In Vitro to in Vivo Extrapolation (IVIVE) to Support New Approach Methodologies (NAMs)-based Safety Assessment

Transcription

1 Center of Excellence for 21 st Century Toxicology A Division of ToxStrategies In Vitro to in Vivo Extrapolation (IVIVE) to Support New Approach Methodologies (NAMs)-based Safety Assessment A Tiered Approach with a Focus on the Consideration of Kinetics and Metabolism Miyoung Yoon ToxStrategies, Inc. Research Triangle Park, NC USA Risk Assessment Specialty Section with the Biological Modeling Specialty Section Jointly Sponsored Webinar Wednesday, December 12, :00 PM 4:30 PM (EDT)

2 Conflict of Interest Statement The author declares no conflict of interest. 1

3 Outline Introduction A tiered approach in NAMs-based safety assessment IVIVE IVIVE applications Prioritization Sensitive population Species sensitivity Alternatives to inhalation testing Future directions Conclusion 2

4 Abbreviations AER - Activity-to-Exposure Ratio AOP Adverse Outcome Pathway CFD Computational Fluid Dynamics Css Concentration at Steady-State HTS High Throughput Screening IVIVE In Vitro to In Vivo Extrapolation MOA Mode of Action MOE Margin of Exposure MOS Margin of Safety MPPD Multiple-Path Particle Dosimetry Model NAMs New Approaches and Methodologies PBPK Physiologically Based Pharmacokinetics POD Point of Departure QSAR Quantitative Structure Activity Relationship 3

5 National Research Council recommendations on 21 st -century safety science Modern biology and computational tools to support efficient risk-based decisions 4

6 Exposure and AOP-based framework for riskbased decisions in modern safety assessment Exposure Dosimetry Dose-Response RISK-based Decisions Mechanism (AOP) (Modified from the slide curtesy of Cecilia Tan) 5

7 Modernizing toxicity testing with new approaches and methodologies Rapid exposure estimation AOP-based in vitro assays - more efficient and humanrelevant alternatives to traditional animal testing IVIVE - quantitative translation of NAMs data to in vivo in the context of safe exposure 6

8 Translation of in vitro toxicity testing results to safe human exposure Streams of data from in vitro and in silico NAMs Equivalent Human Exposure In vitro biokinetics mg/kg/day ppm In vitro assays Computational methods In vitro PoD estimation PBPK & Reverse dosimetry MoE analysis Derivation of Risk values Population assessment 7

9 NAMs in risk-based decision making A tiered approach TESTING & EVALUATION: Ultra-high throughput (HT) NAMs - Rapid exposure estimation SEEM3, ExpoCast etc. - In silico metabolism models - QSAR, read-across, TTC High throughput NAMs - In vitro parent chemical clearance - HT-IVIVE - HT-screening assays (ToxCast/Tox21) Evidence-based toxicology - Systematic review - Problem formulation - Development of testing &risk assessment strategies APPLICATIONS: Prioritization/compound selection - Relative potency - Margin of Exposure (MoE) - Activity:Exposure ratios (AERs) Hazard ID/Mode of Action (MOA) EXA Fit-for-purpose NAMs - SHEDS-HT, etc. - bioactivation/metabolite ID, q-ivive - AOPs, organotypic models, IATAs/Defined Approaches Targeted in vivo testing - PBPK models - Transcriptomics - Targeted endpoint evaluation based on NAMs Risk-based safety decisions - Quantitative dose-response - Predict region of safety for human exposure - Point of departure (PoD) (Slide curtesy of Rebecca Clewell)

