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AFRL-RH-WP-TR-2017-0079 



JET FUEL EXACERBATED 
NOISE-INDUCED HEARING LOSS: 

FOCUS ON PREDICTION OF CENTRAL AUDITORY 
PROCESSING DYSFUNCTION 

Teresa R. Sterner 
Peter J. Robinson 
C. Eric Hack 
Lining Qi 
Latha Narayanan 
Sarah T. Law 

Henry M. Jackson Foundation 
for the Advancement of Military Medicine 
Bioeffects Division 
Molecular' Bioeffects Branch 
Wright-Patterson AFB OH 


Tammie R. Covington 

Henry M. Jackson Foundation 
for the Advancement of Military Medicine 
Aeromedical Research Department 
United States Air Force School of Aerospace Medicine 
Wright-Patterson AFB OH 


Elaine A. Merrill 
Nadja Grobe 
Dominique N. Brown 
David R. Mattie 

Bioeffects Division 
Molecular Bioeffects Branch 


September 2017 

Final Report for Oct 2015 to Mar 2017 


Distribution A: Approved for 
public release; distribution 
unlimited. (PA Case No. 
88ABW-2017-6190,11 Dec 
2017) 


Air Force Research Laboratory 
711 th Human Performance Wing 
Airman Systems Directorate 
Bioeffects Division 
Molecular Bioeffects Branch 
Wright-Patterson AFB OH 45433-5707 


STINFO COPY 






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Jet Fuel Exacerbated Noise Induced Hearing Loss: Focus on Prediction of Central Auditory Processing Dysfunction 


(AFRL-RH-WP-TR-2017 -0079 ) has been reviewed and is approved for publication in 
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MATTIE.DAVI 


Digitally signed by 
MATTIE.DAVID.R.12301 

D.R.12301018 01880 

Date: 2017.12.05 

80 12:26:12-05'00' 


DAVID R. MATTIE 
Work Unit Manager 
Molecular Bioeffects Branch 


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E.A.1 230536283 Date: 2017.12.05 18:36:27 -06'00' 


STEPHANIE A. MILLER, DR-IV, DAF 
Chief, Bioeffects Division 
Ainnan Systems Directorate 
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Air Force Research Laboratory 


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1. REPORT DATE (DD-MM-YYYY) 
30-09-2017 


4. TITLE AND SUBTITLE 


2. REPORT TYPE 


Jet Fuel Exacerbated Noise Induced Hearing Loss: Focus on 
Prediction of Central Auditory Processing Dysfunction 


6. AUTHOR(S) 

Sterner, Teresa R. 1 ; Robinson, Peter J. 1 ; Hack, C. Eric. 1 ; Qi, 
Lining 1 ; Narayanan, Latha 1 ; Law, Sarah T. 1 ; Covington, Tammie 
R. 2 ; Merrill, Elaine A.*; Grobe, Nadja*; Brown, Dominique N.*; 
Mattie, David R.* 


7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

'HJF, 2728 Q St, Bldg 837, WPAFB OH 45433-5707 
2 HJF, 2510 Fifth Street, Bldg 840, WPAFB OH 45433-7951 
*711 HPW/RHDJ, 2728 Q St, Bldg 837, WPAFB OH 45433-5707 


3. DATES COVERED (From - To) 
10/2015-03/2017 


5a. CONTRACT NUMBER 

FA8650-15-2-6608 


5b. GRANT NUMBER 

NA 


5c. PROGRAM ELEMENT NUMBER 

62202F 


5d. PROJECT NUMBER 

H0FS 


5e. TASK NUMBER 

HO 


5f. WORK UNIT NUMBER 

H0FS3003 


8. PERFORMING ORGANIZATION 
REPORT NUMBER 


9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 

Air Force Materiel Command* 

Air Force Research Laboratory 
711th Human Performance Wing 
Airman Systems Directorate 
Bioeffects Division 
Molecular Bioeffects Branch 

Wright-Patterson AFB OH 45433-5707_ 


12. DISTRIBUTION AVAILABILITY STATEMENT 

Distribution A: Approved for public release; distribution unlimited. (PA Case No 88ABW-2017-6190, 11 Dec 2017) 


13. SUPPLEMENTARY NOTES 


10. SPONSOR/MONITOR'S ACRONYM(S) 

711 HPW/RHDJ 


11. SPONSORING/MONITORING 
AGENCY REPORT NUMBER 

AFRL-RH-WP-TR-2017-0079 


14. ABSTRACT 

Multiple laboratory rat studies link JP-8 jet fuel exposure to enhanced noise induced hearing loss (NIHL). Further. JP-8 jet fuel exposure, with and 
without noise, has been found to result in central auditory system dysfunctions in rats. Aircraft pilots, technicians and maintenance crews have 
frequently shown increased hearing loss. The overall objective of this project was to develop a multi-scale model, together with relevant supporting 
experimental data, to describe jet fuel exacerbated noise induced hearing loss. In vitro experiments were used to measure oxidative stress in cell 
lines representative of the auditory pathway. A novel mixtures exposure physiologically-based pharmacokinetic (PBPK) array model was designed 
to describe hearing loss target tissues along the peripheral and central auditory pathways. Partition coefficients (PC) were measured for five JP-8 
jet fuel hydrocarbon components in cochlea, brain stem and temporal lobe tissues. Tissue composition (water, protein, fat types) was characterized 
for hearing target tissues. A mathematical model of a simple neuronal circuit in the dorsal cochlear nucleus was developed and a model of synaptic 
neurotransmitter kinetics was designed. These models may be linked together in future efforts to describe a JP-8 component exposure resulting in 
neurotransmission alterations. 


15. SUBJECT TERMS 

pharmacokinetic, pharmacodynamics, models, hearing loss, JP-8, jet fuel, exposure 


16. SECURITY CLASSIFICATION OF: 

U 


a. REPORT 


b. ABSTRACT 

U 


C. THIS PAGE 

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17. LIMITATION OF 

18. NUMBER 

ABSTRACT 

OF PAGES 

SAR 

128 


19a. NAME OF RESPONSIBLE PERSON 

D. R. Mattie 


19b. TELEPONE NUMBER (Include area code) 

NA 


I 


Standard Form 298 (Rev. 8-98) 

Prescribed by ANSI-Std Z39-18 



































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11 



TABLE OF CONTENTS 


1.0 Summary.1 

2.0 Introduction.3 

2.1 Jet Fuel, Noise, and Hearing Loss.4 

2.2 Objectives.4 

3.0 In Vitro Studies .5 

3.1 Cell Lines .5 

3.2 Cell Culture and Exposure Methods .6 

3.3 In Vitro Results and Discussion.7 

4.0 Jet Fuel PBPK Model and Parameter Development .9 

4.1 Cochlea Harvesting and Relationship to Body weight .10 

4.2 Rat Tissue Partition Coefficient Measurement .11 

4.3 Rat Tissue Composition Determinations and PC Predictions.15 

4.4 Physiologically-Based Pharmacokinetic Modeling of Mixtures using Array Coding.28 

5.0 Pharmacodynamic Modeling of Jet Fuel Effects on Central Auditory Pathway Encoding 

of Auditory Stimuli.35 

5.1 Justification for Modeling Central Auditory Pathway.36 

5.2 Simple Neuronal Circuit Model.38 

5.3 Evoked Response Waveform Simulation.40 

5.4 Model of Synaptic Neurotransmitter Kinetics.40 

6.0 Conclusions and Future Work.41 

6.1 PBPK Model Parameter Measurement.41 

6.2 PBPK Model Improvements.42 

6.3 Pharmacodynamic Models.43 

7.0 References.43 

Appendix A. Array Pharmacokinetic Model Code.49 

Appendix B. Select M Files for Pharmacokinetic Array Model.64 

Appendix C: Model Simulations for Individual Key Components.99 

Appendix D. Berkeley Madonna Code for Pharmacodynamic Models.114 

Appendix E. Theoretical Prediction of Brain Regional PCs Based on White to Gray Matter 

Ratios.116 


List of Acronyms 


118 
































LIST OF FIGURES 


Figure 1. Cytotoxicity in VOT-E36 and VOT-N33 cell lines exposed to toluene or JP-8 in 

24-well plates.8 

Figure 2. Glutathione relative to controls following 24-hour exposure to JP-8 with and 

without 100 pM oligomycin in VOT-E36 and VOT-N33 cells.9 

Figure 3. Photograph of Rat Cochlea Harvested at WPAFB.10 

Figure 4. Cochlea Pair and Body Weight Comparisons.11 

Figure 5. Water Content.17 

Figure 6. Protein Content.18 

Figure 7. All Observed (Measured) vs. Predicted Tissue:Blood PCs.24 

Figure 8. Relative Difference between All Observed (Measured) and Predicted PCs.24 

Figure 9. Observed (Measured) vs. Predicted PCs Excluding Fat, Nonane, and Decane.25 

Figure 10. Relative Difference between All Observed (Measured) and Predicted PCs 

Excluding Fat, Nonane, and Decane.25 

Figure 11. All Observed vs. Fiterature Reported Tissue:Blood PCs.26 

Figure 12. Comparison of PCs for Skull, Cochlea, and Cochlear Epithelial Cell Pellet.27 

Figure 13. Schematic of Traditional Parallel Style Mixtures PBPK Models.28 

Figure 14. Physiologically-Based Pharmacokinetic Model Schematic.30 

Figure 15. Venous Blood Key Component Concentration Predictions for 1000 mg/m 3 JP-8 

Exposure from Guthrie et al. (2014) Exposure Profile.33 

Figure 16. Cochlea Key Component Concentration Predictions for 1000 mg/m 3 JP-8 

Exposure from Guthrie et al. (2014) Exposure Profile.33 

Figure 17. Brain Stem Key Component Concentration Predictions for 1000 mg/m 3 JP-8 

Exposure from Guthrie et al. (2014) Exposure Profile.34 

Figure 18. Temporal Fobe Key Component Concentration Predictions for 1000 mg/m 3 JP-8 

Exposure from Guthrie et al. (2014) Exposure Profile.34 

Figure 19. Remainder of Brain Key Component Concentration Predictions for 1000 mg/m 3 

JP-8 Exposure from Guthrie et al. (2014) Exposure Profile.35 

Figure 20. Schematic Showing Potential Impact Sites of Toluene on Transmission of Wave 

Function W.37 

Figure 21. Schematic of a Simple Neuron Circuit in the Dorsal Cochlear Nucleus of the CAP..39 

Figure 22. Evoked Response Encoding of Stimulus Intensity.41 

Figure 23. Simulations of Neurotransmitter Concentrations in Synaptic Cleft of Unit Volume 
for Two Different Pre-Synaptic Firing Rates.41 


IV 

























LIST OF TABLES 


Table 1. PC Vial and Cap Evaluation.12 

Table 2. Measured Tissue:Air Partition Coefficients.14 

Table 3. Tissue Water Content.16 

Table 4. Tissue Protein Content.18 

Table 5. Tissue Lipid Content.21 

Table 6. Compound-Specific Parameters for PC Estimation.22 

Table 7. Tissue:Blood PC Predictions Compared to Measured and Literature Values.23 

Table 8. Array PBPK Physico-Chemical Parameters for Key Hydrocarbons.31 


V 











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VI 



PREFACE 


Primary funding for this project was provided through the Air Force Office of Scientific 
Research (AFOSR) Laboratory Research Initiation Request (LRIR) 14RH09COR, under the 
program management of Pat Bradshaw, PhD (AFOSR/RTB). Partial funding was also provided 
by the Aerospace Toxicology Program in the Air Force Research Laboratory, 711th Human 
Performance Wing, Airman Systems Directorate, Bioeffects Division, Molecular Bioeffects 
Branch (711 HPW/RHDJ). 

This research was conducted under contract FA8650-10-2-6062 with the Henry M. Jackson 
Foundation for the Advancement of Military Medicine (HJF). The program manager for the HJF 
contract was David R. Mattie, PhD (711 HPW/RHDJ), who was also the technical manager for 
this project. 

The study “Fischer 344 Rat (Rattus norvegicus) Pilot Pharmacokinetic Study for Jet Fuel in the 
Hearing Pathways” was approved by the Wright-Patterson Air Force Base (AFB) Installation 
Animal Care and Use Committee (IACUC) as protocol number F-WA-2015-0159-A. The study 
was conducted in a facility accredited by the Association for the Assessment and Accreditation 
of Laboratory Animal Care (AAALAC), International, in accordance with the Guide for the Care 
and Use of Laboratory Animals (NRC, 2011). The study was performed in compliance with 
DODI 3216.1. 

The authors would like to acknowledge LTC Karyn Armstrong (Attending Veterinarian) and the 
Wright-Patterson AFB Vivarium staff of the U.S. Air Force 711 Human Performance Wing (711 
HPW/RHDV), who provided the daily efforts necessary for animal husbandry. 


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1.0 SUMMARY 


Multiple laboratory rat studies have supported JP-8 jet fuel’s role in enhancing noise-induced 
hearing loss. JP-8 jet fuel exposure, with and without noise, has been found to result in central 
auditory system dysfunctions in rats. Across the world, aircraft pilots, technicians and 
maintenance crews have frequently shown increased hearing loss, often assumed to be the 
product of noise alone. However, a preliminary epidemiology study found that jet fuel exposure 
apparently resulted in increased hearing loss in military workers, as compared to military 
exposure to noise alone. 

The overall objective of this project was to develop a multi-scale model, together with relevant 
supporting experimental data, to describe jet fuel exacerbated noise-induced hearing loss. Such 
hearing loss has been attributed to the effects of oxidative stress as well as disruption of signaling 
along the auditory pathway. The efforts toward completion of this objective can be broken down 
into three goals. The first goal was to utilize in vitro experiments to measure oxidative stress in 
cell lines representative of the auditory pathway. Second, the goal was to develop a 
physiologically-based pharmacokinetic (PBPK) model capable of describing the tissue dose of 
JP-8 components along the peripheral and central auditory pathways. The third goal was to 
design a cellular network model that focuses on disruption of the excitatory and inhibitory 
synaptic connections along the auditory pathway. 

Goal 1: Three cochlear cell lines demonstrate sensitivity to JP-8 induced lipid peroxidation and 
glutathione depletion. However, a clear dose response for lipid peroxidation and glutathione 
depletion in any of the three cell line cultures was not evident. The lack of consistent 
dose/response relationships may be indicative of a problem with delivery of volatile chemicals to 
the cells. 

Goal 2: To facilitate building a PBPK model to describe five JP-8 key hydrocarbons, partition 
coefficients (PCs) for hearing pathway target tissues were measured. Further, to allow for future 
prediction of Air Force relevant chemicals in the PBPK model, tissue composition (water, 
protein, fat types) was characterized for hearing target tissues. This composition information can 
be utilized to calculate PCs without animal use. 

Using these data and literature values, an existing PBPK model was expanded and parameterized 
to include cochlea, brainstem and temporal lobe compartments. In order to streamline the 
addition of these tissues, the model was first re-written in an array format, allowing the addition 
of only one set of code per tissue, instead of five sets of code corresponding to each key 
hydrocarbon per tissue added. The parameterized model was then utilized to estimate 
concentrations of the key hydrocarbons in cochlea, brain stem, temporal lobe, and the remaining 
brain tissue for an inhalation exposure to 1000 mg/m 3 JP-8. These predictions indicate the 
anticipated levels of detection needed to measure the key hydrocarbons in these tissues in a 
pharmacokinetic experiment using this exposure scenario. 

Goal 3: A mathematical model of a simple neuronal circuit in the dorsal cochlear nucleus was 
developed. The dorsal cochlear nucleus is located on the dorso-lateral surface of the brainstem 
and is the site of the first synapse for the auditory nerve fibers after transmission from the 


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cochleae. The model includes the following attributes: frequency coding of signal intensity, 
saturation of receptors at high signal intensities, and longer term alterations in signal processing 
mediated by changes in receptor densities at the synapse. Additionally, a model of synaptic 
neurotransmitter kinetics was designed. The resulting pre-synaptic neuron input signal intensity 
can be linked with neurotransmitter levels at its axonal output. Using receptor occupancy 
modeling, this neurotransmitter level then determines the response of the post-synaptic neuron, 
and signal transmission along the central auditory pathway. 

Models from Goals 2 and 3 may be further validated and linked together in future efforts. This 
composite model would describe a JP-8 component exposure resulting in neurotransmission 
alterations at the dorsal cochlear nucleus. 


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2.0 INTRODUCTION 


Excessive noise is known to induce hearing loss through hair cell functional deficits and death in 
the cochlea. Hair cell loss has been associated with oxidative stress in several publications 
(Fechter, 2005; Henderson et al., 2006; Poirrier et al., 2010). Similarly, multiple laboratory 
studies have supported JP-8 jet fuel’s role in enhancing noise-induced hearing loss (NIHL) 
(Fechter et al., 2007, 2010, 2012). The initial hypothesis that JP-8 jet fuel and noise exposure 
both produce oxidative stress in cochlear tissues, resulting in hearing loss when the oxidative 
stress exceeds glutathione reserves, was explored with the use of a mathematical model 
(Robinson et al., 2013, 2015). 

Recent work by Guthrie et al. (2014) indicated that JP-8 jet fuel exposure, with and without 
noise, resulted in minimal effects on peripheral auditory systems. However, this publication 
showed clear central auditory processing dysfunctions from JP-8 exposure, both with and 
without co-exposure at regulatory settings of safe levels of noise (85 dBA). In vitro studies 
published in Robinson et al. (2015) mirrored the relative lack of JP-8 toxicity in cochlear cells. 
Therefore, it was concluded that peripheral auditory system deficits are not the most sensitive 
outcome with JP-8 exposure even when combined with noise, particularly for relatively short 
term and low concentration exposures. Focus then turned to the diminished brain stem signaling 
findings as a likely more sensitive toxicological endpoint involving central auditory processing 
dysfunction. 

The jet fuel JP-8 is a kerosene-range petroleum fuel currently utilized as the single fuel for land- 
based operations by the U.S. Armed Forces (Stucker et al., 1994) and NATO forces (Work, 
2011). Across the world, aircraft pilots, technicians and maintenance crews have frequently 
shown increased hearing loss; often assumed to be the product of noise exposures within their 
work environment alone. Swedish commercial aircraft technicians and mechanics develop 
hearing loss at relatively young ages, when compared to reference populations (Smedje, 2011). 

In France, military fighter, transport and helicopter pilots were all found to be at higher risk for 
developing hearing loss; helicopter pilots were more likely to experience losses at frequencies 
that compromise verbal communication (Raynal et al., 2006). Similarly, 32 to 47 percent of 
Thai helicopter pilots, aircrew, aircraft technicians and mechanics have all been found to have 
persistent hearing loss. Noise, measured at 91 to 110 dBA in cockpit and around the helicopter, 
is consistently a hazard near these aircraft (Jaruchinda et al., 2005). 

However, a preliminary epidemiology study found that fuel exposure (JP-8 or its predecessor, 
JP-4) apparently resulted in increased hearing loss in military workers, as compared to military 
exposure to noise alone. The fuel exposures were determined to be under each respective jet fuel 
occupational exposure limit (OEF) and noise ranged from under the action level (<85 dBA, time 
weighted average (TWA)), under the OEF (85 to 89.99 dBA TWA) and higher than occupational 
limits (90 to 94.99 or 95 and over dBA) (Kaufman et al., 2005). Noise, fuel and additional 
chemical exposures go hand in hand for many military personnel who work around aircraft. 
Royal Australian Air Force F-l 11 fuel tank maintenance workers exposed to fuel, solvents and 
noise were found by Guest et al. (2010) to have higher hearing thresholds compared to published 
data from otologically normal age-matched populations. 


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2.1 Jet Fuel, Noise, and Hearing Loss 

OELs are generally based on exposure to a single agent; much less is known about combined 
exposures such as jet fuel and noise. Animal studies concur with Kaufman et al. (2005) in that 
jet fuels promote hearing loss caused by noise. Fechter et al. (2007) were the first to show that 
exposure to high concentrations of JP-8 followed by exposure to damaging noise levels resulted 
in loss of hair cell sensitivity, as shown by suppression of distortion product otoacoustic 
emissions (DPOAE), and cochlear outer hair cell loss in Long-Evans rats; the effects were 
greater with jet fuel exposure as compared to the effects of noise alone. Additional studies with 
high JP-8 concentrations resulted in similar findings (Fechter et al., 2010 and 2012). 

Hypotheses for hair cell toxicity mechanisms have ranged from disruption of intracellular 
calcium homeostasis (Liu and Fechter, 1997), to disruption of membrane fluidity (Campo et al., 
2001; Liu et al., 1997), and disruption of efferent pathways synapsing at the cochlea (Lataye et 
al., 2000). Free radical generation resulting in oxidative stress was a pathway investigated by 
Fechter (1999) and Rao and Fechter (2000). Oxidative stress was found to be the mechanism 
resulting in hearing loss following exposure to carbon monoxide and acrylonitrile, but only with 
concurrent noise exposure (Fechter, 2005). Therefore, as jet fuel exposures can increase free 
radical production and oxidative stress at the cellular level, jet fuel exposure would be expected 
to enhance hair cell dysfunction and loss. This assumption was substantiated by the finding that 
JP-8 reduced cellular glutathione (GSH) levels by approximately 40 percent in rat lung epithelial 
cells after only one hour of exposure (Boulares et al., 2002). 

However, data in Guthrie et al. (2014) suggest that central auditory processing dysfunction 
(CAPD) is more likely to be an early manifestation of JP-8 induced ototoxicity, especially at 
more occupationally relevant fuel exposure levels. In their study with Long-Evans rats, Guthrie 
et al. showed that exposure to somewhat lower levels of JP-8 (1000 mg/m 3 ) than previous studies 
did not induce peripheral hearing loss. Instead, a CAPD, measured as impaired brainstem 
encoding of stimulus intensity, was shown to exist at four weeks after the exposure. This 
impairment in stimulus encoding was exacerbated by a concurrent low level (non-damaging) 
noise (8 kHz octave band at 85 dBA sound pressure level) exposure. Therefore, CAPDs may 
play early, critical roles in hearing loss among military involved with aircraft. 

For humans, CAPD may be an early biomarker of brain alteration (Bamiou et al., 2000). Aging 
characteristically alters brainstem function in auditory brainstem response (ABR) measurements 
(Boettcher et al., 1993; Popelar et al., 2006; Zhou et al., 2006). A potential mechanism for age 
induced changes in ABR is altered membrane fluidity, which would lead to potential 
impairments of vesicular transport and/or fusion at the synapses within the auditory brainstem. 
Membrane fluidity is known to be affected by solvent/anesthetic exposure (Bamiou et al., 2000). 


2.2 Objectives 

The overall objective of this project was to develop a multi-scale model, together with relevant 
supporting experimental data, to describe jet fuel exacerbated NIHL. Herein we describe the 
efforts toward completion of this objective as broken down into three goals. The first goal was 


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to utilize in vitro experiments to measure oxidative stress in cell lines representative of the 
auditory pathway. Second, the goal was to develop a mixtures pharmacokinetic model capable 
of describing the tissue dose of JP-8 components along the peripheral and central auditory 
pathways. The third goal was to design a cellular network model that focuses on disruption of 
the excitatory and inhibitory synaptic connections along the auditory pathway. 


3.0 IN VITRO STUDIES 

Previous in vitro studies have been used to explore the effect of JP-8, key hydrocarbons, and an 
ototoxic noise surrogate (oligomycin) on cellular function and viability. Robinson el al. (2015) 
detailed a 24-hour proteomic study of cochlear epithelial cells exposed to JP-8. JP-8 was found 
to have marginal cytotoxic effects on House Ear Institute-Organ of Corti 1 (HEI-OC1) auditory 
cells at the highest dose of 500 ppm. Proteomics analyses indicated upregulation of histones 
involved in chromatin remodeling as a result of oxidative stress at this dose level. It has been 
shown that oxidative stress and pro-inflammatory mediators can alter nuclear histone acetylation 
and deacetylation (Rahman, 2003). Although effects were not dramatic, a dose-response effect 
was apparent with increasing concentrations. The purpose of the proteomic study was to identify 
potentially novel biomarkers of JP-8 toxicity in a cochlear cell line, focusing on biomarkers of 
oxidative stress and lipid peroxidation. Novel biomarker identification could not be made due to 
the mild response. 

Overall, little toxicity in HEI-OC1 cells was seen from JP-8. An initial increase in live/dead 
ratios suggests that early induction of protective cellular mechanisms may explain the lack of 
cytotoxic effects at the two-hour time point. Apoptosis and necrosis induced by JP-8 
components (toluene, ethylbenzene, xylene, nonane, and decane) were enhanced by the presence 
of oligomycin; however, the hydrocarbon component mixture alone showed little impact. 
Collectively, these findings support the proteomic results in demonstrating JP-8 had minimal 
effects on cochlear hair (HEI-OC1) cells (Robinson et al., 2015). 

To complete the investigation of JP-8 effects on auditory cells in vitro, dose response studies to 
monitor oxidative stress and viability in three cell lines were tested. These studies were intended 
to correlate JP-8 exposure, reactive oxygen species production, GSH depletion, and cytotoxicity 
(from Robinson et al, 2015) to provide a broader picture of the JP-8 effect on cochlear cells. 


3.1 Cell Lines 

Three cell lines were used in these studies. Conditionally immortalized HEI-OC1 auditory cells, 
isolated from the Organ of Corti, were provided by Federico Kalinec, PhD (David Geffen School 
of Medicine, Department of Head and Neck Surgery, University of California, Los Angeles CA). 
The development and characterization of the HEI-OC1 cells is described in Kalinec et al. (2003). 
This cell line is a recognized in vitro system to investigate the cellular and molecular 
mechanisms involved in ototoxicity, and for screening of the potential ototoxic properties of jet 
fuel components and other chemical stressors. 


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The neuroblast cell line, VOT-N33, and the otic epithelial cell line, VOT-E36, were originally 
derived from similar regions of the ventral otocyst of the Immortomouse© at embryonic day 10.5 
(Lawoko-Kerali el al., 2004). These cells were kindly provided by Dr. Matthew C. Holley 
(Department of Biomedical Science, University of Sheffield, England). As experimental models, 
the VOT-E36 and VOT-N33 cell lines express a number of sensory epithelia and spiral ganglion 
specific markers making them useful tools for the in vitro study of the influence and the 
mechanism of ototoxic agent on auditory cells. A description of the source, timing and 
derivation of these cell lines is reviewed by Rivolta and Holley (2002). 


3.2 Cell Culture and Exposure Methods 

The HEI-OCI cells were grown in high glucose Dulbecco’s modified Eagle’s medium (DMEM) 
with 10 percent fetal bovine serum (FBS). VOT-E36 and VOT-N33 cells were cultured in 
minimal essential medium (MEM) with Glutamax, 10 percent FBS, and 50 Units/mL gamma- 
interferon. The cells were allowed to proliferate in an incubator with 5 percent CO 2 at 33°C to 
approximately 80 percent confluency before being harvested for assays. Assays were conducted 
in Falcon™ black-walled, clear-flat bottom 96-well or clear flat bottom 24-well tissue culture 
plates (BD Biosciences, San Jose CA) in either 200 or 1000 pL of their respective culture media. 
Seeding cell densities were optimized at 15,000 and 50,000 cells/well for 96- and 24-well plates, 
respectively. These conditions were selected by seeding plates at various densitites and 
identifying the density resulting in the greatest viability after 24-hour incubation at 33°C and 5 
percent CO 2 . Viability was measured using the (3-(4,5-dimethylthiazol-2-yl)-5-(3- 
carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) cell proliferation assay 
(abeam, Cambridge MA). 

JP-8 cytotoxicity was measured by MTS, using varying concentration of either dimethyl 
sulfoxide (DMSO) or ethanol as carriers, to optimize dose delivery. In addition, the impact of 
serum content in exposure media on JP-8 dose/response was assessed (0, 1 or 10 percent FBS). 
Following seeding and a 24-hour incubation, media was replaced with fresh media, with or 
without FBS, plus JP-8, ranging from 0 to 5000 ppm. The MTS assay was conducted as 
described by the abeam protocol abl97010. 

JP-8 induced lipid peroxidation was assessed using an Image-iT® Fipid Peroxidation Kit 
(Thermo Fisher Scientific, Waltham MA). Following exposures, Fipid Peroxidation Sensor 
(Component A), was added to each well at a final concentration of 10 pM and incubated for 30 
minutes at 37°C. The media containing the sensor was then removed, cells were washed three 
times with phosphate buffered saline (PBS), and fluorescence was read at excitation and 
emission peaks of 581 and 591 nm, respectively. 

Glutathione depletion induced by JP-8, with and without oligomycin was measured. Oligomycin 
inhibits the production of adenosine triphosphate (ATP) and was used in previous studies as an 
in vitro surrogate for noise (Robinson el al., 2015). In these studies, JP-8 was delivered in the 
presence of 1 percent fetal bovine serum and 0.4 percent DMSO. Immediately after, the plate 
was sealed and incubated for 24-hour. Glutathione depletion was measured using Invitrogen™ 
ThiolTracker Violet dye. Following JP-8 exposure, the incubation medium was removed from 


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the wells and cells in each well were rinsed with 100 pL Dulbecco’s PBS (D-PBS), which was 
removed each time. Prewarmed 100 |aL aliquots of ThiolTracker™ Violet dye (Thermo Fischer 
Scientific, Waltham MA) working solution (20 pM) in D-PBS was added to each well and 
allowed to incubate at 33°C for 30 minutes. At that time the dye solution was replaced with D- 
PBS and cells were imaged using fluorescence excitation and emission peaks of 404 and 526 nm, 
respectively. 


3.3 In Vitro Results and Discussion 

The effect of carriers, dimethyl sulfoxide or ethanol, on JP-8 and toluene cytotoxicity was 
assessed using the MTS assay. Both DMSO and ethanol are amphiphilic compounds whose 
miscibility with water and ability to dissolve lipophilic compounds like JP-8, make them useful 
solvents for in vitro studies involving insoluble compounds. A clear dose response to JP-8 and 
toluene was not achieved in 96-well plates with either solvent, suggesting that dose delivery of 
these volatile compounds was not consistent. However, the assay was repeated in 24-well plates 
using DMSO as a carrier, resulting in improved dose/response relationships (Figure 1), 
suggesting less loss of volatile components during preparation of larger volumes. The use of 
ethanol as a carrier was not attempted in 24-well plates. 

Based upon the cell proliferation assay, MTS, little difference was seen in JP-8 toxicity when the 
exposure media contained 0, 1 or 10 percent FBS. The clearest dose response curve was 
obtained in the presence of 1.0 percent FBS, which was subsequently used in all other assays. 

All three cell lines demonstrated sensitivity to JP-8 induced lipid peroxidation and glutathione 
depletion induced by cumene hydroperoxide, a positive control, with the neural line VOT-N33 
being more sensitive than the epithelial cell lines. However, in the presence of JP-8 
concentrations ranging from 10 to 3000 ppm for 4 and 24 hours, with either 0.5 percent dimethyl 
sulfoxide or 0.5 percent ethanol, a clear dose response for lipid peroxidation was not evident 
(data not shown). A slight, but not significant, trend in increasing lipid peroxidation with JP-8 
concentration was seen in the HEI-OC1 cells, whereas a slight trend in the opposite direction was 
seen in both VOT cell lines. 

Glutathione depletion induced by JP-8, with and without oligomycin, resulted in opposite trends 
in VOT-E36 and VOT-N33 cells. JP-8 was delivered in the presence of 1 percent fetal bovine 
serum and 0.4 percent DMSO. VOT-E36 demonstrated slight but insignificant glutathione 
depletion in the presence of JP-8. However, in the presence of JP-8 and 100 pM oligomycin, a 
significant increase in glutathione was seen (Figure 2). The trend was reversed in the neuroblast 
culture. In the VOT-N33 cell line, relative glutathione increased in the presence of JP-8 at lower 
doses up to 1000 ppm, but in the presence of both JP-8 and oligomycin, relative glutathione 
remained near control values. The negative glutathione value seen at the lowest combined JP-8 
and oligomycin dose may possibly be an artifact of cell death, as normalization to cell number 
was not performed with this assay (Figure 2). There was a slight but insignificant difference 
between controls (0 ppm JP-8) and 100 pM oligomycin in both cells lines. 


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Much of the data collected suggest a protective effect from the combination of JP-8 with either 
DMSO or ethanol. However, the lack of consistent dose/response relationships is indicative of a 
problem with delivery of volatile chemicals to the cells. Dose/response studies in the three cell 
lines with DMSO alone indicated a slight decline in viability at a concentration of 1 percent. 
Aviation jet fuels used for civil and military aircrafts are a kerosene type. To avoid peroxide 
production after the refinery process, a specific antioxidant is added. The antioxidants generally 
used are hindered phenols in a concentration range of 10 to 20 pg/mL (Bernabei et al., 2000). It 
is possible that the antioxidants in JP-8 are protective against lipid peroxidation and glutathione 
depletion. In addition, antioxidant properties of DMSO and ethanol have been reported 
(Bonnefont-Rousselot et al., 2001; Sanmartm-Suarez et al., 2011). Our results raise doubts 
about the use of these solvents in the evaluation of potential oxidative or antioxidant properties 
of immiscible hydrocarbons. Plates were tightly sealed immediately after dosing, allowing the 
volatile portion to come to equilibrium with the media. However, JP-8 appears to dissociate and 
component concentrations reaching the cells may not be representative of the mixture 
concentrations used. Binding to serum may serve as a carrier to deliver compounds to cells, 
given the on/off rate of binding is rapid. In our studies the percent of serum used had little effect 
on dose/response; however, its effect may have been masked by a significant loss of volatiles in 
the exposure system. In addition, because the plates were tightly sealed and no buffer was used, 
an increase in medium pH may have affected cell viability. 



24 h Tnlupnp Cvtotoxiritv in VOT-N33 cplk 



Toluene (mM) 


24 h IP-8 f\/tntnvirit\/ in \/OT-F8fi re>llc 



0 1000 2000 3000 

JP-8 (ppm) 



0 1000 2000 3000 

JP-8 (ppm) 


Figure 1. Cytotoxicity in VOT-E36 and VOT-N33 cell lines exposed to toluene or JP-8 in 
24-well plates. Media contained 1 percent FBS and 0.5 percent DMSO for 24 hours. 


