Addressing Population Variability in Risk Assessment: Challenges and Opportunities SRP Risk e-Learning Webinar 31 May 2018 Weihsueh A. Chiu, PhD Texas A&M University 1
Conflict of Interest Statement Neither myself nor any of my coauthors, including members of our immediate families, have any financial interest or affiliation with a commercial organization that has a direct or indirect interest in the subject matter of my presentation. 2
Outline • Motivation for addressing population variability and susceptibility • Opportunities using emerging population- based in vivo, in vitro, and in silico approaches – Hazard identification and mechanisms of toxicity – Dose-Response Assessment • Challenges in risk characterization 3
Claudius Galenus (Galen of Pergamum) 129-217 AD “ But remember throughout that no external cause is efficient without a predisposition of the body itself. Otherwise, external causes which affect one would affect all. ” Slide courtesy of D. Threadgill
“ Uncertainty ” or “ Safety ” Factors NOAEL Percent Incidence of Response RfD = --------------- 100 Magnitude of response 100 75 50 UF A =10 UF H =10 25 0 UF A-TK =3 UF A-TD =3 UF H-TK =3 UF H-TD =3 Dose Dose (Avg. daily dose) UF H UF A RfD NOAEL 5
How well can we characterize variability? • Available for relatively few chemicals (~100). Source-to-Outcome Continuum Epidemiologic & • Limited power to examine population variability/susceptibility. clinical studies • Generalizing from occupational/patient cohorts to the population. Source/media concentrations • Available for relatively few chemicals (<1000). Exposure Animal • Uncertain interspecies differences. bioassays • Homogeneous (genetics, diet, etc.) experimental animals. External doses • Available for relatively few chemicals (~100 PBPK; <1000 total). Toxicokinetics TK • Few examples analyzing population variability or uncertainty. models Internal concentrations • Available for more chemicals (~10,000). • Uncertain relationship to health risk. Toxicodynamics • Genetically homogeneous in vitro systems. In vitro Biological response measurements assays, • Available for only a few endpoints (~10?). Systems Toxicity Adverse • Qualitative, not quantitative. dynamics • Most are artificially linear constructs. pathways Outcome Physiological/health status • Variability/susceptibility not included. Pathways • Available for relatively few chemicals (<1000). • Do not adequately address uncertainty, variability, susceptibility (10-fold factor). 6 Toxicity values and risk characterization • In most cases, do not explicitly estimate risk.
Population Variability in Susceptibility Remains a Risk Assessment Challenge Animals, in vitro, Humans or in silico data Individual Gas Inhalation/ Lung Exchange exhalation Slowly Perfused Predictions Rapidly Perfused for an Average Ingestion Fat Jérémy (France) B6C3F1 Hybrid Mice Kidney Male (or Female) Sto ma ch Martijn (Holland) Urine Inte stine Liver HeLa cells Yuki (Japan) Feces Metabolism PBPK models Todd (USA) J. HAMBLIN The Atlantic (Oct 10, 2013) ? Predictions for a Variable Population Population See review Chiu & Rusyn (2018) doi:10.1007/s00335-017-9731-6
New Population-Based Approaches and Tools • Genetically diverse mouse populations • Diversity Panel • Collaborative Cross, Diversity Outbred • Populations of human cells • Cell lines • Inducted pluripotent stem cells • Computational modeling of populations All involve studying populations instead of individuals in an experimental and/or computational setting.