10 NAMs in riskbased decision making Evidence-based toxicology Evidence-based toxicology - Systematic review - Problem formulation - Development of testing &risk assessment strategies TESTING & EVALUATION: APPLICATIONS: TESTING & EVALUATION: APPLICATIONS: EXAMPLES OF USES: Ultra-high throughput (HT) NAMs Ultra-high throughput (HT) NAMs Prioritization/compound selection Prioritization/compound selection - Rapid exposure estimation SEEM3, ExpoCast etc. - Rapid exposure estimation SEEM3, ExpoCast etc. - In silico metabolism models - In silico metabolism models - QSAR, read-across, TTC - QSAR, read-across, TTC High throughput NAMs High throughput NAMs - Systematic review - Problem formulation - Development of testing &risk assessment strategies - In vitro parent chemical clearance - In vitro parent chemical clearance - HT-IVIVE - HT-IVIVE - HT-screening assays (ToxCast/Tox21) - HT-screening assays (ToxCast/Tox21) Targeted in vivo testing - Transcriptomics - Targeted endpoint evaluation based on NAMs - Relative potency - Margin of Exposure (MoE) - Activity:Exposure ratios (AERs) Fit-for-purpose NAMs - SHEDS-HT, etc. Fit-for-purpose NAMs - SHEDS-HT, etc. Risk-based safety decisions - bioactivation/metabolite ID, q-ivive - AOPs, organotypic models, - Quantitative dose-response -IATAs/Defined bioactivation/metabolite Approaches ID, q-ivive - Predict region of safety for human - AOPs, organotypic models, IATAs/Defined exposure Approaches - Point of departure (PoD) Targeted in vivo testing - PBPK models - Transcriptomics - Targeted endpoint evaluation - based PBPK on models NAMs - Relative potency - TSCA existing chemical prioritization - Margin of Exposure (MoE) - Activity:Exposure - Lead compound ratios (AERs) selection - Identify potential endocrine disruptors (EDSP, Health Canada) Hazard ID/Mode of Action (MOA) Hazard ID/Mode of Action (MOA) - Skin sensitization assessment (OPP) Risk-based safety decisions - Support human relevant decisions - Quantitative dose-response - Reduce uncertainty factors - Predict region of safety for human exposure - Reduce animal testing - Point of departure (PoD) 9 EXA

11 IVIVE applications to safety testing current status Prioritization based on MOE or AER is the most well-known example in chemical safety assessment Referred to as HT-IVIVE (e.g., ToxCast dosimetry and MOE analysis) Simple in vitro liver system (e.g., hepatocytes) estimates in vivo hepatic clearance along with other in vitro kinetic data (e.g., protein binding) Rapid PBPK and reverse dosimetry convert the in vitro bioactivity concentration (e.g., AC50) to the equivalent human exposure (e.g., daily oral equivalent dose) In this IVIVE application, nominal concentration in vitro in bioactivity assays is simply used for extrapolation 10

12 IVIVE opportunities in chemical assessment Prioritization HT-IVIVE (Toxcast, Tox21 etc.) for MOE or AER analysis In vitro based safety assessment as an ultimate goal AOP-based fit-for-purpose in vitro assays In vitro POD -> human safe exposure -> derivation of risk values Interim progress In vitro-supported cumulative risk assessment In vitro based PBPK models for risk assessment In vitro-parameterized PBPK to predict internal exposure (IVIVE-PBPK) Pesticide PBPK models currently being considered for use in risk assessment by EPA OPP IVIVE to support the development of in vitro alternatives for inhalation testing Incorporation of historical in life data to support in vitro based safety assessment 11

13 Prioritizing compounds using in vitro bioactivity data and exposure estimation In vitro HTS + HT IVIVE Total US Population = estimate dose to cause bioactivity Decreasing priority Margin between potential hazard and potential exposure (AER) HT exposure predictions (Ring et al., 2017) 12

14 Putting in vitro bioactivity data into context

15 Ultimate goal: In vitro based safety assessment

16 Rapid evaluation of cumulative margin of safety IVIVE-PBPK model In vitro point of departure (EC10, estrogenic activity) Relative potency factor based on in vitro MOS = In vitro EC10 In vivo plasma conc. (Campbell et al., 2015) Reverse dosimetry In vivo plasma concentration Human biomarker data (NHANES urinary conc.)

