Rapid Development of Physiologically Based Pharmacokinetic (PBPK) Models for Human Health Risk Assessment: Application of Diverse Approaches to Estimating Metabolism

Presented May 12, 2021 Society of Toxicology Risk Assessment Specialty Section Webinar Series

by Lisa M. Sweeney, Ph.D., DABT, CHMM UES, Inc., assigned to US Air Force Research Laboratory 711th Human Performance Wing, Wright-Patterson, Air Force Base, Ohio USA [email protected]

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021. 1 Disclaimers

• I am a contractor working with Air Force Research Laboratory/711 Human Performance Wing and the views expressed in this presentation are my own and do not necessarily reflect the views of the Air Force, Department of Defense, or my employer, UES, Inc. Outline • Context/Background/Inspiration • Goals • Specific aims • Outline and methods of case study approach (work in progress) • Progress/Interim Findings • Deeper dive on Quantitative Structure Activity Relationship (QSAR) resource evaluation • Lessons Learned • Plans • Summary • Acknowledgements • References Context/Background

• Strategic Objective: apply new and improved approaches to characterizing risk in DOD work settings, particularly the Air Force operational environment • Applications to PBPK modeling • Physiological parameters: reflect Air Force- relevant stressors (Covington et al. 2019; Sweeney 2020 a, b, c; Sweeney et al. 2020) • New Approach Methods (NAMs), surrogates, and/or read across for points of departure and physicochemical or biochemical parameters • How are NAMs, surrogates, and read across (potential) improvements? • Speed USAF photo by SrA Ryan Callaghan • Cost • Human relevance • Ethical concerns of traditional in vivo approaches

4 Inspiration

• Paini et al. (2019) proposed both read across and in silico approaches for PBPK modeling • In the read across or surrogate approach (Lu et al. 2016), one would use an existing PBPK model for a structurally similar chemical • Alternatively, models can be developed from in silico resources (see Madden et al. 2019) or in vitro sources • Limited guidance and examples on using NAMs for developing, assessing, and applying PBPK models • Most of the databases and tools developed to date have limited applicability to environmental and occupational chemicals • The Air Force has an ongoing need to understand potential human health risks from inhalation exposure to compounds in the work environment. • The increasingly popular high-throughput in vitro techniques technically challenging for volatile organic compounds due to nonspecific losses through volatilization and to test components (e.g., plastic plates) • QSARs are thus an especially important resource for properties of materials present in the vapor form • The airborne hazards of concern to the Air Force include jet fuels, combustion exhaust, and repair shops • Variable and often incompletely characterized composition • Not all components are well-studied both from toxicological and toxicokinetic standpoints • Rather than necessarily trying to develop a single “best” predictor, consideration of multiple approaches can yield a range of estimates that reflect the uncertainty of the process and the merits of different data sets and approaches. Goals and Strategy

• Goals: Develop work flows for (1) rapid development of PBPK models for application to chemicals of new/emerging interest to DOD with respect to risk in the operational environment and (2) characterization of model confidence • Strategy to narrow the scope: • Start with a case study or case series of a previously modeled chemical(s) with some human and/or rodent in vitro and in vivo data available • Rather than necessarily trying to develop a single “best” predictor, consideration of multiple approaches can yield a range of estimates that reflect the uncertainty of the process and the merits of different data sets and approaches • Vmax and Km were selected as chemical specific parameters of interest (partition coefficients were assumed to be more confidently assessed in vitro and using QSAR Outline and Methods of Case Study Approach—Work in Progress

• Parameterization • Develop Vmax and Km estimates from in vivo, in vitro, and in silico data for the subject chemical • Literature searches • Identify online in silico tools • Evaluate QSARs per Patel et al. (2018) • Interspecies extrapolation • In vitro (or in silico) to in vivo extrapolation (scaling) • Limiting cases • Identify potential surrogate substances with existing PBPK models • US EPA CompTox Chemicals Dashboard (Williams et al. 2017) Outline and Methods of Case Study Approach—Work in Progress