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VOT-E36 


VOT N33 



Figure 2. Glutathione relative to controls following 24-hour exposure to JP-8 with and 
without 100 pM oligomycin in VOT-E36 and VOT-N33 cells. Exposure media contained 0.5 
percent DMSO, 1 percent FBS. *p <0.05 (n=3) 


4.0 JET FUEL PBPK MODEL AND PARAMETER DEVELOPMENT 

A pharmacokinetic study to measure JP-8 components in the cochlea and other major tissues is 
needed to inform a physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) model 
to predict the impact of JP-8 concentrations on noise-induced hearing loss. In vivo studies in rats 
(Fechter et al., 2012) have shown that exposure to 1500 mg/m 3 JP-8 in combination with non¬ 
damaging noise levels may induce low levels of peripheral (cochlear) hearing loss, while 
exposure to non-damaging noise combined with lower (1000 mg/m 3 ) JP-8 atmospheres results in 
central auditory pathway damage to hearing without peripheral changes (Guthrie et al., 2014). 
Fechter et al. (2012) did not monitor the central auditory pathway for changes. A PBPK/PD 
model of the process will help in predicting the thresholds of damage when exposed to both jet 
fuels and noise. 

JP-8 is a mixture of thousands of components that fluctuate depending on the source of the crude 
oil from which it is refined (Shafer et al., 2006). The key hydrocarbons in this proposal are 
common constituents of JP-8 well-known for ototoxicity, including the aromatics toluene, 
ethylbenzene, p-xylene (Johnson and Morata, 2010). Also included are nonane and decane, 
components of white spirits, which are known to affect the central auditory pathway (Fund et al., 
1996; SCOEF, 2007). 

Hearing physiology is commonly broken into two segments, the peripheral hearing pathway, 
which includes all portions of the ear, and the central auditory pathway, which includes the 
nerves and nuclei signaling hearing reception into the brain. Therefore, the cochlea and its hair 
cells, which make up the peripheral auditory pathway, are not the only tissues of interest in a 
PBPK/PD model. Portions of the brain containing the central auditory pathway include the 
brainstem and the temporal lobe, which houses the auditory cortex. 

Given that these tissues of interest have not been characterized in such a way as to facilitate a 
PBPK/PD model, ex vivo rat studies were performed to develop these data. First, tissue to air 

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partition coefficients for each of the key hydrocarbons were measured in the tissues. Second, 
target tissue composition was characterized. Water, fats (neutral lipids, neutral phospholipids, 
and acidic phospholipids), and protein content were measured for use in quantitative structure- 
property relationship (QSPR) algorithms designed to mathematically predict chemical-specific 
tissue to air partition coefficients. This knowledge allows the PBPK/PD model to be adapted to 
additional chemicals of concern in the future. 


4.1 Cochlea Harvesting and Relationship to Bodyweight 

A common practice among PBPK modelers is to weigh a tissue/organ and calculate the fraction 
of the total body weight that the tissue represents. This approximation of the volume of the 
tissue provides a unitless factor that facilitates subsequent predictions of tissue concentration. 

The resulting value also allows for scaling of tissue volume for different sized animal models. 

Two technicians from the Wright-Patterson Air Force Base (WPAFB) Vivarium 
(711HPW/RHDV) were sent to the laboratory of Richard J. Salvi, PhD (Department of 
Communicative Disorders and Sciences, University at Buffalo, The State University of New 
York, Buffalo NY) on February 11, 2015. The WPAFB technicians observed and learned the 
process of removing rat cochlea intact from the skull (Figure 3). 

This capability has allowed dissection and removal of 177 individual cochleae from male Fischer 
344 and Sprague-Dawley rats; cochlea were weighed and then stored at -80 °C. All cochlea 
were measured from rats used as part of this pilot study. Additionally, rat skulls were obtained 
through several tissue sharing agreements with other projects in 711 HPW/RHDJ and the Navy 
Medical Research Unit Dayton. 



Figure 3. Photograph of Rat Cochlea Harvested at WPAFB. Magnification level was 
unspecified. 


4.1.1 Results. Single cochlea weights were compared to male rat body weight from both strains 
of rats. Cochlea weight in general increases with body size, but is not adequately described as a 
percent of body weight due to the fact that cochlea are large among young rats and do not 
appreciably increase in size after the rat reaches maturation. Therefore, the typical description of 
a tissue as a fraction of bodyweight utilized in most PBPK models will not work for this tissue. 
Early in this cochlea comparison work, the cochlea was estimated to stay roughly the same size 


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across most body weights (0.025 g for one cochlea, see the red line in Figure 4A). This is a 
sufficient description of cochlea weight for a model, given the very small size of this tissue. 

After 177 cochleae were gathered, a more accurate way to describe cochlea weight was found. 
This mathematical best fit line by linear regression is shown as the black line in Figure 4A. 
Visual inspection of cochlea weights by strain against the regression line indicate that the 
description is adequate for both strains of rat (graphs not shown). 


Given that the model requires the weight be provided in kg for a pair of cochlea, the units were 
converted and the data points doubled, assuming that any given rat would have identical cochlea 
(by weight) on each side of their head (Figure 4B). The PBPK model in this study then 
calculates the paired cochlea size based on bodyweight of the simulated rat, using the equation in 
Figure 4B. 


A. 


“ o 
[§3d ^ 

"5 

£ 0 

M o 

© o 
u 

V 0 

OX) 

0 


040 
.035 - 
030 - 
,025 - 
020 
015 - 
010 
005 - 


0.000 





0.0 


200.0 400.0 

Body Weight (g) 


600.0 


0.0001 n 


ct 



Figure 4. Cochlea Pair and Body Weight Comparisons. (A) Body weight is compared to 177 
single cochlea weights. The red line shows an assumed constant cochlea weight of 0.025 g while 
the black line shows numerical best fit to the data. (B) Body weight is shown in kg compared to 
doubled cochlea weights in kg to determine the equation needed for model calculation of cochlea 
size based on body weight. The red line again shows a constant paired cochlea weight (0.00005 
kg) and the black line shows the numerical best fit as described by the equation posted on the 
graph. 


4.2 Rat Tissue Partition Coefficient Measurement 

Cochlea and other harvested tissues from male Fischer 344 (F-344) rats were utilized to measure 
in vitro partition coefficients of JP-8 constituents. These methods were published by scientists 
from 711 HPW/RHDJ (under its former office symbols), most recently by Mahle et al. (2007). 


4.2.1 Vial Evaluation. Vial optimization was performed with one key hydrocarbon, toluene. A 
3 L DuPont Tedlar® bag (Sigma-Aldrich, St. Louis MO) of toluene gas was prepared by 
injecting the bag with 992 ppm toluene first, agitating the bag over a heat gun, allowing the bag 
to saturate overnight and, then emptying the bag. This preparation coats the bag’s interior 
surface with toluene and prevents further absorption during the experiment. A known 


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concentration of toluene (992 ppm) was infused into the prepared bag and heated to volatilize 
any remaining liquid. Two vial and cap options with polytetrafluoroethylene (PTFE)/silicone 
septa were evaluated for leakage: 10 mL glass vials with magnetic screw thread caps (3.0 mm 
thick septa) and 20 mL glass vials with regular crimp caps (3.3 mm thick septa). All vials, caps 
and septa were purchased from Thermo Fisher Scientific (Rockwood TN). 

Two injection methods were also evaluated: direct injection of toluene and injection of toluene 
after removing an equivalent volume of air from the vial, which theoretically reduces the strain 
of over-inflation on the seal. Following injection of 2.5 mL of 992 ppm toluene, the glass vials 
were incubated at 37 °C for 10 minutes, 3 hours, or 92 hours. 

For analysis, one mL of headspace was extracted and then injected onto an Agilent G1540A gas 
chromatograph-flame ionization detector (GC-FID) system (Agilent, Santa Clara CA) fitted with 
an RTX-1 fused-silica column (30 m x 0.32 mm ID x 0.25 pm film) (Restek, Bellefonte PA). A 
Combi-Pal autosampler (CTC Analyticas, Carrboro NC) was used for automatic injections. A 
split injection mode (2:1) was applied after each 1 mL sample. The injector was set at a 
temperature of 250°C, and helium (constant pressure 10 psi, flow rate 2.1 mL/minute, average 
velocity 35 cm/second). Toluene loss was calculated for each time point. 

The 10 mL glass vial with the magnetic screw cap lost less toluene after incubation at 37°C for 3 
or 92 hours (Table 1). This finding is likely due to better and consistent tightening of a screw 
cap versus a crimp. Further, the theoretical need to remove air in order to place a like volume of 
chemical inside the vial was found to be unnecessary as the vial septum is pierced twice in this 
method. Single injection vials (those from which no air is drawn before injecting the volatile) 
lost less toluene than vials pierced twice; any advantage in reducing intra-vial air pressure was 
negated. 


Table 1. PC Vial and Cap Evaluation 


Experimental Setup 

Percent Toluene Loss 

3 Hours 

92 Hours 

25 mL vial with 
crimp cap 

no air draw out first 

29.78 

54.61 

draw 2.5 mL air out of vial 
before infusing 2.5 mL toluene 
into vial 

19.20 

43.20 

10 mL vial with 
magnetic screw cap 

no air draw out first 

12.77 

37.36 

draw 2.5 mL air out of vial 
before infusing 2.5 mL toluene 
into vial 

21.51 

46.81 


4.2.2 PC Measurement Methods and Optimization. Mixed xylene isomers (o-, m-, p- 
isomers, 96 percent with 4 percent ethylbenzene) were used for this study; during analysis, the 
largest xylene isomer peak with the earliest retention time was utilized. Peaks from toluene, 
ethylbenzene, mixed xylenes, nonane, and decane mixture were verified by the single standards. 


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A 3 L Tedlar bag was saturated similarly to the procedure described above with a mixture of the 
five key hydrocarbons. Then the bag was prepared by installation of 32.6 mL (3000 ppm) 
toluene, 37.9 mL (3000 ppm) ethylbenzene, 37.7 mL (3000 ppm) mixed xylenes, 91.3 mL (5000 
ppm) nonane, and 99 mL (5000 ppm) decane. The bag was allowed to equilibrate overnight and 
then reheated to ensure volatilization prior to PC vial preparation. 

Partition coefficients were measured for rat blood, cochlea, fat, kidney, skull, and three brain 
sections: frontal lobe, brain stem and temporal lobe. Tissue samples (ranging from 15 to 100 
mg) were weighed. Samples were then smeared on the interior walls of the vials (10 mL glass 
vials, magnetic screw thread caps, 3.0 mm thick PTFE/silicone septa) to maximize the contact 
surface of the tissue with the volatile compounds. Due to the bony nature of the skull and 
cochlea, these tissues were crushed inside the vial using a sterile aluminum rod in order to 
provide maximum surface area for absorption. The vials were capped securely and gas mixture 
(2.5 mL) from the Tedlar bag was then injected into the vial. Empty control vials were also 
injected with 2.5 mL of the gas mixture; these vials were designed to account for vapor loss 
through the septum and adherence of the chemicals to the glass vial walls. 

Each type of tissue was optimized for best absorption time. Frozen samples (-80 °C) from rats 
were utilized for optimization, while fresh samples excised from male F-344 rats were used for 
data collection. Results from the optimization stage indicated that all tissues should be incubated 
at 37°C for 1 hour, except fat and brain samples, which were incubated for 2 hours prior to 
sampling (1 mL headspace gas) and analysis by GC-FID. Tissue:air partition coefficients were 
calculated using Equation 1 from Gargas et al. (1989): 


Ptiss 


Cref(Vvial ) Ctiss(Vvial ^tiss) 
Ctiss(Vtiss ) 


Equation 1 


..., where Ptiss is the partition coefficient for the tissue, C re f is the concentration in the reference 
vial, Ctiss is the headspace concentration in the sample vial, Vviai is 10 mL and Vtiss is the volume 
of tissue where 1 mg is equivalent to 0.001 mL. 


4.2.3 Results. PCs were measured during two efforts, the first in April of 2016 and the second 
in August of the same year. In April, six male F-344 rats were utilized for the experiment. 
Additional samples were required due to high variability. Low absorbance by a single pair of 
cochlea, equivalent to approximately 50 mg of bony tissue, was also an issue. In August, an 
additional 15 rats were used to improve the measurement of PCs. Cochleae from five rats were 
pooled, resulting in an experimental sample n of three. 

Tissue:air partition coefficient measurements were calculated using Equation 1 and the results 
analyzed using R software version 3.1.3 (R Core Team, 2015). Visual inspection of the plots 
from the April and August collections showed consistency between the days, so the two sets 
were combined for further analysis, except for cochlea data, for which only the August data were 
used. 


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The data for each tissue and chemical were plotted to examine the skewness of the data and to 
identify potential outliers. Sample 1 of fat measured in April was very low relative to all other 
samples across all chemicals. The median, however, was not affected by this outlier, so the 
median PC value was used for further comparisons. A summary of the resulting tissue:air PC 
data is shown in Table 2. Tissue:blood PCs utilized in the model were computed by dividing the 
tissue:air values by the blood:air PC. 


Table 2. Measured Tissue: Air Partition Coefficients 


Chemical 

Tissue 

N 

Mean 

Median 

SD 

Minimum 

Maximum 

Decane 

blood 

11 

8.6 

7.9 

5.0 

-0.2 

15.7 

brain frontal 

9 

26.7 

16.9 

24.7 

0.6 

78.5 

brain stem 

9 

24.5 

15.0 

33.8 

-8.8 

99.5 

brain temporal 

9 

14.6 

11.2 

13.9 

-1.4 

34.7 

cochlea 

9 

-1.3 

-1.1 

44.4 

-60.5 

89.2 

fat 

6 

2069.1 

2037.1 

217.7 

1827.5 

2459.7 

kidney 

17 

14.0 

10.5 

15.2 

-8.2 

36.9 

skull 

6 

13.2 

14.7 

8.7 

3.3 

23.9 

Ethylbenzene 

blood 

11 

46.3 

41.1 

13.0 

33.0 

72.8 

brain frontal 

9 

62.3 

55.5 

21.5 

34.4 

105.5 

brain stem 

9 

93.1 

87.8 

36.3 

42.1 

171.4 

brain temporal 

9 

60.7 

65.7 

12.4 

41.4 

73.5 

cochlea 

9 

18.5 

15.4 

12.6 

5.7 

45.0 

fat 

6 

2816.9 

2747.7 

545.6 

1795.5 

3300.2 

kidney 

17 

163.1 

140.5 

77.9 

81.4 

391.1 

skull 

6 

15.2 

14.7 

4.3 

9.6 

22.4 

Nonane 

blood 

11 

6.3 

5.9 

4.7 

0.3 

17.3 

brain frontal 

9 

13.0 

9.5 

19.3 

-16.3 

46.2 

brain stem 

9 

20.8 

16.5 

25.1 

-14.1 

78.1 

brain temporal 

9 

12.5 

14.8 

8.4 

1.2 

22.2 

cochlea 

9 

-2.8 

-1.9 

9.8 

-16.0 

12.5 

fat 

6 

848.2 

799.2 

154.4 

714.6 

1148.0 

kidney 

17 

13.7 

14.5 

12.1 

-6.0 

47.5 

skull 

6 

6.5 

5.9 

2.4 

3.9 

10.5 


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Table 2. (continued) 


Chemical 

Tissue 

N 

Mean 

Median 

SD 

Minimum 

Maximum 

Toluene 

blood 

11 

26.2 

22.8 

5.8 

20.3 

38.6 

brain frontal 

9 

39.5 

34.8 

18.0 

24.4 

83.4 

brain stem 

9 

54.0 

49.7 

22.8 

17.0 

91.6 

brain temporal 

9 

33.9 

37.0 

8.2 

19.8 

45.1 

cochlea 

9 

20.8 

18.1 

14.4 

3.6 

44.3 

fat 

6 

1283.5 

1264.9 

245.7 

826.7 

1481.3 

kidney 

17 

67.7 

65.3 

22.3 

30.2 

110.5 

skull 

6 

10.3 

10.8 

3.6 

4.6 

15.5 

Xylene 

blood 

11 

47.1 

42.3 

13.6 

32.3 

73.1 

brain frontal 

9 

69.3 

62.4 

23.0 

39.9 

115.3 

brain stem 

9 

106.9 

104.1 

38.1 

53.2 

188.2 

brain temporal 

9 

69.2 

72.9 

13.8 

50.2 

84.7 

cochlea 

9 

16.9 

14.3 

12.0 

4.0 

41.9 

fat 

6 

3273.8 

3189.2 

652.6 

2068.9 

3888.0 

kidney 

17 

148.0 

130.5 

67.7 

81.2 

355.1 

skull 

6 

16.3 

16.0 

4.7 

10.4 

23.9 


4.3 Rat Tissue Composition Determinations and PC Predictions 

Although PCs may be measured in target tissues such as cochlea or brain stem for a limited 
number of key JP-8 hydrocarbons, QSPR algorithms can be utilized to calculate PCs for 
additional chemicals that may affect hearing. QSPR algorithms require specific data on tissue 
composition in order to accurately predict PCs. Composition data needed include water, protein, 
neutral lipid, acidic phospholipid, and neutral phospholipid contents (Ruark el al., 2014). 


4.3.1 Tissue Water Content for Partition Coefficient Prediction. Water content data are 
required to better understand retention and partition of chemicals of interest in the different 
tissues of a living organism. To examine water content, fresh rat tissues (brain stem, temporal 
lobe and frontal lobe, skull, cochlea, kidney and fat) were obtained. Data were collected from 10 
rats, in order to provide statistically relevant data for PBPK modeling. 

In order to delineate the brainstem for excision, the following procedure was used. The first cut 
was made at Bregma line -9.9. The second cut was made as close as possible to Bregma line - 
12.6 to collect a slice of rat brainstem that contained the cochlear nuclei. The cerebellum was 
not saved. The slice of brainstem was within the regions shown in Plates 41 through 50 in 
Kruger et al. (1995) and may not have always included the regions in Plates 49 and/or 50. 
Although the cochlear nuclei can extend into the junction of the pons and medulla in humans, the 
slices collected would be in the caudal portion of the pons in the rat. 


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To determine water percentage, tissues were frozen at -80 °C and then subjected to lyophilization 
using a Labconco FreeZone Freeze Dry System (Labconco, Kansas City MO). The 
lyophilization was performed at -40 °C for 48 hours. Samples were brought up to ambient room 
temperature before filling the lyophilizer chamber with air. Water content was determined by 
weighing tissues before and after lyophilization. 

Water content was calculated as a percentage of wet tissue weight for each rat and averaged over 
ten rats (Table 3, Figure 5). The mean (± SD) water content of cochlea and skull were similar: 
29.90 (± 3.61) percent and 24.27 (± 3.88) percent, respectively. The brainstem, temporal lobe, 
and frontal lobe contained about 70.82 (± 3.43), 79.82 (± 0.49), and 79.25 (± 0.62) percent water, 
respectively. These values are similar to 77.4 percent, the value reported for the whole brain by 
Ruark el al. (2014). The kidney contained 75.01 (± 0.58) percent water, which is also similar to 
the reported value of 71.7 percent. Lyophilization of the left and right renal fat pads resulted in 
5.75 (± 0.98) and 4.73 (± 1.03) percent water, respectively, which is less than a third of the value 
reported by Ruark et al. (2014), 17.5 percent. This result could be due to fat samples being from 
different regions of the rat. 


Table 3. Tissue Water Content 


Tissue 

Water Content (% wet tissue weight) 

Cochlea 

29.90 (±3.61) 

Skull 

24.27 (± 3.88) 

Brainstem 

70.82 (± 3.43) 

Temporal Lobe 

79.82 (± 0.49) 

Frontal Lobe 

79.25 (± 0.62) 

Kidney 

75.01 (±0.58) 

Left Renal Fat Pad 

5.75 (±0.98) 

Right Renal Fat Pad 

4.73 (± 1.03) 


Note: Water content is given as average percent wet tissue weight. 


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90 



Cochlea Skull Brainstem Temporal Frontal Lobe Kidney Left Renal Fat Right Renal 

Lobe Pad Fat Pad 

Tissue 


Figure 5. Water Content. The water content is given as an average percent (± 1 standard 
deviation) wet tissue weight from a sample size of ten rat tissues. 


4.3.2 Protein Content for PC Prediction. The protein content fractions for rat kidney, renal fat 
pads, brainstem, temporal lobe, and frontal lobe were determined using freeze-dried tissues from 
the water content measurement. Tissues were homogenized with 1 percent Triton X-100 in PBS 
containing IX protease inhibitors at a concentration of 1 mL per 150 mg of tissue in 5 mL glass 
homogenizing vials. The skull and cochlea samples were crushed over liquid nitrogen using a 
mortar and pestle before homogenizing. Following homogenization, kidney samples were 
diluted 1:80 with 1 percent Triton X-100 in PBS; renal fat pads and brain regions were diluted 
1:10. Cochlea and skull homogenates were not diluted. All tissues were then analyzed using a 
bicinchoninic acid (BCA) assay (Thermo Scientific, Waltham MA). All tissues had a sample 
size of 10, except for the cochlea. Cochlea samples required pooling in order to obtain an 
absorbance reading within the standard curve using the BCA assay. Five cochlea pairs were 
pooled in order to achieve this. To increase the experimental n from 2 to 3, a pooled sample 
(five additional cochlea pairs) from Sprague-Dawley rats were added to the study. 

Ten freeze-dried kidney homogenates had an average (± standard deviation) protein content of 
0.188 (± 0.030) mg/mg tissue (Table 4 and Figure 6), which is close to the reported value of 0.18 
by Ruark et al. (2014). The left and right renal fat pads contained 0.014 (+ 0.006) and 0.023 (± 
0.008) mg protein/mg tissue, respectively. This value falls below the reported value of 0.06 
(mg/mg) from Ruark et al. (2014). This result could be due to taking fat samples from different 
regions of the rat. The brainstem, temporal lobe, and frontal lobe contained 0.100 (± 0.010), 
0.095 (± 0.010), and 0.104 (± 0.005) mg protein/mg tissue, respectively. These particular 
regions had slightly higher protein content than the reported value of 0.08 for the whole brain by 
Ruark et al. 2014. The skull and cochlea had similar values of 0.007 (± 0.002) and 0.008 (+ 
0.002) mg protein/mg tissue, respectively. Cochlea from Fisher 344 and Sprague Dawley rats 
showed similar protein amounts with a percent standard error between 7.2 and 12.4 percent, as 
defined by standard deviation* 100/average. 


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Table 4. Tissue Protein Content 


Tissue 

Protein Content 
(mg/mg tissue) 

Kidney 

0.188 (±0.030) 

Brainstem 

0.100 (±0.010) 

Temporal Lobe 

0.095 (± 0.010) 

Frontal Lobe 

0.104 (±0.005) 

Left Renal Fat Pad 

0.014 (± 0.006) 

Right Renal Fat Pad 

0.023 (± 0.008) 

Skull 

0.007 (± 0.002) 

Cochlea 

0.008 (± 0.002) 


Note: The protein content of the (wet weight) tissues is reported as an average (+1 standard 
deviation) of ten samples. 


0.25 


0.2 



Kidney Brainstem Temporal Frontal Left renal Right renal Skull Cochlea 

fat pad fat pad 


Tissue 


Figure 6. Protein Content. The reported values are the average protein content in mg/mg 
tissue from ten rat tissues (± 1 standard deviation). 


4.3.3 Lipid Class Determination for Partition Coefficient Prediction. Research was 
conducted on methods to quantify different classes of lipids in tissues to aid in the development 
of modeling parameters. The three classes of lipids that hold importance in estimating partition 
coefficients for PBPK modeling are the neutral, neutral phospholipids and acidic phospholipids 


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(Ruark et al, 2014). Multiple methodologies for quantitation of these lipids were discussed with 
solid phase extraction being the most viable and efficient. 

The lipid content of the kidney, renal fat pads, brainstem, frontal lobe, temporal lobe, skull, and 
cochlea was determined by eluting four classes of lipids. Neutral lipids, free fatty acids, neutral 
phospholipids, and acidic phospholipids were eluted from the tissue using solid phase extraction. 
All tissues except the skull and cochlea were first homogenized in distilled water at a 
concentration of 1.67 pL/mg of tissue. An aliquot equivalent to 170 mg was used for the 
extraction. The skull and cochlea were ground over liquid nitrogen before homogenization in 
distilled water. A sample size of ten was used for the kidney, temporal lobe, and fat. However, 
samples had to be pooled to achieve the appropriate mass for the remaining tissues. The 
brainstem, frontal lobe, and skull samples were pooled into five groups of two, and the cochlea 
had two groups of ten pairs each. 

In order to separate the total lipids from the homogenized tissue, the qualitative method of Bligh 
and Dyer was utilized (1959). For every 1 mL of homogenized tissue sample, 3.75 mL of 1:2 
(volume:volume (v:v)) chloroform-methanol was added, followed by 1.25 mL chloroform 
(CHCb), and then 1.25 mL H 2 O, vortexing vigorously for one minute between each step. The 
mixture was then centrifuged at 1000 rpm at room temperature for five minutes. This causes a 
separation between a water phase, an interphase, and an organic chloroform phase. The 
chloroform phase at the bottom of the vial was collected using a Pasteur pipette and 2.5 mL 
CHCb/mL sample was added to the remaining two phases. The mixture was vortexed for one 
minute and centrifuged as previously described. The new chloroform layer was collected and 
added to the original chloroform layer. The lipid-containing chloroform was evaporated under 
nitrogen until about 100 pL of the extract remained. 

The procedure for separating lipid classes was modified from Kim and Salem (1990). A 500 mg, 
3 mL Bond Elut aminopropyl column was used to elute the lipids. The column was placed on a 
vacuum manifold with pre-weighted glass test tubes underneath to separately collect the lipid 
fractions. The column was first equilibrated with two washes of hexane (3 mL each). The flow 
rate of the column was adjusted to approximately 3 mL per minute. The chloroform extract was 
then added to the column. Neutral lipids were eluted first using 3 mL CHCb-isopropanol (2:1). 
Free fatty acids were eluted next using 3 mL ethyl ether-acetic acid (100:2) and the neutral 
phospholipids were eluted using 3 mL methanol. Lastly, acidic phospholipids were eluted using 
3 mL of hexane-isopropanol-ethanol-0.1 M ammonium acetate in water-formic acid 
(420:350:100:50:0.5) containing 5 percent phosphoric acid (volume:volume). 

The neutral lipids, free fatty acids, and neutral phospholipids were dried under nitrogen and then 
weighed. The acidic phospholipids were evaporated under nitrogen for 10 minutes until the 
hexane layer was completely removed. Water and chloroform (1 mL each) were added to the 
remaining acidic phospholipid fraction. After vortexing vigorously for one minute, the vial was 
left to separate. Separation was complete after approximately 15 minutes. The chloroform layer 
was collected from the bottom of the vial with a Pasteur pipette and saved. This process of 
chloroform addition, vortexing, and separation was repeated twice more, with the collected 
extract layers added to the first. This resulted in approximately 3 mL of combined chloroform 
extract total containing the acidic phospholipids. About 500 mg of anhydrous sodium sulfate 


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was added for 1 hour to dry the combined chloroform phase. After drying, the chloroform 
extract was transferred to a pre-weighted glass test tube and dried under a stream of nitrogen. 


The masses of the four lipid classes were recorded by measuring the weight change of the test 
tubes into which each class was eluted. The masses of the neutral lipids, free fatty acids, neutral 
phospholipids, and acidic phospholipids are listed in Table 5 and shown in Figure 7. The kidney, 
temporal lobe, and fat data are averaged from a sample size of ten; the brainstem, frontal lobe, 
and skull are averaged from a sample size of five; and the cochlea are averaged from a sample 
size of two. The Total Lipids category listed in the table is the accumulation of all four classes. 

Amounts of total lipids and individual lipid classes measured herein confirmed values for 
adipose, kidney, and whole brain reported by Ruark et al. (2014). The highest total lipid 
measurement was detected in renal fat pads; 93 percent of this fat source consists of neutral 
lipids. Kidney and brain regions consist of a higher percentage of neutral phospholipids as 
compared to other lipid classes. 

Individual brain regions displayed differences in total lipid content. The highest lipid content 
was detected in the brainstem when compared to frontal and temporal lobes. Distinct regional 
lipid composition differences in the rat brain have been found previously. Chavko et al. (1993) 
reported twice as much total lipid in the brainstem as compared to frontal and temporal lobes; 
our data concur with this conclusion. 

The total lipid content of skull or cochlea has not been reported previously. Cochlea from Fisher 
344 and Sprague Dawley rats showed similar lipid amounts with a percent standard error 
between 4.8 and 12.2 percent, as defined by standard deviation* 100/average. Both tissues 
showed very low amounts of total lipids when compared to adipose, kidney and brain. Skull and 
cochlea had similar amounts of neutral lipids and neutral phospholipids while acidic 
phospholipids were three times higher in cochlea. These distinct differences in lipid profiles of 
skull and cochlea are in contrast with the very similar water and protein content profiles for these 
tissues as measured in Sections 4.3.1 and 4.3.2 above. 


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Table 5. Tissue Lipid Content 


Tissue 

(n) 

Neutral 

Lipids 

Free 

Fatty Acids 

Neutral 

Phospholipids 

Acidic 

Phospholipids 

Total 

Lipids 

Kidney 

10.9 

4.4 

26.7 

8.0 

50.0 

(10) 

(± 1-9) 

(± 1.7) 

(±6.4) 

(±3.2) 

(± 8.8) 

Brainstem 

23.1 

7.2 

61.6 

18.6 

110.5 

(5) 

(±6.1) 

(±2.1) 

(±6.9) 

(±4.3) 

(±4.0) 

Temporal 

12.9#,%, A 

5.4 

2i.8#.%,& 

H9$,% 

52.0 # - % ' & 

Lobe 

(±5.2) 

(± 1.6) 

(±4.1) 

(±3.2) 

(±2.2) 

(10) 

Frontal 

8.3 # 

6.3 

23 .5 # ,%,& 

13_6%,@ 

51_7#,%,& 

Lobe 

(± 1.2) 

(± 1.2) 

(±2.7) 

(± 1-8) 

(±2.0) 

(5) 

Renal 

780.1" 

15.6 

34.7 

9.2 

839.5 

Fat Pad 

(± 116.4) 

(±7.6) 

(± 12.6) 

(±5.1) 

(± 119.0) 

(10) 

Skull 

2.4 # 

1.1 

3.1 # 

1.3 # 

7.8 # 

(5) 

(± 0.3) 

(±0.4) 

(±0.5) 

(±0.7) 

(±0.5) 

Cochlea 

3.0 # 

1.6 

4.5 # 

4.4 # 

13.6 # 

(2) 

(±0.4) 

(±0.1) 

(±0.3) 

(±0.3) 

(±0.5) 


Note: The average (± standard deviation) content of each lipid fraction for selected tissues is reported in 
pg lipids/mg tissue. "Oily residue. # p<0.0001 vs. brainstem, $ p<0.01 vs. brainstem, % p<0.0001 vs. skull, 
A p<0.01 vs. cochlea, ®p<0.05 vs. cochlea, & p<0.001 vs. cochlea. 


4.3.4 Predicted Partition Coefficients. Tissue:blood PCs were predicted using the tissue 
contents (protein, water and lipid) measured above and the algorithm published by Ruark et al. 
(2014). The algorithm calculation was implemented in R version 3.1.3 (R Core Team, 2015). 

For neutrally charged compounds such as those studied in this report, the algorithm accepts an 
octanokwater PC (K ow ), and a fraction of compound unbound in the plasma (f u b). The f U b was 
assumed to be small, due to the high likelihood of significant binding of these nonpolar 
chemicals to albumin. The sensitivity of the model to this parameter was examined, and found to 
be low. This unexpected lack of sensitivity to fub is due to the fact that we are predicting 
tissue:blood, rather than tissue:plasma partitions, and the red blood celkplasma PC is used to 
make the conversion, softening the impact of plasma protein binding. The Kow used and 
references are shown in Table 6. The value for xylene is an average of the ortho- and meta¬ 
isomers. 


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Table 6. Compound-Specific Parameters for PC Estimation 


Chemical 

Kovv 

Source 

Decane 

6.69 

Montgomery (2007) 

Nonane 

4.46 

Joshi et al. (2010) 

Ethylbenzene 

3.13 

Wasik et al. (1981) 

Xylene 

3.2 

Wasik et al. (1981) 

Toluene 

2.69 

Montgomery (2007) 


Tissue:blood PC values from the published literature were collected for comparison with our 
measured PCs as well as our predicted values. These data are shown in Table 7. The literature 
values for decane in kidney are from Merrill et al. (2008), those for decane in other tissues are 
from Perleberg et al. (2004), and those for the other chemicals are from Table A-2 of Sterner et 
al. (2004). The literature values for xylene are averages of ortho-, meta-, and para- isomers. The 
literature brain PC values are not region-specific, but are included for comparison to brain-region 
data as an approximate match. 

Several plots were constructed to facilitate a visual comparison of the data. Figure 7 shows a 
comparison of the observed (i.e., measured) versus predicted PCs. If the predictions and 
observations matched perfectly, the symbols would fall on the solid line in the figure. It is 
apparent that there are some points with poor agreement. The relative error was calculated and is 
shown in Figure 8 to get a clearer picture of the discrepancies. It appears that there are a couple 
of clusters that fall further from the line. The outlying points were found to be the predictions 
for fat (all chemicals), and decane and nonane (all tissues). 

Figures 9 and 10 are the same as Figure 7 and 8, respectively, except that the fat, decane, and 
nonane data have been removed. Better agreement is found between the predicted and observed 
PC values. 


