Continuing Education Course - toxicology.orgContinuing Education Course Sunday, March 15 | 8:15 AM...

59th Annual Meeting & ToxExpoMarch 15–19, 2020 • Anaheim, California

AM06: Modern Modeling Strategies to Address Uncertainty and Variability in

Dose-Response Assessment

Continuing Education CourseSunday, March 15 | 8:15 AM TO 12:00 NOON

Chair(s) Kan Shao, Indiana University

Weihsueh A. Chiu, Texas A&M University

Primary EndorserRisk Assessment Specialty Section

Other Endorser(s)Biological Modeling Specialty Section

Regulatory and Safety Evaluation Specialty Section

Presenters Kan Shao, Indiana University

Weihsueh A. Chiu, Texas A&M UniversityAlison Harrill, NIEHS

Caroline Ring, ToxStrategies Inc.

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2 #2020SOT #toxexpo #2020SOT #toxexpo

3 #2020SOT #toxexpo #2020SOT #toxexpo

8:30 AM–9:15 AM Benchmark Dose Modeling Strategies for Uncertainty Quantifications in Dose-Response Assessment Kan Shao, IndianaUniversity, Bloomington, IN 4

9:15 AM–10:00 AM Probablistic Dose-Response Assessment to Quantatively Address Uncertainty and Variability Weihsueh A. Chiu, Texas A&M University, College Station, TX 19

10:00 AM–10:30 AM Break

10:30 AM–11:15 AM Modeling Dose-Response across Populations: Quantification of Inter-individual Variability Alison Harrill, NIEHS, Research Triangle Park, NC 32

11:15 AM–12:00 Noon Simulation of Population Variability in High-Throughput Toxicokinetic Modeling in Support of Dose-Response Assessment Caroline Ring, ToxStrategies Inc., Austin, TX 51

Modern Modeling Strategies to Address Uncertainty and Variability in

Dose-Response Assessment

Benchmark Dose Modeling Strategies for Uncertainty Quantifications in Dose-Response Assessment

Kan Shao, PhDIndiana UniversityBloomington, IN

Email: [email protected]

Conflict of Interest

• The author declares no conflict of interest.

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Presenter 1 | AM06

Abbreviations

• BBMD: Bayesian Benchmark Dose modeling system

• BMD: benchmark dose modeling • BMDL: benchmark dose lower

confidence limit • BMDS: benchmark dose software• BMR: benchmark response • CSF: cancer slope factor• DR: dose-response

• EFSA: European Food Safety Authority

• LOAEL: lowest observed adverse effect level

• MCMC: Markov chain Monte Carlo• NOAEL: no observed adverse effect

level • POD: point of departure • US EPA: US Environmental

Protection Agency

Outline of the Presentation• Introduction on BMD methodology

– Comparison between the NOAEL/LOAEL and BMD approach

• Bayesian BMD modeling for uncertainty quantification– Quantifying within model uncertainty

• Fitting DR models using MCMC algorithm • Evaluating model fit

– Quantifying between-model uncertainty• Comparing models by model weight• Calculating model-averaged BMD

• Case studies

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Presenter 1 | AM06

NOAEL/LOAEL Approach

NOAEL/LOAEL Steps:

• Compare response in exposure groups with the counterpart in control group starting from the lowest exposure group

• Identify the lowest dose level where response is significantly different from control; this level is LOAEL

• The tested dose level immediately smaller than the LOAEL is the NOAEL

NOAELLOAEL

(Data from NTP 2006) (Data from NTP 2006)

490

480

460

500 49

1

5313

LOAEL

Benchmark Dose (BMD) Methodology

BMDBMDL

BMR

BMD method steps:

• Fit a dose-response model (dose-response curve) to the data

• Determine a benchmark dose-response (BMR) level, i.e., the level of change in the endpoint to be considered as adverse

• Calculate the benchmark dose (the dose level that causes the BMR level change in responses, i.e., the intersection of the DR curve and the predetermined BMR level) with its statistical lower bound (BMDL)

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Presenter 1 | AM06

NOAEL/LOAEL Approach versus BMD Method

Dose Selection:

• NOAEL and LOAEL are limited to doses tested in experiment only

• BMD is NOT limited to the tested doses

• NOAEL may be missing

LOAEL

BMDBMDL


Impact of sample size• NOAEL/LOAEL: the ability of a bioassay to detect a treatment response decreases as sample size decreases

(i.e., N ↓ = NOAEL ↑)• BMD method: appropriately considers sample size; as sample size decreases, uncertainty in estimated

response rate increases (i.e., N ↓ = BMDL ↓)

BMDL BMD BMDL BMD

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Presenter 1 | AM06


Cross-study comparison• NOAEL/LOAEL: observed response levels at the NOAEL or LOAEL are not consistent across studies and

cannot be compared

• BMDs derived from different studies may be comparable if endpoint and BMR are the same

BMD = 21.2

Dose Response

0 2/86

1.55 1/50

7.15 9/50

38.56 14/45

Dose Response

0 0/49

2.56 0/48

5.69 0/46

9.79 0/50

16.57 1/49

29.7 13/53NOAEL

LOAEL

NOAEL

LOAEL

BMD = 25.9


• BMD method: characteristics in dose-response information (e.g., dose selection, dose spacing, sample size, how steep or shallow the response is) are taken into consideration and reflected in the variability or uncertainty in results

• NOAEL/LOAEL: the characteristics are not considered

BMD = 21.2

NOAEL

LOAEL

NOAEL

LOAEL

BMD = 25.9

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Presenter 1 | AM06

Comparison of NOAEL and BMD MethodSubject NOAEL/LOAEL Approach BMD Method

Dose selection Limited to doses in study only Not restricted to doses tested

Sample sizeThe ability of a bioassay to detect a treatment response decreases as sample size decreases (i.e., N ↓ = NOAEL ↑)

The ability of a bioassay to detect a treatment response increases as sample size decreases (i.e., N ↓ = BMDL ↓)

Cross-study comparisonObserved response levels at the NOAEL or LOAEL are not consistent across studies and cannot be compared

BMDs derived from different studies may be comparable if endpoint and BMR are the same

Variability and uncertainty in experimental results

Characteristics that influence variability or uncertainty in results (dose selection, dose spacing, sample size) not taken into consideration

The characteristics taken into consideration

Dose-response information

Information, such as shape of the dose-response curve (i.e., how steep or shallow the response is) not taken into consideration

Dose-response information taken into consideration

May be missing from study

NOAEL may be missing, then an uncertainty factor (usually 10) is applied to LOAEL

Typically BMDL can be derived

BMD Modeling for Uncertainty Quantification

•Quantifying within-model uncertainty– Fitting DR models using MCMC algorithm – Evaluating model fit

•Quantifying between-model uncertainty– Comparing models by model weight– Calculating model averaged BMD

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Presenter 1 | AM06

Quantifying Within-Model Uncertainty

• Fitting DR models using MCMC algorithm – Bayesian inference to estimate model parameters based on Bayes’ rule

– For a two-parameter (parameters a and b) dichotomous DR model:

– Log-likelihood function:

𝑝𝑝(𝜽𝜽|𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷) ∝ 𝑝𝑝 𝜽𝜽 𝑝𝑝(𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷|𝜽𝜽)

𝑃𝑃 𝐷𝐷, 𝑏𝑏 𝑑𝑑, 𝑛𝑛, 𝑦𝑦 ∝ 𝑃𝑃 𝑑𝑑, 𝑛𝑛, 𝑦𝑦 𝐷𝐷, 𝑏𝑏 𝑃𝑃 𝐷𝐷, 𝑏𝑏 = 𝑃𝑃 𝐷𝐷, 𝑏𝑏 ς𝑖𝑖=1𝐺𝐺 𝑃𝑃 𝑑𝑑𝑖𝑖, 𝑛𝑛𝑖𝑖, 𝑦𝑦𝑖𝑖 𝐷𝐷, 𝑏𝑏

= 𝑃𝑃(𝐷𝐷, 𝑏𝑏)ෑ𝑖𝑖=1

𝐺𝐺[𝑓𝑓(𝑑𝑑𝑖𝑖)]𝑦𝑦𝑖𝑖 [1 − 𝑓𝑓(𝑑𝑑𝑖𝑖)]𝑛𝑛𝑖𝑖−𝑦𝑦𝑖𝑖

log 𝑝𝑝 𝑑𝑑, 𝑛𝑛, 𝑦𝑦 𝐷𝐷, 𝑏𝑏 =𝑖𝑖=1

𝐺𝐺log 𝑛𝑛𝑖𝑖

𝑦𝑦𝑖𝑖+ 𝑦𝑦𝑖𝑖 log 𝑓𝑓 𝑑𝑑𝑖𝑖 𝐷𝐷, 𝑏𝑏 + 𝑛𝑛𝑖𝑖 − 𝑦𝑦𝑖𝑖 log[1 − 𝑓𝑓(𝑑𝑑𝑖𝑖|𝐷𝐷, 𝑏𝑏)]