• Performance • Compare predictive performance of various Vmax and Km estimates with respect to fit to an example human in vivo chemical time course • Bias, average fold error • Risk assessment implications • Internal dose metrics at toxicologically relevant exposure concentrations and durations for various Vmax and KM estimates • Chronic • Acute • Sensitivity analyses (not yet initiated) Progress: Case study, possible case series, and surrogates

• Selected 1,2,4-trimethylbenzene, with other C9 aromatics as potential candidates for a case series Progress: Surrogates

• Identification via a comprehensive PBPK model database linked with structural information would be most efficient • Personal knowledge and literature searches were needed to match candidates with mammalian PBPK models • 1,2,3,5- and 1,2,4,5-tetramethylbenzene (durene and isodurene; Jalowiecki and Janasik, 2007) • Human liver volume of 3.9 L reported and possibly in Vmax scale up from microsomes

• Multiple model options for • 30 publications examined • 12 did not explicitly report bodyweight scaling factors for Vmax isodurene durene • Multiple families of models • Authors sometimes reused VmaxC values, but altered the bodyweight scaling factor • Multiple model options for o-, m-, p-, and mixed and styrene as well Progress: Summary of human 1,2,4-TMB Vmax and KM estimates

• Total of two limiting cases and 17 VmaxC and KM pairs LIMITING CASES (2): IN VIVO DATA (4): No metabolism Calibration with rat data (2) Complete hepatic clearance Calibration with human data alone Calibration with mix of rat and human data IN VITRO DATA (3): One rat liver slice study: USER-IMPLEMENTED QSARs with 1,2,4-TMB SOURCE Generic scaling DATA (5): Categorical scaling Human in vitro data (1) One human liver microsome study Two investigations using overlapping rat data (4) Generic Vmax scaling DERIVED FROM COMMERCIAL OR Categorical Vmax scaling GOVERNMENT IMPLEMENTED CLEARANCE ESTIMATORS (4): USER-IMPLEMENTED QSAR without Two estimates of clearance 1,2,4-TMB SOURCE DATA (1): For each clearance estimate, two KM Rat and rabbit in vitro data assumptions were used Progress: Vmax and KM

• In vivo, in vitro, and in silico data presented in order of expected reliability (subjective/personal judgement) • Subject chemical Vmax and KM data from in vivo • Optimized with human data • Sweeney et al. (2020); rat model KM value (US EPA, 2016) and human VmaxC optimized to one human in vivo data set near the TLV (Kostrezewski et al. 1997) (comparator) • Jarnberg and Johanson (1999); average of 10 individually optimized human Vmax and KM values; simulations they presented used one individual’s parameter values—not the average • Optimized with rat data; simulations of human in vivo data judged to be adequate • US EPA (2016) optimized rat VmaxC and KM values (modified from Hissink et al. 2007) • Hissink et al. (2007) optimized rat VmaxC and KM values Progress: Vmax and KM from in vitro data

• Subject chemical Vmax and KM from human in vitro data • Lewis et al. 2003 (human liver microsomes) • In vitro to in vivo extrapolation (IVIVE) • 34 mg microsomal protein/g human liver (Barter et al. 2007, as cited by Lipscomb and Poet, 2008) • Liver mass 2.6% of human body weight (Brown et al. 1997) • 70 kg body weight for a standard human • Subject chemical Vmax and KM from rat in vitro data • Mortensen et al. 2000 (rat liver slices) • IVIVE • Typical liver slice weight and liver weight reported (Mortensen et al. 1997) • Two approaches used for interspecies extrapolation • Categorical approach for likely CYP2E1 substrates (Beliveau et al. 2005) • Traditional BW0.7 scaling Progress: Vmax and KM from QSARs calibrated with subject chemical data • QSAR evaluation per Patel et al. (2018) strategy will be discussed in more detail • Human in vitro data: Lewis et al. (2003) • IVIVE same as Lewis et al. (2003) in vitro data • Rat in vivo and in vitro data • Price and Krishnan (2011) • Sarigiannis et al. (2017) (subset of Price and Krishnan 2011 endpoint data) • Interspecies extrapolation same as Mortensen et al. (2000) rat in vitro data Progress: Vmax and KM from other QSAR(s)