17 Margin of safety for adult female

18 Conc IVIVE and rapid PBPK for compound discovery support Objective: Selection and ranking using NAMs together with historical in vivo toxicity database Tools: Rapid PBPK and IVIVE models NAMs in vitro bioactivity Rapid PBPK & IVIVE Historical in vivo data QSAR-based parameterization Outcomes: HT-metabolic clearance (Clint) Rapid prediction of in vivo exposure in test and target species for decision making in early development Compound selection & ranking based on kinetics & toxicity Cmax Time

19 Using in vitro toxicity data with Rapid PBPK to determine relative species sensitivity Problem: 2-amino-2-methyl-1-propanol showed differences in test species sensitivity (hepatic steatosis) Objective: Determine whether rat is a conservative model for human Predict human POD Approach: Evaluate species differences in kinetics between rat and human Use MOA-relevant in vitro assay to determine dose-response for both species Couple rapid PBPK and in vitro results to determine relative sensitivity 18

20 Using in vitro toxicity data with Rapid PBPK to determine relative species sensitivity Rapid PBPK for rat used to guide in vitro study design Fit-for-purpose in vitro model (HepatoPac) was used for steatosis investigation IVIVE used to estimate in vivo equivalent doses in rat and human to compare species sensitivity (Slattery et al., 2018) (Slide curtesy of Rebecca Clewell) 19

21 In vitro/in silico-based generic PBPK for rapid PBPK modeling and HT-IVIVE Recent advances with in silico/in vitro-based tools Particularly for distribution and hepatic clearance Ready to use or generic PBPK software tools e.g., SimCyp, GastroPlus for drugs A significant surge in the development of generic PBPK platforms for environmental chemicals in support of new toxicity testing and regulatory needs (e.g., new TSCA) EPA NCCT s httk, LRI-PLETHEM Linkage with rapid exposure prediction tools for source to outcome modeling 20

22 Rapid PBPK modeling Rapid PBPK Modeling Database for PBPK parameters Automated IVIVE equations QSAR tools for Chemical Properties Running Environment for PBPK Model Tailored Model with User Interface Generic model structure Rapid parameterization Automated IVIVE to use in vitro data QSAR tools Physico-chemical properties Partition coefficient/vd Hosting of database for parameters Ability to connect to exposure prediction tools (e.g., SHEDS-HT) Open source programs (mostly R-based) Model execution environment Flexibility to add an user interface 21 21

23 In vitro-based PBPK models for risk assessment Early age population risk assessment as an example 22

24 PBPK model parameterization with IVIVE Has been used in environmental chemical risk assessment started with volatiles and later on for others including pesticides (reviewed in Yoon et al., 2012) Has been well accepted for generic PBPK modeling for pharmaceutical compounds (e.g., Simcyp, Simulation Plus, PKSim etc.) Because of a broader range of chemical properties for environmental chemicals compared to pharmaceuticals, the applicability of IVIVE has been evaluated specifically for environmental chemicals Rotroff et al., 2010; Wetmore et al., 2013; Yoon et al., 2014;Wambaugh et al.,

25 IVIVE for PBPK model parameterization - Carbaryl In vitro data provides the key parameter - metabolism (Yoon et al., 2011 and 2015) 24

26 Biological scaling of in vitro metabolism data 25

27 IVIVE-PBPK for early-life sensitivity evaluation Age-Specific Human Physiology Age-Specific Metabolism Life-Stage PBPK Model Age- Specific Internal Exposure Age-Specific Exposure Margin of Exposure 26 26

28 Age-Dependent Physiological Parameter Database Body Weight (BW) Liver Blood Flow (QL) Several databases for US population Targeted for risk assessment applications Customizable Based on NHANES and other population database References Clewell et al., 2004 (life stage PBPK model); Wu et al., 2015 (updated life stage model); McNally et al., 2015 (PopGen); Ring et al., 2017 (httk) 27 27

