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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)
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
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)
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)
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
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
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
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
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 .5 C Y P 3 A 4
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 1 A 2
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 B 6
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 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
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 1 -c
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 .5 C E S 2 -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
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
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