• Pirovano et al. (2015) (purified, nonrecombinant rat and rabbit liver CYP enzymes) • Benzyl alcohols, neutral organics, anilines, , amides, amines, esters, and aldehydes (training: 81, test: 40; not explicitly identified but could be guessed from figures) • Approximately 2000 descriptors from the Online Chemical Modeling Environment (OCHEM) were considered, but narrowed down to 6 or fewer • QSAR equations and statistics were reproduced with high precision after an earlier “obsolete” version of a descriptor estimation routine was substituted for the most recent version

Performance of putative QSAR training and test sets based on CYP endpoints and descriptors of Pirovano et al. (2015). KM units: µM, Vmax units: µmol/min/mg microsomal protein Progress: Clearance estimates from in silico software/applications

• KM estimates were taken from in vitro and in vivo estimates (minimum and maximum values) • Vmax estimates were backed out from in silico metabolic clearance estimates (metabolic clearance = Vmax/KM) • Other in silico estimators are available; these examples are not exhaustive, but were freely available • Sipes et al. 2017 • ADMET Predictor 7.2 clearance estimates (in original units of µL/min/mg liver microsomal protein) reported in supplementary material • Considers CYP 1A2, 2C9, 2C19, 2D6, and 3A4 • IVIVE same as Lewis et al. 2003 • Open Structure-activity/property Relationship App (OPERA), National Toxicology Program (NTP), 2020 • Estimated with units of µL/min/106 cells using a 5-nearest neighbors approach, with low confidence (personal communication from Kamel Mansouri) • IVIVE same as Lewis et al. 2003, with exception that scaling was based on hepatocellularity data of Barter et al. (2007) rather than microsomal protein yield. Deeper Dive on QSAR Resource Evaluation

• Process described by Patel et al. (2018) Assessment and Reproducibility of QSAR Models by the Nonexpert. J Chem Inf Model 58: 673-682. • Select paper for analysis • Evaluate adherence to Organisation for Economic Cooperation and Development (OECD) Principles for the validation of QSARs (OECD 2004) • Follow proscribed workflow for QSAR model implementation • Three QSAR papers that incorporated 1,2,4-TMB data will be discussed • Spoiler: all three had flaws Findings: Summary of QSARs evaluated

Study Endpoints Nature of experimental Descriptors Chemical Domain, n system Lewis et al. Vmax and Human liver microsomes Computed molecular orbital Alkylbenzenes (7) 2003 Vmax/KM energies and experimental logP (log of the octanol:water partition coefficient) values Price and Vmax and Rat, not explicitly reported Structural fragments Volatile organic chemicals (53) Krishnan 2011 KM (in vivo, microsomes, or liver slices) Sarigiannis et Vmax and Rat, not explicitly reported Abraham solvation descriptors Subset of Price and Krishnan al. 2017 KM (in vivo, microsomes or (2011) data set; halogenated liver slices) hydrocarbons, alcohols, ketones, hydrocarbons, ethers, esters, and aromatic hydrocarbons (29) • Due to errors, none of the QSARs were suitable for use “as is” • The Price and Krishnan (2011) endpoint values were not well referenced • Allometric scaling of the Price and Krishnan (2011) endpoint values was inconsistent • Reporting was generally incomplete

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021 Lewis et al. 2003 (alkylbenzenes)

• Problems with published documentation • One QSAR was provided that did not match the reported descriptors • One log Vmax value and one log (Vmax/KM) value did not match the tabulated Vmax and KM data • Electronic structural descriptor values were provided that were not used in any reported QSAR

Corrected QSAR Expression (± standard error for coefficients and intercept) n r Standard F P error 1. Log Vmax = (13.333 ± 0.984) –(1.0620 ± 0.101) × ΔE 6 0.982 0.0375 109.7 0.00047 3. Log Vmax = (27.3278 ± 5.9711) × ΔE – (1.46183 ± 0.31567) × (ΔE)2 – (124.568 ± 7 0.944 0.0661 16.4 0.0119 28.212) 4. Log (Vmax/KM) = (7.777 ± 1.160) × logP – (1.2426 ± 0.1812) × (logP)2 – (10.2735 7 0.964 0.0428 26.4 0.00495 ± 1.8492) All intercept and coefficient p values < 0.05