29 F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n F ra c tio n o f a d u lt e n z y m e e x p re s s io n IVIVE predicts in vivo metabolic clearance across ages Expressed C P M enzyme Clint C Y PP2 2C C9 9 C Y PP2 2B B6 6 C Y PP2 2C C C Y PP3 A3 A4 4 C E SS1 1-m -m C E SS1 -c 1 -c C E SS2 -m 2 -m C E SS2 -c 2 -c C Y P 2 C 9 C Y P 2 B 6 6 m o n t h s C Y P 21Y C 9 ISEF C Y P 2 CMPPGL 1 9 Ontogeny Liver Weight C YEnzyme P 2 C 1 9 abundance C Y P 3 A 4 C E S 1 -m C E S 1 -c C 25Y E S 2 (Adult) -m C E S 2 -c Enzym e IVIVE Adult hepatic Clint C P M C Y P 2 B 6 C Y P 3 A 4 C E S 1 -m C E S 1 -c C E S 2 -m C E S 2 -c 5Y 0.5Y 1Y 5Y 1 0Y C Y P 3 A C E S 1 -m A g e (Y e a r s ) 1.5 C E S 2 -c A g e (Y e a r s ) A g e (w e e k s ) Enzyme ontogeny 1.5 C E S 1 -c C Y P 2 C A g e (Y e a r s ) A g e (w e e k s ) C Y P 1 A A g e (w e e k s ) (McCarver et al., 2017) 0.5Y 1Y 1.5 C E S 2 -m A g e (Y e a r s ) C Y P 2 C A g e (w e e k s ) C Y P 2 B A g e (w e e k s ) Life-stage hepatic Clint 6 m o n th s 1Y 0.5 yr 1 yr 5 yr 10 yr m e dults int, in binant zym es R elative enzym e contribution in adults tow ards CPM total C L int, in vivo Enzym e A ge-specifc relative Ontogeny enzym e contribution tow ards C PM total C L int, in vivo 5Y (Docket ID:EPA-HQ-OPP ) 5Y 25 yr 28

30 C oncentration (ng/g) C oncentration (ng/g) C oncentration ( 100 Supporting risk assessment for sensitive populations Y 5Y 19Y 25Y A data-derived extrapolation factor (DDEF) is calculated using the simulated distribution of target tissue exposure in each age population Y 5Y 19Y 25Y This DDEF can be used to address uncertainty for agerelated PK difference (Docket ID:EPA-HQ-OPP ) Cis-permethrin in males of different ages (1 mg/kg/day) Sensitive Juvenile_Cmax 50th percentile Adult_Cmax 50th percentile

31 Integrated approaches for inhalation safety assessment Respiratory MPPD tract dosimetry models CFD (MPPD, models CFD) Portal of entry exposure IVIVE AOP-based in vitro respiratory tract or systemic target assays IVIVE In vitro kinetic assays Lung metabolism diffusion/transport blood air partition Systemic exposure in vitro kinetic assays Liver metabolism binding Transport

32 Integrated approaches for inhalation safety effects assessment Dose-response for local Respiratory MPPD tract dosimetry models CFD (MPPD, models CFD) Portal of entry exposure IVIVE AOP-based in vitro respiratory tract or systemic target assays IVIVE Dose-response for systemic effects In vitro kinetic assays Lung metabolism diffusion/transport blood air partition Systemic exposure in vitro kinetic assays Liver metabolism binding Transport

33 Rapid estimation of systemic exposure General equation Css = Cair/[(1/PB)+(QL/QP)*Clint/(QL+Clint)] for poorly soluble & poorly metabolized (e.g., perchloroethylene), Css = Cair*PB for soluble & extremely well metabolized (e.g., isopropanol), Css = QP*Cair/QL Blood:air partition coefficient (PB); Liver metabolic clearance (Clint); Ventilation rate (QP); Liver blood flow (QL) (Andersen, 1981; Clewell et al., 2004; Yoon et al., 2014) 32