BMD Modeling in the BBMD System

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Presenter 1 | AM06


• Model fitting and evaluation– Textual and graphical

output– Posterior predictive

p-value– Posterior sample

visualization


• Model fitting and evaluation—textual and graphical output

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Presenter 1 | AM06


• Model fitting and evaluation—posterior predictive p-value (PPP):– Used to examine how well the model fit the data– Used likelihood as the key statistic– Difference between BMDS and BBMD

• BMDS: A likelihood ratio (the likelihood of the fitted model over the likelihood of the saturated model) is assumed following a 𝜒𝜒2 distribution; null hypothesis is rejected if the p-value is too small (i.e., model fitting is not adequate). P > 0.1

• BBMD: Posterior samples are first used to generate predicted responses, then likelihood values calculated using the predicted responses and original data are computed and compared. Finally, the probability that one type of likelihood is larger than the other is estimated.

– Pr[𝑇𝑇(𝑦𝑦, 𝜃𝜃𝑙𝑙) > 𝑇𝑇(𝑦𝑦𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝,𝑙𝑙, 𝜃𝜃𝑙𝑙)]– 0.05 ≤ PPP ≤ 0.95

Quantifying Within-Model Uncertainty• Model fitting and evaluation—

posterior sample visualization

– MCMC algorithm probabilistically quantifies the uncertainty in the input dose-response data and in the model fitting process

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Presenter 1 | AM06

Quantifying Between-Model Uncertainty• Model Weight Calculation

– Introduced in Wasserman (2000); compute model weight using two equations below for model comparison:

• Model-Averaged BMD Calculation– According to Hoeting et al., (1999), the model averaged BMD can be expressed as:

– Two components: (1) posterior distribution of BMD from each individual model; (2) posterior model weight for each model

log ෝ𝑚𝑚𝑗𝑗 = 𝓁𝓁𝑗𝑗 −𝑞𝑞𝑗𝑗2 log 𝑛𝑛Pr ℳ𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 =

ෝ𝑚𝑚𝑗𝑗σ𝑘𝑘=1𝐾𝐾 ෝ𝑚𝑚𝑘𝑘

Pr 𝐵𝐵𝐵𝐵𝐷𝐷𝑚𝑚𝑚𝑚 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 =𝑘𝑘=1

𝐾𝐾Pr(𝐵𝐵𝐵𝐵𝐷𝐷𝑘𝑘|ℳ𝑘𝑘, 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷)Pr(ℳ𝑘𝑘|𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷)

Quantifying Between-Model Uncertainty

• Model weight and model averaged BMD estimation– Model weight calculation

quantifies the model uncertainty by assigning percentage weight to models based on how well the model fits the data

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Presenter 1 | AM06

Benchmark Dose Calculation

Based on central tendency

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝐶𝐶𝐶𝑅𝑅𝐶𝐶𝐶𝐶𝑅𝑅 = 𝑓𝑓 𝐵𝐵𝐵𝐵𝐵𝐵 ± 𝑓𝑓 0𝑓𝑓(0)

𝑓𝑓 𝐵𝐵𝐵𝐵𝐵𝐵 = 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝐴𝐴𝑅𝑅𝑅𝑅 𝐶𝐶𝐶𝑅𝑅𝐶𝐶𝐶𝐶𝑅𝑅 ± 𝑓𝑓 0

Dichotomous Data Continuous Data Categorical Data

Extra risk:

𝐵𝐵𝐵𝐵𝑅𝑅 = 𝑓𝑓 𝐵𝐵𝐵𝐵𝐵𝐵 − 𝑓𝑓(0)1 − 𝑓𝑓(0)

Added risk:

𝐵𝐵𝐵𝐵𝑅𝑅 = 𝑓𝑓 𝐵𝐵𝐵𝐵𝐵𝐵 − 𝑓𝑓 0

Pr 𝑌𝑌 ≥ 𝐴𝐴 𝐵𝐵 = 𝐵𝐵𝐵𝐵𝐵𝐵 − Pr 𝑌𝑌 ≥ 𝐴𝐴 𝐵𝐵 = 01 − Pr 𝑌𝑌 ≥ 𝐴𝐴 𝐵𝐵 = 0 = 𝐵𝐵𝐵𝐵𝑅𝑅

Extra risk:

Added risk:

Pr 𝑌𝑌 ≥ 𝐴𝐴 𝐵𝐵 = 𝐵𝐵𝐵𝐵𝐵𝐵 − Pr 𝑌𝑌 ≥ 𝐴𝐴 𝐵𝐵 = 0 = 𝐵𝐵𝐵𝐵𝑅𝑅

Uncertainty Quantification in BMD Estimation

• MCMC algorithm probabilistically quantifies the uncertainty in the input dose-response data and in the model fitting process

• Model weight quantifies the model uncertainty by assigning percentage weight to models based one how well the model fits the data

• Model averaged BMD addresses both within- and between-model uncertainty in BMD estimation

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Presenter 1 | AM06

Case Studies—BMD Analyses Using BBMD System

• Case I: BMD modeling for dichotomous data

• Case II: BMD modeling using individual continuous data

Case I: Liver Tumor Induced by TCDD

Dose N Incidence0 86 2

1.55 50 17.15 50 9

38.56 45 14

Trend test (Cochran-Armitage test): P-value = 1.258e-7 (z-score: 5.16)

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Presenter 1 | AM06

Fitted Dose-Response Curves

Logistic LogLogistic Probit LogProbit

Quantal-Linear Multistage2 Weibull Dichotomous Hill

Model Fitting, BMD, and CSF Estimation

• BMR is defined as 10% extra risk

• The best-fit model is the dichotomous hill model because its model weight is 45.3%

• If using the default model for cancer endpoint, quantal-linear model should be used because model weight is 29% (>8.81% of multistage 2 model)

• Model-averaged BMD can be another choice for POD

Logistic LogLogistic Probit LogProbit Quantal-Linear

Multistage2 Weibull Dichotomous Hill

PPP 0.503 0.525 0.508 0.580 0.509 0.506 0.540 0.571Model Weight 2.35% 6.94% 3.15% 0.152% 29% 8.81% 4.33% 45.3%BMD 20.5 15.3 18.8 35.7 9.55 11.1 18.3 5.85BMDL 16.4 7.02 14.9 25.0 6.46 7.20 8.35 2.84

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Presenter 1 | AM06

BMD and CSF Estimation and SelectionQuantal-Linear Dichotomous-Hill Model-Averaged

9.94 (7.7, 12.86)9.55 (6.46, 15.21) 5.85 (2.84, 8.91)

BMD Estimation

0.01 (0.0078, 0.013)0.01 (0.0066, 0.0155) 0.017 (0.0112, 0.0353)

CSF Estimation

Case II: Continuous Endpoint Induced by PFOA

Dose Response0.11 2.250.08 2.460.18 3.140.09 1.96

… … 107.0 0.308122.0 0.406114.0 0.327125.0 0.379

N = 50

• Dose metric = serum concentration of PFOA in units of mg/L

• Response = serum concentration of free thyroxine (fT4) in male rats in units of ng/dL

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Presenter 1 | AM06

Fitted Dose-Response Curves

Exponential 2 Exponential 3 Exponential 4 Exponential 5

Linear Power Michaelis-Menten Hill

Model Fitting Results and BMD Estimation

• BMR is defined as the 10% relative change in response• No data are available close to the BMD level, so the dose-response shape in that

range is model dependent• Model averaged BMD may be a solution

– BMD MA = 15.3 and BMDL MA = 10.2

Exponential 2 Exponential 3 Exponential 4 Exponential 5 Linear Power Michaelis-Menten

Hill

PPP 0.530 0.524 0.527 0.531 0.523 0.526 0.530 0.524Model Weight 0 0 23.6% 36.9% 0 0 1.16% 38.3%BMD 6.55 7.16 2.14 14.6 16.1 18.2 0.68 24.8BMDL 5.69 6.02 1.78 4.92 14.7 15.5 0.37 14.9

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Presenter 1 | AM06

Probabilistic Dose-Response Assessment to Quantitatively

Address Uncertainty and VariabilityWeihsueh A. Chiu, PhDTexas A&M University

College Station, TXPhone: 979.845.4106


Conflict of Interest Statement

The author declares no conflict of interest.