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Table 7. Tissue:Blood PC Predictions Compared to Measured and Literature Values 


Chemical 

Tissue 

Measured 

Predicted 

Literature 

Source 

Decane 

brain frontal 

2.1 

10 

4.8 

Perleberg et al. (2004) 

brain stem 

1.9 

21 

4.8 

Perleberg et al. (2004) 

brain temporal 

1.4 

10 

4.8 

Perleberg et al. (2004) 

cochlea 

-0.14 

7.6 

NA 

NA 

fat 

260 

160 

330 

Perleberg et al. (2004) 

kidney 

1.3 

9.6 

3.0 

Merrill et al. (2008) 

skull 

1.9 

5.4 

NA 

NA 

Ethylbenzene 

brain frontal 

1.4 

1.3 

0.74 

Sterner et al. (2004) 

brain stem 

2.1 

2.8 

0.74 

Sterner et al. (2004) 

brain temporal 

1.6 

1.3 

0.74 

Sterner et al. (2004) 

cochlea 

0.4 

1.0 

NA 

NA 

fat 

67 

21 

33 

Sterner et al. (2004) 

kidney 

3.4 

1.3 

0.65 

Sterner et al. (2004) 

skull 

0.4 

0.73 

NA 

NA 

Nonane 

brain frontal 

1.6 

7.7 

5.0 

Sterner et al. (2004) 

brain stem 

2.8 

16 

5.0 

Sterner et al. (2004) 

brain temporal 

2.5 

7.7 

5.0 

Sterner et al. (2004) 

cochlea 

-0.32 

5.8 

NA 

NA 

fat 

140 

120 

240 

Sterner et al. (2004) 

kidney 

2.5 

7.4 

NA 

NA 

skull 

1.0 

4.2 

NA 

NA 

Toluene 

brain frontal 

1.5 

0.53 

4.2 

Sterner et al. (2004) 

brain stem 

2.2 

1.1 

4.2 

Sterner et al. (2004) 

brain temporal 

1.6 

0.54 

4.2 

Sterner et al. (2004) 

cochlea 

0.80 

0.41 

NA 

NA 

fat 

56 

8.4 

55 

Sterner et al. (2004) 

kidney 

2.9 

0.51 

4.7 

Sterner et al. (2004) 

skull 

0.47 

0.30 

NA 

NA 

Xylene 

brain frontal 

1.5 

1.5 

1.4 

Sterner et al. (2004) 

brain stem 

2.5 

3.2 

1.4 

Sterner et al. (2004) 

brain temporal 

1.7 

1.5 

1.4 

Sterner et al. (2004) 

cochlea 

0.34 

1.1 

NA 

NA 

fat 

75 

24 

51 

Sterner et al. (2004) 

kidney 

3.1 

1.5 

1.2 

Sterner et al. (2004) 

skull 

0.38 

0.83 

NA 

NA 


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Figure 7. All Observed (Measured) vs. Predicted Tissue:Blood PCs. Solid line represents 
perfect correspondence. 


& 

O 



1 

2 


9 

8 


o 


- 

o 

o 


o 

o 

o 

c 

0 

c 

o 

o 

o 

o 

0 

o 

o 

0 

o 

o 


t -1-1-1-1-1-1-r 

0 5 10 15 20 25 30 35 


Index 


Figure 8. Relative Difference Between All Observed (Measured) and Predicted PCs. 

Horizontal line represents perfect correspondence. 


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Figure 9. Observed (Measured) vs. Predicted PCs Excluding Fat, Nonane, and Decane. 

Solid line represents perfect correspondence. 



Index 


Figure 10. Relative Difference Between All Observed (Measured) and Predicted PCs 
Excluding Fat, Nonane, and Decane. Horizontal line represents perfect correspondence. 


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Figure 11 shows a comparison of measured (Section 4.2.2) PC values with those obtained from 
the literature (Table 7). Overall, the measurements and literature values follow a consistent 
trend, though the measurement of the fat:blood PC for nonane and decane (i.e., the two right¬ 
most circles) are somewhat lower than the values found in the literature. Measurement of the 
fat:air PC is quite variable, so the deviation is not unexpected. 

The standard deviations of the cochlea PC measurements were high relative to the central 
estimates, and the mean and median were negative for some compounds. This result is most 
likely due to the very small size of the cochlea, and the fact that the cochlea is mostly bone with 
relatively little parenchymal tissue. 

As discussed in Section 4.2.3, the cochlea PC measurement standard deviations were high and 
some mean and median values were negative, resulting in low confidence in the measured 
cochlea PCs. An alternative to measuring whole cochlea PCs, namely measuring epithelial cells 
derived from rat cochlea, was attempted. Cochlear epithelial cells (HEI-OC1) were cultured in 
vitro, spun down at 500 x g, and resuspended in PBS twice to form a cell pellet for analysis. 
Theoretically, based on the cochlea being largely bone with a smaller amount of cochlear 
epithelial cells, the PC should fall in between that of the skull (bone) and the pellet. Measured 
PCs for bone, cochlea and cell pellets were graphed to examine the trend of the PCs across these 
tissues (Figure 12). For three of the five chemicals, the expected trend is not observed, further 
reducing confidence in the measured cochlea PCs. 



Figure 11. All Observed vs. Literature Reported Tissue:Blood PCs. Solid line represents 
perfect correspondence. 

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Due to the difficulties in obtaining measured cochlea PCs (discussed in Section 4.2.2) and the 
lack of literature PCs for this tissue, cochlea PCs were estimated for use in the PBPK model 
using a combination of measured and predicted values. Noting that the predicted values are 
consistently slightly higher than that of skull, the cochlea PC was estimated by scaling the skull 
PC measurement by the ratio of the cochlea to skull PC predictions (see Section 4.4 below). In 
this way, the skull PC measurement that is believed to be more accurate is utilized and scaled by 
using the QSPR PC prediction model. 


20 - 


10 


0- 


30 - 


Decane 

• 










9 













Q 

1 



skull 


cochlea 

Nonane 


pellet 


50 - 


40 


30 - 


20 


Ethylbenzene 


• 


























A 


< 

r - 

i i i 


skull cochlea 

Toluene 


pellet 


20 - 


O 

CL 

< 

ai 


c/5 10- 

C/5 


30 - 


20 - 


0 - 


50 

40 

30 

20 


skull 


cochlea 


pellet 


10 - 


Xylene 


• 















• 


« 

1 

1 1 1 

skull cochlea pellet 


Tissue 


skull 


cochlea 


pellet 


Figure 12. Comparison of PCs for Skull, Cochlea, and Cochlear Epithelial Cell Pellet. The 

symbols in the figure represent the tissue:air PC, and they should increase from skull to cochlea 
to pellet for each chemical. 


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4.4 Physiologically-Based Pharmacokinetic Modeling of Mixtures using Array Coding 

Traditionally mixtures models are simply coded as parallel individual models with lines of code 
for each individual chemical in the mixture, for each and every tissue in each model (Figure 13). 
Interactions between the chemicals in the mixture are coded within the pertinent tissue; for 
example, competitive metabolism is coded in the liver. Increasing the number of chemicals in 
the model increases the lines of code required. This practice results in very long models and 
many opportunities to incorporate errors into the code. 


DECANE 


NONANE 


XYLENES 


ETHYLBENZENE 


TOLUENE 


intravenous dose inhalation/exhalation 



Figure 13. Schematic of Traditional Parallel Style Mixtures PBPK Models. Interactions 
between the chemicals in the mixture are coded within the pertinent tissue and are not shown in 
the schematic. 


An existing published model for a volatile organic chemical (Clewell el al, 2001) was adapted to 
an array version in order to streamline the modification process for the mixtures PBPK model 
being developed in this project. The array model has the same structure (tissues) for all 
chemicals in the mixture. Each tissue is coded only once, regardless of the number of 


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components in the mixture. Physiological parameters that would not change based on the 
chemical are still defined as scalar values, but chemical-specific parameters and physico¬ 
chemical properties, which cause each chemical in the model to behave differently, are defined 
as arrays. Examples of these properties include molecular weights, tissue:blood partition 
coefficients, and metabolic constants. 

The prototype array model was coded to simulate the kinetics of up to five chemicals 
simultaneously in five tissues or tissue groups: brain, fat, liver, rapidly perfused tissues, and 
slowly perfused tissues. Once the array model was validated against the original non-array 
version, a cochlea compartment was added and the brain compartment was split into three 
parallel tissues, brainstem, temporal lobe, and the remaining brain tissue (Figure 14). 

Rat physiological parameters were based on the published version from which the array model 
was originally adapted (Clewell et al., 2001). The array model was parameterized based largely 
on physicochemical parameters from published models for toluene, ethylbenzene, and xylene 
(Haddad et al., 1999), nonane (Robinson and Merrill, 2008), and decane (Merrill el al., 2008) 
(Table 8). Some parameters required fitting using various inhalation data sources; fitting was 
necessitated by minor differences in coding from published models. 

Cochlea blood flow is well characterized in the literature (Hillerdal, 1987; Hillerdal et al., 1987; 
Larsen et al., 1984) and averaged for use in modeling by Robinson et al. (2013). The proportion 
of blood in cochlea tissue was calculated from guinea pig data in Morizono et al. (1968). The 
volume of paired cochlea tissue was measured in this study (Section 4.2). The partition 
coefficients were also derived using results from this study (Sections 4.3 and 4.4). 

Brain region volumes were determined both from literature (Delp et al., 1991) and from temporal 
lobes excised for this study. Blood flow to the brain regions were calculated using Gjedde et al. 
(1980). The proportion of blood in brain tissue was found in Brown et al. (1997) and was 
assumed to be the same between regions. Partition coefficients for the brain regions for different 
chemicals were found in previously published PBPK models and then scaled using the differing 
partition coefficients measured in this study (Table 8). 

The prototype model best described chemicals which readily partition into tissues from the blood 
and therefore their movement into a tissue is described by the partition coefficient and the flow 
of blood into the tissue (i.e., flow limited). For some chemicals, specifically nonane and decane, 
additional time is required for the chemical to cross membranes into the tissue; this movement is 
described as diffusion limited. As two of the key hydrocarbons being evaluated in this model are 
diffusion limited, each tissue in the array model was adapted to accommodate diffusion limited 
movement between the tissues and the blood entering the tissues. The array model code, written 
for acslX (Aegis Technologies Group, Orlando FL), is found in Appendix A; the M files required 
for its execution are found in Appendix B. 

Pharmacokinetic studies for each key component were used to parameterize and validate the 
model structure. Once reasonable fits were obtained for individual components (Appendix C), 
the model was utilized to predict target tissue concentrations following a select JP-8 exposure. 
Guthrie et al. (2014) exposed Long-Evans rats (males, mean starting body weight 105 g) to 1000 


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mg/m 3 JP-8, 6 hours per day, 5 days a week, for 4 weeks, without noise. This important 
exposure resulted in central auditory system deficits but not peripheral hearing changes. 


A. 


inhalation/exhalation 


44 



metabolism 


B. 


p 

M 

Tissue Blood 

t 

1 ^ Tissue 

_/ 


Figure 14. Physiologically-Based Pharmacokinetic Model Schematic. (A) General model 
schematic. (B) Each tissue represented above is divided into the tissue itself and the tissue 
blood. This convention allows the model to simulate diffusion limited chemicals. 


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Table 8. Array PBPK Physico-Chemical Parameters for Key Hydrocarbons 


Parameter 

Constant 

Name 

Toluene 

Ethyl 

benzene 

Xylene 

Nonane 

Decane 

BloodiAir PC 

PC 

18.0 a 

42.7 a 

46.0 a 

5.2 b 

5.0 C 

Tissue:Blood Partition Coefficients (Unitless) 

Brain Remainder 

PBm 

2.0 d 

1.22* 

1.38* 

5.0 b 

10.0** 

Brain Stem 

PStm 

(1.81/1.26)PBm e 

1.93* 

2.29* 

(2.9/1.7)PBnf 

(1.56/1.75)PBm e 

Temporal Lobe 

PTL 

(Od/l^PBrn 5 

1.44* 

1.61* 

(2.6/1.7)PBm 5 

(1.16/1.75)PBm e 

Cochlea 

PCoc 

0.54 f 

0.44 f 

0.47 f 

1.45 f 

2.15 f 

Fat 

PFat 

56.7 a 

36.4 a 

40.4 a 

282.0 b 

328.0 C 

Liver 

PLiv 

4.64 a 

1.96 a 

1.98 a 

8.0** 

3.0** 

Rapidly Perfused 
Tissue 

PRap 

4.64 a 


1.98 a 

2.0 b 

3.0 C 

Slowly Perfused 
Tissue 

PSlw 

1.54 a 


m 

4.0 b 

0.85 c 

Inhalation. Metabolic & Clearance Parameters 

Maximum Rate 
of Reaction 

Vmax 

3.44 a 

6.39** 

6.49 a 

0.1** 

0.005** 

Affinity Constant 

Km 

0.13 a 

1.04** 

0.45 a 

0.1** 

0.1** 

Urinary Clearance 

ClUrC 

0.004** 

0.04** 

0.004** 

0.04** 

0.004** 

Upper Respiratory 
Tract Scrubbing 

Scrub 

0 

0 

0 

0.4** 

0.7 C 

Permeability:Area Affinity Constants 

Brain Remainder 

PABrn 

1000? 

1000? 

1000? 

0.5 b 

0.005** 

Brain Stem 

PAStm 

1000? 

1000? 

1000? 

0.5 h 

0.005 b 

Temporal Lobe 

PATL 

1000? 

1000? 

1000? 

0.5 b 

0.005 b 

Cochlea 

PACoc 

1000? 

1000? 

1000? 

1.0‘ 

1.0) 

Fat 

PAFat 

1000? 

1000? 

1000? 

0.5** 

0.07** 

Liver 

PALiv 

1000? 

1000? 

1000? 

0.07 b 

0.15 c 

Rapidly Perfused 
Tissues 

PARap 

1000? 

1000? 

1000? 

1.0 b 

0.005** 

Slowly Perfused 
Tissues 

PASlw 

1000? 

1000? 

1000? 

0.5 b 

0.14** 


Notes: *Measured; **Fittodata; “Haddad et al. (1999); b Robinson & Merrill (2008); “Merrill et al.(2008); 
d Unpublished in-house model; e Scaled using measured PC ratio between regions & fit value; ‘Scaled from 
measured skull PC using predicted cochlea:skull ratio; g No diffusion limitation; h Same as PABrn; 'Same as PARap; 
'PARap in Merrill et al. (2008) 


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In order to simulate this exposure, the relative contribution of the key components to the JP-8 
fuel utilized in the study had to be calculated. The study used a Jet A blend composed for 
research purposes; this blend, known as POSF 4658, is composed of equal parts Jet A fuels from 
five different manufacturers. POSF log book numbers are provided by the Air Force Research 
Laboratory Fuels and Energy Branch (AFRL/RQTF) located at WPAFB OH, formerly known as 
the Air Force Wright Aeronautical Laboratories (AFWAL/POSF). Tandem gas chromatography 
analysis by the 2006 Shafer et al. method indicates that POSF 4658 contains 0.16 percent 
toluene, 0.12 percent ethylbenzene, 0.66 percent xylenes, 1.14 percent nonane, and 2.55 percent 
decane; percent values are by weight. 

All toxicity tests using POSF 4658 are performed with the commercial Jet A fuel blend 
following the addition of a standard additive package required for Air Force JP-8 fuel; the 
additive package comprises less than 0.15 percent by weight. As this is a small percentage, the 
presence of the additive package in the fuel was not figured into the estimation of the key 
component concentrations for the purpose of simulation. 


4.4.1 PBPK Model Results and Discussion. Target tissue estimations following a single day 
under the Guthrie et al. (2014) 1000 mg/m 3 JP-8 inhalation exposure scenario are shown in 
Figures 15 through 19. Although JP-8 contains a higher concentration of nonane than xylenes, a 
higher peak venous blood concentration of xylenes is expected (Figure 15). Merrill et al. (2008) 
incorporated upper respiratory tract scrubbing into their decane model to account for the relative 
decrease in uptake compared to the inhaled concentration. This scrubbing factor was utilized in 
the array model and, for nonane, a smaller scrubbing factor was fit to simulate published data 
(Table 8). In the Guthrie exposure scenario prediction (Figure 15), scrubbing reduces the nonane 
uptake and a higher xylenes concentration prediction is observed. 

Due to nonane and decane having relatively high partition coefficients compared to the aromatic 
compounds, these alkanes are predicted to have the highest concentrations in target tissues, 
despite the scrubbing that occurs during inhalation (Figures 16 through 19). Central auditory 
deficits were observed at this exposure concentration by Guthrie et al. (2014); peripheral 
(cochlear) effects are not. Central auditory effects are associated with alkanes such as nonane 
and decane (Lund et al., 1996; SCOEL, 2007) while cochlear effects are generally attributed to 
aromatics such as toluene, ethylbenzene and specific xylenes (Johnson and Morata, 2010). 

Ligure 16 indicates that relatively small concentrations of aromatics are predicted to be found in 
the cochlea in this inhalation scenario (less than 0.005 mg/L for toluene and ethylbenzene). In 
contrast, Ligures 17 and 18 show that relatively high concentrations of the alkanes are predicted 
to partition to the brain stem and temporal lobe during exposure (more than 0.3 mg/L decane in 
the brain stem); these brain tissues are expected to be the active sites for central auditory 
disruption. 

These estimations also are useful for planning a pharmacokinetic study designed to measure key 
components in these tissues after a similar daily exposure. Ligure 16 indicates the very low 
predicted level of detection required to successfully measure key component concentrations in 
the cochlea. 


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Predictions for Guthrie et al. (2014) Exposure 
5 Key Hydrocarbons in JP-8 



0 12 34 56 78 9 10 11 12 


Hours 


0-Toluene 

0- Ethylbenzene 

0- Xylene 

0- Nonane 

0- Decane 


Figure 15. Venous Blood Key Component Concentration Predictions for 1000 mg/m 3 JP-8 
Exposure from Guthrie et al. (2014) Exposure Profile 


Predictions for Guthrie et al. (2014) Exposure 


5 Key Hydrocarbons in JP-8 



0 - Toluene 

0- Ethylbenzene 

0 - Xylene 

0 - Nonane 

0 - Decane 


Figure 16. Cochlea Key Component Concentration Predictions for 1000 mg/m 3 JP-8 
Exposure from Guthrie et al. (2014) Exposure Profile 


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Predictions for Guthrie et al. (2014) Exposure 
5 Key Hydrocarbons in JP-8 



0- Toluene 

0- Ethylbenzene 

0 - Xylene 

0 - Nonane 

0- Decane 


Figure 17. Brain Stem Key Component Concentration Predictions for 1000 mg/m 3 JP-8 
Exposure from Guthrie et al. (2014) Exposure Profile 


Predictions for Guthrie et al. (2014) Exposure 
5 Key Hydrocarbons in JP-8 



0 - Toluene 

0- Ethylbenzene 

0 - Xylene 

0 - Nonane 

0- Decane 


Figure 18. Temporal Lobe Key Component Concentration Predictions for 1000 mg/m 3 JP- 
8 Exposure from Guthrie et al. (2014) Exposure Profile 


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Predictions for Guthrie et al. (2014) Exposure 


5 Key Hydrocarbons in JP-8 



@-Toluene 

@- Ethylbenzene 

@ - Xylene 

@- Nonane 

0- Decane 


Figure 19. Remainder of Brain Key Component Concentration Predictions for 1000 mg/m 3 
JP-8 Exposure from Guthrie et al. (2014) Exposure Profile 


5.0 PHARMACODYNAMIC MODELING OF JET FUEL EFFECTS ON CENTRAL 
AUDITORY PATHWAY ENCODING OF AUDITORY STIMULI 

Initially, the working hypothesis was that free radical (FR) generation and oxidative stress 
combine to form the primary mechanism through which ototoxicants disrupt hearing and through 
which they potentiate the effects of noise on hearing loss. This concept led to a 
pharmacodynamic model for FR mediated cochlear damage. This model was first outlined in 
Robinson et al. (2013) and further developed in Robinson et al. (2015). The model provided a 
method to combine dosimetric predictions of JP-8 at the target site (via a PBPK model), together 
with its effect, and the effect of noise, on the generation of free radicals in the cochlea, and their 
impact on inducing cellular damage and hearing loss. The model assumed that the inactivation 
of cellular targets, leading to hearing loss, was determined by the area under the FR 
concentration-time curve for the target (cochlea). The model included lipid peroxidation, the 
generation of lipid peroxides, such as polyunsaturated fatty acid radicals (PUFAR) and peroxy 
polyunsaturated fatty acid radicals (PPUFAR), and the model was linked to biomarkers including 
the oxidative stress marker malondialdehyde and glutathione depletion to facilitate future testing 
of the FR hypothesis. Finally, loss of functionality (hearing loss) and hair cell death were 
described as outputs of the model. 

The oxidative stress model for jet fuel exacerbation of noise-induced hearing loss is fully 
described in Robinson et al. (2015) and will not be discussed further here, since following 
analysis of the CAPD shown in the Guthrie et al. (2014) data, it became apparent that the effect 
of jet fuel on central auditory pathway (CAP) encoding of auditory stimuli is a more sensitive 
endpoint for hearing loss. Literature research resulted in the hypothesis that binding of toluene 
and other jet fuel components to gamma-aminobutyric acid (GABA) receptors (allosteric 
modulation) likely leads to increased initial activity and the adaptive response so that binding 
over time may lead to down regulation of the GABA receptor, thereby diminishing GABAa 

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currents (see Figure 20) (Bale et al., 2005a; Krasowski and Harrison, 2000). Voltage-dependent 
Ca ++ channels (VDCCs) regulate neurotransmitter release in the central synaptic network. 
Toluene also can mimic the effects of VDCC blockers (Maguin et al., 2009). In this context, a 
simple neuronal circuit model was developed in order to provide a basis for simulating evoked 
response waveforms (Figure 20), and a model framework for describing synaptic 
neurotransmitter kinetics was proposed. Model codes for the pharmacodynamic models are 
found in Appendix D. 


5.1 Justification for Modeling Central Auditory Pathway 

Recent evidence suggests that auditory processing dysfunctions are early manifestations of JP-8 
induced ototoxicity. Chronic exposure to jet fuel component toluene can impair the functioning 
of the central nervous system, sharing many effects with nervous system depressant compounds 
such as ethanol, barbiturates and anesthetics (Maguin et al., 2009). Anesthetics and related 
compounds were thought to perturb the fluidity of the plasma membrane (LeBel and Schatz, 
1989). Several kinds of ion channels expressed in neurons are thought to be affected. For 
instance, NMDA (Cruz et al., 1998), GABAa (Krasowski and Harrison, 2000), glycine 
(Beckstead et al., 2000), ATP (Woodward et al., 2004), serotonin (Lopreato et al., 2003), and 
nicotinic acetylcholine (Bale et al., 2002, 2005b) receptors are sensitive to toluene. In addition, 
toluene alters the function of different voltage-dependent ion channels (Cruz et al., 2003) 
including VDCCs (Tillar et al., 2002). Significant differences are seen between groups of 
normal-hearing solvent mixtures-exposed humans compared to normal-hearing humans without 
solvent mixtures exposure, in a battery of central auditory functioning tests which include: pure- 
tone audiometry, dichotic digits, pitch pattern sequence, filtered speech, random gap detection, 
masking level difference, and hearing-in-noise tests (Fuente et al., 2011). 

Gene expression analysis of JP-8 exposed rats revealed a modulation of several genes, including 
GABA transporter 3 (GAT-3) (Lin et al., 2001). Components of JP-8 such as toluene have been 
shown to be GABAa allosteric modulators. Hester et al. (2011) have shown that toluene 
exposure was associated with induction or repression of genes in pathways associated with 
synaptic plasticity, including long-term depression, GABA receptor signaling and mitochondrial 
function. Toluene exposures have also been shown to depress the auditory nervous system in 
rats by interfering with muscle contractions which help to dampen the effects of loud noises 
(Maguin et al., 2009). Acute exposures to volatile aromatics such as those found in jet fuel 
enhance inhibitory GABAa, while prolonged exposures leads to diminished GABAa currents 
(Bale et al., 2005a). Changes in the plasticity of the auditory central nervous system can result in 
tinnitus, an epileptic-like auditory phenomenon (Shulman et al., 2002). Both GABAa and 
GABAb receptor level changes, as well as glutamic acid decarboxylate (GAD-37), have been 
implicated in CAPDs (Kou et al., 2013). Gene expression analysis of JP-8 exposed rats 
compared to the control group revealed a modulation of several related genes, including GAT-3 
(Lin et al., 2001). Jet fuel exposure alone may increase susceptibility to noise-induced hearing 
loss and tinnitus. 


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W| 



Figure 20. Schematic Showing Potential Impact Sites of Toluene on Transmission of Wave 
Function W. Potential sites include (a) via voltage dependent calcium channels (VDCC); (b) 
via binding to and (allosteric) modulation of GABAa receptors; and (c) via longer term 
alterations in post-synaptic receptor. 


Our recent in vivo studies on Long-Evans rats (Guthrie et al., 2014) showed that exposure to 
subtoxic levels of JP-8 (1000 mg/m 3 ) did not induce peripheral hearing loss. However, there was 
impaired brainstem encoding of stimulus intensity at four weeks after the exposure. 

Furthermore, this impairment in stimulus encoding was exacerbated by low level (non¬ 
damaging) noise (8 kHz octave band at 85 dBA sound pressure level) exposure. The results 
revealed that subototoxic exposures to noise and JP-8 resulted in normal peripheral auditory 
function concomitant with central auditory processing deficits. Thus it appears that transmission 
deficits in the auditory pathway may play a critical, and early, role in hearing loss in addition to 
oxidative stress-induced peripheral hair cell impairment and loss. 

Altered growth functions of the brainstem components of the auditory brainstem response are 
also characteristic of aging (Boettcher et al., 1993; Popelar et al., 2006; Zhou et al., 2006). 
Potential mechanisms for such a process may include altered membrane fluidity (characteristic of 
solvent/anesthetic exposure) leading to potential impairments of vesicular transport and/or fusion 
at the synapses within the auditory brainstem. In humans, CAPD may be a first sign of subtle 


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brain alteration (Bamiou et al., 2000). Further, abnormal stimulus-response function in the 
central auditory nervous system has been associated with non-hearing disorders such as 
migraines and depression (Ambrosini et al., 2003; Gallinat et al., 2000). Oxidative stress may 
also play a role in CAPD (Henderson et al., 2006). In addition, postulated mechanisms for 
tinnitus show an interesting connection between loss of peripheral cells and compensatory 
mechanisms in the remaining auditory pathway (Schaette and Kempter, 2012). 

The ABR is a non-invasive biomarker, particularly well-suited for quantifying deficits in 
auditory information processing of complex noise. These effects were exacerbated when 
coupled with noise (8 kHz octave band at 85 dBA sound pressure level (SPL)) (Guthrie et al., 
2014). In addition, several solvents such as toluene and xylene, which are also JP-8 components, 
have been shown to induce both CNS toxicity and mid-frequency auditory impairments. In 
house in vitro studies with HEI-OC1 cells (immortalized mouse cochlear epithelial cells) indicate 
marginal cytotoxicity. Given the minimal effects of JP-8 on peripheral auditory systems, both in 
vivo and in vitro, central auditory processing dysfunctions may be an early manifestation of JP-8 
induced toxicity. 

Based on these data and a review of current literature, the preliminary hypothesis is that chronic 
JP-8 exposure alters the distribution of excitatory and inhibitory neurotransmission-related 
proteins, which likely play a key role in hearing loss and, potentially, the development of 
tinnitus. To test the hypothesis, a cellular network model was built to focus on disruption of 
receptors GAB Aval and NMDA (containing NR2A subunit) along the auditory pathway, linking 
potential mechanisms with the ABR. Model parameters for each neuron type include: receptor 
densities at the synapse, neurotransmitter kinetic parameters (release/synthesis, 
removal/degradation) at the synapse and receptor binding kinetics (affinities, maximum 
capacities). The preliminary model describes qualitatively the ABR data (changes in the 
auditory evoked potential waveform as a result of fuel exposure, Guthrie et al. (2014)), in terms 
of hypothetical alterations in receptor densities. 

Although this is a preliminary model at this stage, mechanism-based pharmacodynamic (PD) 
models such as the one described here can be used to provide a quantitative basis for 
extrapolation of CAPD responses over different doses (fuel and/or noise). When linked to the 
PBPK model described in Section 4 above, the resulting PBPK/PD models would ultimately 
allow extrapolation of CAPD effects in animals to predict potential quantitative hearing loss 
effects in humans. As specific causative mechanisms are further uncovered, this work could be 
expanded to include new approaches to protection and possible future therapeutic intervention. 


5.2 Simple Neuronal Circuit Model 

In order to link the delivered dose of jet fuel components described above with a potential 
mechanism for fuel-induced alterations in auditory processing, and in particular for observed 
deficits in measured evoked response encoding of stimulus intensity, we assume that fuel 
exposure may alter synaptic transmission through alterations in synaptic receptor densities. To 
explore this mechanism, a mathematical model of a simple neuronal circuit in the dorsal cochlear 
nucleus was developed based on Kou et al. (2013) (Figure 21). The model includes the 


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following attributes: frequency coding of signal intensity, saturation of receptors at high signal 
intensities, and longer term alterations in signal processing mediated by changes in receptor 
densities at the synapse. 



Auditory pathway 
(excitatory) 


Interneurons 

(inhibitory) 


Figure 21. Schematic of a Simple Neuron Circuit in the Dorsal Cochlear Nucleus of the 
CAP. The model is based on a neuron circuit described in Kou et al. (2013). Sfi, Sd and Sc2, 
represent signal intensities transmitted by the fusiform cell, and each of the cartwheel cells. R 
represents receptor densities; subscripts/and c refer to the fusiform and cartwheel cells; and 
subscripts i and e refer to inhibitory and excitatory connections, respectively. PBPK predicted 
dosimetry of jet fuel components (not shown) are linked to the circuit model via modulation of 
receptor densities (i?, and Re). 


The signal intensities Sfi, Sd and Sc2, transmitted by the fusiform cell, and each of the cartwheel 
cells, respectively, are given by: 


~ _ ^ejl ' So 

fl ~ S n + K 


e.f 1 


Pj,f\ ' (S c , + S c 2 ) 

S cl +S c2+ K e,fl 


Equation 2 


o _ ^e,c\ ' P 0 

^1 ' 


P 0 +K e , cl 


Pe,c2 ' Pq 


Pq + ^e,c2 


Pj,c2 ' S el 

S ci + K i.c2 


Equation 3 


Equation 4 


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.. .where subscripts i and e refer to inhibitory and excitatory connections; K represents signal 
intensities that half the maximum response; and Po represents parallel fiber (inhibitory) input. 


5.3 Evoked Response Waveform Simulation 

The ABR allows for simultaneous evaluation of the peripheral cochlear nerve and the ascending 
auditory brainstem pathway. Guthrie et al. (2014) measured impairment in brainstem response 
in rats to changes in sound stimulus level. The green line in Figure 22 shows the effect of prior 
long-term fuel exposure on WII waveform, compared with control (black thick line). The upper 
dotted curve shows preliminary model simulations using the simple neuron circuit model 
described above. The lower dotted curve shows the effect of reducing the inhibitory receptor 
density Ru-i by a factor of 10 from the upper curve, leading to a decrease in the evoked response 
that roughly corresponds to the impact of fuel exposure. 


5.4 Model of Synaptic Neurotransmitter Kinetics 

In the simple neuron circuit model proposed here, neurotransmitter levels in the synaptic cleft 
link the firing rate of the pre-synaptic neuron (related to the sound intensity), with the activation 
of receptors on the post-synaptic membrane (via receptor ), and the transmission of the signal. 
The blocking effect of jet fuel components such as toluene on VDCCs will directly impact the 
release of neurotransmitters, and can be incorporated into the model at this point. 

The model of synaptic neurotransmitter kinetics works on the following assumptions: A) 
presynaptic axonal firing rate (frequency/) is proportional to input sound intensity; B) each pre- 
synaptic action potential causes the release of a standard unit of neurotransmitter (NT); and C) 
neurotransmitters are removed from the synaptic cleft with a first-order rate constant k, which 
represent the “refractory period” of the signal (turnover of NT). Altering pre-synaptic firing 
rates (/) then would affect NT concentrations in the synaptic cleft (Figure 23). After a 
sufficiently long period of time, NT levels in the synaptic cleft would reach a plateau value, as 
shown by the dotted line in Figure 23. 

The pre-synaptic neuron input signal intensity can now be linked with neurotransmitter levels at 
its axonal output. Using receptor occupancy modeling, this neurotransmitter level will then 
determine the response of the post-synaptic neuron, and (ultimately) signal transmission along 
the CAP. 


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1.0 


j= 0.8 


> 

<u 

\ S \ 

C 

& 

«/) 

oc 

T3 

0 > 

-* 

o 

> 

Ui 


0.6 

0.4 

0.2 


0.0 



Sound Signal Intensity (dB) 


100 


Figure 22. Evoked Response Encoding of Stimulus Intensity. See text for explanation. 




Time Time 

Figure 23. Simulations of Neurotransmitter Concentrations in Synaptic Cleft of Unit 
Volume for Two Different Pre-Synaptic Firing Rates. Firing rates for these simulations are/ 
= 1 (left) and/= 2 (right). 


6.0 CONCLUSIONS AND FUTURE WORK 


6.1 PBPK Model Parameter Measurement 

Overall, obtaining reasonable partition coefficients for the PBPK model was more challenging 
than predicted. The inherent properties (size, bony nature) of cochlea made it difficult to get 
measured PCs. Due to the unavailability of previously measured PCs for cochlea, it is difficult 


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to determine if the predicted values were appropriate or not. Cochlea and other honey structures 
are heterogeneous tissues with compositions consisting largely of hydroxyapatite (a mineral 
phase) (Boskey, 2013). The algorithms in Ruark et al. (2014) do not account for this 
composition. As the cochleae are a very small tissue in the body, the overall PBPK model is not 
very sensitive to the cochlea PC for an individual chemical; however, the predicted tissue 
concentration for the cochleae will be greatly affected. Therefore, the predicted concentration 
could be an order of magnitude higher or lower than predicted, making lower detection limits in 
the cochlea tissue even more important in order to quantify the hydrocarbon level present. 

The equation from Ruark et al. (2014) appeared to be inadequate to predict fat partition 
coefficients for any of these very lipophilic compounds. Further, the equation did not agree well 
with measurements of nonane and decane partition coefficients in any tissues. These two 
aliphatic hydrocarbons have different chemical properties than toluene, ethylbenzene and xylene, 
and are not at all related to the chemicals (drugs) in the database from which the equation was 
developed. A reinvestigation of the Ruark equations to accommodate highly lipophilic 
compounds and better predict fat concentrations is recommended. 

