In Vitro to in Vivo Extrapolation (IVIVE) to Support New ... · MOS –Margin of Safety ... - Devel...

Center of Excellence for 21st Century ToxicologyA 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, 20183:00 PM–4:30 PM (EDT)

Conflict of Interest Statement

The author declares no conflict of interest.

1

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

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

National Research Council recommendations

on 21st-century safety science Modern biology and computational

tools to support efficient risk-based

decisions

4

Exposure and AOP-based framework for risk-

based decisions in modern safety assessment

Exposure

Dosimetry

Mechanism (AOP)

RISK-based

DecisionsDose-Response

5(Modified from the slide curtesy of Cecilia Tan)

Modernizing toxicity testing with new

approaches and methodologies

Rapid exposure estimation

AOP-based in vitro assays - more efficient and human-

relevant alternatives to traditional animal testing

IVIVE - quantitative translation of NAMs data to in vivo in the

context of safe exposure

6

Translation of in vitro toxicity testing results to safe

human exposure

In vitro assays

Computational

methods

In vitro PoD

estimation

Equivalent Human

Exposure

PBPK &

Reverse

dosimetry

Streams of data from in vitro

and in silico NAMs

In vitro

biokinetics

MoE analysis

Derivation of Risk values

Population assessment

mg/kg/day

ppm…

7

NAMs in risk-based

decision making – A

tiered approach

8

Evidence-based toxicology- Systematic review- Problem formulation- Development of testing &risk assessment strategies

Prioritization/compound selection- Relative potency- Margin of Exposure (MoE)

- Activity:Exposure ratios (AERs)

Hazard ID/Mode of Action (MOA)

Risk-based safety decisions- Quantitative dose-response- Predict region of safety for human

exposure- Point of departure (PoD)

TESTING & EVALUATION: APPLICATIONS:

- TSCA existing chemical prioritization

- Lead compound selection

- Identify potential endocrine disruptors (EDSP, Health Canada)

EXAMPLES OF USES:

- Skin sensitization assessment (OPP)

- Support human relevant decisions

- Reduce uncertainty factors

- Reduce animal testing

Ultra-high throughput (HT) NAMs- Rapid exposure estimation – SEEM3, ExpoCast etc.- In silico metabolism models- QSAR, read-across, TTC

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

High throughput NAMs- In vitro parent chemical clearance- HT-IVIVE- HT-screening assays (ToxCast/Tox21)

(Slide curtesy of Rebecca Clewell)

NAMs in risk-

based decision

making

9











EXAMPLES OF USES:



















EXAMPLES OF USES:









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

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 assessment11

Prioritizing compounds using in vitro bioactivity data and

exposure estimationIn vitro HTS + HT IVIVE = estimate dose to cause bioactivity

HT exposure predictions

Margin between potential hazard and potential exposure (AER)

Total US Population

Decreasing priority

(Ring et al., 2017) 12

Putting in vitro

bioactivity data

into context

Ultimate goal: In

vitro based safety

assessment

Rapid evaluation of cumulative margin of

safety

MOS = In vitro EC10

In vivo plasma conc.

IVIVE-PBPK model

In vivo plasma concentration

In vitro point of departure (EC10, estrogenic activity)

(Campbell et al., 2015)Human biomarker data(NHANES urinary conc.)

Relative potency factor based on in vitro

Reverse dosimetry

Margin of safety for adult female

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

QSAR-based parameterization

HT-metabolic clearance (Clint)

Outcomes:

Rapid prediction of in vivo exposure in test and target species for decision making in early development

Cmax

NAMs

in vitro bioactivity

Rapid PBPK & IVIVE

Historical in vivo data

Time

Co

nc

Compound selection & ranking based on kinetics & toxicity

Using in vitro toxicity data with Rapid PBPK to

determine relative species sensitivity

18

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

Using in vitro toxicity data with Rapid PBPK to

determine relative species sensitivity

19

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

(Slide curtesy of Rebecca Clewell)(Slattery et al., 2018)

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

Rapid PBPK Modeling

Database for PBPK parameters

QSAR tools for Chemical Properties

Automated IVIVE equations

Running Environment for PBPK Model

Tailored Model with

User Interface

Database for PBPK parameters

QSAR tools for Chemical Properties

Automated IVIVE equations

Running Environment for PBPK Model

Rapid PBPK modeling

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 interface21

21

In vitro-based PBPK models for risk assessment

Early age population risk assessment as an example 22

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., 2018

23

(Yoon et al., 2011 and 2015)