Challenges for Hazard Identification Animals, in vitro, Humans or in silico data Individual Predictions ? for an Average Jérémy (France) B6C3F1 Hybrid Mice Male (or Female) Martijn (Holland) Yuki (Japan) • Todd (USA) J. HAMBLIN The Atlantic (Oct 10, 2013) • Human relevance of single strain rodent (positive and Predictions negative findings) for a Variable • No information about human Population population variability Population
Hazard Identification: Why Use Population-Based Models? Range of Human Mouse Responses Poor models of humans Good models of humans Extrapolation • Reduce chances of being “unlucky” and picking a strain that is a “poor” model of humans • Obtaining information about potential range of population variability
Extreme Transgressive Variation Average Daily Running Distance 20 preCC 129s1/SvlmJ A/J Average Running Distance (km) C57Bl6/J Cast/EiJ NOD/LtJ 15 NZO/HILtJ PWK/PhJ WSB/EiJ 10 5 Harrill & McAllister (2018) https://doi.org/10.1289/EHP1274 Mouse and Human Response Phenotypes to 0 Ebola Virus Infection AJP - Endocrinology and Metabolism, 2011 Mouse (47 strains) Human (n=86) Resistant or Resistant or Partially Partially Resistant Resistant Lethal w/o Lethal w/o HF HF Lethal w/ HF Lethal w/ HF Sources: Rasmussen et al. 2014 (mouse), McElroy et al. 2014 (human)
Hazard Identification: Proof of Principle Using Population-Based Mouse Models Might miss hazard 37 different inbred if only testing one mouse strains of these strains Humans 6 different inbred mouse strains (lower dose) Distributions of responses overlap 0.1 1 10 100 1000 Fold-Change in Serum ALT Alison H. Harrill et al. Genome Res. 2009;19:1507-1515
Challenges for Characterizing Mechanisms of Toxicity and Susceptibility Animals, in vitro, Humans or in silico data Individual Predictions ? for an Average Jérémy (France) B6C3F1 Hybrid Mice Male (or Female) Martijn (Holland) Yuki (Japan) ? Todd (USA) J. HAMBLIN The Atlantic (Oct 10, 2013) • Knockout studies probe one gene at a time Predictions • Difficult to distinguish inter- for a Variable and intra-species susceptibility Population differences Population
Population-Based Models to Investigate Mechanisms of Toxicity and Susceptibility Experiments with Genetically Diverse Populations Environ. Factors Toxicity Genes Genes/pathways associated with susceptibility or resistance to toxicity from environmental factors
Mechanisms of Toxicity and Susceptibility: Proof of Principle Using Population-Based Mouse Models Insights into Liver toxicity: Humans APAP (1 g every 6 hrs for 1 week) mechanism of toxicity Liver toxicity: Mouse population Confirmed CD44 in human Status cohorts Recovery Apoptosis & CD44 Candidate GWAS in Inflammation mice Susceptibility Gene Alison H. Harrill et al. Genome Res. 2009;19:1507-1515
Challenges for Dose-Response Assessment Animals, in vitro, Humans or in silico data Individual Predictions ÷10 for an Average Jérémy (France) B6C3F1 Hybrid Mice Male (or Female) Martijn (Holland) ÷ Yuki (Japan) Todd (USA) J. HAMBLIN The Atlantic (Oct 10, 2013) 10 • Single strain dose-response assumed to be representative of Predictions population for a Variable • 10-fold inter- and intra-species Population factors assumed to be adequate Population (conservative?)
Population Variability in Toxicokinetics NOAEL Population-based RfD = --------------- Physiologically-Based UF A x UF H Pharmacokinetic (PBPK) Models • Monte Carlo simulation • Bayesian approaches • Emerging experimental UF A =10 UF H =10 models UF A-TK =3 UF A-TD =3 UF H-TK =3 UF H-TD =3 17
Human population variability of trichloroethylene pharmacokinetics Bayesian Population Ratio of 95th percentile/ Source-to-Outcome Continuum 50th percentile PBPK Model individual Source/media concentrations parameters vary by individual [~50 individuals total] Human Exposure inter-individual variability External doses TCE oxidized 1.11 Toxicokinetics Inhaled air Exhaled air by P450 (1.05, 1.22) Respiratory Respiratory Tract Tissue Respiratory Internal concentrations Tract Lumen Tract Lumen Total TCA 2.09 (Inhalation) (Exhalation) Oxidation produced (1.81, 2.51) (Dead space) Toxicodynamics Gas Exchange TCE conj. with 6.61 Biological response IA Rapidly Perfused measurements GSH (3.95, 11.17) Oral Systems Slowly Perfused dynamics Stomach Fat Physiological/health status Venous Blood Duodenum Depending on the toxic moiety (which may be different Gut PV for different effects), humans could have very low or Liver very high variability. Oxidation & Conjugation Kidney IV Source: Chiu et al., 2009
Using a population of mouse strains to address TCE toxicokinetic variability B6C3F1 strain DBA/2J strain KK/HIJ strain Bayesian Population Source-to-Outcome Continuum TCA PBPK Model Source/media concentrations parameters vary by strain [17 strains total] Exposure DCA External doses Toxicokinetics Inhaled air Exhaled air Respiratory Respiratory Tract Tissue Respiratory Internal concentrations Tract Lumen Tract Lumen (Inhalation) (Exhalation) Oxidation DCVG (Dead space) Toxicodynamics Gas Exchange Biological response IA Rapidly Perfused measurements Oral Systems Slowly Perfused dynamics Stomach Fat Physiological/health status Venous DCVC Blood Duodenum Gut PV Liver Oxidation & Conjugation Kidney 19 IV Source: Chiu et al., 2014
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