A theoretical approach to predicting partition coefficients in different brain regions, based on 
regional white matter to gray matter ratios, is presented in Appendix E. Although insufficient 
data on regional brain compositions were found to progress the theory beyond a preliminary 
concept, it should be noted that this capability is not restricted to hearing loss simulation, but 
could be beneficial to any project that involves determining regional brain delivery of a chemical 
(and for which such data is limited). 


6.2 PBPK Model Improvements 

Since central auditory pathway dysfunction has been found as an early indicator of JP-8 hearing 
loss in rats (Guthrie et al., 2014), the PBPK model was expanded to contain not only a cochlea 
compartment, but also a brainstem and temporal lobe (central auditory pathway regions) plus a 
brain remainder compartment. In order to streamline the addition of these tissues, the model was 
first re-written into array format, allowing the addition of only one set of code per tissue, instead 
of five sets of code corresponding to each key hydrocarbon per tissue added. Addition of tissues 
into a PBPK model requires parameterization, including tissue weights (fraction of body weight), 
partition coefficients, and blood flows (fraction of cardiac output). Data reported herein and 
literature values were both utilized to parameterize the PBPK model. 

The parameterized model was then utilized to estimate concentrations of the key hydrocarbons in 
cochlea, brain stem, temporal lobe, and the remaining brain tissue for the 1000 mg/m 3 JP-8 
exposure scenario detailed by Guthrie et al. (2014). These predictions indicate the anticipated 
levels of detection needed to measure the key hydrocarbons in these tissues in a pharmacokinetic 
experiment using this exposure scenario. The PBPK model in its current state requires validation 
with tissue concentrations from such an experiment. Following validation, predicted 
concentrations of hydrocarbons in target brain regions can be linked to waveforms in the neural 
network model to estimate the magnitude of effect. 


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6.3 Pharmacodynamic Models 

The multistage model described in Section 5 above needs to be parameterized in a future project 
using experimental data. In such an experiment, brain slices collected from JP-8 exposed rats 
could be used for quantification of changes in the inhibitory signaling system (e.g., GABAa 
receptor subtypes, GABA transaminase, GAT-3) within specific auditory nuclei along the central 
auditory pathway, using immunohistochemistry and other techniques. 

Predictions from our model, suitably parameterized and validated, can be compared with human 
exposure/effect studies currently being conducted in Japan. Our laboratory has initiated an 
international agreement with the Japanese Air Self Defense Force Aeromedical Laboratory for 
the “Comparison of Operational Jet Fuel and Noise Exposures.” 


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APPENDIX A. ARRAY PHARMACOKINETIC MODEL CODE 


Note that acslX allows for longer lines than Microsoft Word, in which this appendix is 
formatted. Use of this code in acslX will require careful vetting to ensure that lines are begun in 
a format acceptable in acslX and to ensure that code is not arbitrarily split between lines. 


PROGRAM: JFHL_5HC.CSL 

! NOTE: CINT is only 2x TSQInf - may have to decrease CINT for SQ somulations 
! Model currently set up to run for 5 chemicals 

! Will run for fewer chemicals without modifications as long as parameters are set 
accordingly 

! Can change the model to run for more or less by only changing the "5" in the line 
"PARAMETER (NChem=5)" 


! ******************* set init.m to load at run time ***************** 


INITIAL 

INTEGER NChem 
PARAMETER (NChem=5) 

DIMENSION MW(NChem), PB(NChem), PLq(NChem), PMuc(NChem), PBrn(NChem), PStm(NChem), 
PTL(NChem) 

DIMENSION PCoc (NChem), PFat(NChem), PLiv (NChem), PRap(NChem), PSlw(NChem), 

PSQ(NChem) 

DIMENSION PABrn(NChem), PAStm(NChem), PATL(NChem), PACoc(NChem), PAFat(NChem), 

PALiv(NChem), PARap(NChem), PASlw(NChem), PASQ(NChem) 

DIMENSION VMaxC(NChem), KM(NChem), KFC(NChem), ClUrC(NChem), kUrtC(NChem), 

Scrub(NChem) 

DIMENSION kAD(NChem), kAS(NChem), kTD(NChem), kTSD(NChem) 

DIMENSION Cone(NChem), IVDose(NChem), PDose(NChem), PDrink(NChem), SQDose(NChem) 
DIMENSION VMax(NChem), KF(NChem), ClUr(NChem), kUrt(NChem) 

DIMENSION IAArt(NChem), IABrn(NChem), IABrnBld(NChem), IAStm(NChem), 

IAStmBld(NChem), IATL(NChem), IATLBld(NChem) 

DIMENSION IACoC(NChem), IACocBld(NChem), IAFat(NChem), IAFatBld(NChem), 

IALiv(NChem), IALivBld(NChem) 

DIMENSION IARap(NChem), IARapBld(NChem), IASlw(NChem), IASlwBld(NChem), IASQ(NChem), 
IASQBld(NChem) 

DIMENSION AIO(NChem), Drink(NChem), IVR(NChem), SQR(NChem), TotDose(NChem) 

DIMENSION IAExh(NChem), IAMuc(NChem), IAO(NChem), IAExc(NChem), IADu(NChem), 

IASt(NChem), IAMetl(NChem), IAMet2(NChem) 

DIMENSION IAUrn(NChem), IAUCCArt(NChem), IAUCCBrn(NChem), IATotlnh(NChem), 

IATotIV(NChem), IATotDrink(NChem), IATotSQ(NChem) 

DIMENSION DAUCCArt(NChem), DAUCCBrn(NChem), PAUCCArt(NChem), PAUCCBrn(NChem) 
DIMENSION CAlv(NChem), CAlvPPM(NChem), CP(NChem), CEnd(NChem), CEndPPM(NChem), 

PerEnd(NChem), CMix(NChem), CMixPPM(NChem), PerMix(NChem) 

DIMENSION RACh(NChem), RAMuc(NChem), RAArt(NChem), RAExh(NChem), RABrn(NChem), 
RABrnBld(NChem), RAStm(NChem), RAStmBld(NChem) 

DIMENSION RATL(NChem), RATLBld(NChem), RACoc(NChem), RACocBld(NChem), RAFat(NChem), 
RAFatBld(NChem) 

DIMENSION RAO(NChem), RAExc(NChem), RADu(NChem), RASt(NChem) 

DIMENSION RALiv(NChem), RALivBld(NChem), RAMetl(NChem), RAMet2(NChem), RARap(NChem), 
RARapBld(NChem) 

DIMENSION RASlw(NChem), RASlwBld(NChem), RASQ(NChem), RASQBld(NChem), RAUrn(NChem), 
RATotlnh(NChem) 

DIMENSION CInh(NChem), CMuc(NChem), CArt(NChem), CBrn(NChem), CBrnBld(NChem), 

CBrnDif(NChem) 


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DIMENSION CStm(NChem), CStmBld(NChem), CStmDif(NChem), CTL(NChem), CTLBld(NChem), 
CTLDif(NChem) 

DIMENSION CCoc(NChem), CCocBld(NChem), CCocDif(NChem), CFat(NChem), CFatDif(NChem), 
CFatBld(NChem) 

DIMENSION CLiv (NChem), CLivBld(NChem), CLivDif (NChem), CRap(NChem), CRapBld(NChem), 
CRapDif(NChem) 

DIMENSION CSFC(NChem), CSlw(NChem), CSlwBld(NChem), CSlwDif(NChem), CSQ(NChem), 
CSQBld(NChem), CSQDif(NChem) 

DIMENSION CVen(NChem) 

DIMENSION ACh(NChem), AMuc(NChem), AArt(NChem), AUCCArt(NChem), AExh(NChem), 

ABrn (NChem), ABrnBld(NChem), AUCCBrn(NChem) 

DIMENSION AStm(NChem), AStmBld(NChem), ATL (NChem) , ATLBld(NChem), ACoc(NChem), 
ACocBld(NChem) 

DIMENSION AFat(NChem), AFatBld (NChem), AO(NChem), AExc(NChem), ADu(NChem), 

ASt(NChem) 

DIMENSION ALiv(NChem), ALivBld(NChem), AMetl(NChem), AMet2(NChem), ARap(NChem), 
ARapBld(NChem) 

DIMENSION ASlw(NChem), ASlwBld(NChem), ASQ(NChem), ASQBld(NChem), AUrn(NChem) 

DIMENSION ATotlnh(NChem), ATotIV(NChem), ATotDrink(NChem), ATotSQ(NChem) 

DIMENSION CBrnTotBld(NChem), CBrnTot(NChem), TDose(NChem), AmtBody(NChem), 

MassBal(NChem) 


INTEGER i, jl, j 2, j3, j4, j5, j 6, 
il2, il3, il4, il5, il6, il7, il8 
LOGICAL CC 


j7, il, i2, i3, i4, i5, i6, i7. 


i9, ilO, ill, 


! To control whether closed or open chamber 


! Total Pulmonary Ventilation Rate (L/hr for 1 kg animal) 

CONSTANT QPC =24.75 ! Total pulmonary ventilation (L/hr - 1 kg) 

! Blood Flows (fraction of cardiac output) 

CONSTANT QCC =14.6 ! Cardiac output (L/hr - 1 kg animal) 

CONSTANT QBrnC = 0.013 ! Brain Remainder (0.02 - QStmC - QTLC) 

CONSTANT QStmC = 0.004 ! Brain Stem 

CONSTANT QTLC = 0.003 ! Temporal Lobe 

CONSTANT QCocC = 0.00004 ! Cochlea pair 

CONSTANT QFatC =0.07 ! Fat 

CONSTANT QLivC = 0.183 ! Liver 

CONSTANT QRapC = 0.557 ! Rapidly perfused 

CONSTANT QSlwC = 0.16996 ! Slowly perfused (includes skin and 

subcutaneous compartment) 


CONSTANT QSQg = 0.012 
(L/ (hr*g), based on rat muscle 


! Flow to subcutaneous compartment per g 
! (slowly perfused), from Sterner et al. 2013 


! Tissue Volumes (fraction of body weight) 

CONSTANT BW = 0.22 ! Body weight (kg) 

CONSTANT VAlvC = 0.007 ! Alveolar blood 

CONSTANT VBrnC = 0.004 ! Brain Remainder (0.006 - VStmC - VTLC) 

CONSTANT VStmC = 0.001 ! Brain Stem 

CONSTANT VTLC = 0.001 ! Brain Temporal Lobe 

CONSTANT VFatC =0.10 ! Fat 

CONSTANT VLivC = 0.034 ! Liver 

CONSTANT VMucC = 0.0001 ! Mucous 

CONSTANT VRapC = 0.044 ! Rapidly perfused 

CONSTANT VSlwC =0.65 ! Slowly perfused (includes skin, cochlea, SQ) 

CONSTANT DS = 0.15 ! Dead space volume (fraction) 


CONSTANT VolSQ = 0.282 ! mL ~= g; Max value for this protocol = 0.282 

mL = 0.282 g 

! Parameter max value should be no larger than 

25 mL/kg BW for a rat, 


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! per AALAS Learnrng Library, 2005 
! Tissue Volumes (fraction of tissue volume) 


CONSTANT 

VBrnBldC 

= 0.03 

CONSTANT 

VStmBldC 

= 0.03 

CONSTANT 

VTLBldC 

= 0.03 

CONSTANT 

VCocBldC 

= 0.0183 

CONSTANT 

VFatBldC 

= 0.0154 

CONSTANT 

VLivBldC 

= 0.034 

CONSTANT 

VRapBldC 

= 0.2075 

CONSTANT 

VSlwBldC 

= 0.0333 

CONSTANT 

VSQBldC 

= 0.0167 


flow, the other half is injection 

! Molecular Weights (mg/mmole) 
CONSTANT MW = NChem*1.0 

! Tissue/Blood Partition Coefficients 


CONSTANT 

PB 

= NChem*l.0 

CONSTANT 

PLq 

= NChem*1.0 

CONSTANT 

PMuc 

= NChem*l.0 

CONSTANT 

PBrn 

= NChem*l.0 

CONSTANT 

PStm 

= NChem*1.0 

CONSTANT 

PTL 

= NChem*l.0 

CONSTANT 

PCoc 

= NChem*l.0 

CONSTANT 

PFat 

= NChem*1.0 

CONSTANT 

PLiv 

= NChem*l.0 

CONSTANT 

PRap 

= NChem*1.0 

CONSTANT 

PSlw 

= NChem*1.0 

CONSTANT 

PSQ 

= NChem*l.0 


! Brain Remainder 
! Brain Stem 
! Brain Temporal Lobe 
! Cochlea 
! Fat 
! Liver 

! Rapidly Perfused Tissues 
! Slowly Perfused Tissues 
! Equals 1/2 VSlwBldC 

! Only half the compartment will have blood 


! Blood/air 
! Saline/air 
! Mucous/air 
! Brain Remainder 
! Brain Stem 
! Brain Temporal Lobe 
! Cochlea 
! Fat 
! Liver 

! Rapidly perfused tissue 
! Slowly perfused tissue 
! Subcutaneous compartment 


! Tissue Permeability Coefficients 
CONSTANT PABrn = NChem*1000.0 
CONSTANT PAStm = NChem*1000.0 
CONSTANT PATL = NChem*1000.0 
CONSTANT PACoc = NChem*1000.0 
CONSTANT PAFat = NChem*1000.0 
CONSTANT PALiv = NChem*1000.0 
CONSTANT PARap = NChem*1000.0 
CONSTANT PASlw = NChem*1000.0 
CONSTANT PASQ = NChem*1000.0 


Brain remainder diffusion limitation 

Brain stem diffusion limitation 

Brain temporal lobe diffusion limitation 

Cochlea diffusion limitation 

Fat diffusion limitation 

Liver diffusion limitation 

Rapidly perfused tissues diffusion limitation 
Slowly perfused tissues diffusion limitation 
Subcutaneous compartment diffusion limitation 


! Metabolism Parameters 

CONSTANT VMaxC = NChem*0.0 
CONSTANT KM = NChem*1.0 
CONSTANT KFC = NChem*0.0 


! Maximum reaction rate (mg/hr) 

! Michaelis-Menten (mg/L) 

! First order rate constant (/hr) 


! Uptake and Clearance Parameters 

CONSTANT ClUrC = NChem*0.0 

CONSTANT kUrtC = NChem*0.0 

CONSTANT Scrub = NChem*0.0 

absorbed into blood or mucus) 


CONSTANT 

kAD 

NChem*0.0 

CONSTANT 

kAS 

NChem*0.0 

CONSTANT 

kTD 

NChem*0.0 

CONSTANT 

kTSD = 

NChem*0.0 

Dosing Parameters 


CONSTANT 

Cone 

= NChem* 

CONSTANT 

IVDose 

= NChem* 

CONSTANT 

PDose 

= NChem* 

CONSTANT 

PDrink 

= NChem* 


! Urinary clearance (L/hr) 

! URT uptake into mucus (L/hr) 

! Fraction of inhaled dose scrubbed (not 

! Absorption from duodenum (/hr) 

! Absorption from stomach (/hr) 

! Excretion (/hr) 

! Transfer - stomach to duodenum (/hr) 


Inhaled concentration (ppm) 

IV dose (mg/kg) 

Oral dose (mg/kg) 

Drinking water dose (mg/kg/day) 


51 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 







CONSTANT 

SQDose 

= NChem* 0.0 

! Subcutaneous dose (mg/kg) 

CONSTANT 

TChng 

= 0.0 

! Length of inhalation exposure (hrs 

CONSTANT 

DaysWk 

= 1.0 

! Number of exposure days per week 

CONSTANT 

TMax 

= 24.0 

! Maximum time for exposures 

CONSTANT 

TInf 

= 0.20 

! Length of IV injection (hrs) 

CONSTANT 

TSQInf 

= 0.02 

! Length of SQ injection (hrs) 

CONSTANT 

Rats 

= 1.0 

! Number of animals in experiment 

Chamber Parameter 



CONSTANT 

CC 

.FALSE. 

! Default to open chamber 

CONSTANT 

VChC = 

9.1 

! Volume of closed chamber 

CONSTANT 

kLCC = 

0.0 

! Chamber loss 

Simulation 

Control 

Parameters 


CONSTANT 

TStop = 

24.0 


CINTERVAL 

CINT = 

0.01 



! Set Chamber Volume and Loss for Open and Closed Chamber Exposures 
IF (.NOT. CC) THEN 

VCh = 1.0e+20 ! Large chamber = open chamber 

kLC =0.0 

ELSE 

VCh = VChC - (Rats * BW) ! Calculate net chamber volume 

kLC = kLCC 
ENDIF 

! Scaled Pulmonary Ventilation Rate (L/hr) 

QCN = QCC * (BW**0.7 5) 

QP = QPC * (BW**0.7 5) 

QAlv = 0.67 * QP 


! Scaled Blood Flows (L/hr) and Tissue Volumes (L) 


QSQC = QSQg * VolSQ 
size of injection, 
al., 2013 


! [L/ (hr*g) * g] = L/ (hr) 

! Compartment is assumed to affect tissue equal to the 
! with appropriate flow for that size; See Sterner et 


QBrn = QBrnC * QCN 


QStm = QStmC * QCN 
QTL = QTLC * QCN 


QCoc 

= QCocC 

~k 

QCN ! cochlea pair 

QFat 

= QFatC 

~k 

QCN 

QLiv 

= QLivC 

~k 

QCN 

QRap 

= QRapC 

:k 

QCN 

QSQ 

= QSQC 

* 

QCN 

VSQC 

= ( (VolSQ 

*2.0)71000.0)/BW 


fraction BW 

Volume of subcutaneous tissue 


compartment is 2x the size of the injection 

twice its size by the injection) 

VAlv = VAlvC * BW 
VMuc = VMucC * BW 

VBrn = (VBrnC * (1 - VBrnBldC) ) * BW 

VStm = (VStmC * (1 - VStmBldC) ) * BW 

VTL = (VTLC * (1 - VTLBldC) ) * BW 

VCocM = 0.00007 *(BW**0.2348) 
pair mass (kg) 

VCoc = VCocM * (1 - VCocBldC) 

VFat = (VFatC * (1 - VFatBldC) ) * BW 

VLiv = (VLivC * (1 - VLivBldC) ) * BW 


(equal tissue affected balooned to 


Data based calculation of cochlea 


52 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 




VRap = (VRapC * (1 - VRapBldC) ) * BW 
VSQ = (VSQC * (1 - VSQBldC) ) * BW 


VBrnBld = VBrnBldC * (VBrnC * BW) 


VStmBld = VStmBldC * (VStmC * BW) 


VTLBld 

= VTLBldC 

* 

(VTLC 


BW) 

VCocBld = 

VCocBldC 

-k 

VCocM 



VFatBld = 

VFatBldC 

: k 

(VFatC 

* 

BW) 

VLivBld = 

VLivBldC 


(VLivC 


BW) 

VRapBld = 

VRapBldC 

~k 

(VRapC 

~k 

BW) 

VSlwBld = 

VSlwBldC 


(VSlwC 

~k 

BW) 

VSQBld = 

VSQBldC 

★ 

(VSQC 


BW) 

QSlw = 

(QSlwC * 

QCN) - QSQ 


QC 

QBrn + QStm 

+ QTL 

+ 

QCoc 

QTot = 

QCN - QC 






+ QFat + QLiv + QRap + QSlw 


+ 


QSQ 


VSlw = ((VSlwC * (1 - VSlwBldC) ) * BW) - VCoc - VCocBld - VSQ - VSQBld 

VTotTis = VBrn + VStm + VTL + VCoc + VFat + VLiv + VRap + VSlw + VSQ 
VTotBld = VBrnBld + VStmBld + VTLBld + VCocBld + VFatBld + VLivBld + VRapBld + 
VSlwBld + VSQBld 

VTot = (VTotTis + VTotBld)/BW 


DO 1030 i=l, NChem 

! Scaled Metabolism Parameters 

VMax(i) = VMaxC(i) * (BW**0.75) 

KF(i) = KFC(i) / (BW**0.25) 

! Scaled Clearance Rates 

ClUr(i) = ClUrC(i) * (BW**0.75) 

kUrt(i) = (min(kUrtC(i), QPC)) * (BW**0.75) 

! Initial Amounts (mg) 

IAArt(i) =0.0 
IABrn(i) =0.0 
IAStm(i) =0.0 
IATL(i) =0.0 
IABrnBld(i) =0.0 
IAStmBld(i ) =0.0 
IATLBld(i) =0.0 
IACoc(i) =0.0 

IACocBld(i) =0.0 
IAFat(i) =0.0 

IAFatBld(i) =0.0 
IALiv(i) =0.0 

IALivBld(i) =0.0 
IARap(i) =0.0 

IARapBld(i) =0.0 
IASlw(i) = 0.0 

IASlwBld(i) = 0.0 
IASQ(i) =0.0 

IASQBld(i) =0.0 


! Scaled and Initial Doses 

AI0(i) = (Conc(i) * VCh * MW(i)) / 24450.0 
in chamber 

Drink(i) = (PDrink(i) * BW) / 24.0 
dose (mg/hr) 

IVR(i) =0.0 
SQR(i) =0.0 
TotDose (i) =0.0 
balance check 


Initial amount 

Drinking water 

IV dose 
SQ Dose 

Facilitate mass 


53 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 




! Initialize Initial Values for Integrating State Variables 
IAExh(i) =0.0 
IAMuc(i) =0.0 
IAO(i) =0.0 
IAExc(i) =0.0 
IADu(i) =0.0 
IASt(i) = 0.0 
IAMetl(i) = 0.0 
IAMet2(i) = 0.0 
IAUrn(i) =0.0 
IAUCCArt(i) =0.0 
IAUCCBrn(i) =0.0 
IATotlnh(i) =0.0 
IATotIV(i) = 0.0 
IATotDrink(i) =0.0 
IATotSQ(i) =0.0 


! Initialize Starting Values 
DAUCCArt(i) =0.0 
DAUCCBrn(i) =0.0 
PAUCCArt(i) =0.0 
PAUCCBrn ( i) =0.0 
PerEnd(i) = 0.0 
PerMix(i) = 0.0 
1030: CONTINUE 


i 


Initialize 
Cl Zone 
CSFZone 
DayExp 


Starting Values 
= 1.0 
= 0.0 
= 1.0 


END 


End of Initial 


DYNAMIC 

ALGORITHM IALG = 2 ! Gear stiff 

method 


DISCRETE DoseOn ! Start dosing 

INTERVAL Doselnt =24.0 ! Interval to 

repeat dosing 

SCHEDULE DoseOffIV .AT. T + TInf 
SCHEDULE DoseOfflhl .AT. T + TChng 
SCHEDULE DoseOffSQ .AT. T + TSQInf 

IF (((T .LT. TMax) .AND. (DayExp .LE. DaysWk)) .OR. CC) THEN 
Cl Zone = 1.0 
ENDIF 


DayExp = DayExp + 1.0 

IF (DayExp .GT. 7.0) THEN 
DayExp = 0.5 
ENDIF 

/ TInf ! Rate of intravenous dosing 

(PDose(jl) * BW) 


DO 1040 jl=l, NChem 

IF (T .LE. TInf) THEN 

IVR(jl) = (IVDose(j1) * BW) 
(mg/hr) 

TotDose(jl) = TotDose(jl) t 


54 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



ENDIF 

1040: CONTINUE 

DO 104 5 j 6=1, NChem 

IF (T. LE. TSQInf) THEN 

SQR(j6) = (SQDose(j6) * BW) / TSQInf ! Rate of SQ dosing (mg/hr) 

ENDIF 

1045: CONTINUE 
END 


DISCRETE DoseOffIV 
DO 1050 j2=1, NChem 
IVR(j 2) = 0.0 
1050: CONTINUE 
END 


DISCRETE DoseOfflhl 
IF (.NOT. CC) THEN 
Cl Zone = 0.0 
ENDIF 
END 


DISCRETE DoseOffSQ 
DO 1055 j7=l, NChem 
SQR(j 7) = 0.0 
1055: CONTINUE 
END 


DISCRETE Calc ! Calculate daily average AUC (D) using previous value (P) 

! Note that this is calculated for the Brain Remainder and not the whole brain 
INTERVAL Calclnt =24.0 

DO 1060 j3=1, NChem 

DAUCCArt(j 3) = (AUCCArt(j3) - PAUCCArt(j3)) / (Calclnt / 24.0) 

DAUCCBrn(j 3) = (AUCCBrn(j3) - PAUCCBrn(j3)) / (Calclnt / 24.0) 

IF (T .GT. 0.0) THEN 

PAUCCArt(j3) =AUCCArt(j3) 

PAUCCBrn(j3) =AUCCBrn(j3) 

ENDIF 

1060: CONTINUE 

END ! End of Calc 


Calculate Parameters Used for Plotting 


Hours = T 

Minutes = T * 60.0 
Days = T / 24.0 

DO 2010 il=l, NChem 

! Concentration in Alveolar Air (mg/L and ppm) 

CAlv(il) = CArt(il) / PB(il) 

CAlvPPM(il) = (CArt(il) / PB(il)) * (24450.0 / MW(il)) 

! Concentration in Inhaled Air (ppm) 

CP(il) = (Clnh(il) * 24450.0) / MW(il) 


55 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 






! Concentration in End-Exhaled Air (mg/L, ppm and percent) 

CEnd(il) = RAExh(il) / QAlv 

CEndPPM(il) = CEnd(il) * (24450.0 / MW(il)) 

IF (Cone(il) .GT. 0.0) THEN 

PerEnd(il) = (CEnd(il) / ((Conc(il) * MW(il)) / 24450.0)) * 100.0 
ENDIF 


! Concentration in Mixed Exhaled Air (mg/L, ppm and percent) 

CMix(il) = ((1.0 - DS) * CEnd(il)) + (DS * Clnh(il)) 

CMixPPM(il) = CMix(il) * (24450.0 / MW(il)) 

IF (Cone (il) .GT. 0.0) THEN 

PerMix(il) = (CMix(il) / ((Conc(il) * MW(il)) / 24450.0)) * 100.0 
ENDIF 

2010: CONTINUE 


CArtTot =0.0 
AExhAll =0.0 

DO 2020 i2=l, NChem 

! Total Concentration in Arterial Blood (mg/L) 
CArtTot = CArtTot + CArt(i2) 

! Total Amount Exhaled (mg) 

AExhAll = AExhAll + AExh(i2) 

2020: CONTINUE 


DERIVATIVE 

i_ 


! Amount in Inhaled Air (mg) 

ACh = INTVC(RACh, AI0) 


! Concentration in Inhaled Air (mg/L) and Rate of Change in Chamber Concentration 
(mg/hr) 

PROCEDURAL (CInh, RACh = QAlv, VCh, kLC, kUrt, CIZone, CMuc, CArt) 

DO 3030 i3=l, NChem 

CInh(i3) = ( (ACh(13) / VCh) * (1-Scrub (13)) ) * CIZone 

RACh(i3) = (Rats * ((-QAlv * CInh(i3)) + (QAlv * (CArt(i3) / PB(i3))) + 

(kUrt(i3) * ( (CMuc (i3) / PMuc(i3)) - (CArt(i3) / PB(i3))))))& 

& - (kLC * ACh (13) ) 

3030: CONTINUE 
END 


! Amount in Mucous (mg) 

AMuc = INTVC(RAMuc, IAMuc) 

! Amount in Arterial Blood (mg) 

AArt = INTVC(RAArt, IAArt) 

AUCCArt = INTVC(CArt, IAUCCArt) 

! Amount in Exhaled Air (mg) 

AExh = INTVC(RAExh, IAExh) 

PROCEDURAL (CMuc, CArt = VAlv, VMuc) 

DO 3040 i4=l, NChem 

CMuc(i4) = AMuc(i4) / VMuc ! Concentration 

of in mucous (mg/L) 

CArt(i4) = AArt(i4) / VAlv ! Concentration 

of in arterial blood (mg/L) 

3040: CONTINUE 


56 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 





END 


i 


i 


PROCEDURAL (CVen, RAMuc, RAArt, RAExh = QBrn, CBrnBld, QStm, CStmBld, QTL, CTLBld, 
QCoc, CCocBld, QFat, CFatBld, QLiv, CLivBld, & 

& QRap, CRapBld, QSlw, CSlwBld, QSQ, CSQBld, IVR, QC, kUrt, CInh, CMuc, 
PMuc, CArt, PB, QAlv, CVen, RAUrn, QAlv) 

DO 3050 i5=l, NChem 

! Concentration in Mixed Venous Blood (mg/L) 

CVen(i5) = (QBrn*CBrnBld(i5) + QStm *CStmBld(i5) t QTL*CTLBld(i5) + 

QCoc*CCocBld (i5) t QFat*CFatBld(i5) + QLiv*CLivBld(i5) & 

& + QRap*CRapBld(i5) + QSlw*CSlwBld(i5) t QSQ*CSQBld(i5) t IVR(i5)) / QC 

! Rate of Change in Mucus (mg/hr) 

RAMuc(i5) = (kUrt(i5) * (CInh(i5) - (CMuc(i5) / PMuc(i5)))) - (kUrt(i5) * 

( (CMuc(i5) / PMuc(i5)) - (CArt(i5) / PB(i5)))) 


! Rate of Change in Arterial Blood (mg/hr) 

RAArt(i5) = (QAlv * CInh(i5)) - (kUrt(i5) * (CInh(i5) - (CMuc(i5) / PMuc(i5)))) 
- (QAlv * (CArt(i5) / PB(i5))) & 

& t (QC * (CVen(15) - CArt(i5))) - RAUrn(15) 

! Rate of Change in Exhaled Air (mg/hr) 

RAExh(15) = (QAlv * (CArt(i5) / PB(i5))) + (kUrt(i5) * ((CMuc(i5) / PMuc(i5)) - 
(CArt(15) / PB(i5)))) 

3050: CONTINUE 
END 


! Brain Remainder Blood and Brain Remainder Tissue 


ABrnBld = INTVC(RABrnBld, IABrnBld) 
brain remainder blood (mg) 

ABrn = INTVC(RABrn, IABrn) 
brain remainder tissue (mg) 

AUCCBrn = INTVC(CBrn, IAUCCBrn) 

curve for the concentration in brain remainder tissue (hr*mg/L) 


! Amount in 
! Amount in 
! Area under the 


PROCEDURAL (CBrnBld, CBrn, CBrnDif, RABrnBld, RABrn = ABrnBld, 
PBrn, QBrn, CArt, PABrn) 

DO 3060 i6=l, NChem 

CBrnBld(16) = ABrnBld(16) / VBrnBld 
in brain remainder blood (mg/L) 

CBrn (i6) = ABrn(i6) / VBrn 
in brain remainder tissue (mg/L) 

CBrnDif(16) = CBrn(i6) / PBrn(i6) 
available for diffusion (mg/L) 

RABrnBld(i6) = (QBrn *(CArt(i6) - CBrnBld(i6)))+(PABrn(16) 
CBrnBld(i6))) 

in brain remainder blood (mg/hr) 

RABrn(i6) = PABrn(i6) * (CBrnBld(16) - CBrnDif(16)) 
in brain remainder tissue (mg/hr) 


VBrnBld, ABrn, VBrn, 

! Concentration 
! Concentration 
! Concentration 
* (CBrnDif(16) - 

! Rate of change 
! Rate of change 


3060: CONTINUE 


57 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 









! Brain Stem and Brain Stem Tissue 


AStmBld = INTVC(RAStmBld, IAStmBld) ! Amount in 

brain stem blood (mg) 

AStm = INTVC(RAStm, IAStm) ! Amount in 

brain stem tissue (mg) 

PROCEDURAL (CStmBld, CStm, CStmDif, RAStmBld, RAStm = AStmBld, VStmBld, AStm, VStm, 
PStm, QStm, CArt, PAStm) 

DO 3062 il6=1, NChem 

CStmBld(il6) = AStmBld(il6) / VStmBld ! 

Concentration in brain stem blood (mg/L) 

CStm(il6) = AStm(il6) / VStm ! 

Concentration in brain stem tissue (mg/L) 

CStmDif(il6) = CStm(il6) / PStm(il6) ! 