In vitro data provides the key parameter -metabolism

IVIVE for PBPK model parameterization - Carbaryl

24

Biological scaling of in vitro metabolism data

25

Age-

Specific

Internal

ExposureLife-Stage PBPK Model

Age-Specific

Human

Physiology

Age-Specific

Metabolism

Age-Specific

Exposure

Margin of Exposure

IVIVE-PBPK for early-life sensitivity evaluation

26

26

Age-Dependent Physiological Parameter

Database

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

Body Weight (BW)

Liver Blood Flow (QL)

27

IVIVE predicts in vivo metabolic clearance across ages

28

C Y P 2 C 9

C Y P 2 B 6

C Y P 2 C 1 9

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

6 m o n t h s

1Y

5Y

Expressed enzyme Clint Life-stage hepatic Clint

0 5 0 0 1 0 0 0

0 .0

0 .5

1 .0

1 .5C Y P 2 C 1 9

A g e (w e e k s )Fra

ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 0 0 1 0 0 0

0 .0

0 .5

1 .0

1 .5C Y P 2 C 9


ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 0 0 1 0 0 0

0 .0

0 .5

1 .0

1 .5 C Y P 3 A 4


ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 0 0 1 0 0 0

0 .0

0 .5

1 .0

1 .5C Y P 1 A 2


ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 0 0 1 0 0 0

0 .0

0 .5

1 .0

1 .5C Y P 2 B 6


ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 1 0 1 5 2 0

0 .0

0 .5

1 .0

1 .5C E S 1 -m

A g e (Y e a rs )Fra

ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 1 0 1 5 2 0

0 .0

0 .5

1 .0

1 .5C E S 2 -c


ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 1 0 1 5 2 0

0 .0

0 .5

1 .0

1 .5C E S 1 -c


ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

0 5 1 0 1 5 2 0

0 .0

0 .5

1 .0

1 .5 C E S 2 -m


ctio

n o

f a

du

lt e

nz

ym

e e

xp

re

ss

ion

Enzyme ontogeny

(McCarver et al., 2017)

ISEF × MPPGL × Liver Weight × Enzyme abundance

Adult hepatic Clint

IVIVE

C Y P 2 C 9

C Y P 2 B 6

C Y P 2 C 1 9

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

0.5Y

5Y

C Y P 2 C 9

C Y P 2 B 6

C Y P 2 C 1 9

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

25Y (Adult)

1Y

R elative enzym e

contribution in adults

tow ards C PM C L int, in

vitro using recom binant

C Y P and C ES enzym es

R elative enzym e

contribution in

adults tow ards CPM

total C L int, in v ivo

A ge-specifc relative

enzym e contribution

tow ards C PM total

C L int, in v ivo

1 0Y

Enzym e

Ontogeny

C P M

C Y P 2 C 9

C Y P 2 B 6

C Y P 2 C 1 9

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

0.5Y

5Y

C Y P 2 C 9

C Y P 2 B 6

C Y P 2 C 1 9

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

25Y (Adult)

1Y

R elative enzym e

contribution in adults

tow ards C PM C L int, in

vitro using recom binant

C Y P and C ES enzym es

R elative enzym e

contribution in

adults tow ards CPM

total C L int, in v ivo

A ge-specifc relative

enzym e contribution

tow ards C PM total

C L int, in v ivo

1 0Y

Enzym e

Ontogeny

C P M

6 m o n th s

1Y

5Y

0.5 yr

1 yr

5 yr

10 yr

25 yr

(Docket ID:EPA-HQ-OPP-2017-0180)

Supporting risk assessment for sensitive populations

29

Sensitive Juvenile_Cmax50th percentile

Adult_Cmax50th percentile

A data-derived extrapolation factor (DDEF) is calculated using the simulated distribution of target tissue exposure in each age population

This DDEF can be used to address uncertainty for age-related PK difference

29

1Y 5Y 19Y 25Y

0

50

100

150

200

Co

nc

en

tra

tio

n (

ng

/g)

1Y 5Y 19Y 25Y

0

100

200

300

400

Co

nc

en

tra

tio

n (

ng

/g)

1Y 5Y 19Y 25Y

0

200

400

600

800

1000

Co

nc

en

tra

tio

n (

ng

/g)