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Abbreviations

• ADI: acceptable daily intake • BMD(L): benchmark dose (lower confidence limit)• BMR: benchmark response• DAF: dosimetric adjustment factor• HDMI: human dose at which a fraction I of the

population shows an effect of magnitude M or greater

• IRIS: Integrated Risk Information System• LOAEL: Lowest-Observed-Adverse-Effect-Level• NAS: National Academy of Sciences

• NOAEL: No-Observed-Adverse-Effect-Level• POD: point of departure• RDA: recommended daily allowance• RfD: reference dose• UF: uncertainty factor• UL: upper level• WHO/IPCS: World Health

Organization/International Programme on Chemical Safety

Learning Objectives

• Why is traditional deterministic dose-response assessment problematic?

• What is probabilistic dose-response assessment?

• How can probabilistic dose-response assessment be performed and used in risk assessment?

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Presenter 2 | AM06

Why Is Traditional Deterministic Dose-Response Assessment Problematic?

• Toxicity values such as the RfD are ambiguously defined

• Lack quantification of uncertainty and variability

• Create a false sense of precision

• Lead to “risk assessments” that are not actually based on risk

NOAEL: Greatest concentration or amount of a substance, found by experiment or observation, that causes no adverse alteration . . . of the target organismdistinguishable from those observed in normal (control) organisms of the same species and strain under the same defined conditions of exposure

BMDL: A statistical lower confidence limit on the dose that produces a predetermined change in response rate of an adverse effect (called the benchmark response or BMR) compared to background

Benchmark dosee.g., BMDBMR

5%Uncertaintydistribution

90% confidenceinterval

Example of Ambiguously Defined Toxicity Value: NOAEL versus BMDL

NOAEL should be viewed as an “approximation” of the BMD!

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Presenter 2 | AM06

RfD: an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime

Ambiguities in the RfD

• POD (point of departure): if NOAEL, then magnitude of effect is unspecified and uncertainty is unquantified

• DAF (dosimetric adjustment factor): accounts for “average” interspecies differences (e.g., allometric scaling), uncertainty unquantified

• UFA (interspecies uncertainty factor): assumed to be conservative, but unclear by how much

• UFH (intraspecies uncertainty factor): factor accounting for variability assumed to be conservative, but unclear by how much and unspecified as to population fraction coveredPe

rcen

t Inc

iden

ce o

f Res

pons

e

Dose0

25

50

75

100

NOAEL

LOAEL

Dose(Avg. daily dose)

Mag

nitu

de o

f res

pons

e

PODRfD UFH UFA DAF

Issues Recognized by the National Academies

• Science and Judgment report (NAS, 1994) recommended presenting quantitative representation of uncertainty

• Science and Decisions report (NAS, 2009) recommended incorporating • Mode-of-action, vulnerable populations, background exposures• Unified approach to both cancer and noncancer endpoints• Probabilistic methods for assessing uncertainty

• Review of the IRIS Program report (NAS, 2014) recommended systematic use of uncertainty analysis and expanded use of Bayesian methods

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Presenter 2 | AM06

Existing RfD Is Ill Suited for Many Decision Contexts

• RfD incorrectly used as a bright line between “safe” and “not safe”

• RfD actually separates “reasonable assurance of safe” from “might not be safe” . . . without precisely defining “safe”

• RfD lacks quantitative prediction of level of protection or confidence, preventing assessment of:

• Economic cost–benefit• Risk-benefit and risk-risk trade-offs/comparisons• Life cycle impact assessment

What Is Probabilistic Dose-Response Assessment?

Comprehensive framework developed by WHO/IPCS for probabilistic dose-response assessment• Follows NAS recommendations to develop toxicity values as

“risk-specific doses” rather than “(sort of) safe(ish) doses”

• Addresses variability by conceptually distinguishing between individual versus population dose-response relationships

• Replaces fixed uncertainty factors with probabilistic factors based on historical data

World Health Organization & International Programme on Chemical Safety. (2018). Guidance Document on Evaluating and Expressing Uncertainty in Hazard Characterization, 2nd ed. World Health Organization. https://apps.who.int/iris/handle/10665/259858

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Presenter 2 | AM06

Foundation of WHO/IPCS Approach Is the “Target Human Dose”

Target Human Dose (HDMI): human dose at

which a fraction I of the population shows an effect of magnitude (or severity) M or greater (for the critical effect considered)• Specifies the “target” magnitude of effect

M (analogous to BMR for the benchmark dose)

• Specifies “target” fraction of the variable population I (incidence)

• Can be estimated probabilistically to derive a confidence interval that characterizes uncertainty

Human Dose(Avg. daily dose)

Mag

nitu

de o

f res

pons

e0

100I = 99%I = 50%

Different percentile individuals

M = 10

Key Concept: HDMI (e.g., HD10

01)WHO (2017): Guidance on Evaluating and Expressing Uncertainty in Hazard Assessment. Harmonization Project Document 11. Chiu WA and Slob W (2015): A Unified Probabilistic Framework for Dose-Response Assessment of Human Health Effects. EHP, DOI: 10.1289/ehp.1409385.

I = 1%

Target Human Dose Can Be Derived Similarly to a RfD, While Also Addressing Its Limitations

Benchmark dose modeling*

Prior historical toxicity data across species*

Prior historical or chemical-specific human data on TK

and TD variability*

Combine uncertainties with lognormal approximation or

Monte Carlo simulation*

IHA

MIM UFUF

DAFBMDHD,

=

HA UFUFDAFNOAELRfD

=

Human Variability Factor for Incidence I

Interspecies Factor for remaining TK and TD

differences

Benchmark Dose for Magnitude of effect M

Dosimetric Adjustment Factor for Body Size

differences

Lower Confidence Bound can be defined as a “Probabilistic RfD”* For details see WHO (2014)

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Target Human Dose (HDMI) and Probabilistic RfD Have More

Precise Definitions Than the RfD

RfD: an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk ofdeleterious effects during a lifetime

Probabilistic RfD: a statistical lower confidence limit on the human dose at which a fraction I of the population shows an effect of magnitude (or severity) M or greater (for the critical effect considered)

Target human dosee.g., HDM

I

5%Uncertaintydistribution

90% confidenceinterval

RfD should be viewed as an “approximation” of the HDM

I!

Demonstrating Feasibility

14

3,064 peer-reviewed toxicity values and endpoints from Wignall et al. (2014)

1,464 RfDs and endpoints608 chemicals (351 with multiple RfDs or endpoints)

• Noncancer, oral RfDs• Based on animal data• Sufficient reporting of

species, points of departure, uncertainty factors, etc.

body weight14%

clinical chemistry9%

enzyme activity2% food and/or

water consumption

2%hematology5%

neurotransmitter3%

organ weight15%

urinalysis1%

clinical signs5%

gross pathology1%

mortality/survival2%

nonneoplastic histopathology

33%

development1%

reproduction2%

neurobehavior1%

multiple2%

other1%

none2%

Continuous endpoints

Dichotomousendpoints

Continuous and/or dichotomousendpoints

Effect Types

Re-calculate using Probabilistic MethodsChiu et al., 2018

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Presenter 2 | AM06

Comparing Traditional and Probabilistic Dose-Response Assessment

mg/

kg−d

10−910−810−710−610−510−410−310−210−1100

101

102

103

104

HDM I (median)

HDM I (90% CI)

Probabilistic RfDPODTraditional RfD1522 Chemical/Endpoint Combinations

A

101 102 103

n=26

●●●●●●n=255

●●n=141

●●●●●●●n=1025

●●●●n=30

● ●●●●n=45ChronicBMDL

SubchronicBMDL

ChronicNOAEL

ChronicLOAEL

SubchronicNOAEL

SubchronicLOAEL

Uncertainty in HDM I

POD

B

0 50 100

●

●

●

●

●

●

●

UncertaintySources:LOAEL

NOAEL

BMDL

Subchronic

Animal TK/TD

Animal BW

Human var.