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021 Price and Krishnan (2011) • Errors in reporting of descriptors for 4 chemicals • 1 typo, 3 used in QSAR derivation • Errors/uncertainties in VmaxC values • Unit conversion error (6-fold) • Inappropriate scaling (linear scaling with respect to body weight in original) • Sources not cited; identified from other papers from group, my personal knowledge, and lit searches • Inconsistencies in scaling • Sources often used BW0.7 while Price and Krishnan used BW0.75 with no adjustment • Authors used 0.25 kg BW to normalize Vmaxs, not study-specific BW • Mixture of sources, including unvalidated rat liver slice data • Lack of statistical information on QSAR expression parameterization

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021 Price and Krishnan (2011) corrections

• VmaxC consistently scaled to BW 0.7 • VmaxC consistently normalized to study specific BW • Sources explicitly identified • Statistical details reported New expression for logVmaxC based on updated Price and Krishnan (2011) descriptors and endpoint values (VmaxC in µmol/h/kg0.7)

Coefficients Standard Error t Statistic P-value Lower 95% Upper 95% Intercept 0 N/A N/A N/A N/A N/A CH3 0.2574 0.122 2.11 0.0409 0.01121 0.5036 CH2 0.07139 0.0430 1.66 0.105 -0.01546 0.1582 CH -0.01259 0.161 -0.0782 0.938 -0.3378 0.3126 C -0.9065 0.291 -3.12 0.00326 -1.493 -0.3202 C=C -0.6961 0.541 -1.29 0.205 -1.788 0.3954 H 0.4538 0.195 2.33 0.0249 0.06027 0.8474 Br 0.6558 0.134 4.88 1.57×10-5 0.3846 0.9270 Cl 0.5419 0.0779 6.96 1.66×10-8 0.3847 0.6990 Fl 0.3503 0.130 2.70 0.00993 0.0885 0.6120 AC 0.7190 1.34 0.536 0.595 -1.987 3.425 H_AC 0.1292 0.265 0.488 0.628 -0.4050 0.6634

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021 Price and Krishnan (2011) corrections

New expression for log KM based on updated Price and Krishnan (2011) descriptors and endpoint values (KM in µM)

Coefficients Standard Error t Statistic P-value Lower 95% Upper 95% Intercept 0 #N/A #N/A #N/A #N/A #N/A CH3 0.149978 0.09518 1.575728 0.122592 -0.0421 0.34206 -5 CH2 0.14739 0.033579 4.389396 7.52 × 10 0.079625 0.215154 CH 0.105545 0.125717 0.839543 0.405918 -0.14816 0.359253 C 0.015109 0.226675 0.066655 0.947173 -0.44234 0.472557 C=C 0.566287 0.422025 1.341835 0.186859 -0.28539 1.417968 H -0.14138 0.15215 -0.92924 0.358076 -0.44843 0.165667 Br 0.055565 0.104851 0.529939 0.598945 -0.15603 0.267162 Cl 0.103657 0.060753 1.706198 0.095357 -0.01895 0.226262 Fl 0.137095 0.101188 1.354852 0.182708 -0.06711 0.341301 AC 1.108443 1.046083 1.059613 0.295377 -1.00264 3.219524 H_AC -0.14618 0.206533 -0.70777 0.482997 -0.56298 0.270622

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021 Sarigiannis et al. (2017) • Endpoint values are a subset of the (corrected) Price and Krishnan (2011) data • Reasons for exclusions are not articulated • In Sarigiannis et al. 2017, the VmaxC for chlorodibromomethane was used in place of the value for bromodichloromethane • Descriptor “v” was in error for two New expression for log VmaxC based on updated Sarigiannis et al. compounds (2017) descriptors and endpoint values (VmaxC in µmol/h/kg0.7) • Transposition of digits (once) Coefficients Standard Error t Statistic P-value Lower 95% Upper 95% Intercept 1.6150 0.215 7.524 1.21 × 10 -7 1.1710 2.0590 • Duplication of value of E 0.1972 0.442 0.447 0.659 -0.7165 1.1109 neighboring entry S -0.1307 0.560 -0.233 0.818 -1.2898 1.0284 A 1.8689 1.187 1.575 0.129 -0.5861 4.3239 B 4.0795 1.025 3.979 0.001 1.9584 6.2006 V -0.6453 0.194 -3.328 0.003 -1.0464 -0.2442