Concentration available for diffusion (mg/L) 

RAStmBld(il6) = (QStm *(CArt(il6) - CStmBld(il6)))+(PAStm(il6) * (CStmDif(il6) - 
CStmBld(il6))) 

! Rate of 

change in brain stem blood (mg/hr) 

RAStm(il6) = PAStm(il6) * (CStmBld(il6) - CStmDif(il6)) ! Rate of 

change in brain stem tissue (mg/hr) 

3062: CONTINUE 
END 

i_ 



! Brain Temporal Lobe and Brain Temporal Lobe Tissue 

ATLBld = INTVC(RATLBld, IATLBld) 
temporal lobe blood (mg) 

ATL = INTVC(RATL, IATL) 
temporal lobe tissue (mg) 

PROCEDURAL (CTLBld, CTL, CTLDif, RATLBld, RATL = ATLBld, VTLBld, ATL, VTL, PTL, QTL, 
CArt, PATL) 

DO 3064 il7=1, NChem 

CTLBld(il7) = ATLBld(il7) / VTLBld ! Concentration 

in brain temporal lobe blood (mg/L) 

CTL(il7) = ATL(i17) / VTL ! Concentration 

in brain temporal lobe tissue (mg/L) 

CTLDif(il7) = CTL(il7) / PTL(il7) ! Concentration 

available for diffusion (mg/L) 

RATLBld(il7) = (QTL *(CArt(il7) - CTLBld(i17)))+(PATL(il7) * (CTLDif(il7) - 
CTLBld(i17))) 

! Rate of change 

in brain temporal lobe blood (mg/hr) 

RATL(il7) = PATL(il7) * (CTLBld(i17) - CTLDif(i17)) ! Rate of change 

in brain tissue (mg/hr) 

3064: CONTINUE 
END 


! Amount rn brarn 
! Amount in brain 


58 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 









! Cochlea Tissue Blood and Cochlea Tissue 


ACocBld = INTVC(RACocBld, IACocBld) ! Amount in 

cochlea blood (mg) 

ACoc = INTVC(RACoc, IACoc) ! Amount in 

cochlea tissue (mg) 

PROCEDURAL (CCoCBld, CCoC, CCoCDif, RACoCBld, RACoC = ACocBld, VCoCBld, ACoC, VCoC, 
PCoC, QCoC, CArt, PACoC) 

DO 3070 il5=1, NChem 

CCocBld(il5) = ACocBld(il5) / VCocBld ! Concentration 

in cochlea blood (mg/L) 

CCoc(il5) = ACoc(il5) / VCoc ! Concentration 

in cochlea (mg/L) 

CCocDif (115) = CCoc(il5) / PCoc(il5) ! Concentration 

available for diffusion (mg/L) 

RACocBld(115) = (QCoc *(CArt(il5) - CCocBld(il5)))+(PACoc(il5) * (CCocDif(il5) - 
CCocBld(il5))) 

! Rate of change 

in cochlea blood (mg/hr) 

RACoc(115) = PACoc(115) * (CCocBld(il5) - CCocDif(115)) ! Rate of change 

in cochlea (mg/hr) 

3070: CONTINUE 
END 

i_ 



! Fat Blood and Fat Tissue 

AFatBld = INTVC(RAFatBld, IAFatBld) 

! Amount in fat blood (mg) 

AFat = INTVC(RAFat, IAFat) ! AMount in fat 

tissue (mg) 

PROCEDURAL (CFatBld, CFat, CFatDif, RAFatBld, RAFat = AFatBld, VFatBld, AFat, VFat, 
PFat, QFat, CArt, PAFat) 

DO 3075 i7=l, NChem 

CFatBld(i7) = AFatBld(i7) / VFatBld ! Concentration 

in fat blood (mg/L) 

CFat(i7) = AFat(i7) / VFat ! Concentration 

in fat tissue (mg/L) 

CFatDif(i7) = CFat(i7) / PFat(i7) ! Concentration 

available for diffusion (mg/L) 

RAFatBld(i7) = (QFat *(CArt(i7) - CFatBld(i7))) + (PAFat(i7) * (CFatDif (i7) - 
CFatBld(i7))) 

! Rate of change 

in fat blood (mg/hr) 

RAFat(i7) = PAFat(i7) * (CFatBld(i7) - CFatDif (17)) ! Rate of change 

in fat tissue (mg/hr) 

3075: CONTINUE 
END 

i_ 


59 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 










I 


! Digestive Tract 
! Amount Absorbed (mg) 

AO = INTVC(RAO, IAO) 

! Amount Excreted (mg) 

AExc = INTVC(RAExc, IAExc) 

! Amount Duodenum (mg) 

ADu = INTVC(RADu, IADu) 

! Amount Leaving the Stomach (mg) 

! (ASt changed to (INTVC(RASt, IASt)) and is replaced in other equations with 
(TotDose(i) - ASt(i)) since ASt 

! could not be defined as (TotDose - INTVC(RASt, 0.0))) 

ASt = INTVC(RASt, IASt) 

! Rate of Change in Amount Absorbed, Amount Excreted, Amount in Duodenum and Amount 
Leaving Stomach (mg/hr) 

PROCEDURAL (RAO, RAExc, RADu, RASt = TotDose) 

DO 3080 i8=l, NChem 

RAO(i8) = (kAS (i8) * (TotDose(i8) - ASt(i8))) + (kAD(i8) * ADu(i8)) 

RAExc(i8) = kTD (i8) * ADu(i8) 

RADu(i8) = (kTSD(i8) * (TotDose(i8) - ASt(i8))) - (kAD(i8) * ADu(i8)) - 
(kTD(i8) * ADu(i8)) 

RASt(i8) = (kAS(i8) * (TotDose(i8) - ASt(i8))) + (kTSD(i8) * (TotDose(i8) - 

ASt (i8)) ) 

3080: CONTINUE 
END 


! Liver Blood and Liver Tissue 

ALivBld = INTVC(RALivBld, IALivBld) ! Amount in liver 

blood (mg) 

ALiv = INTVC(RALiv, IALiv) ! Amount in 

liver tissue (mg) 


AMetl 

metabolized, 

AMet2 

metabolized. 


= INTVC(RAMetl, IAMetl) 
saturable (mg) 

= INTVC(RAMet2, IAMet2) 
first order (mg) 


! Amount 
! Amount 


PROCEDURAL (CLivBld, CLiv, CLivDif, RALivBld, RAMetl, RAMet2, RALiv = ALivBld, 
VLivBld, ALiv, VLiv, PLiv, QLiv, CArt, PALiv, VMax, KF, RAO, Drink) 

DO 3090 i9=l, NChem 

CLivBld(i9) = ALivBld(i9) / VLivBld ! 

Concentration in liver blood (mg/L) 

CLiv(i9) = ALiv(i9) / VLiv ! 

Concentration in liver tissue (mg/L) 

CLivDif (i9) = CLiv(i9) / PLiv(i9) ! 

Concentration available for diffusion (mg/L) 

RALivBld(i9) = (QLiv *(CArt(i9) - CLivBld(i9)))+(PALiv(i9) * (CLivDif(i9) - 
CLivBld(i9))) 

! Rate of 

change in liver blood (mg/hr) 

RAMetl(i9) = (VMax(i9) * CLivDif (i9)) / (KM(i9) + CLivDif(i9)) ! Saturable 

rate of metabolism in liver (mg/hr) 


60 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 








RAMet2(i9) = KF(i9) * CLivDif(i9) * VLiv 
order rate of metabolism in liver (mg/hr) 

RALiv(19) = (PALiv(i9) * (CLivBld(i9) - CLivDif (19) )) 
RAMet2(i9) + RAO(i9) + Drink (i9) 

change in liver tissue (mg/hr) 

3090: CONTINUE 
END 


! First- 


RAMetl(i9) - 

! Rate of 


i 


! Rapidly Perfused Tissue Blood and Rapidly Perfused Tissue 

ARapBld = INTVC(RARapBld, IARapBld) ! Amount in 

rapidly perfused tissue blood (mg) 

ARap = INTVC(RARap, IARap) ! Amount in 

rapidly perfused tissue (mg) 


! Concentration 
! Concentration 
! Concentration 


PROCEDURAL (CRapBld, CRap, CRapDif, RARapBld, RARap = ARapBld, VRapBld, ARap, VRap, 
PRap, QRap, CArt, PARap) 

DO 3100 il0=1, NChem 

CRapBld(ilO) = ARapBld(ilO) / VRapBld 
in rapidly perfused tissue blood (mg/L) 

CRap(ilO) = ARap(i10) / VRap 
in rapidly perfused tissue (mg/L) 

CRapDif(ilO) = CRap(ilO) / PRap(ilO) 
available for diffusion (mg/L) 

RARapBld(ilO) = (QRap *(CArt(il0) - CRapBld(il0)))+(PARap(il0) * (CRapDif(il0) - 
CRapBld(il0))) 

! Rate of change 

in rapidly perfused tissue blood (mg/hr) 

RARap(ilO) = PARap(ilO) * (CRapBld(il0) - CRapDif(ilO)) ! Rate of change 

in rapidly perfused tissue (mg/hr) 

3100: CONTINUE 
END 

i_ 


! Slowly Perfused Tissue Blood and Slowly Perfused Tissue 

ASlwBld = INTVC(RASlwBld, IASlwBld) ! Amount in 

slowly perfused tissue blood (mg) 

ASlw = INTVC(RASlw, IASlw) ! Amount in 

slowly perfused tissue (mg) 

PROCEDURAL (CSlwBld, CSlw, CSlwDif, RASlwBld, RASlw = ASlwBld, VSlwBld, ASlw, VSlw, 
PSlw, QSlw, CArt, PASlw) 

DO 3120 il2=l, NChem 

CSlwBld(il2) = ASlwBld(il2) / VSlwBld ! Concentration 

in slowly perfused tissue blood (mg/L) 

CSlw(il2) = ASlw(il2) / VSlw ! Concentration 

in slowly perfused tissue (mg/L) 

CSlwDif (il2) = CSlw(il2) / PSlw(il2) ! Concentration 

available for diffusion (mg/L) 

RASlwBld(il2) = (QSlw *(CArt(il2) - CSlwBld(il2)))+(PASlw(il2) * (CSlwDif(il2) - 
CSlwBld(i12))) 


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! Rate of change 


in slowly perfused tissue blood (mg/hr) 

RASlw(il2) = PASlw(il2) * (CSlwBld(il2) - CSlwDif (il2)) ! Rate of change 

in slowly perfused tissue (mg/hr) 

3120: CONTINUE 
END 


i 


! Subcutaneous Compartment Blood and Subcutaneous Compartment Tissue 

ASQBld = INTVC(RASQBld, IASQBld) ! Amount in blood 

(mg) 

ASQ = INTVC(RASQ, IASQ) ! Amount in tissue 

(mg) 

PROCEDURAL (CSQBld, CSQ, CSQDif, RASQBld, RASQ = ASQBld, VSQBld, ASQ, VSQ, PSQ, QSQ, 
CArt, PASQ) 

DO 3025 il8=1, NChem 

CSQBld(il8) = ASQBld(il8) / VSQBld ! Concentration in 

blood (mg/L) 

CSQ(il8) = ASQ(il8) / VSQ ! Concentration in 

tissue (mg/L) 

CSQDif(il8) = CSQ (i18) / PSQ(il8) ! Concentration 

available for diffusion (mg/L) 

RASQBld(il8) = (QSQ *(CArt(il8) - CSQBld(il8)))+(PASQ(il8) * (CSQDif(il8) - 
CSQBld(i18))) 

! Rate of change in 

blood (mg/hr) 

RASQ(i18) = PASQ(il8) * (CSQBld(il8) - CSQDif(il8)) + SQR(il8) 

! Rate of change in 

tissue (mg/hr) 

3025: CONTINUE 
END 

i_ 


! Amount in Urine (mg) 

AUrn = INTVC(RAUrn, IAUrn) 


! Rate of Excretion (mg/hr) 

PROCEDURAL (RAUrn = ClUr, CArt) 

DO 3130 il3=l, NChem 

RAUrn(il3) = ClUr(il3) * CArt(il3) 
3130: CONTINUE 
END 


CHECK MASS BALANCE 


! Total Amount Inhaled (mg) 

ATotlnh = INTVC(RATotlnh, IATotlnh) 

! Total Amount Injected (mg) 

ATotIV = INTVC(IVR, IATotIV) 


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! Total Amount Drunk (mg) 

ATotDrink = INTVC(Drink, IATotDrink) 

! Total Amount SQ (mg) 

ATotSQ = INTVC (SQR, IATotSQ) 


! Calculating Total Brain Concentration (mg/kg), Total Dose (mg), Total Amount in Body 
(mg) and Mass Balance (mg) 

PROCEDURAL (CBrnTotBld, CBrnTot, TDose, AmtBody, MassBal, RATotlnh = ABrnBld, 
AStmBld, ATLBld, VBrnBld, VStmBld, VTLBld, ABrn, AStm, ATL, VBrn, VStm, VTL,& 

& QAlv, CInh, ATotlnh, ATotIV, TotDose, ATotDrink, ATotSQ, AMuc, AArt, 
ACoc, ACocBld, AFat, AFatBld, ALiv, ALivBld, ARap, ARapBld, ASlw, ASlwBld, & 

& ASQ, ASQBld, AExh, AUrn, AMetl, AMet2, AExc, ADu, ASt) 

DO 3140 il4=1, NChem 

CBrnTotBld(il4) = (ABrnBld(il4) + AStmBld(il4) + ATLBld(il4))/(VBrnBld + 

VStmBld + VTLBld) 

CBrnTot(il4) = (ABrn(il4) + AStm(il4) + ATL(114))/(VBrn + VStm + VTL) 


RATotlnh(114) = QAlv * CInh(il4) 

TDose(il4) = ATotInh(il4) + ATotIV(il4) + TotDose(il4) + ATotDrink(il4) + 


ATotSQ (i14) 

AmtBody(i14) 
& 
& 

+ ALivBld(il4) & 

& 

ASQBld(i14) & 

& 

ADu(il4) + ASt(i14) 


= AMuc(i14) 
+ AStm(il4) 
+ ACoc(i14) 

+ ARap(i14) 

+ AExh(i14) 


+ AArt(il4) + ABrn(il4) + ABrnBld(il4) & 

+ AStmBld(il4) + ATL(il4) + ATLBld(il4) & 

+ ACocBld(il4) + AFat(il4) + AFatBld(il4) + ALiv(il4) 

+ ARapBld(il4) + ASlw(il4) + ASlwBld(il4) + ASQ(il4) + 

+ AUrn(i14) + AMetl(il4) + AMet2(il4) + AExc(il4) + 


IF (i14 .GT. 1) THEN 

MassBal(il4) = TDose(il4) - AmtBody(il4) 

ELSE 

MassBal (il4) = TDose(il4) - AmtBody(il4) 
ENDIF 

3140: CONTINUE 
END 


TERMT(T.GT.TStop, 'Simulation Finished') 


END 

! End 

of 

Derivative 

END 

! End 

of 

Dynamic 

END 

! End 

of 

Program 


63 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 




APPENDIX B. SELECT M FILES FOR PHARMACOKINETIC ARRAY MODEL 


The following m files are to be used with the csl file in Appendix A. They contain all necessary 
files to execute the simulations in Section 4.4.1 and Appendix C. 

Note that acslX allows for longer lines than Microsoft Word, in which this appendix is 
formatted. Use of this code in acslX will require careful vetting to ensure that lines are begun in 
a format acceptable in acslX and to ensure that code is not arbitrarily split between lines. 


SET M FILE TO LOAD AT RUN TIME 


Init.m 


prepare T HOURS MINUTES DAYS CP CMUC CALV 
PEREND PERMIX 

prepare CBRN CBRNBLD AUCCBRN CCOC CCOCBLD 
prepare CRAP CRAPBLD CSLW CSLWBLD CBRNTOT 
CSQBLD 

prepare CVEN AURN TDOSE AMTBODY MASSBAL 

HVDPRN=0; 

WESITG=0; 

WEDITG=0; 


CALVPPM CART AUCCART AEXH CEND CENDPPM 

CFAT CFATBLD CLIV CLIVBLD AMET1 AMET2 
CBRNTOTBLD CSTM CSTMBLD CTL CTLBLD CSQ 


DOSE RESET M FILE 


ResetDoses.m 


CONC(1)=0.0; 
CONC(2)=0.0; 
CONC(3)=0.0; 
CONC(4)=0.0; 
CONC(5)=0.0; 


IVDOSE(1)=0.0; 
IVDOSE(2)=0.0; 
IVDOSE(3)=0.0; 
IVDOSE(4)=0.0; 
IVDOSE(5)=0.0; 


PDOSE(1)=0.0; 
PDOSE(2)=0.0; 
PDOSE(3)=0.0; 
PDOSE(4)=0.0; 
PDOSE(5)=0.0; 


PDRINK(1)=0.0; 
PDRINK(2)=0.0; 
PDRINK(3)=0.0; 
PDRINK(4)=0.0; 
PDRINK(5)=0.0; 


SQDOSE (1)=0.0; 
SQDOSE(2)=0.0; 
SQDOSE (3)=0.0; 
SQDOSE(4)=0.0; 
SQDOSE(5)=0.0; 


TCHNG=0.0; DAYSWK=1.0; TMAX=24.0; DOSEINT=1000.0; 
TINF=0.2; TSQINF = 0.02; VOLSQ = 0.282; 

RATS=1.0; 


CC=0; VCHC=9.1; KLCC=0.0; 


CINT=0.01 


64 

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SPECIES AND CHEMICAL SPECIFIC M FILES 


Rattus.m 


% Generic rat parameters 

% All cochlea parameters are for paired cochlea. 


% Values from REF1, unless otherwise stated 
BW=0.3; %REF1 = 0.22, REF8&9 =0.3 

QCC=14.6; 

QPC=24.75; 


QBRNC=0.013; 
QSTMC=0.004; 
QTLC=0.003; 
QCOCC=0.00004; 
QFATC=0.07; 
QLIVC=0.183; 
QRAPC=0.557; 
QSLWC=0.16996; 
QSQG=0.012; 
muscle 


%Remainder only; Adds up to 0.02 

%Calculated using brain volumes & REF2 
%REF3 


%Flow to subcutaneous compartment per g (L/(hr*g), based on rat 
% (slowly perfused) (REF4) 


VBRNC=0.004; 
VSTMC=0.001; 
VTLC=0.001; 
VALVC=0.007; 
VFATC=0.10; 
VLIVC=0.034; 
VMUCC=0.0001; 
VRAPC=0.044; 
VSLWC=0.65; 


%Remainder only; 0.006 - VSTMC - VTLC; 0.006 from REF5 Table 3 
%REF5 Table 3 
%in-house research 


VOLSQ=0.282; 
rat (REF6) 


% mL ~= g; Max value for this protocol = 0.282 mL = 0.282 g 
% Parameter max value should be no larger than 25 mL/kg BW for a 


% Blood content values for most tissues from REF5 
VBRNBLDC=0.03; %REF4 

VSTMBLDC=0.03; %Assumed same across the brain 
VTLBLDC=0.03; 

VCOCBLDC=0.0183; %Calculated from data in REF7 
VFATBLDC=0.0154; 

VLIVBLDC=0.034; 

VRAPBLDC=0.2075; 

VSLWBLDC=0.0333; 


% REF1 

% Clewell HJ, III, Gentry PR, Gearhart JM, Covington TP, Banton MI, Andersen ME: 
% Development of a physiologically based pharmacokinetic model of isopropanol 
% and its metabolite acetone. Toxicol Sci 2001, 63(2):160-172. 

% REF2 

% Gjedde et al 1980 Rapid simultan determinat of regional blood flow_blood-brain 
glucose transfer in brain of rats 

% REF3 


65 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



% Robinson et al. 2013 TR 
% REF4 

% Sterner et al. 2014 TMB-4 model 
% REF5 

% Delp et al. 1991 
% REF6 

% AALAS Learning Library, 2005 
% REF7 

% Morizono et al 1968 Cochlear blood vol in Gpig measured w Cr51 label RBC (Hearing 
Loss reference file) 

% REF8 

% Merrill et al 2008 Improved predictive model 4 decane kinetics across species.pdf 
% REF9 

% Robinson and Merrill 2008 Harmonized PBPK mdl 4 nonane as component of jet 
fuel.pdf 


DecaneRat.m 


% Sets decane parameters for rat (chemical 5) 

MW(5)=142.3; 

DS=0.15; %Per REF2: acetone = 0.25, isopropanol = 0.15, typical lipophilics 

= 0.3, csl default=0.15 


PB(5)=5.0; %Current study=7.9, REF1=5.0 

PLQ(5)=0.0041; %Saline:air REF3=0.0041 
PMUC(5)=0.0041; %Set same as water:air REF4 

% Measured brain PCs indicated less partitioning into stem and temporal lobe than 
expected 

% based on fat content and nonane measured PCs 

PBRN(5)=10.0; %Current study frontal lobe = 1.75, REF1= 4.8, Fit to Perleberg 

data 


PSTM(5)=(1.56/1.75)*PBRN(5); %Current study = 1.56, Scaled 
PSTM/measured frontal lobe)) 

PTL(5)=(1.16/1.75)*PBRN(5); %Current study = 1.16, Scaled 
PTL/measured frontal lobe)) 

PCOC(5)=2.15; %Current study: scaled from measured skull PC 

prediction ratio 


PFAT(5)=328.0; 
PLIV (5)=3.0; 
PRAP(5)=3.0; 
PSLW(5)=0.85; 
PSQ(5)=PFAT(5) 


%REF1 =328., Current results 
%Fitting to Perleberg data 
%REF1=3.0 
%REF1=0.85 


211.71 


(PBRN*(measured 
(PBRN*(measured 
using cochlea:skull 


VMAXC(5)=0.005; 
KM(5)=0.1; 

KFC(5)=0.0; 


%REF1=0.4 
%REF1=1.5 

%Not expecting first order metabolism for this compound 


CLURC(5)=0.004; %Urinary clearnace, L/hr, REF2 = 0.004, NonanaRat.m=0.4 
KURTC(5)=0.0; %URT uptake, L/hr, absorption into mucus, 0 = no scrubbing 

%QPC = 24.75 

SCRUB(5)=0.7; %Fraction of concentration in air that is scrubbed out (not 

absorbed), 0=no scrubbing 


66 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



%oral dosing parameters from REF1 
KAD(5)=0.5; 

KAS(5)=2.0; 

KTD(5)=0.2 5; 

KTSD (5)=3.0; 

% If PA = 1000.0 = flow limited 
PABRN(5)=0.005; %REF1=0.009 

PASTM(5)=0.005; 

PATL(5)=0.005; 

PACOC(5)=1.0; %Same as PARAP in REF1 
PAFAT(5)=0.07; %REF1=0.7 

PALIV(5)=0.15; %REF1=0.15 

PARAP(5)=0.005; %REF1= 1.0 

PASLW(5)=0.14; %REF1 did not use, NonaneRat.m=0.5 

PASQ(5)=0.02; %Fit to toluene data 


% REF1 

% Merrill et al 2008 Improved predictive model 4 decane kinetics across species.pdf 
% REF2 

% Clewell HJ, III, Gentry PR, Gearhart JM, Covington TR, Banton MI, Andersen ME: 

% Development of a physiologically based pharmacokinetic model of isopropanol 
% and its metabolite acetone. Toxicol Sci 2001, 63 (2) :160-172. 

%REF3 

% Sterner et al 2004 AFRL-HE-WP-TR-2004-0032 Analysis of algorithms predicting 
blood-air_tissue-blood PCs fr solvent PCs.pdf 

%REF4 

% Schlosser PM, Asgharian BA, Medinsky M. 2010 Chapter 1.04 Inhalation Exposure and 
Absorption of Toxicants. 

% In: McQueen CA (ed). Comprehensive Toxicology. Volume 14. Elsevier e-book 


EthylbenzeneRat.m 

% Sets ethylbenzene parameters for rat (chemical 2) 

MW(2)=106.17; %Wikipedia 

DS=0.15; %Per REF2: acetone = 0.25, isopropanol = 0.15, typical lipophilics 

= 0.3, csl default=0.15 

PB (2)= 4 2.7; %REF1 = 42.7, In house study=41.1 

PLQ(2)=1.69; %Saline:air REF2=1.69 

PMUC(2)=1.69; %Set same as water:air REF3 

PBRN(2)=1.22; %In house study frontal lobe = 1.22, REF2 whole brain=0.80 

PSTM(2)=1.93; %Current study = 1.93 

PTL(2)=1.44; %Current study = 1.44 

PCOC(2)=0.44; %Current study: scaled from measured skull PC using cochlea:skull 

prediction ratio 

PFAT(2)=36.4; %REF1 =1556./42.7=36.4, In-house study=59.62 

PLIV(2)=1.96; %REF1=83.8/42.7=1.96, Current study: kidney = 3.02 

PRAP(2)=1.41; %REF1=60.3/42.7=1.41 

PSLW(2)=0.61; %REF1=26.0/42.7=0.61 

PSQ(2)=PFAT(2)*2; 

VMAXC(2)=6.39; %REF1=3.44 

KM(2)=1,04; %REF1=0.13 

KFC(2)=0.0; %Not expecting first order metabolism for this compound 


67 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



CLURC(2)=0.04; %Urinary clearnace, L/hr 

KURTC(2)=0.0; %URT uptake, L/hr, uptake into mucus, 0.0 = no scrubbing 

%QPC = 24.75 

SCRUB(2)=0.0; %Fraction of concentration in air that is scrubbed out (not 

absorbed), 0=no scrubbing 

%oral dosing parameters from REF1 
KAD(2)=0.5; 

KAS (2) =2.0; 

KTD(2)=0.25; 

KTSD (2 ) =3.0; 

% If PA = 1000.0 = flow limited 
PABRN(2)=1000.0; 

PASTM(2)=1000.0; 

PATL (2) =1000.0,- 
PACOC (2) =1000. 0; 

PAFAT(2)=1000.0; 

PALIV(2)=1000.0; 

PARAP(2)=1000.0; 

PASLW(2)=1000.0; 

PASQ(2)=0.02; %Fit to toluene data 


% REF1 

%Haddad et al 1999 Physiological modeling of the toxicokinetic interactions in a 
quaternary mixture 

% of aromatic hydrocarbons. Toxicol Appl Physiol. 161:249-57. 

%REF2 

% Sterner et al 2004 AFRL-HE-WP-TR-2004-0032 Analysis of algorithms predicting 
blood-air_tissue-blood PCs fr solvent PCs.pdf 

%REF3 

% Schlosser PM, Asgharian BA, Medinsky M. 2010 Chapter 1.04 Inhalation Exposure and 
Absorption of Toxicants. 

% In: McQueen CA (ed). Comprehensive Toxicology. Volume 14. Elsevier e-book 


NonaneRat.m 

% Sets nonane parameters for rat (chemical 4) 

% *******Significant difference between values from different sources 

MW(4)=128.0; 

DS=0.15; %Per REF1: acetone = 0.25, isopropanol = 0.15, typical lipophilics 

= 0.3, csl default=0.15 

PB(4)=5.2; %Current study=5.9, REF5=5.2 

PLQ(4)=0.004; %REF6 decane saline:air PC=0.0041 (Log Kow values are similar so 
assumed saline:air PCs are similar) 

PMUC(4)=0.004; %Set same as water:air REF7 

PBRN(4)=5.0; %Current study frontal lobe = 1.70, REF5 = 5.0 

PSTM(4)=(2.9/1.7)*PBRN(4); %Current study = 2.9, Scaled (PBRN*(measured 

PSTM/measured frontal lobe)) 

PTL(4)=(2.6/1.7)*PBRN(4); %Current study = 2.6, Scaled (PBRN*(measured 

PTL/measured frontal lobe)) 

PCOC(4)=1.45; %Current study: scaled from measured skull PC using cochlea:skull 

prediction ratio 

PFAT(4)=282.0; %REF5 = 282.0, Current results = 145.01, average[145,282]=214 


68 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



PLIV(4)=8.0; %Fitting to data; REF5 found protein binding to be a factor; wonder 
if this could concentrate more in liver than PC alone allows; REF5 used decane value 
PRAP(4)=2.0; %REF5-2.0, Current study: kidney = 2.59 

PSLW(4)=4.0; %REF2: muscle =4.0, , REF5 =4.0 

PSQ(4)=PFAT(4)*2; 


VMAXC(4)=0.1; 
KM(4)=0.1; 

KFC(4)=0.0; 


%REF5=0.0 
%REF5=1.5 

%Not expecting first order metabolism for this compound 


CLURC(4)=0.4; %Urinary clearnace, L/hr, REF1 = 0.004 

KURTC(4)=0.0; %URT uptake, L/hr, deposition in mucus, model uses minimum value 

QPC or this, 0 = no scrubbing 

%QPC = 24.75 default rat 

SCRUB(4)=0.4; %Fraction of concentration in air that is scrubbed out (not 
absorbed), 0=no scrubbing 


of 


%oral dosing parameters from REF1 
KAD(4)=0.5; 

KAS(4)=2.0; 

KTD(4)=0.25; 

KTSD (4)=3.0; 


% If PA = 1000.0 = flow limited 


PABRN(4)=0.5; 
PASTM(4)=0.5; 
PATL (4)=0.5; 
PACOC(4)=1.0; 
PAFAT(4)=0.5; 
PALIV(4)=0.07; 
PARAP(4)=1.0; 
PASLW(4)=0.5; 
PASQ(4)=0.02; 


%REF5=0.5 


%Same as PARAP 
%REF5=0.8 
%REF5=0.07 
%REF5= 1.0 
%REF5=0.5 

%Fit to toluene data 


% REF1 

% Clewell HJ, III, Gentry PR, Gearhart JM, Covington TR, Banton MI, Andersen ME: 
% Development of a physiologically based pharmacokinetic model of isopropanol 
% and its metabolite acetone. Toxicol Sci 2001, 63(2):160-172. 

% REF2 

% Robinson, P.J. and Merrill, E.A. 2008. A harmonized physiologically based 
pharmacokinetic model 

% for nonane as a component of jet fuel. Wright-Patterson AFB, OH: Air Force 
Research Laboratory, 

% Applied Biotechnology Branch. AFRL-RH-WP-TR-2008-0067, ADA502610. 

% REF3 

% Sterner et al 2006 Analysis of algorithms... 

%REF4 

% Joshi et al. 2010 PCs 4 nonaneisomers in rat.pdf 
%REF5 

% Robinson and Merrill 2008 Harmonized PBPK mdl 4 nonane as component of jet 
fuel.pdf 

%REF6 

% Sterner et al 2004 AFRL-HE-WP-TR-2004-0032 Analysis of algorithms predicting 
blood-air_tissue-blood PCs fr solvent PCs.pdf 

%REF7 


69 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



% Schlosser PM, Asgharian BA, Medinsky M. 2010 Chapter 1.04 Inhalation Exposure and 
Absorption of Toxicants. 

% In: McQueen CA (ed). Comprehensive Toxicology. Volume 14. Elsevier e-book 


TolueneRat.m 

% Sets toluene parameters for rat (chemical 1) 

MW(1)=92.14; %Wikipedia 

DS=0.15; %Per REF2: acetone = 0.25, isopropanol = 0.15, typical lipophilics 

= 0.3, csl default=0.15 

PB (1)=18.0; %REF1=REF4=18.0, current study=22.8 

PLQ(1)=1.99; %Saline:air REF2=1.99 

PMUC(1)=1.99; %Set same as water:air REF3 

PBRN(1)=2.0; %Current study frontal lobe = 1.26, REF2= 4.22, REF4=2.0 

PSTM(1)=2.87; %Current study = 1.81, Scaled PBRN* (1.81/1.26) 

PTL(1)=2,13; %Current study = 1.34, Scaled PBRN*(1.34/1.26) 

PCOC(1)=0.54; %Current study: scaled from measured skull PC using cochlea:skull 

prediction ratio 

PFAT(1)=56.7; %REF1 =1021./I8.=56.7, Current study: 46.82, REF4=56.7 

PLIV(1)=4.64; %REF1=83.6/18.=4.64, REF4=4.64 

PRAP(1)=4.64; %Current study: kidney = 2.37, REF1=83.6/18.=4.64, REF4=4.64 

PSLW(1)=1.54; %REF1=27.7/18.=1.54, REF4=1.54 

PSQ(l)=PFAT(1)*2; 

VMAXC(1)=3.44; %REF1=3.44, REF4=4.81 except 3.44 for Haddad data 

KM(1)=0.13; %REF1=0.13, REF4=0.55 except 0.13 for Haddad data 

RFC(1)=0.0; %Not expecting first order metabolism for this compound 

CLURC(1)=0.004; %Urinary clearnace, L/hr, REF4=0 

KURTC(1)=0.0; %URT uptake, L/hr, uptake into mucus, 0.0 = no scrubbing, REF4=0 

SCRUB(1)=0.0; %Fraction of concentration in air that is scrubbed out (not 

absorbed), 0=no scrubbing 

%oral dosing parameters from REF1 
KAD(1)=0.5; 

KAS(1)=2.0; 

KTD(1)=0.25; 

KTSD (1)=3.0; 

% If PA = 1000.0 = flow limited 
PABRN(1)=10 0 0.0; 

PASTM(l)=1000.0; 

PATL (1) =1000.0,- 
PACOC (1) =1000. 0; 

PAFAT(1)=1000.0; 

PALIV(l)=1000.0; 

PARAP(1)=1000.0; 

PASLW(1)=1000.0; 

PASQ(1)=0.02; %Fit to toluene data 


% REF1 

%Haddad et al 1999 Physiological modeling of the toxicokinetic interactions in a 
quaternary mixture 

% of aromatic hydrocarbons. Toxicol Appl Physiol. 161:249-57. 

%REF2 


70 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



% Sterner et al 2004 AFRL-HE-WP-TR-2004-0032 Analysis of algorithms predicting 
blood-air_tissue-blood PCs fr solvent PCs.pdf 

%REF3 

% Schlosser PM, Asgharian BA, Medinsky M. 2010 Chapter 1.04 Inhalation Exposure and 
Absorption of Toxicants. 

% In: McQueen CA (ed). Comprehensive Toxicology. Volume 14. Elsevier e-book 
%REF4 

% Hack Toluene model, rat_toluene.m 


XyleneRatm 

% Sets xylene parameters for rat (chemical 3) 

MW(3)=106.17; %Wikipedia 

DS=0.15; %Per REF2: acetone = 0.25, isopropanol = 0.15, typical lipophilics 

= 0.3, csl default=0.15 

PB(3)=46.0; %REF1=46.0; In house study=42.3 

PLQ(3)=2.03; %Saline:air REF2 m=1.79, o=2.64, p=1.67, average=2.03 
PMUC(3)=2.03; %Set same as water:air REF3 
PBRN(3)=1.38; %In house study frontal lobe = 1.38, REF2 whole brain m=1.2, 

o=1.7, p=l.2 

PSTM(3)=2.29; %Current study = 2.29 

PTL(3)=1.61; %Current study = 1.61 

PCOC(3)=0.47; %Current study: scaled from measured skull PC using cochlea:skull 

prediction ratio 

PFAT(3)=40.4; %REF1 =1859./46.=40.4, In-house study=70.09 

PLIV(3)=1.98; %REF1=90.9/46.=1.98, Current study: kidney = 2.84 

PRAP(3)=1.98; %REF1=90.9/46.=l.98 

PSLW(3)=0.91; %REF1=41.9/46.=0.91 

PSQ(3)=PFAT(3)*2; 

VMAXC(3)=6.49; %REF1=6.49 

KM(3)= 0.4 5; %REF1 = 0.45 

KFC(3)=0.0; %Not expecting first order metabolism for this compound 

CLURC(3)=0.004; %Urinary clearnace, L/hr 

KURTC(3)=0.0; %URT uptake, L/hr, uptake into mucus, 0.0 = no scrubbing 

%QPC = 24.75 

SCRUB(3)=0.0; %Fraction of concentration in air that is scrubbed out (not 

absorbed), 0=no scrubbing 

%oral dosing parameters from REF1 
KAD(3)=0.5; 

KAS (3) =2.0; 

KTD(3)=0.25; 

KTSD(3)=3.0; 

% If PA = 1000.0 = flow limited 
PABRN(3)=1000.0; 

PASTM(3)=1000.0; 

PATL(3)=1000.0; 

PACOC(3)=1000.0; 

PAFAT(3)=1000.0; 

PALIV(3)=1000.0; 

PARAP(3)=1000.0; 

PASLW(3)=1000.0; 

PASQ(3)=0.02; %Fit to toluene data 


71 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



% REF1 

%Haddad et al 1999 Physiological modeling of the toxicokinetic interactions in a 
quaternary mixture 

% of aromatic hydrocarbons. Toxicol Appl Physiol. 161:249-57. 