% Contribution

C

0.10.20.30.40.50.60.70.80.9

1

0.10.20.30.40.50.60.70.80.9

1

0.10.20.30.40.50.60.70.80.91.0

10−2 10−1 100 101 102 103 104

Probabilistic HQ = Exposure/Probabilistic RfD

Incid

ence

(Fra

ctio

n of

Pop

ulat

ion)

Upper 95% confidence

Median estimate

Lower 95% confidence

Probabilistic HQ = 1

D

Traditional and probabilistic RfDs correlate highly, mostly differing by <10-fold for I = 1%

PODs tend to correlate with the upper 95% confidence bound for I = 1%, but with some differences up to ~100-fold

Chiu et al., 2018

Lessons Learned across Many Chemicals and Endpoints

• Broadly improving rigor, transparency, and consistency of dose-response assessment using probabilistic approach is feasible:

• Uncertainty factor distributions derived from historical evidence, not factors of 1, 3, or 10• Combining uncertainty distributions probabilistically avoids “compounding conservatism”• The resulting HDM

I is clear as to the degree of health protection (target incidence and magnitude of effect) and conservatism (% confidence)

• Exposure at the current RfD• Frequently implies upper 95% confidence bound incidence of a few percent• Whether such risks are “acceptable” may vary by context (including endpoint severity)

• Greatest contributors to uncertainty are: • Lack of BMDL (see previous presentation!)• Uncertainty about human variability (see next two presentations!)

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Presenter 2 | AM06

How Can Probabilistic Dose-Response Assessment Be Performed and Used in Risk Assessment?

• Multiple user-friendly tools (off- and online) to perform computations

• Tiered approach suggested to transition from deterministic to probabilistic assessments

• Multiple risk assessment applications beyond setting exposure limits

Offline Tools to Facilitate Computation:APROBA and APROBA-plus Excel Tools

APROBA: calculates HDMI and Prob RfD

(Part of WHO/IPCS Framework)APROBA-plus: adds capability to compare with exposure distributions

https://www.researchgate.net/publication/326422432_APROBA_PLUS-V100_v012_TEMPLATEhttps://www.who.int/ipcs/methods/harmonization/areas/hazard_assessment/en/

WHO/IPCS, 2017 Bokkers et al., 2017

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Presenter 2 | AM06

https://www.researchgate.net/publication/326422432_APROBA_PLUS-V100_v012_TEMPLATE

Online Tools to Facilitate Computation:APROBAweb Rshiny App

19

https://wchiu.shinyapps.io/APROBAweb/Chiu et al., 2018

Online Tools to Facilitate Computation: Bayesian Benchmark Dose Web Portal

20

BBMD analysis of datasets

Proceed to “RfDEstimate”

Distributional estimates of POD

HDMI Distribution

Dose-response function (dose versus I)

https://benchmarkdose.org/

Chiu et al., 2018

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Presenter 2 | AM06

https://wchiu.shinyapps.io/APROBAweb/

Traditional RfD Probabilistic RfDIs a population

incidence I < 1% for the reported critical effect adequately protective?

Derive probabilistic RfDfor smaller value of I

and/or less severe effect

When comparing to exposure estimate, is

HQ with the Traditional RfD < 0.1?

Derive probabilistic RfDfor I = 1% and the

reported critical effect

Risk is likely to be adequately protective with >95% confidence

When comparing to exposure estimate, is

HQ with the Probabilistic RfD < 1?

Evaluate options:

Risk is adequately protective at >95%

confidence

Yes

Yes

No

No Yes

No

• Reduce exposure to HQ < 1

• Reduce uncertainty in exposure estimates

• Reduce uncertainty in dose-response estimates

Typical Priorities for Reducing Dose-Response Uncertainty1. Replace LOAEL/NOAEL with

BMD modeling2. Conduct study/studies to

estimate human variability3. Conduct chronic study to

replace subchronic study4. Conduct study(ies) to estimate

animal-human extrapolation

Higher Tiered Assessment

Example Risk Assessment/Risk Management Workflow Incorporating Traditional RfD, Probabilistic RfD, and Tiered Uncertainty Reduction

Chiu et al., 2018

Applications: Exposure Limits and Beyond

Key Opportunities of Probabilistic Dose-Response Assessment• Forms a more rigorous and transparent basis for reference values • Better characterizes uncertainty and variability• Potential broad application across different types of toxicity values

(RfDs, RDAs, ADIs, cancer slope factors, etc.)• Provides complete intake-response function• Enables assessment of risk-benefit and risk-risk trade-offs, economic

cost–benefit analysis

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Presenter 2 | AM06

RDA UL

Intake

Risk

of A

dver

se E

ffect

(Tox

icity

)

Risk

of A

dver

se E

ffect

(Def

icien

cy)

Deficiency

Could Apply to Nutrition, and Thereby Evaluate Risk-Benefit Trade-Offs

RDA UL

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

0.01 0.1 1 10Human Boron Dose (mg/kg-d)

% Decrease in fetal weight (1% most sensitive human)

UCL

Fit

LCL

Magnitude, Incidence of

Deficiency vs. Adverse Effects

More rigorous than CatReg-based methodology, as it accounts for dose-response of each effect

Could Apply to Life Cycle Impact Assessment

• Primarily used for comparative risk assessments of life cycle impacts

• Requires slope of dose-response curve, not a “safe dose” level

24

Fantke et al., 2018

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Presenter 2 | AM06

Summary and Conclusion

WHO/IPCS probabilistic approaches provides a substantial advance in quantitatively addressing uncertainty and variability in dose-response assessment• Addresses numerous reasons “Why is traditional deterministic dose-

response assessment problematic?”• WHO/IPCS guidance provides a comprehensive framework for “What

is probabilistic dose-response assessment?”• Numerous online and offline software tools and applications

demonstrate “How can probabilistic dose-response assessment be performed and used in risk assessment?”

References• Bokkers BGH, Mengelers MJ, Bakker MI, Chiu WA, and Slob W. (2017). APROBA-Plus: A Probabilistic Tool to Evaluate and

Express Uncertainty in Hazard Characterization and Exposure Assessment of Substances. Food Chem Toxicol, 110, 408–417. doi:10.1016/j.fct.2017.10.038.

• Chiu WA, Axelrad DA, Dalaijamts C, Dockins C, Shao K, Shapiro AJ, Paoli G. (2018). Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects. Environ Health Perspect, 126(6), 067009. doi:10.1289/EHP3368.

• Chiu WA, and Slob W. (2015). A Unified Probabilistic Framework for Dose-Response Assessment of Human Health Effects. Environ Health Perspect, 123(12), 1241–1254. doi:10.1289/ehp.1409385.

• Fantke P, Aylward L, Bare J, Chiu WA, Dodson R, Dwyer R, … McKone TE, (2018). Advancements in Life Cycle Human Exposure and Toxicity Characterization. Environ Health Perspect, 126(12), 125001. doi:10.1289/EHP3871.

• NAS. (1994). Science and Judgment in Risk Assessment. Washington, DC: National Academy Press.• NAS. (2009). Science and Decisions: Advancing Risk Assessment. Washington DC: National Academy Press.• NAS. (2014). A Framework to Guide Selection of Chemical Alternatives. Washington, DC: National Academy Press.• WHO/IPCS. (2017). Guidance Document on Evaluating and Expressing Uncertainty in Hazard Characterization. Geneve: World

Health Organization International Program on Chemical Safety Retrieved from http://www.who.int/ipcs/methods/harmonization/areas/hazard_assessment/en/.

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Presenter 2 | AM06

Modeling Dose-Response across Populations: Quantification of Inter-Individual Variability

Alison Harrill, PhDNIEHS

Research Triangle Park, NCPhone: 984.287.3138




Content is the sole responsibility of the author and does not necessarily represent the views or policies of the National Institutes of Health of the Government of the United States of America.

1

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Presenter 3 | AM06

Abbreviations• CC: collaborative cross• DNT: developmental neurotoxicity• DO: diversity outbred• EC50: effective concentration for 50% of maximal effect• ESC: embryonic stem cell• FR: flame retardant chemical• LOAEL: Lowest-Observed-Adverse-Effect-Level• MUGA: mouse universal genotyping array• NOAEL: No-Observed-Adverse-Effect-Level• NPC: neural progenitor cell• POD: point of departure• SNP: single-nucleotide polymorphism• TDVF: toxicodynamic variability factor

People Respond Differently to Xenobiotics

Harrill 2016. Mouse Population-Based Toxicology for Personalized Medicine and Improved Safety Prediction. Drug Discovery Toxicology: From Target Assessment to Translational Biomarkers. Wiley.