34 Capturing cellular exposure and kinetics in respiratory tract and systemic target for IVIVE Requires AOP-based in vitro assays for IVIVE of respiratory tract and systemic target tissue effects Inhalation Lung Mucociliary clearance Tissues QC Airway/ alveolus Alveolus Lung tissue Lung tissue Cd-MT Lung blood Lung blood Cd-MT Deposition of CdO NP (MPPD model) CdO NP CdO Cd + MT Cd Induction QC Only in liver Q tissue Induction Cd + MT Tissue Cd-MT Lung tissue Cd Cd-MT Lung bloodtissue blood Q tissue (Zhao et al., 2014) Only in kidney 33

35 Critical to recapitulate both exposure and biology for Inhalation IVIVE Advanced in vitro respiratory tract models have promises to recapitulate respiratory tract region and/or cell specific metabolism biological fidelity to describe cellular exposure in different regions of respiratory tract In vitro kinetic modeling as a critical component of Inhalation IVIVE in vitro air to cell exposure (e.g., simulation of air chamber concentration over time and cell partitioning) in vitro specific kinetic behaviors (e.g., particokinetics in Hinderliter et al., 2010, Thomas et al., 2018) 34

36 Expanding areas of applications and increasing confidence in IVIVE Rapid generation of metabolism parameters Expanding domains of applicability of IVIVE Highly lipophilic chemicals Inhaled agents Incorporation of new in vitro models (e.g., human on a chip, organotypic cultures) for IVIVE applications In vitro kinetics as an essential part of IVIVE 35

37 Predicted Clint (L/hr) Predicted Clint (L/hr) Why metabolism parameters still need to be measured Limitations due to in vitro tools (low clearance) Diethylhexyl phthalate (DEHP) W arfarin Caffeine Carbaryl Butylparaben Propylparaben Ethylparaben Atrazine B isphenol-a Acetaminophen Limitation due to domains of applicability (lack of coverage non-cyp enzymes/phase II) Experimentally determined Clint (L/hr) Measured Clint (L/hr) 36

38 Extrapolation to In Vivo How to incorporate new technologies Animals Allometric scaling physiological and biochemical parameters scaled by function of body size Traditional in vitro systems Organotypic culture, tissue-chips, bioreactor etc. Biological scaling (IVIVE) Relating intrinsic functions by accounting for inter system scale and microenvironment differences What would be the best way to scale?

39 Volume/Well (ml) In vitro biokinetic modeling of simple dynamic culture of 3D liver culture over long time frames Replicate 1 Replicate 2 Model Prediction Time (days) Media volume changes due to daily replacement and evaporation over time (Phillips et al., 2018) Concentration/time profile without beads Combination of metabolism and in vitro system-dependent loss of compound

40 In vitro biokinetic model to describe compound disposition in bioreactor and assist in quantitative extrapolation of the results 39

41 Teasing out in vitro kinetic issues to increase confidence in IVIVE More chemical goes in than comes out Accumulation = In (with flow) Out (with flow) Adsorption + Desorption Eventually we reach steady state: Adsorption = Desorption (Enders et al., 2017) 40

42 IVIVE a key component in translation of NAMs results in the context of human safety Potential Target Tissue In Vitro Toxicity Assays Nature of Toxicity CSBP Doseresponse modeling QSAR QSPR Information on assays conditions Prediction of chemical kinetics In vivo Human Safe Exposure Estimate Metabolite-ID Absorption Distribution Metabolism Excretion PB/PK Model Reverse Dosimetry In Vitro Kinetic Assays (modified from Yoon et al. 2012) 41

43 Acknowledgements Funding American Chemistry Council-Long Range Research Initiatives (ACC-LRI) Council for the Advancement of Pyrethroid Human Health Risk Assessment (CAPHRA) EPA STAR grant NIEHS U19 grant ANGUS chemical Colleagues and Collaborators (Hamner/ScitoVation/ToxStrategies/US EPA) Harvey Clewell Rebecca Clewell Mel Andersen Martin Phillips Jenny Pedersen Gina Song Marjory Moreau David Billings Yuansheng Zhao Jerry Campbell Jeremy Leonard Pergentino Balbuena Lavanya Lao Erin Burgunder Jeff Enders Salil Pendse Alina Efremenko 42