%REF2 

% Sterner et al 2004 AFRL-HE-WP-TR-2004-0032 Analysis of algorithms predicting 
blood-air_tissue-blood 

% PCs fr solvent PCs.pdf 

%REF3 

% Schlosser PM, Asgharian BA, Medinsky M. 2010 Chapter 1.04 Inhalation Exposure and 
Absorption of Toxicants. 

% In: McQueen CA (ed). Comprehensive Toxicology. Volume 14. Elsevier e-book 


M FILE FOR SIMULATIONS IN SECTION 4.4.1 


Predict_Guthriel4.m 


%Simulates 5 key hydrocarbons for Guthrie et al. 2014 exposure to predict rat target 
tissue concentrations 

%Guthrie, O.W., Xu, H., Wong, B.A., Mclnturf, S.M., Reboulet, J.E., Ortiz, P.A. and 
Mattie, D.R. 2014. 

% Exposure to low levels of jet-propulsion fuel impairs brainstem encoding of 

stimulus intensity. 

% J Toxicol Environ Health A 77(5): 261-280. 

resetdoses 

rattus 

toluenerat 

ethylbenzenerat 

xylenerat 

nonanerat 

decanerat 


BW=0.105; %average starting BW in Guthrie et al. 2014 

%Calculates concentration of each component in POSF 4658, converted from 1000 mg/m3 to 
ppm, assuming STP 

%Weight percent values from tandem gas chromatography analysis by the 2006 Shafer et 
al. method 

%POSF 4658 contains 0.16 percent toluene, 0.12 percent ethylbenzene, 

% 0.66 percent xylenes, 1.14 percent nonane, and 2.55 percent decane 

CONC (1) = ((0.0016*1000)*24.45)/MW(1); 

CONC (2) = ((0.0012*1000)*24.45)/MW(2); 

CONC(3)=((0.0066*1000)*24.45)/MW(3); 

CONC(4) = ((0.0114*1000)*24.45)/MW(4) ; 

CONC (5) = ((0.0255*1000)*24.4 5)/MW(5); 

TCHNG=6.0; %Simulates 1 day of 4 week study 

TSTOP=12.0; 

start @nocallback 

plotcven = plot (0, _t, _cven(:,l), '-b', _t, _cven(:,2),'-k'); 

plot (plotcven, 1, _t, _cven(:,3),'-r', _t, _cven(:,4),'-g'); 


72 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 




plot (plotcven, 
plotccoc = plot (0, 
plot (plotccoc, 
plot (plotccoc, 
plotcbrn = plot (0, 
plot (plotcbrn, 
plot (plotcbrn, 
plotcstm = plot (0, 
plot (plotcstm, 
plot (plotcstm, 
plotctl = plot (0, 
plot (plotctl, 
plot (plotctl, 


1, _t, _cven(:,5) , '-m'); 

_t, _ccoc(:,l), '-b', _t, _ccoc(:,2),'-k'); 

1, _t, _ccoc(:,3),'-r', _t, _ccoc(:,4),'-g'); 
1, _t, _ccoc(:,5) , '-m'); 

_t, _cbrn(:,l), '-b', _t, _cbrn(:,2),'-k'); 

1, _t, _cbrn(:,3),'-r', _t, _cbrn(:,4),'-g'); 
1, _t, _cbrn(:,5),'-m'); 

_t, _cstm(:,l), '-b', _t, _cstm(:,2),'-k'); 

1, _t, _cstm(:,3),'-r', _t, _cstm(:,4),'-g'); 
1, _t, _cstm(:,5),'-m'); 

_t, _ctl(:,1), '-b', _t, _ctl(:,2) , '-k'); 

1, _t, _ ctl(:,3),r', _t, _ctl(:,4),g'); 

1, t, ctl (:,5), '-m'); 


set @preference=BackslashEscapeS 


pltscript (plotcven, 
ExposureX";") 

pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven. 


"Chart.Header.Text = \"Predictions for Guthrie et al. (2014) 

"Chart.SubHeader.Text = \"5 Key Hydrocarbons in JP-8V';") 
"Chart.SubHeader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Venous Blood [mg/L]\";") 

"Chart.Axes.Bottom.Title.Text=\"Hours\) 

"Chart.Series[0].Title=\"Toluene\";") 

"Chart.Series[1] . Title=\"EthylbenzeneX";") 

"Chart.Series[2].Title=\"XyleneX";") 

"Chart.Series[3].Title=\"NonaneX";") 

"Chart.Series[4].Title=\"DecaneX";") 


pltscript(plotccoc, 
ExposureX";") 

pltscript(plotccoc, 
pltscript(plotccoc, 
pltscript (plotccoc, 
pltscript(plotccoc, 
pltscript (plotccoc, 
pltscript (plotccoc, 
pltscript(plotccoc, 
pltscript (plotccoc, 
pltscript (plotccoc. 


"Chart.Header.Text = \"Predictions for Guthrie et al. (2014) 

"Chart.SubHeader.Text = \"5 Key Hydrocarbons in JP-8X";") 
"Chart.SubHeader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Cochlea [mg/L]\";") 

"Chart.Axes.Bottom.Title.Text=\"Hours\";") 

"Chart.Series[0].Title=\"TolueneX";") 

"Chart.Series[1].Title=\"EthylbenzeneX";") 

"Chart.Series[2].Title=\"XyleneX";") 

"Chart.Series[3].Title=\"NonaneX";") 

"Chart.Series[4].Title=\"DecaneX";") 


pltscript (plotcbrn, 
ExposureX";") 

pltscript (plotcbrn, 
pltscript (plotcbrn, 
pltscript(plotcbrn, 
[mg/L]\";") 

pltscript (plotcbrn, 
pltscript(plotcbrn, 
pltscript(plotcbrn, 
pltscript (plotcbrn, 
pltscript(plotcbrn, 
pltscript(plotcbrn. 


"Chart.Header.Text = \"Predictions for Guthrie et al. (2014) 

"Chart.SubHeader.Text = \"5 Key Hydrocarbons in JP-8X";") 
"Chart.SubHeader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Remainder of the Brain 

"Chart.Axes.Bottom.Title.Text=\"Hours\";") 

"Chart.Series[0].Title=\"Toluene\";") 

"Chart.Series[1] .Title=\"EthylbenzeneX";") 

"Chart.Series[2].Title=\"XyleneX";") 

"Chart.Series[3].Title=\"NonaneX";") 

"Chart.Series[4].Title=\"DecaneX";") 


pltscript(plotcstm, 
ExposureX";") 

pltscript(plotcstm, 
pltscript(plotcstm, 
pltscript(plotcstm, 
pltscript(plotcstm, 
pltscript(plotcstm, 
pltscript(plotcstm, 
pltscript(plotcstm. 


"Chart.Header.Text = \"Predictions for Guthrie et al. (2014) 

"Chart.SubHeader.Text = \"5 Key Hydrocarbons in JP-8X";") 
"Chart.SubHeader.Visible=true; ") 

"Chart.Axes.Left.Title.Text=\"Brain Stem [mg/L]\";") 

"Chart.Axes.Bottom.Title.Text=\"Hours\";") 

"Chart.Series[0].Title=\"TolueneX";") 

"Chart.Series[1].Title=\"EthylbenzeneX";") 

"Chart.Series[2].Title=\"XyleneX";") 


73 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



pltscript(plotcstm, "Chart.Series[3]. Title=\"Nonane!";") 
pltscript(plotcstm, "Chart.Series[4].Title=\"Decane!";") 


pltscript (plotctl. 
Exposure!";") 

pltscript (plotctl, 
pltscript (plotctl, 
pltscript (plotctl, 
pltscript (plotctl, 
pltscript (plotctl, 
pltscript (plotctl, 
pltscript (plotctl, 
pltscript (plotctl, 
pltscript(plotctl, 


"Chart.Header.Text = \"Predictions for Guthrie et al. (2014) 

"Chart.Subheader.Text = \"5 Key Hydrocarbons in JP-8X";") 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Temporal Lobe [mg/L]\";") 
"Chart.Axes.Bottom.Title.Text=\"Hours!" ; ") 

"Chart.Series[0].Title=\"Toluene!";") 

"Chart.Series[1].Title=X"Ethylbenzene!";") 

"Chart.Series[2] .Title=!"Xylene!" ; " ) 

"Chart.Series[3].Title=!"Nonane!) 

"Chart.Series[4].Title=!"Decane!) 


set @preference=NoBackslashEscapeS 


M FILES FOR SIMULATIONS IN APPENDIX C 


Toluene Study Simulation M Files 


Cl_Haddad.m 

%Haddad, S., Tardif, R., Charest-Tardif, G. and Krishnan, K. 1999. Physiological 
modeling 

% of the toxicokinetic interactions in a quaternary mixture of aromatic 

hydrocarbons. 

% Toxicol.Appl.Pharmacol. 161(3): 249-257. 

resetdoses 

rattus 

toluenerat 

%T, CVEN 


had50 = 

[ 

4.0 

0.472 

4.0 

0.546 

4.5 

0.210 

4.5 

0.281 

4.5 

0.354 

5.0 

0.093 

5.0 

0.136 

5.0 

0.182 

5.5 

0.083 

5.5 

0.133 

5.5 

0.108 

6.0 

0.045 

6.0 

0.059 

6.0 

0.072 

%T, CVEN 

hadlOO 

= [ 

4.0 

0.751 

4.0 

1.033 

4.0 

1.464 

4.5 

0.547 

4.5 

0.632 


74 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



4.5 

0.798 

5.0 

0.281 

5.0 

0.345 

5.0 

0.435 

5.5 

0.199 

5.5 

0.290 

5.5 

0.230 

6.0 

0.112 

6.0 

0.133 

6.0 

0.173 

%T, CVEN 

had200 = 

[ 

4.0 

3.205 

4.0 

4.042 

4.0 

5.099 

4.5 

2.334 

4.5 

3.120 

4.5 

3.714 

5.0 

1.559 

5.0 

1.856 

5.0 

2.274 

5.5 

0.826 

5.5 

1.352 

5.5 

1.072 

6.0 

0.602 

6.0 

0.716 

6.0 

0.804 


% Study specific values as found in Hack tolune model, Haddad.m 
BW=0.235; 

VMAXC(1)=3.44; 

KM(1)=0.13; 


CONC(1)=50.0, TCHNG=4.0; 

TSTOP=8.0; 

start @nocallback 


plotcven = plot (0, had50(:,l), had50(:,2), '+b', _t, _cven(:,1), '-b'); 


CONC (1)=100.0; 
start @nocallback 

plot (plotcven, 1, hadl00(:,l), hadl00(:,2), '+k', _t, _cven(:,l), '—k'); 

CONC(1)=200.0; 
start @nocallback 

plot (plotcven, 1, had200(:,l), had200(;,2), '+g', _t, _cven(:,1), '—g'); 


set @preference=BackslashEscapeS 


pltscript (plotcven. 
Study!";") 

pltscript (plotcven. 
Hours!";") 

pltscript(plotcven, 
pltscript (plotcven, 
[mg/L]!";") 

pltscript(plotcven, 
pltscript (plotcven. 


"Chart.Header.Text = V'Haddad et al. (1999) Toluene Inhalation 
"Chart.Subheader.Text = !"50, 100, 200 ppm Inhalation for 4 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=!"Toluene in Venous Blood 

"Chart.Axes.Bottom.Title.Text=!"Hours!";") 

"Chart.Series[0].Title=!"50 ppm!";") 


75 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



pltscript(plotcven, "Chart.Series[1].Title=\"Simulation 50 ppm\";") 
pltscript(plotcven, "Chart.Series[2].Title=\"100 ppm\";") 
pltscript(plotcven, "Chart.Series[3].Title=\"Simulation 100 ppm\";") 
pltscript(plotcven, "Chart.Series[4].Title=\"200 ppm\";") 
pltscript(plotcven, "Chart.Series[5].Title=\"Simulation 200 ppm\";") 

set @preference=NoBackslashEscapeS 


Cl_Lam.m 

%Lam, C.W., Galen, T.J., Boyd, J.F. and Pierson, D.L. 1990. Mechanism of transport and 
distribution 

% of organic solvents in blood. Toxicol.Appl.Pharmacol. 104(1): 117-129. 

%Male Sprague-Dawley rats, BW about 300 g. 

% 2-hour whole-body inhalation of 488 +/- 24 ppm toluene. 

% n = 5 rats, put in 10 minutes apart. 

% 30 L closed chamber with 5 rats, but CONC is maintained at 488 ppm, rather than 

depleted, so run as open chamber. 

%Data from Plasma+RBC column. Table 1, page 121 

resetdoses 

rattus 

toluenerat 

%T (hr), CVEN (mg/L) 
lam = [ 

2 10.90 

2 15.03 

2 14.98 

2 15.02 

2 15 . 09 ]; 

% Study specific values as found in Hack tolune model, Lam.m 
BW=0.3; 

CONC(1)=488.0, TCHNG=2.0; 

TSTOP=4.0; 
start @nocallback 

plotcven = plot (0, lam(:,l), lam(:,2), '+b', _t, _cven(:,l), '-b'); 


set @preference=BackslashEscapeS 


pltscript(plotcven, 
StudyV; ") 

pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
[mg/L]\";") 

pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven. 


"Chart.Header.Text = \"Lam et al. (1990) Toluene Inhalation 

"Chart.SubHeader.Text = \"488 ppm Inhalation for 2 Hours/";") 
"Chart.SubHeader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Toluene in Venous Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\";") 

"Chart.Series[0].Title=\"488 ppm\";") 

"Chart.Series[1].Title=\"Simulation 488 ppm\";") 


set @preference=NoBackslashEscapeS 


76 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



Cl_Romer.m 


%Romer, K.G., Federsel, R.J. and Freundt, K.J. 1986. Rise of inhaled toluene, ethyl 
benzene, 

% m-xylene, or mesitylene in rat blood after treatment with ethanol. Bull Environ 

Contam 

% Toxicol 37 (6) : 874-876. 

%Adult female SPF Sprague-Dawley rats 
% 200 - 220 g BW 

%Groups of 3 rats exposed in a 20 L glass chamber, air flow 1.25 L/min, for 2 hr. 

% 20 L closed chamber with 3 rats, but CONC is maintained at 220 ppm, rather than 

depleted, so run as open chamber. 

%Aromatic cone varied < 5%, delivered via evaporator. 

% Sham-treated groups given 5 mL/kg physiological saline. 

%Exposure cone monitored via GC using 100 uL air samples. 

%Blood (0.02 mL) collected from retro-orbital plexus. 

% Blood cone of aromatics determined by GC. 

resetdoses 

rattus 

toluenerat 

%T (hr), CVEN(mg/L) 
romer = [ 

2.0 5.23 

2.0 5.04 

2.0 5.43]; 


BW=0.210; 


CONC (1)=220.0; 

TCHNG=2.0; 

TSTOP=3.0; 
start @nocallback 

plotcven = plot (0, romer(:,1), romer(:,2), '+b', _t, _cven(:,1), '-b'); 


set @preference=BackslashEscapeS 


pltscript(plotcven, 
StudyV; ") 

pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
[mg/L]\";") 

pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven. 


"Chart.Header.Text = V'Romer et al. (1986) Toluene Inhalation 

"Chart.Subheader.Text = \"220 ppm Inhalation for 2 Hours/";") 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Toluene in Venous Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\";") 

"Chart.Series[0].Title=\"220 ppm mean value/";") 

"Chart.Series[1].Title=Z"Simulation 220 ppm/";") 


set @preference=NoBackslashEscapeS 


Cl_Tardif.m 

%Tardif, R., Charest-Tardif, G. and Brodeur, J. 1996. Comparison of the influence 
% of binary mixtures versus a ternary mixture of inhaled aromatic hydrocarbons 

% on their blood kinetics in the rat. Arch Toxicol 70(7); 405-413. 

%Adult male SD rats 


77 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



235-245 g 


resetdoses 

rattus 

toluenerat 

%T, CVEN from Figure 1, mean followed by +SD then -SD 


tarlOO 

= [ 

4.1 

1.14 

4.5 

0.69 

5.0 

0.36 

5.5 

0.23 

6.0 

0.13 

4.1 

1.53 

4.5 

0.82 

5.0 

0.44 

5.5 

0.29 

6.0 

0.19 

4.1 

0.76 

4.5 

0.55 

5.0 

0.27 

5.5 

0.17 

6.0 

0.08 


%T, CVEN from Figure 2, mean followed by +SD then -SD 
tar200 = [ 

4.1 4.34 

4.5 3.17 

5.0 1.96 

5.5 1.19 

6.0 0.79 

4.1 5.29 

4.5 3.94 

5.0 2.31 

5.5 1.42 

6.0 0.89 

4.1 3.37 

4.5 2.47 

5.0 1.66 

5.5 0.93 

6.0 0.64]; 

BW=0.240; 

CONC(1)=100.0; 

TCHNG=4.0; 

TSTOP=6.0; 
start @nocallback 

plotcven = plot (0, tarl00(:,l), tarl00(:,2), '+b', _t, _cven(:,l), '-b'); 

CONC(1)=200.0; 
start @nocallback 

plot (plotcven, 1, tar200(:,l), tar200(:,2), '+k', _t, _cven(:,l), '-k'); 


set @preference=BackslashEscapeS 

pltscript (plotcven, "Chart.Header.Text = V'Tardif et al. (1996) Toluene Inhalation 
StudyV; ") 

pltscript (plotcven, "Chart.SubHeader.Text = \"100, 200 ppm Inhalation for 4 
Hours)";") 


78 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



pltscript(plotcven, 
pltscript(plotcven, 
[mg/L]\";") 

pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven. 


"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Toluene in Venous Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\) 

"Chart.Series[0].Title=\"100 ppm\";") 

"Chart.Series[1].Title=\"Simulation 100 ppm\";") 
"Chart.Series[2].Title=\"200 ppm\";") 

"Chart.Series[3].Title=\"Simulation 200 ppm\";") 


set @preference=NoBackslashEscapeS 


Ethylbenzene Study Simulation M Files 


C2_Haddad.m 

%Haddad, S., Tardif, R., Charest-Tardif, G. and Krishnan, K. 1999. Physiological 
modeling 

% of the toxicokinetic interactions in a quaternary mixture of aromatic 

hydrocarbons. 

% Toxicol.Appl.Pharmacol. 161(3): 249-257. 


resetdoses 

rattus 

ethylbenzenerat 

%T, CVEN (mean, +SD, -SD) 


had50 > 

= [ 

4.1 

0.66 

4.5 

0.46 

5.0 

0.28 

5.5 

0.17 

6.0 

0.13 

4.1 

0.78 

4.5 

0.53 

5.0 

0.32 

5.5 

0.20 

6.0 

0.17 

4.1 

0.59 

4.5 

0.33 

5.0 

0.22 

5.5 

0.15 

6.0 

0.09 

%T, CVEN 

hadlOO 

= [ 

4.1 

1.52 

4.5 

1.02 

5.0 

0.70 

5.5 

0.45 

6.0 

0.31 

4.1 

1.68 

4.5 

1.15 

5.0 

0.89 

5.5 

0.58 

6.0 

0.36 

4.1 

1.36 

4.5 

0.94 

5.0 

0.57 

5.5 

0.38 


79 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



6.0 

0.28 

%T, CVEN 

had200 

= [ 

4.1 

5.73 

4.5 

3.79 

5.0 

2.70 

5.5 

1.92 

6.0 

1.36 

4.1 

6.57 

4.5 

4.24 

5.0 

3.02 

5.5 

2.21 

6.0 

1.66 

4.1 

5.05 

4.5 

3.42 

5.0 

2.44 

5.5 

1.72 

6.0 

1.12 


BW=0. 235; 


CONC (2)=50.0, TCHNG=4.0; 

TSTOP=8.0; 
start @nocallback 

plotcven = plot (0, had50(:,l), had50(:,2), '+b', _t, _cven(:,2), '-b'); 

CONC(2)=100.0; 
start @nocallback 

plot (plotcven, 1, hadl00(:,l), hadl00(:,2), '+k', _t, _cven(:,2), '—k'); 

CONC (2)=200.0; 
start @nocallback 

plot (plotcven, 1, had200(:,l), had200(:,2), '+g', _t, _cven(:,2), '—g'); 


set @preference=BackslashEscapeS 


pltscript (plotcven. 
Inhalation StudyV';") 

pltscript (plotcven, 
Hours\";") 

pltscript(plotcven, 
pltscript (plotcven, 
[mg/L]\";") 

pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven. 


"Chart.Header.Text = V'Haddad et al. (1999) Ethylbenzene 
"Chart.Subheader.Text = \"50, 100, 200 ppm Inhalation for 4 


"Chart.SubHeader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Ethylbenzene in Venous Blood 


"Chart.Axes.Bottom.Title.Text=\"Hours\) 
"Chart.Series[0].Title=\"50 ppm\";") 

"Chart.Series[1].Title=\"Simulation 50 ppm\";") 
"Chart.Series[2].Title=\"100 ppm\";") 

"Chart.Series[3].Title=\"Simulation 100 ppm\";" 
"Chart.Series[4].Title=\"200 ppm\";") 

"Chart.Series[5].Title=\"Simulation 200 ppm\";" 


) 

) 


set @preference=NoBackslashEscapeS 


80 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



C2_Tardif.m 


%Tardif, R., Charest-Tardif, G. and Brodeur, J. 1996. Comparison of the influence 
% of binary mixtures versus a ternary mixture of inhaled aromatic hydrocarbons 

% on their blood kinetics in the rat. Arch Toxicol 70(7): 405-413. 

%Adult male SD rats 
% 235-245 g 

resetdoses 

rattus 

ethylbenzenerat 

%T, CVEN from Figure 1, mean followed by +SD then -SD 


tarlOO 

= [ 

4.1 

1.92 

4.5 

1.27 

5.0 

0.86 

5.5 

0.53 

6.0 

0.35 

4.1 

2.11 

4.5 

1.42 

5.0 

1.08 

5.5 

0.66 

6.0 

0.41 

4.1 

1.79 

4.5 

1.14 

5.0 

0.63 

5.5 

0.43 

6.0 

0.29 


%T, CVEN from Figure 4, mean followed by +SD then -SD 


tar200 

= [ 

4.1 

7 . 67 

4.5 

4.96 

5.0 

3.47 

5.5 

2.50 

6.0 

1.71 

4.1 

8.22 

4.5 

5.51 

5.0 

3.82 

5.5 

2.78 

6.0 

2.01 

4.1 

6.92 

4.5 

4.42 

5.0 

3.25 

5.5 

2.20 

6.0 

1.39 


BW=0.240; 


CONC(2)=100.0; 

TCHNG=4.0; 

TSTOP=6.0; 
start @nocallback 

plotcven = plot (0, tarl00(:,l), tarl00(:,2), '+b', _t, _cven(:,2), '—b 1 ); 

CONC (2)=200.0; 
start @nocallback 

plot (plotcven, 1, tar200(:,l), tar200(:,2), '+k', _t, _cven(:,2), '—k'); 


81 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



set @preference=BackslashEscapeS 


pltscript(plotcven. 
Inhalation StudyV';") 

pltscript (plotcven. 
Hours\";") 

pltscript (plotcven, 
pltscript (plotcven, 
[mg/L]\";") 

pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven. 


"Chart.Header.Text = V'Tardif et al. (1996) Ethylbenzene 
"Chart.Subheader.Text = V'100, 200 ppm Inhalation for 4 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Ethylbenzene in Venous Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\) 

"Chart.Series[0].Title=\"100 ppm\";") 

"Chart.Series[1].Title=\"Simulation 100 ppm\";") 

"Chart.Series[2].Title=\"200 ppm\";") 

"Chart.Series[3].Title=\"Simulation 200 ppm\";") 


set @preference=NoBackslashEscapeS 


Xylene Study Simulation M Files 


C3_Haddad.m 

%Haddad, S., Tardif, R., Charest-Tardif, G. and Krishnan, K. 1999. Physiological 
modeling 

% of the toxicokinetic interactions in a quaternary mixture of aromatic 

hydrocarbons. 

% Toxicol.Appl.Pharmacol. 161(3): 249-257. 


resetdoses 

rattus 

xylenerat 

%T, CVEN (mean, +SD, -SD) 


had50 > 

= [ 

4.1 

0.49 

4.5 

0.32 

5.0 

0.24 

5.5 

0.17 

6.0 

0.12 

4.1 

0.55 

4.5 

0.40 

5.0 

0.29 

5.5 

0.21 

6.0 

0.15 

4.1 

0.43 

4.5 

0.23 

5.0 

0.20 

5.5 

0.14 

6.0 

0.09 

%T, CVEN 

hadlOO 

= [ 

4.1 

1.20 

4.5 

0.86 

5.0 

0.50 

5.5 

0.37 

6.0 

0.24 

4.1 

1.46 


82 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



4.5 

1.02 

5.0 

0.62 

5.5 

0.45 

6.0 

0.33 

4.1 

1.10 

4.5 

0.73 

5.0 

0.44 

5.5 

0.33 

6.0 

0.17 

%T, 

CVEN 

had200 = [ 

4.1 

6.26 

4.5 

4.13 

5.0 

2.83 

5.5 

1. 97 

6.0 

1.33 

4.1 

7.60 

4.5 

5.68 

5.0 

3.93 

5.5 

2.83 

6.0 

1. 97 

4.1 

4 . 96 

4.5 

2.80 

5.0 

1.77 

5.5 

1.12 

6.0 

0.71 

BW=0 

.235; 


CONC (3)=50.0, TCHNG=4.0; 

TSTOP=8.0; 
start @nocallback 

plotcven = plot (0, had50(:,l), had50(:,2), '+b', _t, _cven(:,3), '-b'); 

CONC(3)=100.0; 
start @nocallback 

plot (plotcven, 1, hadl00(:,l), hadl00(:,2), '+k', _t, _cven(:,3), '—k'); 

CONC (3)=200.0; 
start @nocallback 

plot (plotcven, 1, had200(:,l), had200(:,2), '+g', _t, _cven(:,3), '—g'); 


set @preference=BackslashEscapeS 


pltscript (plotcven. 
Inhalation StudyV';") 

pltscript (plotcven, 
Hours\";") 

pltscript(plotcven, 
pltscript (plotcven, 
[mg/L]\";") 

pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven. 


"Chart.Header.Text = V'Haddad et al. (1999) m-Xylene 
"Chart.Subheader.Text = \"50, 100, 200 ppm Inhalation for 4 
"Chart.SubHeader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"m-Xylene in Venous Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\) 

"Chart.Series[0].Title=\"50 ppm\";") 

"Chart.Series[1].Title=\"Simulation 50 ppm\";") 

"Chart.Series[2].Title=\"100 ppm\";") 

"Chart.Series[3].Title=\"Simulation 100 ppm\";") 

"Chart.Series[4].Title=\"200 ppm\";") 

"Chart.Series[5].Title=\"Simulation 200 ppm\";") 


83 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



set @preference=NoBackslashEscapeS 


C3_Tardif.m 


%Tardif, R., Charest-Tardif, G. and Brodeur, J. 1996. Comparison of the influence 
% of binary mixtures versus a ternary mixture of inhaled aromatic hydrocarbons 

% on their blood kinetics in the rat. Arch Toxicol 70(7): 405-413. 


%Adult male SD rats 
% 235-245 g 


resetdoses 

rattus 

xylenerat 


%T, CVEN from Figure 1, mean followed by +SD then -SD 


tarlOO 

= [ 

4.1 

1.26 

4.5 

0.86 

5.0 

0.51 

5.5 

0.37 

6.0 

0.24 

4.1 

1.44 

4.5 

1.01 

5.0 

0.62 

5.5 

0.43 

6.0 

0.30 

4.1 

1.09 

4.5 

0.71 

5.0 

0.43 

5.5 

0.31 

6.0 

0.15 


%T, CVEN from Figure 3, mean followed by +SD then -SD 


tar200 

= [ 

4.1 

6.32 

4.5 

4.26 

5.0 

2.83 

5.5 

1.99 

6.0 

1.37 

4.1 

7.80 

4.5 

5.74 

5.0 

3.95 

5.5 

2.83 

6.0 

2.04 

4.1 

4.89 

4.5 

2.83 

5.0 

1.75 

5.5 

1.12 

6.0 

0.74 


BW=0.240; 


CONC(3)=100.0; 

TCHNG=4.0; 

TSTOP=6.0; 
start @nocallback 

plotcven = plot (0, tarl00(:,l), tarl00(:,2), '+b', _t, _cven(:,3), '—b'); 


84 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



CONC (3)=200.0; 
start @nocallback 

plot (plotcven, 1, tar200(:,l), tar200(:,2), '+k', _t, _cven(:,3), '—k'); 


set @preference=BackslashEscapeS 


pltscript (plotcven. 
Inhalation Study\";") 

pltscript (plotcven, 
Hours\";") 

pltscript(plotcven, 
pltscript (plotcven, 
[mg/L]\";") 

pltscript(plotcven, 
pltscript (plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript (plotcven. 


"Chart.Header.Text = V'Tardif et al. (1996) m-Xylene 
"Chart.Subheader.Text = \"100, 200 ppm Inhalation for 4 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Xylene in Venous Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\) 

"Chart.Series[0].Title=\"100 ppm\";") 

"Chart.Series[1].Title=\"Simulation 100 ppm\";") 

"Chart.Series[2].Title=\"200 ppm\";") 

"Chart.Series[3].Title=\"Simulation 200 ppm\";") 


set @preference=NoBackslashEscapeS 


Nonane Study Simulation M Files 


C4_inhouse_inhal.m 

%Robinson, P.J. and Merrill, E.A. 2008. A harmonized physiologically based 
pharmacokinetic model 

% for nonane as a component of jet fuel. Wright-Patterson AFB, OH: Air Force 

Research 

% Laboratory, Applied Biotechnology Branch. AFRL-RH-WP-TR-2008-0067, ADA502610. 

resetdoses 

rattus 

nonanerat 


%(t, even, cfat, 
ih!90dl00 = [ 

cliv. 

cslw) 


0.5 

0.372 

NaN 

NaN 

NaN 

0.5 

0.427 

NaN 

NaN 

NaN 

0.5 

0.482 

NaN 

NaN 

NaN 

1.0 

0.370 

NaN 

NaN 

NaN 

1.0 

0.523 

NaN 

NaN 

NaN 

1.0 

0.676 

NaN 

NaN 

NaN 

2.0 

0.306 

NaN 

NaN 

NaN 

2.0 

0.516 

NaN 

NaN 

NaN 

2.0 

0.726 

NaN 

NaN 

NaN 

3.0 

0.496 

NaN 

NaN 

NaN 

3.0 

0.676 

NaN 

NaN 

NaN 

3.0 

0.856 

NaN 

NaN 

NaN 

4.0 

0.808 

13.93 

1.44 

1.0 

4.0 

0.891 

27.24 

3.83 

2.76 

4.0 

0.974 

40.55 

6.22 

4.52 

4.08 

0.292 

NaN 

NaN 

NaN 

4.08 

0.43 

NaN 

NaN 

NaN 

4.08 

0.568 

NaN 

NaN 

NaN 

4.16 

0.2251 

NaN 

NaN 

NaN 

4.16 

0.362 

NaN 

NaN 

NaN 


85 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



4.16 

0.498 

NaN 

NaN 

NaN 

4.25 

0.191 

NaN 

NaN 

NaN 

4.25 

0.304 

NaN 

NaN 

NaN 

4.25 

0.417 

NaN 

NaN 

NaN 

4.5 

0.097 

NaN 

NaN 

NaN 

4.5 

0.205 

NaN 

NaN 

NaN 

4.5 

0.313 

NaN 

NaN 

NaN 

5.0 

0.053 

NaN 

NaN 

NaN 

5.0 

0.114 

NaN 

NaN 

NaN 

5.0 

0.175 

NaN 

NaN 

NaN 

6.0 

0.004 

NaN 

NaN 

NaN 

6.0 

0.037 

NaN 

NaN 

NaN 

6.0 

0.07 

NaN 

NaN 

NaN 

7.0 

0.003 

NaN 

NaN 

NaN 

7.0 

0.03 

NaN 

NaN 

NaN 

7.0 

0.057 

NaN 

NaN 

NaN 

8.0 

0.0 

8.29 

0.0 

0.0 

8.0 

0.019 

18.65 

0.05 

0.7 

8.0 

0.042 

29.01 

0.22 

1.65] ; 

%(t, CV1, CF1, 
ihl90d500 = [ 
0.6 1.311 

CL1, CS1) 

NaN 

NaN 

NaN 

0.6 

2.051 

NaN 

NaN 

NaN 

0.6 

2.791 

NaN 

NaN 

NaN 

1 .1 

1.406 

NaN 

NaN 

NaN 

1 .1 

2.263 

NaN 

NaN 

NaN 

1 .1 

3.12 

NaN 

NaN 

NaN 

2.1 

2.934 

NaN 

NaN 

NaN 

2.1 

3.736 

NaN 

NaN 

NaN 

2.1 

4.538 

NaN 

NaN 

NaN 

3.1 

2.201 

NaN 

NaN 

NaN 

3.1 

3.635 

NaN 

NaN 

NaN 

3.1 

5.069 

NaN 

NaN 

NaN 

4 .1 

3.504 

66.01 

25.03 

11.71 

4 .1 

4.43 

131.11 

39.63 

17.33 

4 .1 

5.356 

196.21 

54.23 

22.95 

4.18 

3.0 

NaN 

NaN 

NaN 

4.18 

3.75 

NaN 

NaN 

NaN 

4.18 

4.5 

NaN 

NaN 

NaN 

4.26 

1.902 

NaN 

NaN 

NaN 

4.26 

2.785 

NaN 

NaN 

NaN 

4.26 

3.668 

NaN 

NaN 

NaN 

4.35 

1.322 

NaN 

NaN 

NaN 

4.35 

1.953 

NaN 

NaN 

NaN 

4.35 

2.584 

NaN 

NaN 

NaN 

4 . 6 

1.34 

NaN 

NaN 

NaN 

4 . 6 

1.768 

NaN 

NaN 

NaN 

4 . 6 

2.196 

NaN 

NaN 

NaN 

5.1 

0.811 

NaN 

NaN 

NaN 

5.1 

1.184 

NaN 

NaN 

NaN 

5.1 

1.557 

NaN 

NaN 

NaN 

6.1 

0.218 

NaN 

NaN 

NaN 

6.1 

0.41 

NaN 

NaN 

NaN 

6.1 

0.602 

NaN 

NaN 

NaN 

7.1 

0.195 

NaN 

NaN 

NaN 

7.1 

0.365 

NaN 

NaN 

NaN 

7.1 

0.535 

NaN 

NaN 

NaN 

8.1 

0.152 

52.64 

0.34 

0.0 

8.1 

0.251 

82.2 

0.48 

4 .96 

8.1 

0.35 

111.76 

0.62 

10.08]; 

%(t, CV1, CF1, CL1, CS1) 

86 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



ihl90dl000 = [ 


0.7 

1.682 

NaN 

NaN 

NaN 

0.7 

6.933 

NaN 

NaN 

NaN 

0.7 

12.184 

NaN 

NaN 

NaN 

1.2 

3.385 

NaN 

NaN 

NaN 

1.2 

6.95 

NaN 

NaN 

NaN 

1.2 

10.515 

NaN 

NaN 

NaN 

2.2 

9.405 

NaN 

NaN 

NaN 

2.2 

15.211 

NaN 

NaN 

NaN 

2.2 

21.017 

NaN 

NaN 

NaN 

3.2 

7.789 

NaN 

NaN 

NaN 

3.2 

16.928 

NaN 

NaN 

NaN 

3.2 

26.067 

NaN 

NaN 

NaN 

4.2 

4.724 

66.86 

47.91 

23.61 

4.2 

16.467 

360.28 

99.9 

45.0 

4.2 

28.21 

653.7 

151.91 

66.39 

4.28 

6.904 

NaN 

NaN 

NaN 

4.28 

13.625 

NaN 

NaN 

NaN 

4.28 

20.346 

NaN 

NaN 

NaN 

4.36 

2.902 

NaN 

NaN 

NaN 

4.36 

10.88 

NaN 

NaN 

NaN 

4.36 

18.858 

NaN 

NaN 

NaN 

4.45 

3.02 

NaN 

NaN 

NaN 

4.45 

9.281 

NaN 

NaN 

NaN 

4.45 

15.542 

NaN 

NaN 

NaN 

4.7 

1.649 

NaN 

NaN 

NaN 

4.7 

5.073 

NaN 

NaN 

NaN 

4.7 

8.497 

NaN 

NaN 

NaN 

5.2 

0.888 

NaN 

NaN 

NaN 

5.2 

4.403 

NaN 

NaN 

NaN 

5.2 

7.918 

NaN 

NaN 

NaN 

6.2 

0.638 

NaN 

NaN 

NaN 

6.2 

2.378 

NaN 

NaN 

NaN 

6.2 

4.118 

NaN 

NaN 

NaN 

7.2 

0.514 

NaN 

NaN 

NaN 

7.2 

1.483 

NaN 

NaN 

NaN 

7.2 

2.452 

NaN 

NaN 

NaN 

8.2 

0.247 

155.42 

2.2 

7.07 

8.2 

0.687 

316.26 

7.15 

21.11 

8.2 

1.127 

477.1 

12.1 

35.15 


BW=0.3; 

CONC(4)=100.0, TCHNG=4.0; 

TSTOP=10; 

start @nocallback 


plotcven = plot 

(0, ihl90dl00( : , 1) 

, ihl90dl00 (: 

,2) , 

'+b', t. 

even (: 

,4) , 

'-b 

plotcfat = plot 

(0, ihl90dl00(:,1) 

, ihl90dl00 (: 

,3) , 

'+b', t. 

cfat (: 

,4) , 

'-b 

plotcliv = plot 

(0, ihl90dl00(:,1) 

, ihl90dl00 (: 

,4) , 

'+b', t. 

cliv (: 

,4) , 

'-b 

plotcslw = plot 

(0, ihl90dl00(:,1) 

, ihl90dl00 (: 

,5) , 

'+b', _t, 

cslw (: 

,4) , 

'-b 

CONC (4)=500.0, 

rCHNG(4)=4.1; 







start @nocallback 







plot (plotcven. 