Cis-permethrin in males of different ages (1 mg/kg/day)

(Docket ID:EPA-HQ-OPP-2017-0180)

Integrated approaches

for inhalation safety

assessment

Portal of entry exposure

Systemic exposure

MPPDCFD

models

AOP-based in vitro

respiratory tract or systemic

target assays

In vitro kinetic assaysLung metabolism

diffusion/transport

blood air partition

in vitro kinetic assaysLiver metabolism

bindingTransport

IVIVE

IVIVE

Respiratory tract dosimetry models

(MPPD, CFD)

Integrated approaches

for inhalation safety

assessment

Portal of entry exposure

Systemic exposure

MPPDCFD

models

AOP-based in vitro

respiratory tract or systemic

target assays

In vitro kinetic assaysLung metabolism

diffusion/transport

blood air partition

in vitro kinetic assaysLiver metabolism

bindingTransport

IVIVE

IVIVE

Respiratory tract dosimetry models

(MPPD, CFD) Dose-response for systemic effects

Dose-response for local effects

Css = Cair/[(1/PB)+(QL/QP)*Clint/(QL+Clint)]

Rapid estimation of systemic exposure

32

Css = Cair*PB

Css = QP*Cair/QL

for poorly soluble & poorly metabolized (e.g., perchloroethylene),

for soluble & extremely well metabolized (e.g., isopropanol),

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)

General equation

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

33

Lung Tissues

Lung blood

Lung tissue

Tissue

Tissue blood

Cd + MT Cd-MT

Induction

Qtissue Qtissue

Cd Cd-MT

Only in kidney

Only in liver

Lung blood

Lung tissue

Lung tissue

Lung blood

Inhalation

alveolusAirway/ Alveolus

Cd-MT Cd + MT

Induction

QCQC

CdO

CdO NP

Cd-MT Cd

Deposition of CdO NP

(MPPD model)

Mucociliary clearance

(Zhao et al., 2014)

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

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

Why metabolism parameters still need to be

measured

36

Limitations due to

in vitro tools (low

clearance)

Limitation due to

domains of

applicability (lack of

coverage non-CYP

enzymes/Phase II)

Measured Clint (L/hr)

Pre

dic

ted

C

lin

t (L

/h

r)

B isphenol-A

Ethylparaben

Atrazine

Carbaryl

Diethylhexylphthalate(DEHP)

W arfar in

Caffeine

Butylparaben

Acetaminophen

Propylparaben

0.1

1

10

100

1000

10000

100000

0.1 1 10 100 1000 10000 100000

Pre

dic

ted

Clin

t(L

/hr)

ExperimentallydeterminedClint(L/hr)

Extrapolation to In Vivo – How to incorporate

new technologies

Allometric scaling physiological and biochemical parameters scaled by function of body size

What would be the best way to scale?

Biological scaling (IVIVE)Relating intrinsic functions by accounting forinter system scale and microenvironment differences

Animals

Traditional in vitro systems

Organotypic culture, tissue-chips,

bioreactor etc.

37

In vitro biokinetic modeling of simple dynamic

culture of 3D liver culture over long time frames

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15

Vo

lum

e/W

ell

(mL)

Time (days)

Replicate 1

Replicate 2

Model Prediction

Media volume changes due to daily

replacement and evaporation over

time

Concentration/time profile

without beads

Combination of metabolism

and in vitro system-dependent

loss of compound(Phillips et al., 2018)

In vitro biokinetic model to describe compound

disposition in bioreactor and assist in quantitative

extrapolation of the results

39

Teasing out in vitro kinetic issues to increase

confidence in IVIVE

40

Eventually we reach steady state: Adsorption = Desorption

More chemical goes in than comes out

Accumulation = In (with flow) – Out (with flow) – Adsorption + Desorption

(Enders et al., 2017)

IVIVE – a key component in translation of NAMs

results in the context of human safety

Potential Target

Tissue

PB/PK Model

In vivo Human

Safe Exposure Estimate

In Vitro

Toxicity Assays

In Vitro

Kinetic AssaysQSAR

QSPR

Information

on assays

conditions

Prediction of

chemical kineticsNature of

Toxicity

• Metabolite-ID

• Absorption

• Distribution

• Metabolism

• Excretion

Reverse Dosimetry

(modified from Yoon et al. 2012)

CSBP Dose-

response

modeling

41

Acknowledgements

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

42

Harvey Clewell

Rebecca Clewell

Mel Andersen

Martin Phillips

Jenny Pedersen

Gina Song

Marjory Moreau

David Billings

Yuansheng Zhao

Jerry Campbell

Jeremy Leonard

PergentinoBalbuena

Lavanya Lao

Erin Burgunder

Jeff Enders

Salil Pendse

Alina Efremenko

Colleagues and Collaborators

(Hamner/ScitoVation/ToxStrategies/US EPA)Funding

References

Andersen. 1981. Saturable metabolism and its relationship to toxicity. Crit Rev Toxicol. 1981 May;9(2):105-50.