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021 Sarigiannis et al. (2017)

Coefficients Standard Error t Statistic P-value Lower 95% Upper 95% New expression for log KM based on Intercept 0.2373 0.361 0.6568 0.5178 -0.5100 0.9845 updated Sarigiannis et al. (2017) descriptors E -0.0363 0.743 -0.0489 0.9614 -1.5740 1.5013 and endpoint values (KM in µM) S 0.4667 0.943 0.4949 0.6254 -1.4840 2.4174 A -2.6389 1.997 -1.3213 0.1994 -6.7704 1.4926 B -2.5017 1.726 -1.4498 0.1606 -6.0712 1.0679 V 0.6700 0.326 2.0533 0.0516 -0.0050 1.3450

DISTRIBUTION A. MSC/PA Case # 2021-0056. Cleared 3/3/2021 Progress: Performance of Vmax and Km estimates

• Subset of parameter estimates for clarity • OPERA was indistinguishable from “no metabolism” • The chemical specific in vitro estimates tended to over predict metabolic clearance and QSARs based on broader data sets under estimated metabolism Progress: Toxicity Reference Value implications of Vmax and KM estimates

Acute Exposure Threshold Limit Value Reference Guideline Level Concentration (AEGL) 2 Single 8 h exposure 8 h/d, 5 days/week Continuous exposure 738 mg/m3 123 mg/m3 0.06 mg/m3

National Research American Conference of U.S. EPA (2016) Council (2012) Governmental Industrial Hygienists (ACGIH, 2018)

• Duration required for periodicity/steady state for chronic exposure was determined with VmaxC = 0, with simulation until the AUC for the last week increased by less than 1% over the preceding week (10 weeks) • While multiple metrics could be considered, only blood Cmax is presented for illustration purposes • Impact for this metric varies with exposure conditions, likely due to differential sensitivity to Vmax and/or KM Lessons learned/confirmed to date:

• Published PBPK models often have incomplete information on Vmax scaling • Vmax and KM estimation is not a simple matter even for a limited in data set • QSARs generated to support PBPK modeling do not conform to current transparency standards • Clerical errors identified • Scaling and protocol inconsistencies identified • Referencing/sourcing of underlying data was challenging even for a practitioner with extensive experience and knowledge of the field • Consideration of multiplicity of models for a single chemical was not addressed in previous surrogate example (Lu et al. 2016) and is a substantial issue for many chemicals Future plans and possibilities

• Closure on QSAR usability efforts • Complete and further evaluate metrics from TRV impact analysis • Conduct sensitivity analysis • Articulate a rationale for anticipated PBPK model reliability/predictivity based on calibration/validation approaches and findings • Complete similar analysis for a C9-aromatic without an existing PBPK model, but with human in vivo data • Complete similar analysis for C9-aromatics without human in vivo data or an existing PBPK model; develop an approach for making recommendations in the absence of validation data • Apply similar approach to partition coefficient estimates • Apply similar approach to a different chemical category Summary

• In an effort to improve and expedite data-driven risk assessment for occupational settings, we are exploring the use of NAMs, surrogates, and read-across for PBPK model development • An initial case study is underway with a subject chemical with existing, validated human PBPK models and limited in vivo and in vitro toxicokinetic data • Existing QSARs were generally found to require correction and/or improved documentation to establish confidence • The approach being implemented is expected to evolve with experience and multidisciplinary, multi-stakeholder feedback from the scientific community Acknowledgements

• This effort was financially supported under the Virtual Airman SimulaTion (VAST) task • AFRL contributors • Jeff Gearhart • Matt Linakis • Teri Sterner • Tammie Covington • Slava Chushak References

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