1, ihl90d500(:, 1) , 

ihl90d500 (: , 

2) , 

' +k', _t. 

even (: 

4) , 

-k' 

plot (plotcfat. 

1, ihl90d500(:, 1) , 

ihl90d500 (:, 

3) , 

+ 

1 

r+ 

cfat ( : 

4) , 

-k' 

plot (plotcliv, 

1, ihl90d500(:, 1) , 

ihl90d500 (:, 

4) , 

+k', t. 

cliv ( : , 

4) , 

-k' 

plot (plotcslw, 

1, ihl90d500(:, 1) , 

ihl90d500 (:, 

5) , 

' +k', _t. 

cslw (: , 

4) , 

-k' 


CONC (4)=1000.0, TCHNG(4)=4.2; 
start @nocallback 


87 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 






plot (plotcven, 1, ihl90d500(:,1), ihl90dl000(:,2), '+g', _t, _cven(:,4), '—g'); 
plot (plotcfat, 1, ihl90d500(:,1), ihl90dl000(:,3), '+g', _t, _cfat(:,4), '-g'); 
plot (plotcliv, 1, ihl90d500(:,1), ihl90dl000(:,4), '+g', _t, _cliv(:,4), '-g'); 
plot (plotcslw, 1, ihl90d500(:,1), ihl90dl000(:,5), ’+g’, _t, _cslw(:,4), ’-g’); 


set @preference=BackslashEscapeS 

pltscript (plotcven, "Chart.Header.Text = \"In-House Nonane Inhalation StudyV';") 
pltscript(plotcven, "Chart.Subheader.Text = \"100, 500, 1000 ppm Inhalation for 4 
Hours\";") 

pltscript(plotcven, "Chart.Subheader.Visible=true;") 

pltscript(plotcven, "Chart.Axes.Left.Title.Text=\"Nonane in Venous Blood 
[mg/L]\";") 

pltscript(plotcven, "Chart.Axes.Bottom.Title.Text=\"Hours\) 

pltscript(plotcven, "Chart.Series[0].Title=\"100 ppm\";") 

pltscript(plotcven, "Chart.Series[1].Title=\"Simulation 100 ppm\";") 

pltscript(plotcven, "Chart.Series[2].Title=\"500 ppm\";") 

pltscript(plotcven, "Chart.Series[3].Title=\"Simulation 500 ppm\";") 

pltscript (plotcven, "Chart.Series[4].Title=\"1000 ppm\";") 

pltscript (plotcven, "Chart.Series[5].Title=\"Simulation 1000 ppm\";") 

pltscript(plotcfat, "Chart.Header.Text = \"In-House Nonane Inhalation StudyV';") 
pltscript(plotcfat, "Chart.Subheader.Text = \"100, 500, 1000 ppm Inhalation for 4 
Hours\";") 

pltscript(plotcfat, "Chart.Subheader.Visible=true;") 

pltscript (plotcfat, "Chart.Axes.Left.Title.Text=\"Nonane in Fat [mg/L] 
pltscript(plotcfat, "Chart.Axes.Bottom.Title.Text=\"Hours\) 
pltscript(plotcfat, "Chart.Series[0].Title=\"100 ppm\";") 
pltscript (plotcfat, "Chart.Series[1].Title=\"Simulation 100 ppm\";") 
pltscript(plotcfat, "Chart.Series[2].Title=\"500 ppm\";") 
pltscript(plotcfat, "Chart.Series[3].Title=\"Simulation 500 ppm\";") 
pltscript (plotcfat, "Chart.Series[4].Title=\"1000 ppm\";") 
pltscript(plotcfat, "Chart.Series[5].Title=\"Simulation 1000 ppm\";") 

pltscript(plotcliv, "Chart.Header.Text = \"In-House Nonane Inhalation StudyV';") 
pltscript (plotcliv, "Chart.Subheader.Text = \"100, 500, 1000 ppm Inhalation for 4 
Hours\";") 

pltscript(plotcliv, "Chart.Subheader.Visible=true;") 

pltscript(plotcliv, "Chart.Axes.Left.Title.Text=\"Nonane in Liver [mg/L]\";") 

pltscript(plotcliv, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 

pltscript (plotcliv, "Chart.Series[0] .Title=\"100 ppm\";") 

pltscript(plotcliv, "Chart.Series[1].Title=\"Simulation 100 ppm\";") 

pltscript(plotcliv, "Chart.Series[2].Title=\"500 ppm\";") 

pltscript (plotcliv, "Chart.Series[3].Title=\"Simulation 500 ppm\";") 

pltscript(plotcliv, "Chart.Series[4].Title=\"1000 ppm\";") 

pltscript(plotcliv, "Chart.Series[5].Title=\"Simulation 1000 ppm\";") 

pltscript (plotcslw, "Chart.Header.Text = \"In-House Nonane Inhalation StudyV';") 
pltscript(plotcslw, "Chart.Subheader.Text = \"100, 500, 1000 ppm Inhalation for 4 
Hours\";") 

pltscript(plotcslw, "Chart.Subheader.Visible=true;") 

pltscript(plotcslw, "Chart.Axes.Left.Title.Text=\"Nonane in Slowly Perfused 
Tissues [mg/L]\";") 

pltscript(plotcslw, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 

pltscript(plotcslw, "Chart.Series[0].Title=\"100 ppm\";") 

pltscript (plotcslw, "Chart.Series[1].Title=\"Simulation 100 ppm\";") 

pltscript(plotcslw, "Chart.Series[2].Title=\"500 ppm\";") 

pltscript(plotcslw, "Chart.Series[3].Title=\"Simulation 500 ppm\";") 

pltscript (plotcslw, "Chart.Series[4].Title=\"1000 ppm\";") 

pltscript(plotcslw, "Chart.Series[5].Title=\"Simulation 1000 ppm\";") 

set @preference=NoBackslashEscapeS 


88 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 





C4_Lof99.m 


%Lof, A., Lam, H.R., Gullstrand, E., Ostergaard, G. and Ladefoged, 0. 1999. 
Distribution of 

% dearomatised white spirit in brain, blood, and fat tissue after repeated 

exposure of 

% rats. Pharmacol.Toxicol. 85(2): 92-97. 

% Rats dosed with 400 and 800 ppm white spirits; 3.6% n-nonane = 14.4 and 28.8 ppm 
nonane, respectively 

resetdoses 

rattus 

nonanerat 


%(T, CVen, CBrnTot, CFat) 


lof991o = [ 



102 . 

0.1 

0.73 

27.26 

102 . 

0.14 

1.17 

32 . 

102 . 

0.06 

0.29 

22.52 

270. 

0.09 

0.65 

32.5 

270. 

0.15 

0.85 

39.7 

270. 

0.03 

0.45 

25.3 

438 . 

0.1 

0.54 

29.06 

438 . 

0.13 

0.62 

34.41 

438 . 

0.07 

0.46 

23.71 

440 . 

0.03 

0.24 

33.85 

440 . 

0.04 

0.27 

41.22 

440 . 

0.02 

0.21 

26.48 

442 . 

0.02 

0.19 

29.75 

442 . 

0.03 

0.24 

35.48 

442 . 

0.01 

0.14 

24.02 

444 . 

0.02 

0.14 

28.54 

444 . 

0.03 

0.18 

32.46 

444 . 

0.01 

0.1 

24.62 

462 . 

0.01 

0.18 

23.75 

462 . 

0.01 

0.24 

24.58 

462 . 

0.01 

0.12 

22.92] ; 

(T, 

CVen, CBrnTot, CFat) 

lof99hi = [ 



102 . 

0.26 

1.2 

69.82 

102 . 

0.41 

1.58 

81 . 94 

102 . 

0.11 

0.82 

57.7 

270. 

0.21 

1.31 

90.39 

270. 

0.31 

1.54 

111.72 

270. 

0.11 

1.08 

69.06 

438 . 

0.32 

1.27 

99.51 

438 . 

0.44 

1 . 4 

127.56 

438 . 

0.2 

1.14 

71.46 

440 . 

0.06 

0.32 

88.96 

440 . 

0.08 

0.4 

101.56 

440 . 

0.04 

0.24 

76.36 

442 . 

0.03 

0.34 

74.76 

442 . 

0.04 

0.51 

82 . 

442 . 

0.02 

0.17 

67.52 

444 . 

0.02 

0.2 

69.3 

444 . 

0.03 

0.23 

75.36 

444 . 

0.01 

0.17 

63.24 

462 . 

0.02 

0.18 

50.07 

4 62 . 

0.02 

0.25 

58.5 

462 . 

0.02 

0.11 

41.64] 


89 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



BW=0.3; 


CONC(4)=14.4; 

TCHNG=6.0; DOSEINT=24.0; DAYSWK=5.0; TMAX=504.0; 

CINT=0.1; 

TSTOP=525.0; %not sure why it was 529 before - this is the next interval of 25 higher 
than last dosing 
start @nocallback 

plotcven = plot (0, lof991o ( :,1), lof991o(:,2), '+b', _t, _cven(:,4), '-b'); 

plotcbrntot = plot (0, lof991o (:,1), lof991o (:,3), 'tb', _t, _cbrntot(:,4), '—b'); 
plotcfat = plot (0, lof991o(:,1), lof991o(:,4), 'tb', _t, _cfat(:,4), '-b'); 

CONC(4)=28.8; 
start @nocallback 

plot (plotcven, 1, lof99hi (:,1), lof99hi(:,2), '+k', _t, _cven(:,4), '—k'); 
plot (plotcbrntot, 1, lof99hi(:,1), lof99hi ( :,3), '+k', _t, _cbrntot(:,4), '—k'); 

plot (plotcfat, 1, lof99hi(:,1), lof99hi(:,4), '+k', _t, _cfat(:,4), '—k'); 


set @preference=BackslashEscapeS 

pltscript (plotcven, "Chart.Header.Text = \"Lof et al. (1999) White Spirits 

Inhalation StudyV;") 

pltscript(plotcven, "Chart.Subheader.Text = \"14.4 and 28.8 ppm for 6 HoursV;") 
pltscript(plotcven, "Chart.Subheader.Visible=true;") 

pltscript(plotcven, "Chart.Axes.Left.Title.Text=\"Nonane in Venous Blood 
[mg/L]\";") 

pltscript(plotcven, "Chart.Axes.Bottom.Title.Text=\"Hours\) 
pltscript(plotcven, "Chart.Series[0] .Title=\"14.4 ppm\";") 
pltscript(plotcven, "Chart.Series[1].Title=\"Simulation 14.4 ppm\";") 
pltscript (plotcven, "Chart.Series[2] .Title=\"28.8 ppm\";") 
pltscript(plotcven, "Chart.Series[3].Title=\"Simulation 28.8 ppm\";") 

pltscript(plotcbrntot, "Chart.Header.Text = \"Lof et al. (1999) White Spirits 
Inhalation StudyV';") 

pltscript(plotcbrntot, "Chart.Subheader.Text = \"14.4 and 28.8 ppm for 6 
Hours)";") 

pltscript(plotcbrntot, "Chart.Subheader.Visible=true;") 

pltscript(plotcbrntot, "Chart.Axes.Left.Title.Text=\"Nonane in Total Brain 
[mg/L]\";") 

pltscript(plotcbrntot, "Chart.Axes.Bottom.Title.Text=\"Hours)”;") 
pltscript(plotcbrntot, "Chart.Series[0].Title=\"14.4 ppm\";") 
pltscript (plotcbrntot, "Chart.Series[1].Title=\"Simulation 14.4 ppm\";") 
pltscript(plotcbrntot, "Chart.Series [2] .Title=\"28.8 ppm\";") 
pltscript(plotcbrntot, "Chart.Series[3].Title=\"Simulation 28.8 ppm\";") 

pltscript (plotcfat, "Chart.Header.Text = V'Lof et al. (1999) White Spirits 

Inhalation Study)";") 

pltscript (plotcfat, "Chart.Subheader.Text = \"14.4 and 28.8 ppm for 6 Hours)";") 
pltscript(plotcfat, "Chart.Subheader.Visible=true;") 

pltscript(plotcfat, "Chart.Axes.Left.Title.Text=)"Nonane in Fat [mg/L])";") 

pltscript (plotcfat, "Chart.Axes.Bottom.Title.Text=)"Hours)";") 

pltscript(plotcfat, "Chart.Series[0].Title=)"14.4 ppm)";") 

pltscript(plotcfat, "Chart.Series[1].Title=)"Simulation 14.4 ppm)";") 

pltscript (plotcfat, "Chart.Series[2] .Title=)"28.8 ppm)";") 

pltscript(plotcfat, "Chart.Series[3].Title=)"Simulation 28.8 ppm)";") 

set @preference=NoBackslashEscapeS 


90 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



C4_Zahlsen90.m 


%Zahlsen, K., Nilsen, A.M., Eide, I. and Nilsen, O.G. 1990. Accumulation and 
distribution of 

% aliphatic (n-nonane), aromatic (1,2,4-trimethylbenzene) and naphthenic 

% (1,2,4-trimethylcyclohexane) hydrocarbons in the rat after repeated inhalation. 

Pharmacol. 

% Toxicol. 67 (5) : 436-440. 


resetdoses 

rattus 

nonanerat 


%(T, CVen 

, CBrnTot) 

zahl90 = 

[ 


12. 

21.8 

179 

60. 

17.3 

153 

156. 

15.4 

153 

228 . 

11.5 

132 

348 . 

10.2 

123 


% At the start, the animals weighed 150-200 g; Assumed 200 g overall due to growth 
BW=0.2; 

CONC (4)=1025.2; %Target was 1000 ppm, measured on 5 days, average 1025.2 ppm 
TCHNG=12.0; DOSEINT=24.0; DAYSWK=7.0; TMAX=360.0; %TMAX=336.0 depending on how you 
read it 
CINT=0.1; 

TSTOP=360.0; 

start @nocallback 

plotcven = plot (0, zahl90(:,l), zahl90(:,2), '+b 1 , _t, _cven(:,4), '-b'); 

plotcbrntot = plot (0, zahl90(:,l), zahl90(:,3), '+b', _t, __cbrntot(:,4), '—b'); 


set @preference=BackslashEscapeS 

pltscript(plotcven, "Chart.Header.Text = V'Zahlsen et al. (1990) Nonane Inhalation 

StudyV; ") 

pltscript(plotcven, "Chart.SubHeader.Text = V'1000 ppm for 12 HoursV 1 ;") 
pltscript(plotcven, "Chart.SubHeader.Visible=true;") 

pltscript(plotcven, "Chart.Axes.Left.Title.Text=\"Nonane in Venous Blood 
[mg/L]\";") 

pltscript(plotcven, "Chart.Axes.Bottom.Title.Text=\"Hours\) 
pltscript(plotcven, "Chart.Series[0].Title=\"1000 ppm\";") 
pltscript(plotcven, "Chart.Series[1].Title=\"Simulation 1000 ppm\";") 

pltscript (plotcbrntot, "Chart.Header.Text = V'Zahlsen et al. (1990) Nonane 

Inhalation StudyV';") 

pltscript(plotcbrntot, "Chart.SubHeader.Text = \"1000 ppm for 12 HoursV';") 
pltscript(plotcbrntot, "Chart.SubHeader.Visible=true;") 

pltscript(plotcbrntot, "Chart.Axes.Left.Title.Text=\"Nonane in Total Brain 
[mg/L]\";") 

pltscript(plotcbrntot, "Chart.Axes.Bottom.Title.Text=\"Hours/";") 
pltscript(plotcbrntot, "Chart.Series[0].Title=\"1000 ppm\";") 
pltscript(plotcbrntot, "Chart.Series[1].Title=\"Simulation 1000 ppm\";") 

set @preference=NoBackslashEscapeS 


91 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



Decane Study Simulation M Files 


C5_Lof99.m 

%Lof, A., Lam, H.R., Gullstrand, E., Ostergaard, G. and Ladefoged, 0. 1999. 
Distribution of 

% dearomatised white spirit in brain, blood, and fat tissue after repeated 

exposure of 

% rats. Pharmacol.Toxicol. 85(2): 92-97. 


% Rats dosed with 400 and 800 ppm white spirits; 16.6% n-decane = 106.4 and 212 ppm 
decane, respectively 


resetdoses 

rattus 

decanerat 

%(T, CVen, CBrnTot, CFat) 

%Additional data are present in the original cmd file, but commented out 


lof991o 

= 

[ 





102 . 

0. 

. 7 

3. 

35 

150. 

3 

102 . 

0. 

00 

4 . 

99 

178 . 

94 

102 . 

0. 

.54 

1 . 

71 

121. 

66 

270. 

0. 

00 

00 

2. 

64 

182. 

8 

270. 

1 . 

. 1 

3. 

27 

222. 

1 

270. 

0. 

.66 

2. 

01 

143. 

5 

438 . 

0. 

. 7 

2. 

34 

172 . 

1 

438 . 

0. 

.81 

2. 

89 

198. 

4 

438 . 

0. 

.59 

1 . 

79 

145. 

8 

440 . 

0. 

.3 

1 . 

24 

202. 

8 

440 . 

0. 

.35 

1 . 

34 

256. 

19 

440 . 

0. 

.25 

1 . 

14 

149. 

41 

442 . 

0. 

.24 

0. 

78 

190. 

8 

442 . 

0. 

.29 

0. 

93 

218. 

12 

442 . 

0. 

.19 

0 . 

63 

163. 

48 

444 . 

0 . 

.21 

0 . 

54 

191. 

7 

444 . 

0 . 

.25 

0 . 

72 

228. 

18 

444 . 

0 . 

.17 

0 . 

36 

155. 

22 

462 . 

0 . 

.13 

0 . 

45 

166. 

5 

462 . 

0 . 

.16 

0 . 

88 

187 . 

19 

462 . 

0 . 

. 1 

0 . 

02 

145. 

81] ; 

(T, CVen, 

CBrnTot, 

CFat) 

lof99hi 

= 

[ 





102 . 



2 

; . 09 

6.08 

4 

102 . 

2 . 

.78 

7 . 

62 

493. 

34 

102 . 

1 . 

. 4 

4 . 

54 

387. 

06 

270. 

1 . 

.73 

6. 

24 

537. 

7 

270. 

2 . 

.12 

6. 

96 

642. 

8 

270. 

1 . 

.34 

5. 

52 

432. 

6 

438 . 

2 . 

.21 

5. 

95 

590. 

5 

438 . 

2 . 

.75 

7 . 

19 

770. 

5 

438 . 

1 . 

. 67 

4 . 

71 

410. 

5 

440 . 

0 . 

.57 

2. 

91 

547 . 

3 

440 . 

0 . 

. 64 

3. 

24 

616. 

49 

440 . 

0 . 

.5 

2. 

58 

478 . 

11 

442 . 

0 . 

.44 

1 . 

96 

511 . 

9 

442 . 

0 . 

.48 

2. 

8 

57! 

L . 78 

442 . 

0 . 

. 4 

1 . 

12 

452 . 

02 

444 . 

0 . 

.36 

1 . 

14 

489. 

6 


92 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



444 . 

0.42 1.37 

534.2 

444 . 

0.3 0.91 

445. 

462 . 

0.16 0.57 

426. 

462 . 

0.18 0.97 

462.76 

462 . 

0.140.17 

389.24 

BW=0.3; 




CONC (5)=106.4; 

TCHNG=6.0; DOSEINT=24.0; DAYSWK=5.0; TMAX=504.0; 

CINT=0.1; 

TSTOP=525.0; %not sure why it was 529 before - this is the next interval of 25 higher 
than last dosing 
start @nocallback 


plotcven = plot (0, 

lof 9 91o ( :, 1) 

, lof991o(: , 2) 

, ' +b', 

_t. 

even (:,5) , 

' -b' ) ; 


plotcbrntot = plot 

(0, lof991o (: 

,1), lof991o (: 

,3), '+b' , 

t, ebrntot ( 

: ,5) , 

' -b') 

plotcfat = plot (0, 

lof9 91o(;,1) 

, lof991o ( :,4) 

, ' +b' , 

__t. 

cfat(:,5), 

' -b' ) ; 


CONC(5)=212.0; 
start @nocallback 








plot (plotcven, 1, 

lof99hi(:,1), 

lof99hi(;,2), 

' +k' , 

_t. 

even(;,5), ' 

- k ' ) ; 


plot (plotcbrntot. 

1, lof9 9hi(:, 

1), lof9 9hi(:, 

3), ' +k" 

1 

t _ 

t, ebrntot ( : 

,5), 1 

’ - k ' ) ; 

plot (plotcfat, 1 , 

lof99hi(: , 1) , 

lof99hi( ;, 4 ) , 

' +k' , 

_t. 

cfat( ;, 5) , ' 

-k') ; 



set @preference=BackslashEscapeS 


pltscript(plotcven. 
Inhalation StudyV';") 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
[mg/L]\";") 

pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven, 
pltscript(plotcven. 


"Chart.Header.Text = V'Lof et al. (1999) White Spirits 

"Chart.Subheader.Text = V'106.4 and 212 ppm for 6 HoursV';") 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Decane in Venous Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\) 

"Chart.Series[0].Title=\"106.4 ppm\";") 

"Chart.Series[1].Title=\"Simulation 106.4 ppm\";") 

"Chart.Series[2].Title=\"212 ppm\";") 

"Chart.Series[3].Title=\"Simulation 212 ppm\";") 


pltscript(plotcbrntot. 
Inhalation StudyV';") 

pltscript(plotcbrntot. 
Hours)";") 

pltscript(plotcbrntot, 
pltscript(plotcbrntot, 
[mg/L]\";") 

pltscript(plotcbrntot, 
pltscript(plotcbrntot, 
pltscript(plotcbrntot, 
pltscript(plotcbrntot, 
pltscript(plotcbrntot. 


"Chart.Header.Text = V'Lof et al. (1999) White Spirits 

"Chart.Subheader.Text = \"106.4 and 212 ppm for 6 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Decane in Total Brain 

"Chart.Axes.Bottom.Title.Text=\"Hours)” ; " ) 

"Chart.Series[0].Title=\"106.4 ppm\";") 

"Chart.Series[1].Title=\"Simulation 106.4 ppm\";") 
"Chart.Series[2].Title=\"212 ppm\";") 

"Chart.Series[3].Title=\"Simulation 212 ppm\";") 


pltscript(plotcfat. 
Inhalation Study)";") 
pltscript(plotcfat, 
pltscript(plotcfat, 
pltscript(plotcfat, 
pltscript(plotcfat, 
pltscript(plotcfat, 
pltscript(plotcfat, 
pltscript(plotcfat. 


"Chart.Header.Text = V'Lof et al. (1999) White Spirits 

"Chart.Subheader.Text = \"106.4 and 212 ppm for 6 Hours)";") 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=)"Decane in Fat [mg/L])";") 
"Chart.Axes.Bottom.Title.Text=)"Hours)";") 

"Chart.Series[0].Title=)"106.4 ppm)";") 

"Chart.Series[1].Title=)"Simulation 106.4 ppm)";") 

"Chart.Series[2].Title=)"212 ppm)";") 


93 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



pltscript (plotcfat, "Chart.Series[3].Title=\"Simulation 212 ppm\";") 


set @preference=NoBackslashEscapeS 


C5_Perleberg04.m 

%Perleberg, U.R., Keys, D.A. and Fisher, J.W. 2004. Development of a physiologically 
% based pharmacokinetic model for decane, a constituent of Jet Propellent-8. 

% Inhal.Toxicol. 16(11-12): 771-783. 


resetdoses 

rattus 

decanerat 

% (1-T, 2-CArt, 3-CLiv, 4-CBrnTot, 5-CRap, 6-CFat, 7-conc-bone-marrow, 8-conc-skin) 


perll200 = 
4.08 4.34 

[ 

33.74 

120.38 

11.09 

104.91 

260.89 

8.18 

4.08 

4.79 

37.96 

131.21 

13.08 

122.68 

315.37 

9.83 

4.08 

3.89 

29.52 

109.55 

9.10 

87.15 

206.42 

6.53 

4.50 

0.63 

17.48 

94.85 

11.02 

60.51 

195.57 

8.72 

4.50 

0.82 

23.02 

108.64 

13.91 

81.42 

272.81 

10.82 

4.50 

0.44 

11 . 94 

81.07 

8.12 

39.60 

118.33 

6.622 

5.00 

0.58 

5.17 

73.29 

5.84 

112.89 

NaN 

24.12 

5.00 

0.72 

6.15 

75.55 

7.44 

142.72 

NaN 

37.15 

5.00 

0.45 

4.18 

71.04 

4.23 

83.05 

NaN 

11.09 

6.00 

0.26 

1.16 

45.37 

4 . 97 

88.52 

158.30 

10.43 

6.00 

0.32 

1. 64 

48.94 

6.32 

107.13 

200.46 

13.81 

6.00 

0.20 

0.69 

41.80 

3.62 

69.91 

116.14 

7.050 

8.00 

0.15 

0.55 

14.76 

3.23 

94.43 

NaN 

8.363 

8.00 

0.19 

0.74 

15.76 

3.76 

118.91 

NaN 

13.45 

8.00 

0.10 

0.37 

13.76 

2.71 

69.94 

NaN 

3.277 

10.00 

NaN 

NaN 

6.90 

3.82 

101.23 

57.139 

20.98 

10.00 

NaN 

NaN 

7.77 

5.48 

123.56 

66.751 

29.04 

10.00 

NaN 

NaN 

6.02 

2.17 

78.89 

47.528 

12.93 

16.00 

NaN 

NaN 

2.09 

2.85 

74.96 

6.140 

0.861 

16.00 

NaN 

NaN 

2.28 

4.15 

83.96 

6.911 

1.463 

16.00 

NaN 

NaN 

1. 90 

1.55 

65.96 

5.370 

0.259 

28.00 

NaN 

NaN 

0.36 

2.45 

55.34 

1.348 

1.004 

28.00 

NaN 

NaN 

0.45 

3.66 

64.11 

1. 672 

1.572 

28.00 

NaN 

NaN 

0.27 

1.24 

46.57 

1.025 

0.435 


% (1-T, 2-CArt, 3-CLiv, 4-CBrnTot, 5-CRap, 6-CFat, 7-conc-bone-marrow, 8-conc-skin) 
perl781 = [ 

4.00 2.68 34.8 61.1 12.1 51.1 109.64 NaN 

4.00 3.58 45.1 65.9 16.7 72.3 163.83 NaN 

4.00 1.78 24.5 56.2 7.42 29.7 55.469 NaN]; 

% (1-T, 2-CArt, 3-CLiv, 4-CBrnTot, 5-CRap, 6-CFat, 7-conc-bone-marrow, 8-conc-skin) 


perl273 = [ 
4.00 0.49 

4.39 

18.3 

2.14 

14.3 

52.479 

1.45 

4.00 0.65 

5.30 

21.1 

2.76 

23.3 

66.393 

1.45 

4.00 0.34 

3.49 

16.1 

1.53 

5.34 

38.565 

0.79 

BW=0.211; 







CONC(5)=1200. 