Not everyone responds to a drug or chemical the same way

3

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Presenter 3 | AM06

Inter-Individual Differences in Toxicity Responses

• Root causes• Differences in chemical/drug exposure• Susceptible life stage (old versus young)• Comorbidities• Lifestyle factors• Genetics

• Drug metabolism and transporter genes• Genes involved in pharmacodynamic processes

• Precision medicine• Everyone has a “rare” disease• E.g., subtypes of diabetes and

metabolic disease or cancers

Probabilistic Dose-Response in Population Space

• For safety assessment, it’s important to appreciate population dynamics so that dose thresholds protect the most sensitive people in the population

• Risk assessors need to account for shifts in dose-response across populations, but these require model systems that provide input parameters that inform exposure risks

5

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Presenter 3 | AM06

Consider Genetic Variation in Risk Assessment

Genetic SequenceEpigenome

EnvironmentHealthStatus

• Gene x environment differences are important

• 2009: NRC—for risk assessment “attention should be directed to vulnerable individuals and subpopulations that may be particularly susceptible”

• 100s of drug labels contain drug-gene interaction information

• Abacavir—HLA-B*5701 and hypersensitivity

• Warfarin—CYP2C9 and VKORC1 genotypes require lower initiation dose

Dose Shifts across Individuals in a Population

Dose

Res

pons

e (p

erce

nt)

EC50

5 10 15 20 25 30 350

50

100 EC50 may differ widely across individuals within a population

A single strain “snapshot” may not be adequately protective for sensitive subpopulations

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Presenter 3 | AM06

Considering Inter-Individual Variability for Chemicals?

• In risk assessment for chemicals, inter-individual variability is captured via uncertainty factors

• Use of genetically diverse populations for screening informs a data-driven uncertainty factor

÷10 ÷10

inter-individualvariability

inter-speciesvariability

A historical factor of 10 is used to capture variability among humans

Is it protective enough?

Can use a probabilistic approach to replace the default factor with data-driven uncertainty factorsRodent → Human Human → Human

EC50animalEC50human

BMD10human

Genetic Variability

• Genetic variability within a species (e.g., mouse or human) and in spatially/geographically distant groups arises from selective pressures or random mutations that are passed on to offspring, as well as genetic drift

• Can be interrogated in model systems; however, the system has tohave some genetic variability present

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Presenter 3 | AM06

Genetic Variability and Toxicology

• Most guideline toxicity testing is done in a limited genetic context• Rodent strains or stocks

• In vitro—few human donors• Genetically different• But not representative of the population at large

Animal Models Used in Toxicology Are Not DiverseInbred Strain• Isogenic—immortal clones of genetically identical

individuals• Complete DNA sequence known for many

strains• Strain information can be archived and

accumulated over time for many drugs• Low signal/noise ratio in experiments when other

factors well controlled• Examples:

• C57BL/6J• Balb/cByJ• 129S1/SvImJ

Common Outbred Stock• Genetically undefined• Phenotypic variation greater than single inbred

strain, but less than when using multiple inbred strains

• Phenotypic characteristics of stock can change rapidly due to random genetic drift, selective breeding, genetic contamination

• Stocks of same name from different breeders are likely different, affects cross-lab validation

• Examples: • Sprague Dawley rats• CD-1 miceC57BL/6J Sire

=C57BL/6J Male Sibling

C57BL/6J Male Offspring=

C57BL/6J Male Offspring

Inbred mice are genetically identical

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Presenter 3 | AM06

• Models to consider:• Human cell lines or epidemiological data

• Genetically diverse nonclinical species• Rodent (mouse or rat)• Drosophila (flies)• C. elegans (nematodes)• Zebrafish

Increase genetic diversity via inclusion of divergent strains with a variety of polymorphisms

Human-Relevant Toxicology Requires Diverse Models

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Presenter 3 | AM06

CAST/EiJ WSB/EiJC57BL/6J PWK/PhJA/J 129S1/SvImJ NZO/H1LtJNOD/LtJ

Heterozygous at most loci; different

allele from dam and sire

Uses for DO Mice in Chemical Risk Assessment

Mode of ActionHazard ID

Elem

ent

Pote

ntia

l App

roac

hes f

or P

opul

atio

n-Ba

sed

Risk

Ass

essm

ent

Predict adverse effects that

only occur in genetically sensitive

individuals

Identify hazards that conventional models may

miss

Inform extrapolation of rodent to human via data

to replace standard uncertainty factors

Elucidate shape of dose-response relationship for

variety of endpoints in populations

Quantify threshold doses and BMDL10 for adverse

events that occur in sensitive individuals

Dose-Response

Estimate population risk with data-driven

relationship between exposure and dose

Elucidate interplay between variability

in toxicokinetics with variable

toxicodynamics

’Omics platform identification of key molecular changes

associated with increased risk

Identify genetic sequence variants

that underlie toxicity sensitivity

Exposure Assessment

Establish exposure

biomarkers for biomonitoring

Measure population-

wide differences in toxicokinetics

to estimate internal dose

Harrill and McAllister, Environmental Health Perspectives 2017

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Presenter 3 | AM06

• JWH-018 (spice/K2) is a synthetic cannabinoid drug that is a more potent CB receptor agonist than THC

• Wide variability in tetrad testsChange�in�body�temperature

0

5

10

15

20

day 1 day 2 day 3 day 4 day 5

# of

ani

mal

s

Day of injection

Convulsions per day (not rated)

Convulsions observed in some Dos—this has never been reported with conventional mouse strains (Balb/c) for this dose of JWH-018

Harrill and Fantegrossi, unpublished data

N=50

DO Mice Detect Sensitive Responders: Neurotoxicity

Change in body temperature

DO Mice Can Detect Human-Relevant Kidney Injury

ANOVA P<0.0001*P<0.05 for post hoc comparison to vehicle

Significant difference between vehicle treated group andValproic Acid (N=50 mice)

Observed severe hydronephrosis in a few animals by gross pathology

Animal 2058

BUN 35 U/LALT 26 U/LAST 65 U/L

Animal 2358


Animal 2057


Valproic acid is known to be associated with Fanconi’s syndrome: impairment of the proximal tubule and reabsorption of Na, bicarbonate, K, Phosphate, Glucose, Amino acids, Uric Acid, low MW proteins and peptides

Valproic acid use has led to kidney injury observed in children

ANOVA P<0.0001*P<0.05 for post hoc comparison to vehicle

Significant difference between vehicle treated group andValproic Acid (N=50 mice)

Observed severe hydronephrosis in a few animals by gross pathology

Animal 2058


Animal 2358


Animal 2057


Valproic acid is known to be associated with Fanconi’s syndrome: impairment of the proximal tubule and reabsorption of Na, bicarbonate, K, Phosphate, Glucose, Amino acids, Uric Acid, low MW proteins and peptides

Harrill Group, unpublished data

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Presenter 3 | AM06

Vehicl

e

Cisplat

in0

50

100

150

BU

N m

g/dL

Vehicle Cisplatin

BUN

Evaluate Biomarker Performance

Benchmark Biomarkers to Pathology

Grade�0

Grade�1 Grade�2

A B

C D

Vehicle

Cisplatin

Cisplatin

Vehicle

Harrill et al., Experimental Biology and Medicine 2017

DO Mice Can Detect Human-Relevant Kidney Injury

DO Can Detect Idiosyncratic, Human-Relevant Liver InjuryALT AST Total Bilirubin

Female DO mice administered zileuton for 7 days at 300 mg/kg e.g., less than human maximum therapeutic dose by allometric scaling

Susceptibility Mode of Action

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Presenter 3 | AM06

DO Can Model Idiosyncratic Drug-Induced Liver Injury

• Idiosyncratic toxicities have been difficult to study because dose-response relationship is unclear and because mode of action is debated

• DO mice provide a model to detect sensitive responders

Green tea extract containing supplements causes rare and non-dose-dependent liver injury in susceptible people

Resistant

Responders

Extreme responders “idiosyncratic” DILI

Church et al., Food and Chem Tox. 2015

Clinical Chemistry in DO—Not Different Than Conventional Mice

B6C3F1: Handbook of Toxicology, 3rd Ed. From NIEHS DataMouse: From vet school pages of UMN and WikiVet, and *UPenn

