The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics

High-throughput toxicogenomics as an advanced toolboxof Tox21plays an increasingly important role in facilitating the toxicityassessment of environmental chemicals. However, toxicogenomic dose-responseanalyses are typically challenged by limited data, which may resultin significant uncertainties in parameter and benchmark dose (BMD)estimation. Integrating historical data via prior distribution usinga Bayesian method is a useful but not-well-studied strategy. The objectiveof this study is to evaluate the effectiveness of informative priorsin genomic dose-response modeling and BMD estimation. Specifically,we aim to identify plausible informative priors and evaluate theireffects on BMD estimates at both gene and pathway levels. A generalinformative prior and eight time-specific (from 3 h to 29 d) informativepriors for seven commonly used continuous dose-response modelswere derived. Results suggest that the derived informative priorsare sensitive to the specific data sets used for elicitation. Realdata-based simulations indicate that BMD estimation with the time-specificinformative priors can achieve increased or equivalent accuracy, significantlydecreased uncertainty, and a slightly enhanced correlation with thepoints of departure estimated from apical end points than the counterpartswith noninformative priors. Overall, our study systematically examinedthe effects of historical data-based informative priors on BMD estimates,highlighting the benefits of plausible information priors in advancingthe practice of toxicogenomics.