44 References Andersen Saturable metabolism and its relationship to toxicity. Crit Rev Toxicol May;9(2): Campbell et al., A case study on quantitative in vitro to in vivo extrapolation for environmental esters: methyl-, propyl- and butylparaben, Toxicology, 2015, 332: Clewell et al., Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on tissue dosimetry. Toxicol Sci Jun;79(2): Clippinger et al., Alternative approaches for acute inhalation toxicity testing to address global regulatory and nonregulatory data requirements: An international workshop report. Toxicol In Vitro. 2017, 48: Enders et al., Evaluation of Non-Specific Binding to Different Organic Polymeric Components in Flow-Based Advanced Cell Culture Systems for Toxicity Testing. Poster presentation at the 56 th Annual SOT Meeting, Baltimore, MD, Mar 13-16, 2017, Abstract#3201 Hartman et al., An in vitro approach for prioritization and evaluation of chemical effects on glucocorticoid receptor mediated adipogenesis. Toxicol Appl Pharmacol May 19;355: Hinderliter et al., ISDD: A computational model of particle sedimentation, diffusion, and target cell dosimetry for in vitro toxicity studies. Part Fibre Toxicol. Nov 30;7(1): McCarver et al., Developmental expression of drug metabolizing enzymes: impact on disposition in neonates and young children, Dryad Digital Repository., 2017, Date Published: July 31, doi.org/ /dryad.71pp6.

45 References McNally et al., Reprint of PopGen: A virtual human population generator. Toxicology. Jun 5;332: Paini et al., Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making, Computational Toxicology, Volume 9, Pages Phillips et al., Xenobiotic metabolism in alginate-encapsulated primary human hepatocytes over long timeframes. Appl in Vitro Toxicol, Sep 2018.ahead of print, Ring et al., Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability. Environ Int Sep;106: Rotroff et al., Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicol Sci Oct;117(2): Slattery et al., Application of In Silico and In Vitro Methods to Address Data Gaps in Chemical Risk Assessment: A Case Study with 2-Amino-2-Methylpropanol, Poster presentation at the 57 th Annual SOT Meeting, San Antonio, TX, Mar 11-15, 2018, Abstract #2872 Thomas et al., ISD3: a particokinetic model for predicting the combined effects of particle sedimentation, diffusion and dissolution on cellular dosimetry for in vitro systems. Part Fibre Toxicol Jan 25;15(1):6. Wambaugh et al., Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics. Toxicol Sci May 1;163(1): Wetmore et al., Relative impact of incorporating pharmacokinetics on predicting in vivo hazard and mode of action from high-throughput in vitro toxicity assays. Toxicol Sci Apr;132(2):

46 References Wu et al., Can the observed association between serum perfluoroalkyl substances and delayed menarche be explained on the basis of puberty-related changes in physiology and pharmacokinetics? Environment international. 82:61-8. Yoon et al., Quantitative in vitro to in vivo extrapolation of cell-based toxicity assays results, Critical Reviews in Toxicology, 42(8): Yoon et al., Use of in vitro data in PBPK models: An example of in vitro to in vivo extrapolation with carbaryl in "Parameters for Pesticide QSAR and PBPK/PD models for Human Risk Assessment", J. B. Knaak, C. Timchalk, R. Tornero- Velez (Eds)., Vol. 1099, pp American Chemical Society Yoon et al., Evaluation of simple in vitro to in vivo extrapolation approaches for environmental compounds, Toxicology in Vitro, 28(2): Yoon et al., Use of in vitro data in developing a physiologically based pharmacokinetic model: Carbaryl as a case study, Toxicology, 332:52-66 Yoon et al., Moving Beyond Prioritization Toward True In Vitro Safety Assessment, Applied In Vitro Toxicology, 2 (2), Zhao et al., Pharmacokinetic modeling of inhaled cadmium oxide (CdO) nanoparticles in pregnant mice to interpret observed developmental effects (53rd Annual Meeting Mar 22-27, Phoenix, AZ, Society of Toxicology, 2014)