AnalyteDO

Reference Range

B6C3F1 Reference

Range

"Mouse" Reference

RangeAnalyte

DO Reference

RangeAlbumin (g/dl) 2.4 - 3.2 2.5 - 4.2 2.5 - 3.0 Sperm concentration 0 - 27.7ALP (U/L) 35 - 97 20 - 85 35 - 96 Sperm motility 19.4 - 79.8ALT (U/L) 11 - 46 20 - 50 17 - 77 Path velocity 80.2 - 178.6Anion Gap 8.8 - 30.8 Progressive velocity 50.3 - 169.5BUN (mg/dl) 16 - 39 12 - 34 8 - 33 Track speed 157.6 - 300.4Ca (mg/dl) 8.6 - 9.8 7.1 - 10.1 Lateral Amplitude 8.7 - 14.0Cholesterol (mg/dl) 72 - 96 80 - 130 50 - 250 Beat frequency 28.3 - 42.7CK (U/L) 24 - 270 Straightness 62.8 - 88.9Cl (mEq/dl) 108 - 118 88 - 110 Linearity 29.5 - 63.9CO2 (mEq/L) 13 - 33 % Hyperactivity 0 - 8.08Creatinine (mg/dl) 0.1 - 0.2 0.2 - 0.8 0.2 - 0.9Fasting glucose (mg/dl) 69 - 157 81 - 165 62 - 175Glob (g/dl) 1.6 - 2.7 Glucose AUC 31283 - 64065HDL (mg/dl) 47 - 113 Glucose AUC/mg 461.1 - 1076.1Iphos (mg/dl) 4.8 - 9.8 5.7 - 9.2 Fasting T0 glucose 69 - 157K (mEq/dl) 4.2 - 7.4 3.6 - 7.3 5.0 - 7.5 Glucose T0/T180 0.33 - 1.23LDL (mg/dl) 6-22Na (mEq/dl) 145 - 155 147 - 163 140 - 160NEFA (mEq/dl) 0.8 - 2.1 Wk 1 insulin (ng/ml) 0.112 - 3.19SDH (U/L) 9.9 - 32.9 18 - 57 Wk 14 insulin (ng/ml) 0.0727 - 3.49Total bile acids (uMol/L) 0.4 - 4.2 Wk 1 leptin (ng/ml) 0.350 - 5.20Total protein (mg/dl) 4.2 - 5.3 4.0 - 6.0 3.5 - 7.2 Wk 14 leptin (ng/ml) 0.415 - 17.20Triglycerides (mg/dl) 69 - 388

Data based on 75 adult male DO mice maintained on D12450J diet.

Harrill et al., NTP Research Report 6. 2018

Conservation of biology

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Presenter 3 | AM06

Considering Inter-Individual Variability for Chemicals?

• In risk assessment for chemicals, inter-individual variability is captured via uncertainty factors

÷10 ÷10

inter-individualvariability

inter-speciesvariability

A historical factor of 10 is used to capture variability among humans

Is it protective enough?

Can use a probabilistic approach to replace the default factor with data-driven uncertainty factors

Rodent → Human Human → Human

EC50animalEC50human

BMD10human

Use the DO Mice to Model Dose Shifts across Population

Dose

Res

pons

e (p

erce

nt)

EC50

5 10 15 20 25 30 350

50

100 Because DO mice are genetically unique, they respond differently to the same stimulus

We can use this information to replace default uncertainty factors

23

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Presenter 3 | AM06

DO ES Cells isolated and differentiated into neural progenitor cells

Investigating Developmental Neurotoxicity Using DO

NPC ES

Figure�12�

Nestin positive NPCs Secondary Antibody Control

DAPI SSEA1

Nes n��

ES�cells�but�not�NPCs�express�the�ES�marker�SSEA1�

Markers�for�differen a on�to�NPCs�

GLAST�

mES� NPC�

NPCs are positive for Nestin and GLAST expression and negative for SSEA1 expressionTed Choi, Predictive Biology

Experimental Methods Development: Ensuring Diversity Using SNP Data

Hierarchical clustering of

cell lines

Kinship matrix of cell lines using SNP data derived from the Mouse Universal Genotyping Array

Harrill Group, Unpublished Data

• Cell lines colored by mating set identifier

• Highest relatedness within mating trios

• Relatively low levels of relatedness across cell lines, indicating that diverse genetic backgrounds are represented

• 200 lines (100 male/100 female)

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Presenter 3 | AM06

• Pilot Project:• 200 DO cell lines (100 male, 100 female)• 6 chemicals• 12 dose concentrations (up to 100 uM)• Alamar Blue

FR: flame retardant chemical

Utilizes experimental and computational framework for assessing population dynamics in response to neurotoxic agents

Proof of Concept: Developmental Neurotoxicity

Rotenone� 2,2’,4,4’,5-pentabromodiphenyl�

ether

Methyl�mercuric(II)�chloride

Dieldrin Ethinyl estradiol Phenol,�isopropylated,�phosphate�(3:1)

Sel

ecte

d ch

emic

als

Mitochondrial toxicity Phased-out FR Known DNT

Known DNT Mixed evidence on estrogenic neuro FX

Replacement FR

DO Mating TriosES cells isolated ESC

NPC

100 male100 female

Identical NPC 12-dose-response plates6 chemicals

42hAlamar blue

114hAlamar blue

0–114hReal-time cell

analysisGrowth data

24hLysis buffer added

/frozenTempO-Seq

Study pending

MUGA, RNA-Seq

RNA-Seq

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Presenter 3 | AM06

Framework for Using Population Data for DNTDO Neural Progenitor Cell Lines

Measure cytotoxicity

Calculate EC10 for each parameter and cell line

Calculate default distribution (if data normal and unimodal; prior)

Chemical-specific toxicodynamic variability estimation

Determine level of protection and level of conservatism

Assess impacts on uncertainty factors

Method derived from: Chiu et al. ALTEX 2017

Population Dynamics in Dose Range: Sex Differences• Most distributions are approximately

normal on log10 scale.

• Cytotoxicity/alamar blue at 114h.

• A “Reference” Cell line was repeated across experimental plate batches. Repeated reference cell line (gray) has a narrower distribution versus the population distributions of males (pink) and females (blue).

BDE99 - m

ale

BDE99 - f

emale

BDE99- re

feren

ce

Dieldrin

- male

Dieldrin

- fem

ale

Dieldrin

- refe

rence

Estrad

iol - male

Estrad

iol - fem

ale

Estrad

iol - ref

erence

IPP - male

IPP - fem

ale

IPP - refe

rence

MeHgCl -

male

MeHgCl -

female

MeHgCl -

refere

nce

Rotenone -

male

Rotenone -

female

Rotenone -

refer

ence

-3

-2

-1

0

1

2

log(

EC10

)

LogEC10BDE99 Dieldrin Estradiol IPP MeHgCl Rotenone

M F Ref M F Ref M F Ref M F Ref M F Ref M F Ref


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Presenter 3 | AM06

Population Dynamics: Combining Male and Female

BDE99

BDE99- re

feren

ce

Dieldrin

Dieldrin

- refe

rence

Estrad

iol

Estrad

iol - ref

erence IPP

IPP - refe

rence

MeHgCl

MeHgCl -

refere

nce

Rotenone

Rotenone -

refer

ence

-3

-2

-1

0

1

2

log(

EC10

)

LogEC10 - Both SexesBDE99 Dieldrin Estradiol IPP MeHgCl RotenoneM&F Ref M&F Ref M&F Ref M&F Ref M&F Ref M&F Ref

• Most distributions are approximately normal on log10 scale.

• Cytotoxicity/alamar blue at 114h.

• A “Reference” Cell line was repeated across experimental plate batches. Repeated reference cell line (gray) has a narrower distribution versus the population distributions of males (pink) and females (blue).

• Methylmercury and rotenone toxicity have wide populations distributions in the dose-response.