Campbell et al., 2015. A case study on quantitative in vitro to in vivo extrapolation for environmental esters: methyl-, propyl- and butylparaben, Toxicology, 2015, 332: 67-76

Clewell et al., 2004. Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on tissue dosimetry. Toxicol Sci. 2004 Jun;79(2):381-93.

Clippinger et al., 2017. Alternative approaches for acute inhalation toxicity testing to address global regulatory and non-regulatory data requirements: An international workshop report. Toxicol In Vitro. 2017, 48:53-70.

Enders et al., 2017. Evaluation of Non-Specific Binding to Different Organic Polymeric Components in Flow-Based Advanced Cell Culture Systems for Toxicity Testing. Poster presentation at the 56th Annual SOT Meeting, Baltimore, MD, Mar 13-16, 2017, Abstract#3201

Hartman et al., 2018. An in vitro approach for prioritization and evaluation of chemical effects on glucocorticoid receptor mediated adipogenesis. Toxicol Appl Pharmacol. 2018 May 19;355:112-126.

Hinderliter et al., 2010. ISDD: A computational model of particle sedimentation, diffusion, and target cell dosimetry for in vitro toxicity studies. Part Fibre Toxicol. Nov 30;7(1):36. 15

McCarver et al., 2017. Developmental expression of drug metabolizing enzymes: impact on disposition in neonates and young children, Dryad Digital Repository., 2017, Date Published: July 31, doi.org/10.5061/dryad.71pp6.

References

McNally et al., 2015. Reprint of PopGen: A virtual human population generator. Toxicology. Jun 5;332:77-93.

Paini et al., 2019. Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making, Computational Toxicology, Volume 9, Pages 61-72

Phillips et al., 2018. Xenobiotic metabolism in alginate-encapsulated primary human hepatocytes over long timeframes. Appl in Vitro Toxicol, Sep 2018.ahead of print, http://doi.org/10.1089/aivt.2017.0029

Ring et al., 2017. Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability. Environ Int. 2017 Sep;106:105-118.

Rotroff et al., 2010. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. ToxicolSci. 2010 Oct;117(2):348-58.

Slattery et al., 2018. 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 57th Annual SOT Meeting, San Antonio, TX, Mar 11-15, 2018, Abstract #2872

Thomas et al., 2018. 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. 2018 Jan 25;15(1):6.

Wambaugh et al., 2018. Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics. Toxicol Sci. 2018 May 1;163(1):152-169.

Wetmore et al., 2013. Relative impact of incorporating pharmacokinetics on predicting in vivo hazard and mode of action from high-throughput in vitro toxicity assays. Toxicol Sci. 2013 Apr;132(2):327-46.

References

Wu et al., 2015. 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., 2012. Quantitative in vitro to in vivo extrapolation of cell-based toxicity assays results, Critical Reviews in Toxicology, 42(8): 633-652

Yoon et al., 2012. 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. 323-338. American Chemical Society

Yoon et al., 2014. Evaluation of simple in vitro to in vivo extrapolation approaches for environmental compounds, Toxicology in Vitro, 28(2):164-70

Yoon et al., 2015. Use of in vitro data in developing a physiologically based pharmacokinetic model: Carbaryl as a case study, Toxicology, 332:52-66

Yoon et al., 2016. Moving Beyond Prioritization Toward True In Vitro Safety Assessment, Applied In Vitro Toxicology, 2 (2), 67-73.

Zhao et al., 2014. 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)

In Vitro to in Vivo Extrapolation (IVIVE) to Support New ... · MOS –Margin of Safety ... - Devel...

Documents

Transcript of In Vitro to in Vivo Extrapolation (IVIVE) to Support New ... · MOS –Margin of Safety ... - Devel...