Abstract
Characterizing human variability in susceptibility to chemical toxicity is a critical issue in regulatory decision-making, but is usually addressed by a default 10-fold safety/uncertainty factor. Feasibility of population-based in vitro experimental approaches to more accurately estimate human variability was demonstrated recently using a large (~1000) panel of lymphoblastoid cell lines. However, routine use of such a large population-based model poses cost and logistical challenges. We hypothesize that a Bayesian approach embedded in a tiered workflow provides efficient estimation of variability and enables a tailored and sensible approach to selection of appropriate sample size. We used the previously collected lymphoblastoid cell line in vitro toxicity data to develop a data-derived prior distribution for the uncertainty in the degree of population variability. The resulting prior for the toxicodynamic variability factor (the ratio between the median and 1% most sensitive individuals) has a median (90% CI) of 2.5 (1.4-9.6). We then performed computational experiments using a hierarchical Bayesian population model with lognormal population variability with samples sizes of n = 5 to 100 to determine the change in precision and accuracy with increasing sample size. We propose a tiered Bayesian strategy for fit-for-purpose population variability estimates: (1) a default using the data-derived prior distribution; (2) a pilot experiment using samples sizes of ~20 individuals that reduces prior uncertainty by > 50% with > 80% balanced accuracy for classification; and (3) a high confidence experiment using sample sizes of ~50-100. This approach efficiently uses in vitro data on population variability to inform decision-making.
Highlights
The growing list of chemical substances in commerce and the complexity of exposures in the environment present enormous challenges for ensuring safety while promoting innovation
While characterization of human variability in susceptibility to chemical toxicity is a critical issue in toxicology, public health, and risk assessment, it is usually addressed by a generic 10-fold safety/uncertainty factor despite encouragement to generate and use chemical-specific data (WHO/IPCS, 2005)
3.1 Estimating population variability for each chemical For most chemicals, convergence was reached for all parameters with a chain length of 8000, where the first 4000 “warmup” samples of each chain were discarded, and the final 4000 samples were used for evaluation of convergence, model fit, and inference
Summary
The growing list of chemical substances in commerce and the complexity of exposures in the environment present enormous challenges for ensuring safety while promoting innovation. In addition to addressing only a fraction of the chemicals in commerce, current hazard testing approaches usually do not take into account the genetic diversity within populations, overlooking uncertainties about how genetic variability might interact with environmental exposures to affect risk (Rusyn et al, 2010). While characterization of human variability in susceptibility to chemical toxicity is a critical issue in toxicology, public health, and risk assessment, it is usually addressed by a generic 10-fold safety/uncertainty factor despite encouragement to generate and use chemical-specific data (WHO/IPCS, 2005). The recent use of population-based animal in vivo (Rusyn et al, 2010; Chiu et al, 2014) and human in vitro (Abdo et al, 2015a,b; Eduati et al, 2015; Lock et al, 2012) experimental models that incorporate genetic diversity provides an opportunity to more precisely estimate human variability and increase confidence in decision-making. The technical feasibility and the scientific and practical value of large-scale in vitro population-based experimental approaches to more accurately estimate human
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