Using Population Dose-Response Data to Quantify Toxicodynamic Variability

EC10,50

EC10,01

Toxicodynamic Variability FactorTDVF01 = (EC10,50 / EC10,01)TDVF05 = (EC10,50 / EC10,05)

Toxicodynamic Variability MagnitudeTDVM = log10(TDVF)

The default fixed uncertainty factor at US EPA for toxicodynamic variability is 101/2

Corresponds to:TDVF = 3.16TDVM = 0.5

WHO/IPCS 2005

Method: Chiu et al., ALTEX 2017

Coverage

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Presenter 3 | AM06

Bayesian Modeling—TDVF Confidence Intervals + EC10

TDVF05 with 90% confidence intervals

Chemical Endpoint TDVF05 (90% CI)

Data-Driven TDVFDirectionFrom3.16

BDE 99 AB 114 h 2.39 (2.00, 2.96) Dieldrin AB 114 h 2.80 (2.42, 3.33)Estradiol AB 114 h 1.82 (1.66, 2.05) IPP AB 114 h 1.71 (1.60, 1.86) MeHgCl AB 114 h 26.9 (10.3, 109) á

Rotenone AB 114 h 11.2 (7.51, 19.1) á

The default fixed uncertainty factor for toxicodynamic variability is

TDVF = 3.16*Preliminary data for research purposes only: not to be used for decision-making

Compare Mouse TFVF with Human TDVF

• Compared TDVF calculated for human LCL studies to mouse NPC TDVF

Chemical Human TDVF Mouse TDVF05 and 90% CIMethylmercury chloride 16.03 11.2 (7.51, 19.1) Dieldrin 3.76 2.80 (2.42, 3.33)

Factors affecting the estimates:Human > 1,000 individuals usedMouse ~90 individuals used after data QC

More individuals = more accurate estimate of population variability

DO mice have more randomized genetic polymorphisms with a 12.5% average minor allele frequency—fewer individuals needed versus human, although species-specific differences are unavoidable

Human data: Chiu et al., ALTEX 2017

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Presenter 3 | AM06

Put the Data in Context for Risk Assessors

Suspicion of neurotoxicity via

conventional screens

In vivo Assessment(animals; epidemiological

evidence)

In vitro Assessment(rodent/human cells; zebrafish; plenaria)

In vitro DO cell analysis to determine population variability in response

Inform RfD estimate

IPP Estradiol BDE99 Dieldrin MeHgCl

2.71 4.36 4.79 4.80 11.34

Pilot Project – DO Toxicodynamic Variability Factors

Default factor:3.16

Identify point of departure (dose)

Other Cell Types Available for Population Studies

• DO/CC:• Embryonic stem cells• Neural progenitor cells (NPCs)• Glial cells• Cardiomyocytes• Further engineering will be required to generate other cell types

• Human:• Lymphoblastoid cell lines (LCLs)• iPSC (several international stem cell banks)• Patient-derived cells for targeted hypotheses

• E.g., Do cells derived from Alzheimer’s patients respond differently to test chemical?

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Presenter 3 | AM06


• Population-based models enable detection of human-relevant hazards that would be missed by conventional models

• Genetic variation contributes to dose-shifts that may affect to which exposure threshold a chemical should be limited

• Calculation of a chemical-specific uncertainty factor using a probabilistic approach may enable protection of genetically sensitive subpopulations

References• Chiu WA, Wright FA, Rusyn I. 2017. A Tiered, Bayesian Approach to Estimating of Population Variability

for Regulatory Decision-Making. ALTEX. 34(3):377–388.• Church RJ, Gatti DM, Urban TJ, Long N, Yang X, Shi Q, Eaddy JS, Mosedale M, Ballard S, Churchill GA et

al., Sensitivity to Hepatotoxicity Due to Epigallocatechin Gallate Is Affected by Genetic Background in Diversity Outbred Mice. Food Chem Toxicol. 2015; 76:19–26. 10.1016/j.fct.2014.11.008.

• Churchill GA, Gatti DM, Munger SC, Svenson KL. The Diversity Outbred Mouse Population. MammGenome. 2012; 23(9-10):713–718. 10.1007/s00335-012-9414-2.

• Harrill AH, Borghoff S, Zorrilla L, Blystone C, Kissling GE, Malarkey D, Shockley K, Travlos G, DeVito MJ. 2018. NTP Research Report on Baseline Characteristics of Diversity Outbred (J:DO) Mice Relevant to Toxicology Studies: Research Report 6. Durham (NC).

• Harrill AH, Lin H, Tobacyk J, Seely JC. Mouse Population-Based Evaluation of Urinary Protein and miRNA Biomarker Performance Associated with Cisplatin Renal Injury. Exp Biol Med (Maywood). 2018;243(3):237–247. doi:10.1177/1535370217740854.

• Harrill AH, McAllister KA. New Rodent Population Models May Inform Human Health Risk Assessment and Identification of Genetic Susceptibility to Environmental Exposures. Environ Health Perspect. 2017; 125(8):086002. 10.1289/EHP1274.

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Simulation of Population Variability in High-Throughput Toxicokinetic Modeling in Support

of Dose-Response AssessmentCaroline Ring

ToxStrategies Inc.Austin, TX

Phone: 512.298.1307Email: [email protected]



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Abbreviations

• AC50: concentration at 50% of maximum activity in an in vitro assay

• AER: activity-exposure ratio• BW: body weight• BMI: body mass index• CDC-NHANES: Centers for Disease Control,

National Health and Nutrition Examination Survey

• GFR: glomerular filtration rate• HT: high-throughput• SEEM3: Systematic Empirical Evaluation of

Models, version 3

• TK: toxicokinetics• US EPA: United States Environmental Protection

Agency

Motivation: Chemical Prioritization Based on Potential Risk

Dose with potentially adverse effect

Potential exposure

Lower Medium Higher Priority

Distribution of doses across population

Distribution of potential exposures across population

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Motivation: High-Throughput Risk-Based Chemical Prioritization for Large Numbers of Chemicals


Potential exposure

Lower Medium Higher Priority

Chemicals may not have in vivo tox studies—estimate using in vitro HT screening assays (e.g., ToxCast/Tox21)

Chemicals may not have detailed exposure data—estimate using HT exposure models (e.g., US EPA SEEM3) (Ring et al., 2019)

How to Relate In Vitro Bioactivity to In Vivo Toxicity and Potential Risk—For a Population?


Potential exposure

Lower Medium Higher

?

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Toxicokinetics (TK): Relate Internal Body Concentration to External Dose/Exposure

External dose

Body concentration

(assumed same as in vitro active

conc.)

Toxicokinetic model:AbsorptionDistributionMetabolism

Excretion

High-Throughput TK: Generic Models That Can Be Parameterized for Many Chemicals with Minimal Chemical-Specific Data Requirements

Gut

Liver

Rest of Body

Volume of Distribution

Lung

Gut

Liver

Rest of Body

Kidney

Arterial BloodVeno

us B

lood

1-compartment model

3-compartment model

Multi-compartment model

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R Package httk: A Free, Open-Source Tool to Do High-Throughput TK

At R command line:> install.packages(“httk”)> library(httk)

Parameters for High-Throughput TK Models

Chemical-specific parametersIntrinsic hepatic clearance rate Measured in HT in vitro assays

(Wetmore et al., 2012, 2014, 2015)Fraction unbound to plasma proteinTissue:blood partition coefficients (for compartmental models)

Predict from phys-chem properties and tissue properties (Pearce et al., 2017)

Physiological parametersTissue masses These vary (and covary) across a human

population!

Correlated Monte Carlo approach:Simulate joint distribution

based on available population data (Ring et al., 2017)

Tissue blood flowsGlomerular filtration rate (passive renal clearance)

Hepatocellularity

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Population Toxicokinetics: Relate Internal Body Concentration to Population Distribution of External Doses/Exposures

External dose

Population TK

Body concentration

(assumed same as in vitro active

conc.)

Population distribution

How to Relate In Vitro Bioactivity to In Vivo Toxicity and Potential Risk—For a Population


Potential exposure

Lower Medium Higher

Population TK

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HTTK-Pop: A Module in httk to Simulate Population Variability in Physiological TK Parameters—Based on Real Data

Demographic and body measures

Tissue masses and blood flows Other parameters

Sex (male or female) Blood Pancreas GFRAge (0–80 years) Brain Skeleton (bone) HepatocellularityRace/ethnicity (5 broad categories)

Gonads Skin Skin surface area

Height Heart Small intestine Serum creatinineBody weight Kidneys Spleen Hematocrit

Large intestine StomachLiver Adipose tissueLung Other tissueSkeletal muscle Cardiac output

HTTK-Pop Simulates Population Physiology Based on Data from CDC-NHANES

Large, ongoing survey of US population: demographics, body measures, medical exam, biomonitoring (health and exposure) . . .Designed to be representative of US population according to census dataDatasets publicly available (http://www.cdc.gov/nchs/nhanes.htm)

Centers for Disease Control, National Health and Nutrition Examination Survey

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To Simulate a Population for TK, HTTK-Pop Samples from Actual NHANES Data

(2007–2012; 24,456 Total Respondents)SEQN Sex Age Race/

EthnicityHeight (cm) Weight (kg) Serum creatinine Hematocrit

52019 Female 32 Non-Hispanic White 164.9 65.9 0.64 41.370597 Female 49 Other 163.4 70.1 0.58 37

65513 Male 32 Mexican American 177.9 134.9 0.64 44.5

56381 Male 23 Non-Hispanic Black 180.9 78.9 1.29 43.3

68209 Male 15 Other Hispanic 164.5 54.4 0.82 52.6

52019 Female 32 Non-Hispanic White 164.9 65.9 0.64 41.3

70597 Female 49 Other 163.4 70.1 0.58 37[… and so on, for the number of individuals requested in the simulated population]

HTTK-Pop Can Simulate Populations withUser-Specified Demographics

User can specify . . . Default, if not specifiedAge limits 0–79 yearsSex (#males, #females) NHANES proportionsRace/ethnicity (5 NHANES categories)

NHANES proportions

BMI categories NHANES proportions

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HTTK-Pop Uses NHANES Data + Literature Regression Relations to Simulate Population TK

Predict non-NHANES quantities

Tissue massesTissue blood flowsGFR (kidney function)Hepatocellularity

Sample NHANES quantities

SexRace/ethnicityAgeHeightWeightSerum creatinineHematocrit

Regression equations from literature

(+ residual marginal variability)

(Similar approach used in SimCYP [Jamei et al., 2009], GastroPlus, PopGen [McNally et al., 2014], P3M [Price et al., 2003], physB [Bosgra et al., 2012], etc.)