. 0; 






TCHNG=4.0; DOSEINT : 

=24.0; 

DAYSWK= 

: 5.0; TMAX=24.0, 



CINT=0.1; 

TSTOP=30.0; 
start @nocallback 


94 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



plotcart = plot (0, perll200 (:, 1), perll200(:, 2), '+b', _t, _cart(:,5), '—b'); 

plotcliv = plot (0, perll200(:,1), perll200(:,3), '+b', _t, _cliv(:,5), '—b'); 

plotcbrntot = plot (0, perll200 ( :, 1), perll200(:, 4), '+b', _t, __cbrntot (: , 5) , '—b'); 

plotcrap = plot (0, perll200 (:, 1), perll200(:, 5), '+b', _t, _crap(:,5), '—b'); 

plotcfat = plot (0, perll200(:, 1), perll200 ( :, 6), '+b', _t, _cfat(:,5), '—b'); 

CONC(5)=781.0; 

TSTOP=5.0; 
start @nocallback 

plot (plotcart, 1, perl781(:,1), perl781(:,2), '+k', _t, _cart(:,5), '—k'); 

plot (plotcliv, 1, perl7 81 (:,1), per1781(:,3), '+k', _t, _cliv(:,5), '—k'); 

plot (plotcbrntot, 1, perl781(:,1), perl781(:,4), '+k', _t, _cbrntot(:,5), '—k'); 

plot (plotcrap, 1, perl781 (:,1), perl781 (:,5), '+k', _t, _crap(:,5), '—k'); 

plot (plotcfat, 1, perl781 (:,1), perl781 (:,6), '+k', _t, _cfat(:,5), '—k'); 

CONC (5)=273.0; 
start @nocallback 

plot (plotcart, 1, perl273 (:, 1), perl273 (:,2), '+g', _t, _cart(:,5), '-g'); 

plot (plotcliv, 1, perl273 (:, 1), per1273 (:,3), '+g', _t, _cliv(:,5), '—g'); 

plot (plotcbrntot, 1, perl273(:,1), perl273(:,4), '+g', _t, _cbrntot(:,5), '-g'); 

plot (plotcrap, 1, perl273 (:, 1), perl273 (:, 5), '+g', _t, _crap(:,5), '—g'); 

plot (plotcfat, 1, perl273 (:, 1), perl273 (:, 6), '+g', _t, _cfat(:,5), '—g'); 


set @preference=BackslashEscapeS 

pltscript (plotcart, "Chart.Header.Text = V'Perleberg et al. (2004) n-Decane 

Inhalation Study\";") 

pltscript (plotcart, "Chart.Subheader.Text = \"273, 781 or 1200 ppm for 4 
Hours\";") 

pltscript(plotcart, "Chart.SubHeader.Visible=true;") 
pltscript (plotcart, "Chart.Axes.Left.Automatic=false;") 
pltscript(plotcart, "Chart.Axes.Left.Minimum=0.01;") 
pltscript(plotcart, "Chart.Axes.Left.Maximum=100.0;") 
pltscript(plotcart, "Chart.Axes.Left.Logarithmic = true;") 

pltscript (plotcart, "Chart.Axes.Left.Title.Text=\"Decane in Arterial Blood 
[mg/L]\";") 

pltscript(plotcart, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 

pltscript (plotcart, "Chart.Series[0].Title=\"1200 ppm\";") 

pltscript (plotcart, "Chart.Series[1].Title=\"Simulation 1200 ppm\";") 

pltscript(plotcart, "Chart.Series[2].Title=\"781 ppm\";") 

pltscript (plotcart, "Chart.Series[3].Title=\"Simulation 781 ppm\";") 

pltscript(plotcart, "Chart.Series[4].Title=\"273 ppm\";") 

pltscript(plotcart, "Chart.Series[5].Title=\"Simulation 273 ppm\";") 

pltscript(plotcliv, "Chart.Header.Text = V'Perleberg et al. (2004) n-Decane 

Inhalation StudyV';") 

pltscript(plotcliv, "Chart.SubHeader.Text = \"273, 781 or 1200 ppm for 4 
Hours\";") 

pltscript(plotcliv, "Chart.SubHeader.Visible=true;") 
pltscript (plotcliv, "Chart.Axes.Left.Automatic=false;") 
pltscript(plotcliv, "Chart.Axes.Left.Minimum=0.01;") 
pltscript(plotcliv, "Chart.Axes.Left.Maximum=100.0;") 
pltscript (plotcliv, "Chart.Axes.Left.Logarithmic = true;") 

pltscript (plotcliv, "Chart.Axes.Left.Title.Text=\"Decane in Liver [mg/L]\";") 

pltscript(plotcliv, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 

pltscript (plotcliv, "Chart.Series[0].Title=\"1200 ppm\";") 

pltscript (plotcliv, "Chart.Series[1].Title=\"Simulation 1200 ppm\";") 

pltscript(plotcliv, "Chart.Series[2].Title=\"781 ppm\";") 

pltscript (plotcliv, "Chart.Series[3].Title=\"Simulation 781 ppm\";") 

pltscript(plotcliv, "Chart.Series[4].Title=\"273 ppm\";") 


95 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



pltscript(plotcliv, "Chart.Series[5].Title=\"Simulation 273 ppm\";") 

pltscript (plotcbrntot, "Chart.Header.Text = V'Perleberg et al. (2004) n-Decane 
Inhalation Study\";") 

pltscript(plotcbrntot, "Chart.Subheader.Text = \"273, 781 or 1200 ppm for 4 
Hours\";") 

pltscript(plotcbrntot, "Chart.Subheader.Visible=true;") 
pltscript(plotcbrntot, "Chart.Axes.Left.Automatic=false;") 
pltscript(plotcbrntot, "Chart.Axes.Left.Minimum=0.01; ") 
pltscript(plotcbrntot, "Chart.Axes.Left.Maximum=2 00.0;") 
pltscript(plotcbrntot, "Chart.Axes.Left.Logarithmic = true;") 
pltscript(plotcbrntot, "Chart.Axes.Left.Title.Text=\"Decane in Total Brain 
[mg/L]\";") 

pltscript(plotcbrntot, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 

pltscript(plotcbrntot, "Chart.Series [0] .Title=\"1200 ppm\";") 

pltscript (plotcbrntot, "Chart.Series[1].Title=\"Simulation 1200 ppm\";") 

pltscript (plotcbrntot, "Chart.Series[2] .Title=\"781 ppm\";") 

pltscript(plotcbrntot, "Chart.Series[3].Title=\"Simulation 781 ppm\";") 

pltscript(plotcbrntot, "Chart.Series[4].Title=\"273 ppm\";") 

pltscript (plotcbrntot, "Chart.Series[5].Title=\"Simulation 273 ppm\";") 

pltscript(plotcrap, "Chart.Header.Text = V'Perleberg et al. (2004) n-Decane 

Inhalation StudyV';") 

pltscript (plotcrap, "Chart.Subheader.Text = \"273, 781 or 1200 ppm for 4 
Hours\";") 

pltscript(plotcrap, "Chart.Subheader.Visible=true;") 
pltscript(plotcrap, "Chart.Axes.Left.Automatic=false;") 
pltscript(plotcrap, "Chart.Axes.Left.Minimum=0.01;") 
pltscript (plotcrap, "Chart.Axes.Left.Maximum=100.0;") 
pltscript(plotcrap, "Chart.Axes.Left.Logarithmic = true;") 

pltscript (plotcrap, "Chart.Axes.Left.Title.Text=\"Decane in Rapidly Perfused 
Tissues [mg/L]\";") 

pltscript(plotcrap, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 

pltscript (plotcrap, "Chart.Series[0].Title=\"1200 ppm\";") 

pltscript (plotcrap, "Chart.Series[1].Title=\"Simulation 1200 ppm\";") 

pltscript(plotcrap, "Chart.Series[2].Title=\"781 ppm\";") 

pltscript (plotcrap, "Chart.Series[3].Title=\"Simulation 781 ppm\";") 

pltscript (plotcrap, "Chart.Series[4] .Title=\"273 ppm\";") 

pltscript(plotcrap, "Chart.Series[5].Title=\"Simulation 273 ppm\";") 

pltscript (plotcfat, "Chart.Header.Text = V'Perleberg et al. (2004) n-Decane 

Inhalation StudyV';") 

pltscript (plotcfat, "Chart.Subheader.Text = \"273, 781 or 1200 ppm for 4 
Hours\";") 

pltscript(plotcfat, "Chart.Subheader.Visible=true;") 
pltscript (plotcfat, "Chart.Axes.Left.Logarithmic = true;") 

pltscript (plotcfat, "Chart.Axes.Left.Title.Text=\"Decane in Fat [mg/L]\";") 

pltscript(plotcfat, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 

pltscript (plotcfat, "Chart.Series[0] .Title=\"1200 ppm\";") 

pltscript (plotcfat, "Chart.Series[1].Title=\"Simulation 1200 ppm\";") 

pltscript(plotcfat, "Chart.Series[2].Title=\"781 ppm\";") 

pltscript (plotcfat, "Chart.Series[3].Title=\"Simulation 781 ppm\";") 

pltscript(plotcfat, "Chart.Series[4].Title=\"273 ppm\";") 

pltscript(plotcfat, "Chart.Series[5].Title=\"Simulation 273 ppm\";") 

set @preference=NoBackslashEscapeS 


96 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



C5_Zahlsen92.m 


%Zahlsen, K., Eide, I., Nilsen, A.M. and Nilsen, O.G. 1992. Inhalation kinetics of C6 
% to CIO aliphatic, aromatic and naphthenic hydrocarbons in rat after repeated 

% exposures. Pharmacol.Toxicol. 71(2): 144-149. 


resetdoses 

rattus 

decanerat 


% (1-T, 2-CArt, 3-CBrnTot, 4-CLiv, 5-CRap, 6-CFat) 
zahl = [ 


12.0 

0.83957 

12.73585 

5.49278 

4.76705 

119.6743 

12.0 

0.93918 

13.71772 

6.95847 

5.53547 

167.2025 

12.0 

0.73996 

11.75398 

4.02709 

3.99863 

72.1461 

36.0 

0.91072 

8.28186 

6.48888 

8.09687 

188.4052 

36.0 

1.01033 

10.08907 

7.31422 

9.07874 

208.0426 

36.0 

0.81111 

6.47465 

5.66354 

7.115 

168.7678 

60.0 

0.96764 

8.56646 

6.53157 

11.05671 

175.029 

60.0 

1.03879 

10.37367 

7.08654 

14 . 9415 

183.9939 

60.0 

0.89649 

6.75925 

5.9766 

7.17192 

166.0641 

72.0 

0.01423 

0.24191 

NaN 

0.24191 

131.6275 

72.0 

0.02846 

0.25614 

NaN 

0.32729 

145.9998 

72.0 

0.001 

0.22768 NaN 

0.15653 117. 

2552] ; 


BW=0.175; 

SCRUB(5)=0.4; 

CONC(5)=100.0; 

TCHNG=12.0; DOSEINT=24.0; DAYSWK=5.0; TMAX=72.0; 

CINT=0.1; 

TSTOP=80.0; 
start @nocallback 

plotcart = plot (0, zahl(:,1), zahl(:,2), '+b', _t, _cart(:,5), '—b'); 

plotcbrntot = plot (0, zahl(:,l), zahl(:,3), '+b', _t, _cbrntot(:,5), '-b'); 


plotcliv 

= plot 

(0, zahl (:,1), zahl(:,4). 

' +b', 

_t. 

_cliv (:,5), 

'-b 

plotcrap 

= plot 

(0, zahl (:,1), zahl(:,5). 

' +b', 

_t. 

_crap (:,5), 

'-b 

plotcfat 

= plot 

(0, zahl (:,1), zahl(:,6). 

' +b', 

_t, 

cfat(:,5), 

'-b 


set @preference=BackslashEscapeS 


pltscript(plotcart. 
Inhalation Study/";") 
pltscript(plotcart, 
pltscript(plotcart, 
pltscript(plotcart, 
[mg/L]\";") 

pltscript(plotcart, 
pltscript(plotcart, 
pltscript(plotcart. 


"Chart.Header.Text = \"Zahlsen et al. (1992) n-Decane 

"Chart.Subheader.Text = \"100 ppm for 12 HoursV';") 
"Chart.Subheader.Visible=true;") 

"Chart.Axes.Left.Title.Text=\"Decane in Arterial Blood 

"Chart.Axes.Bottom.Title.Text=\"Hours\";") 

"Chart.Series[0].Title=\"100 ppm\";") 

"Chart.Series[1].Title=\"Simulation 100 ppm\";") 


pltscript(plotcbrntot. 
Inhalation Study/";") 

pltscript(plotcbrntot, 
pltscript(plotcbrntot, 
pltscript(plotcbrntot, 
[mg/L]\";") 

pltscript(plotcbrntot, 
pltscript(plotcbrntot, 
pltscript(plotcbrntot. 


"Chart.Header.Text = \"Zahlsen et al. (1992) n-Decane 

"Chart.Subheader.Text = \"100 ppm for 12 Hours/";") 
"Chart.Subheader.Visible=true; ") 

"Chart.Axes.Left.Title.Text=\"Decane in Total Brain 

"Chart.Axes.Bottom.Title.Text=\"Hours/" ; " ) 

"Chart.Series[0].Title=i"100 ppm/";") 

"Chart.Series[1].Title=Z"Simulation 100 ppm/";") 


97 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



pltscript(plotcliv, "Chart.Header.Text = V'Zahlsen et al. (1992) n-Decane 

Inhalation StudyV';") 

pltscript(plotcliv, "Chart.SubHeader.Text = V'100 ppm for 12 HoursV';") 

pltscript(plotcliv, "Chart.SubHeader.Visible=true;") 

pltscript(plotcliv, "Chart.Axes.Left.Automatic=false;") 

pltscript(plotcliv, "Chart.Axes.Left.Minimum=0.01;") 

pltscript(plotcliv, "Chart.Axes.Left.Maximum=100.0;") 

pltscript(plotcliv, "Chart.Axes.Left.Logarithmic = true;") 

pltscript(plotcliv, "Chart.Axes.Left.Title.Text=\"Decane in Liver [mg/L]\";") 
pltscript(plotcliv, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 
pltscript(plotcliv, "Chart.Series[0].Title=\"100 ppm\";") 
pltscript(plotcliv, "Chart.Series[1].Title=\"Simulation 100 ppm\";") 

pltscript (plotcrap, "Chart.Header.Text = V'Zahlsen et al. (1992) n-Decane 

Inhalation StudyV';") 

pltscript(plotcrap, "Chart.SubHeader.Text = \"100 ppm for 12 HoursV';") 

pltscript(plotcrap, "Chart.SubHeader.Visible=true;") 

pltscript(plotcrap, "Chart.Axes.Left.Automatic=false;") 

pltscript(plotcrap, "Chart.Axes.Left.Minimum=0.01;") 

pltscript (plotcrap, "Chart.Axes.Left.Maximum=l00.0;") 

pltscript(plotcrap, "Chart.Axes.Left.Logarithmic = true;") 

pltscript (plotcrap, "Chart.Axes.Left.Title.Text=\"Decane in Kidney (Rapidly 
Perfused Tissues) [mg/L]\";") 

pltscript(plotcrap, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 
pltscript(plotcrap, "Chart.Series[0].Title=\"100 ppm\";") 
pltscript(plotcrap, "Chart.Series[1].Title=\"Simulation 100 ppm\";") 

pltscript(plotcfat, "Chart.Header.Text = V'Zahlsen et al. (1992) n-Decane 

Inhalation StudyV';") 

pltscript(plotcfat, "Chart.SubHeader.Text = \"100 ppm for 12 HoursV';") 
pltscript(plotcfat, "Chart.SubHeader.Visible=true;") 

pltscript (plotcfat, "Chart.Axes.Left.Title.Text=\"Decane in Fat [mg/L]\";") 
pltscript(plotcfat, "Chart.Axes.Bottom.Title.Text=\"Hours\";") 
pltscript(plotcfat, "Chart.Series[0].Title=\"100 ppm\";") 
pltscript (plotcfat, "Chart.Series[1].Title=\"Simulation 100 ppm\";") 

set @preference=NoBackslashEscapeS 


98 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



APPENDIX C: MODEL SIMULATIONS FOR INDIVIDUAL KEY COMPONENTS 


These simulations are organized by key component and then by study. The section titles 
correspond to the m file names seen in Appendix B. The study reference is cited in the first 
bullet. The citation is followed by a list of study conditions necessary to simulate the kinetics of 
the chemical. 


TOLUENE RAT EXPOSURE SIMULATIONS 


Cl_Haddad.m 

• Haddad, S., Tardif, R., Charest-Tardif, G. and Krishnan, K. 1999. Physiological modeling 
of the toxicokinetic interactions in a quaternary mixture of aromatic hydrocarbons. 
Toxicol.Appl.Pharmacol. 161(3): 249-257. 

• Male Sprague-Dawley rats 

• Body weight = 0.235 kg 

• 4-hour inhalation exposure 

• 50, 100, or 200 ppm 

• Data points = mean ± 1 SD 


Haddad et al. (1999) Toluene Inhalation Study 


50, 100, 200 ppm Inhalation for 4 Hours 



0 ■ 50 ppm 

0 - Simulation 50 ppm 

0 ■ 100 ppm 

0- Simulation 100 ppm 

0 ■ 200 ppm 

0- Simulation 200 ppm 


Cl_Lam.m 

• Lam, C.W., Galen, T.J., Boyd, J.F. and Pierson, D.L. 1990. Mechanism of transport and 
distribution of organic solvents in blood. Toxicol.Appl.Pharmacol. 104(1): 117-129. 

• Male Sprague-Dawley rats 

• Body weight = estimated 0.3 kg 


99 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 
























• 2-hour inhalation exposure 

• 488 ± 24 ppm 

• Data points = individual values, n =5 


Lam et al. (1990) Toluene Inhalation Study 


488 ppm Inhalation for 2 Hours 



0 ■ 488 ppm 

0- Simulation 488 ppm 


Cl_Romer.m 

• Romer, K.G., Federsel, R.J. and Freundt, KJ. 1986. Rise of inhaled toluene, ethyl 
benzene, m-xylene, or mesitylene in rat blood after treatment with ethanol. Bull Environ 
Contam Toxicol 37(6): 874-876. 

• Female Sprague-Dawley rats 

• Body weight = 0.210 kg (range: 0.200 - 0.220) 

• 2-hour inhalation exposure 

• 220 ppm 

• Data points = mean ± 1 SD 


100 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 


















Romeretal. (1986) Toluene Inhalation Study 
220 ppm Inhalation for 2 Hours 



0 ■ 220 ppm mean value 

0- Simulation 220 ppm 


Cl_Tardif.m 

• Tardif, R., Charest-Tardif, G. and Brodeur, J. 1996. Comparison of the influence of 
binary mixtures versus a ternary mixture of inhaled aromatic hydrocarbons on their blood 
ki netics in the rat. Arch Toxicol 70(7): 405-413. 

• Male Sprague-Dawley rats 

• Body weight = 0.240 kg (range: 0.235 - 0.245) 

• 4-hour inhalation exposure 

• 100 or 200 ppm 

• Data points = mean ± 1 SD 


Tardif et al. (1996) Toluene Inhalation Study 


100, 200 ppm Inhalation for 4 Hours 



0 ■ 100 ppm 

0- Simulation 100 ppm 

0 ■ 200 ppm 

0- Simulation 200 ppm 


101 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 















ETHYLBENZENE RAT EXPOSURE SIMULATIONS 


C2_Haddad.m 

• Haddad et al. (1999) 

• Male Sprague-Dawley rats 

• Body weight = 0.235 kg 

• 4-hour inhalation exposure 

• 50, 100, or 200 ppm 

• Data points = mean ± 1 SD 


Haddad etal. (1999) Ethylbenzene Inhalation Study 
50, 100, 200 ppm Inhalation for 4 Hours 



* 

■ 

50 ppm 

0 

— 

Simulation 50 ppm 

0 

■ 

100 ppm 

a 


Simulation 100 ppm 

0 

■ 

200 ppm 

✓ 

— 

Simulation 200 ppm 


C2_Tardif.m 

• Tardif et al. (1996) 

• Male Sprague-Dawley rats 

• Body weight = 0.240 kg (range: 0.235 - 0.245) 

• 4-hour inhalation exposure 

• 100 or 200 ppm 

• Data points = mean ± 1 SD 


102 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 




















Tardif et al. (1996) Ethylbenzene Inhalation Study 
100, 200 ppm Inhalation for 4 Hours 



0 ■ 100 ppm 

0- Simulation 100 ppm 

0 ■ 200 ppm 

0- Simulation 200 ppm 


XYLENES RAT EXPOSURE SIMULATIONS 


C3_Haddad.m 

• Haddad et al. (1999) 

• Male Sprague-Dawley rats 

• Body weight = 0.235 kg 

• 4-hour inhalation exposure 

• 50, 100, or 200 ppm 

• Data points = mean ± 1 SD 


Haddad et al. (1999) m-Xylene Inhalation Study 
50, 100, 200 ppm Inhalation for 4 Hours 




■ 

50 ppm 

0 

— 

Simulation 50 ppm 

0 

■ 

100 ppm 

0 


Simulation 100 ppm 

0 

■ 

200 ppm 

✓ 

— 

Simulation 200 ppm 


103 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 













































C3_Tardif.m 


• Tardif et al. (1996) 

• Male Sprague-Dawley rats 

• Body weight = 0.240 kg (range: 0.235 - 0.245) 

• 4-hour inhalation exposure 

• 100 or 200 ppm 

• Data points = mean ± 1 SD 


Tardif et al. (1996) m-Xylene Inhalation Study 


100, 200 ppm Inhalation for 4 Hours 



0 ■ 100 ppm 

0 - Simulation 100 ppm 

0 ■ 200 ppm 

0- Simulation 200 ppm 


NONANE RAT EXPOSURE SIMULATIONS 


C4_inhouse_inhal.m 

• Robinson, P.J. and Merrill, E.A. 2008. A harmonized physiologically based 
pharmacokinetic model for nonane as a component of jet fuel. Wright-Patterson AFB, 
OH: Air Force Research Faboratory, Applied Biotechnology Branch. AFRF-RH-WP-TR- 
2008-0067, ADA502610. 

• Female Fischer F344 rats 

• Body weight = estimated 0.3 kg 

• 4-hour inhalation exposure 

• 100, 500 or 1000 ppm 

• Data points = mean ± 1 SD 


104 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 






In-House Nonane Inhalation Study 



0123456789 10 

Hours 


0 ■ 100 ppm 

0- Simulation 100 ppm 

0 ■ 500 ppm 

0- Simulation 500 ppm 

0 ■ 1000 ppm 

0- Simulation 1000 ppm 


In-House Nonane Inhalation Study 
100, 500,1000 ppm Inhalation for 4 Hours 



0 ■ 100 ppm 

0- Simulation 100 ppm 

0 ■ 500 ppm 

0- Simulation 500 ppm 

0 ■ 1000 ppm 

0- Simulation 1000 ppm 


In-House Nonane Inhalation Study 



0123456789 10 

Hours 


0 ■ 100 ppm 

0- Simulation 100 ppm 

0 ■ 500 ppm 

0- Simulation 500 ppm 

0 ■ 1000 ppm 

0 - Simulation 1000 ppm 


105 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 










































































































































































In-House Nonane Inhalation Study 



0123456789 10 

Hours 


■/ 

■ 

100 ppm 

* 

— 

Simulation 100 ppm 

✓ 

■ 

500 ppm 

0 

— 

Simulation 500 ppm 

0 

■ 

1000 ppm 

* 

— 

Simulation 1000 ppm 


C4_Lof99.m 

• Lof, A., Lam, H.R., Gullstrand, E., Ostergaard, G. and Ladefoged, O. 1999. Distribution 
of dearomatised white spirit in brain, blood, and fat tissue after repeated exposure of rats. 
Pharmacol.Toxicol. 85(2): 92-97. 

• Male Wistar rats 

• Body weight = estimated 0.3 kg 

• 6-hour inhalation exposure 

• 400 or 800 ppm white spirits 

o 3.6% n-nonane 
o = 14.4 or 28.8 ppm nonane 

• Data points = mean ± 1 SD 


Lof et al. (1999) White Spirits Inhalation Study 
14.4 and 28.8 ppm for 6 Hours 



0 ■ 14.4 ppm 

0 - Simulation 14.4 ppm 

0 ■ 28.8 ppm 

0- Simulation 28.8 ppm 


106 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 










































































































Nonane in Fat [mg/L] Nonane in Total Brain [mg/L] 


Lof et al. (1999) White Spirits Inhalation Study 
14.4 and 28.8 ppm for 6 Hours 



0 ■ 14.4 ppm 

0- Simulation 14.4 ppm 

0 ■ 28.8 ppm 

0- Simulation 28.8 ppm 


Lof et al. (1999) White Spirits Inhalation Study 
14.4 and 28.8 ppm for 6 Hours 



0 ■ 14.4 ppm 

0 - Simulation 14.4 ppm 

0 ■ 28.8 ppm 

0- Simulation 28.8 ppm 


C4_Zahlsen90.m 

• Zahlsen, K., Nilsen, A.M., Eide, I. and Nilsen, O.G. 1990. Accumulation and distribution 
of aliphatic (n-nonane), aromatic (1,2,4-trimethylbenzene) and naphthenic (1,2,4- 
trimethylcyclohexane) hydrocarbons in the rat after repeated inhalation. 

Pharmacol.Toxicol. 67(5): 436-440. 

• Male Sprague-Dawley rats 

• Body weight = 0.2 kg 

• 12-hour inhalation exposure, 5 consecutive days 

• 1025.2 ppm 

o Target concentration = 1000 ppm 
o Average over 5 days = 1025.2 ppm 

• Data points = mean 


107 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 






























































Nonane in Total Brain [mg/L] Nonane in Venous Blood [mg/L] 


Zahlsen et al. (1990) Nonane Inhalation Study 
1000 ppm for 12 Hours 



0 ■ 1000 ppm 

0- Simulation 1000 ppm 


Zahlsen etal. (1990) Nonane Inhalation Study 


1000 ppm for 12 Hours 



0 ■ 1000 ppm 

0- Simulation 1000 ppm 


DECANE RAT EXPOSURE SIMULATIONS 


C5_Lof99.m 

• Lof et al. (1999) 

• Male Wistar rats 

• Body weight = estimated 0.3 kg 

• 6-hour inhalation exposure 

• 400 or 800 ppm white spirits 

o 16.6% n-nonane 
o = 106.4 or 212 ppm nonane 

• Data points = mean ± 1 SD 


108 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 









































































































Decane in Fat [mg/L] Decane in Total Brain [mg/L] Decane in Venous Blood [mg/L] 


Lof et al. (1999) White Spirits Inhalation Study 
106.4 and 212 ppm for 6 Hours 



0 ■ 106.4 ppm 

0- Simulation 106.4 ppm 

0 ■ 212 ppm 

0- Simulation 212 ppm 


Lof et al. (1999) White Spirits Inhalation Study 


106.4 and 212 ppm for 6 Hours 



0 ■ 106.4 ppm 

0- Simulation 106.4 ppm 

0 ■ 212 ppm 

0- Simulation 212 ppm 


Lof et al. (1999) White Spirits Inhalation Study 
106.4 and 212 ppm for 6 Hours 



0 ■ 106.4 ppm 

0- Simulation 106.4 ppm 

0 ■ 212 ppm 

0- Simulation 212 ppm 


109 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 



















































































































































































































Decane in Liver [mg/L] Decane in Arterial Blood [mg/L] 


C5_Perleberg04.m 


• Perleberg, U.R., Keys, D.A. and Fisher, J.W. 2004. Development of a physiologically 
based pharmacokinetic model for decane, a constituent of Jet Propellent-8. Inhal.Toxicol. 
16(11-12): 771-783. 

• Male Fischer F344 rats 

• Body weight = 0.211 kg 

• 4-hour inhalation exposure 

• 273, 781 or 1200 ppm 

• Data points = mean ± 1 SD 


Perleberg et al. (2004) n-Decane Inhalation Study 
273, 781 or 1200 ppm for 4 Hours 



* 

■ 

1200 ppm 


— 

Simulation 1200 ppm 


■ 

781 ppm 


— 

Simulation 781 ppm 

* 

■ 

273 ppm 


— 

Simulation 273 ppm 


Perleberg et al. (2004) n-Decane Inhalation Study 
273, 781 or 1200 ppm for 4 Hours 



✓ 

■ 

1200 ppm 

a 


Simulation 1200 ppm 

* 

■ 

781 ppm 

V' 


Simulation 781 ppm 

* 

■ 

273 ppm 

V- 


Simulation 273 ppm 


no 

Distribution A. Approved for public release; distribution unlimited (PA Case No 88ABW-2017-6190, 11 Dec 2017) 
































Perleberg et al. (2004) n-Decane Inhalation Study 


273, 781 or 1200 ppm for 4 Hours 



0 ■ 1200 ppm 

0- Simulation 1200 ppm 

0 ■ 781 ppm 

0- Simulation 781 ppm 

0 ■ 273 ppm 

0- Simulation 273 ppm 


Perleberg et al. (2004) n-Decane Inhalation Study 


273, 781 or 1200 ppm for 4 Hours 



■/ 

■ 

1200 ppm 


— 

Simulation 1200 ppm 


■ 

781 ppm 


— 

Simulation 781 ppm 

0 

■ 

273 ppm 

✓ 

— 

Simulation 273 ppm 


Perleberg et al. (2004) n-Decane Inhalation Study 
273, 781 or 1200 ppm for 4 Hours 



0 ■ 1200 ppm 

0- Simulation 1200 ppm 

0 ■ 781 ppm 

0- Simulation 781 ppm 

0 ■ 273 ppm 

0- Simulation 273 ppm 


ill 

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in Total Brain [mg/L] Decane in Arterial Blood [mg/L] 


C5_Zahlsen92.m 


• Zahlsen, K., Eide, I., Nilsen, A.M. and Nilsen, O.G. 1992. Inhalation kinetics of C6 to 
CIO aliphatic, aromatic and naphthenic hydrocarbons in rat after repeated exposures. 
Pharmacol.Toxicol. 71(2): 144-149. 

• Male Sprague-Dawley rats 

• Body weight = 0.175 kg 

• 12-hour inhalation exposure, 5 consecutive days 

• 100 ppm 

• Data points = mean 


Zahlsen etal. (1992) n-Decane Inhalation Study 
100 ppm for 12 Hours 



@ ■ 100 ppm 

@- Simulation 100 ppm 


Zahlsen etal. (1992) n-Decane Inhalation Study 
100 ppm for 12 Hours 



0 ■ 100 ppm 

0- Simulation 100 ppm 


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Zahlsen etal. (1992) n-Decane Inhalation Study 
100 ppm for 12 Hours 



0 ■ 100 ppm 

0- Simulation 100 ppm 


Zahlsen et al. (1992) n-Decane Inhalation Study 


100 ppm for 12 Hours 



0 ■ 100 ppm 

0 - Simulation 100 ppm 


Zahlsen etal. (1992) n-Decane Inhalation Study 
100 ppm for 12 Hours 



0 ■ 100 ppm 

0- Simulation 100 ppm 


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APPENDIX D. BERKELEY MADONNA CODE FOR PHARMACODYNAMIC 

MODELS 


Model for Simple Neuron Circuit in the Dorsal Cochlear Nucleus of the CAP 

METHOD RK4 

STARTTIME = 0 
STOPTIME = 5 
DT = 0.02 

Sfl = Refl*S0*time/(SO*timetKef1)- Rif1* (Scl+Sc2)/ (Scl+Sc2+Kif1) 

; S represents signal strength 
; fl represents fusiform cell #1 

; Sfl is signal strength output from fusiform cell #1 
; S0*time is input signal intensity (click strength) 

; "time" is a dummy variable to increase SO 
; Refl is excitatory receptor density (at fusiform cell#l) 

; Rifl is inhibitory receptor density (at fusiform cell#l) 

; Kefl, Kifl are the "half saturation" signal strengths 

Scl = Recl*P0/(PO+Kecl) 

Sc2 = Rec2*(P0)/(P0+Kec2)-Ric2*Scl/(ScltKic2) 

; c represents cartwheel cell 

limit Scl>=0 
limit Sc2>=0 
limit Sfl>=0 

; negative signal strengths are not permitted 


S0=1 

Ref1=1 

Kef1=1 

Rif1=1 

Kif1=1 

Recl=l 

P0=1 

Kecl=l 

Rec2=l 

Kec2=l 

Rec2=l 

Kic2=l 

Ric2=l 

; most parameters are arbitrary at this point 


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Model for Neurotransmitter Accumulation in Synaptic Cleft 


METHOD RK4 

STARTTIME = 0 
STOPTIME = 10 
DT = 0.02 

NT=STEP(NT0*exp(-k*(time-1)),1)+STEP(NT0*exp(-k*(time-2)),2)+STEP(NT0*exp(-k*(time- 
3)),3)+STEP(NT0*exp(-k*(time-4)),4)+STEP(NT0*exp(-k*(time-5)),5)+STEP(NT0*exp(- 
k*(time-6)),6)+STEP(NT0*exp(-k*(time-7)),7)+STEP(NT0*exp(-k*(time-8)),8) 

NT0=1 
k=0.5 


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APPENDIX E. THEORETICAL PREDICTION OF BRAIN REGIONAL PCS BASED 

ON WHITE TO GRAY MATTER RATIOS 


In cases in which PCs are measured for a chemical in only two regions, PC values for other 
regions can be estimated, in theory, provided regional percentages of gray and white matter are 
known. The assumption is that the gray matter and white matter maintain the same PC values 
across different regions but the proportion of gray matter to white matter changes between 
regions. These different proportions of cell types can be used to predict the PC value for a 
region. 

Let PCi and PC 2 be (measured) PC values for two brain regions, with (known) white matter 
fractions wi and W2, respectively. 


PCi = wi.PCw +(1 - wi)PC g 

Equation 1 

PCi = W2.PCw +(1 - W2)PCg 

Equation 2 

PCw is the partition coefficient of the white matter, and PC g is the partition coefficient for the 
gray matter. The rearrangement of Equation 2 yields: 

PCw = (PC 2 - (1 - W2)PCg)/W2 

Equation 3 


Equation 1 is then substituted: 

PCi = (W1/W2). (PC 2 - (1 - W 2 )PCg) +(1 - Wl)PCg 
= (W1/W2). PC 2 - ( wi/wi) PCg + wiPCg + PCg - wiPCg 

Equation 4 

Rearranging Equation 4 gives PC g in terms of the (measured) regional PC values and (known) 
white matter fractions: 


PC n 


PC 1 — —PC 2 

w 2 


1 - 


Wi 

w 2 


Equation 5 


And substituting Equation 5 into Equation 3 gives an expression for PCw. 


PC — — 
r 

W 2 


PC 2 ~ (1 — w 2 ) 


PC 1 -^ 1 PC 2 

W 2 


1 - 


w 2 


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Equation 6 


A PC value (PC X ) for a different region of interest can then be calculated based on gray and 
white matter PCs (Equations 5 and 6) and the fraction of white matter in the region of interest: 


PCx — Fg.PCg + Fw.PCw 


Equation 7 


This algorithm can be used to: 

• Validate PC measurements for a chemical from multiple brain regions, by checking for 
consistency with white and gray matter fractions, if known. 

• Check measured gray and white matter PC measurements (since gray and white matter 
are difficult to separate out, particularly from small brains), or estimate measured gray 
and white matter PC values directly. 

• Estimate gray and white-matter percentages of different brain regions by back-calculating 
from measured gray/white matter PC values from (at least) two different chemicals. 
Additional chemicals can be used to check for consistency. 

• Combine with predictions (such as the modified Schmitt (2008) model, see Ruark et al. 
(2014)) based on lipid/protein content of regions and gray and white matter, as a further 
consistency check. 

Separate white and gray matter blood flows can also be used in brain kinetic models (both 
regional and whole-brain). In humans, cerebral spinal fluid for whole brain is 55 to 60 mL/100 g 
brain tissue/minute, gray matter is 55 mL/100 g brain tissue/minute, and white matter is 45 
mL/100 g brain tissue/minute (Rengachary and Ellenbogen, 2005). 


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LIST OF ACRONYMS 


ABR auditory brainstem response 

ATP adenosine triphosphate 

BCA bicinchoninic acid 

CAP central auditory pathway 

CAPD central auditory processing dysfunction 

CHCb chloroform 

DMEM Dulbecco’s modified Eagle’s medium 

DMSO dimethyl sulfoxide 

D-PBS Dulbecco’s phosphate buffered saline 

DPOAE distortion product otoacoustic emissions 

F-344 Fischer 344 

FBS fetal bovine serum 

FR free radical 

fub fraction unbound in plasma 

GABA gamma-aminobutyric acid 

GAT-3 GABA transporter 3 

GC-FID gas chromatography with a flame ionization detector 

GSH glutathione 

HEI-OC1 House Ear Institute-Organ of Corti 1 

IACUC Institutional Animal Care and Use Committee 

Kow octanohwater partition coefficient 

MEM minimal essential medium 

MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H- 

tetrazolium 

NIHL noise-induced hearing loss 

NT neurotransmitter 

OEL occupational exposure limit 

PBPK physiologically-based pharmacokinetic 

PBS phosphate buffered saline 

PC partition coefficient 

PD pharmacodynamic 

PPUFAR peroxy polyunsaturated fatty acid radical 

PTFE polytetrafluoroethylene 

PUFAR polyunsaturated fatty acid radical 

QSPR quantitative structure-property relationship 

SD standard deviation 

SPL sound pressure level 

TWA time weighted average 

VDCC voltage-dependent calcium channel 

WPAFB Wright-Patterson Air Force Base 


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