> httk::httkpop_generate(method = “direct resampling”, nsamp=1000)

HTTK-Pop Uses NHANES Data + Literature Regression Relations to Simulate Population TK

• Most tissue masses for adults are allometrically scaled with height (McNally et al., 2014)

• Reference masses and reference heights from Reference Man and Reference Woman (ICRP, 2002)

• Tissues for age < 18 predicted using regression relations from literature (Ogiu et al., 1997)

• Blood, brain, and bone masses for all ages predicted using regression relations from the literature

• Glomerular filtration rate predicted for age > 12 with CKD-EPI equation (function of sex, race/ethnicity, age, serum creatinine) (Levey et al., 2009)

• For age < 12, NHANES did not measure serum creatinine, so GFR was predicted using alternate equation based on body surface area (Johnson et al., 2006)

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HTTK-Pop Generates a Table of Simulated Individuals with Their TK-Relevant Physiology Parameters

SEQN Sex Age Race/Ethnicity

[NHANES quantities: height, weight, serum creatinine, hematocrit]

[Tissue masses and blood flows]

[GFR, hepatocellularity, body surface area…]

52019 Female 32 Non-Hispanic White … … …70597 Female 49 Other … … …

65513 Male 32 Mexican American … … …

56381 Male 23 Non-Hispanic Black … … …68209 Male 15 Other Hispanic … … …

52019 Female 32 Non-Hispanic White … … …70597 Female 49 Other … … …[… and so on, for the number of individuals requested in the simulated population]

httk Can Automatically Process HTTK-Pop Simulated Population Table to Produce the Parameters Expected by httk’s Built-In Generic TK

Models

• Arguments: population physiology table; chemical; which model• Tissues are lumped into the compartments defined by the model• Units are converted to those expected by httk model• Chemical-specific parameters are computed

> simpop <- httk::httkpop_generate(method = “direct resampling”, nsamp=1000) #generate simulated population> simpop_httk <- httk::get_httk_params(indiv_dt = simpop, chemcas = "57-91-0”, model = “3compartmentss”, poormetab = TRUE) #get population TK parameters for a specified chemical and model (this CAS is for 17-alpha-estradiol, chosen as an arbitrary example chemical)

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httk Can Automate All the Population TK Monte Carlo Steps in One Function Call

SEQN [Params]52019 […]70597 […]65513 […]56381 […]68209 […]52019 […]70597 […]

Gut

Liver

Rest of Body

> httk::calc_mc_oral_equiv(conc, chem.name, which.quantile, model, httkpop=TRUE, nsamp, …)

Simulate a population Solve TK model for steady-state body conc.for a given chemical Compute population dist. of equiv. doses

for a specified assay AC50

Compute equiv. dose for specified quantile of population (e.g., most sensitive 5%)

Example: Result of Using httk and HTTK-Popfor Rapid Chemical Prioritization in Overall US Population

Range of inferred median exposures(US EPA ExpoCast)

Range of 5th pctileequiv. doses across assays

Activity-exposure ratio, AER

Ring et al., 2017

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Chemicals by increasing AER for overall US population

<11

1 000

10 000

100 000

1 000 00010 000 000

AER for total population (order of magnitude)

HTTK-Pop Allows Computation of

AER Differences for Potentially Sensitive

Subpopulations

Red = higher priority than for overall US populationBlue = lower priority than for overall US population

Ring et al., 2017

AER

Exposure (ExpoCast) Equiv. dose

AER (Potential Risk) Differences Are Driven by Differences in Population TK for Age > 65, Age 6–11, and Age 12–19

Ring et al., 2017

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Brief Demonstration of How to Use httk and HTTK-Pop

[See R Markdown document httkpop_example.Rmdand its PDF output httkpop_example.pdf]


• HT risk-based chemical prioritization requires comparing HT estimates of toxicity (in vitro screening assays) to HT estimates of exposure

• Toxicokinetics translates between exposures and potentially bioactive internal body concentrations (or concentrations bioactive in vitro)

• httk is an open-source R package for high-throughput TK• HTTK-Pop is a module within httk to simulate population TK

parameters based on CDC-NHANES data• httk + HTTK-Pop = Monte Carlo estimates of potential risk for most

sensitive portion of a population, or for potentially sensitive subpopulations

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Any Questions?

Contact information:

Caroline RingToxStrategies Inc.Austin, TXPhone: 512.298.1307Email: [email protected]

References• Ring CL, Arnot JA, Bennett DH, Egeghy PP, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips KA, Price PS, Shin H-M, Westgate

JN, Setzer RW, Wambaugh JF. 2019. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. Environ Sci Technol 53:719–732. https://doi.org/10.1021/acs.est.8b04056.

• Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF. 2017. Identifying Populations Sensitive to Environmental Chemicals by Simulating Toxicokinetic Variability. Environ Int 106:105–118. http://dx.doi.org/10.1016/j.envint.2017.06.004.

• Pearce RG, Setzer RW, Strope CL, Sipes NS, Wambaugh JF. 2017. httk: R Package for High-Throughput Toxicokinetics. J Stat Soft 79(4). https://doi.org/10.18637/jss.v079.i04.

• Wambaugh JF, Setzer RW, Reif DM, Gangwal S, Mitchell-Blackwood J, Arnot JA, Joliet O, Frame A, Rabinowitz J, Knudsen TB, Judson RS, Egeghy P, Vallero D, Cohen Hubal EA. 2013. High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project. Environ Sci Technol 47(15):8479–88. https://doi.org/10.1021/es400482g.

• Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, Clewell HJ, Dix DJ, Andersen M, Houck KA, Allen B, Judson RS, Singh R, Kavlock RJ, Richard AM, Thomas RS, Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment. Toxicological Sciences. 125:157–74; 2012.

• Wetmore BA, Allen B, Clewell HJ, Parker T, Wambaugh JF, Almond LM, Sochaski MA, Thomas RS, Incorporating Population Variability and Susceptible Subpopulations into Dosimetry for High-Throughput Toxicity Testing. Toxicological Sciences. 142:210–24; 2014.

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References• Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A, The Simcyp Population-Based ADME Simulator.

Expert Opinion on Drug Metabolism & Toxicology. 5:211–23; 2009.• Price PS, Conolly RB, Chaisson CF, Gross EA, Young JS, Mathis ET, Tedder DR, Modeling Inter-Individual Variation in

Physiological Factors Used in PBPK Models of Humans. Critical Reviews in Toxicology. 33:469–503; 2003.• McNally K, Cotton R, Hogg A, Loizou G, PopGen: A Virtual Human Population Generator. Toxicology. 315:70–85; 2014.• Bosgra S, van Eijkeren J, Bos P, Zeilmaker M, Slob W, An Improved Model to Predict Physiologically Based Model

Parameters and Their Inter-Individual Variability from Anthropometry. Critical Reviews in Toxicology. 42:751–67; 2012.• ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values. Annals of the ICRP:

Pergamon; 2002.• Johnson TN, Rostami-Hodjegan A, Tucker GT, Prediction of the Clearance of Eleven Drugs and Associated Variability in

Neonates, Infants and Children. Clin Pharmacokinet. 45:931–56; 2006.• Levey AS, Stevens LA, Schmid CH, Zhang YP, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J,

Co, C.K.D.E. A New Equation to Estimate Glomerular Filtration Rate. Annals of Internal Medicine. 150:604–12; 2009.• Ogiu N, Nakamura Y, Ijiri I, Hiraiwa K, Ogiu T, A Statistical Analysis of the Internal Organ Weights of Normal Japanese

People. Health Phys. 72:368–83; 1997.

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