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

High-throughput toxicogenomics as an advanced toolbox
of Tox21
plays an increasingly important role in facilitating the toxicity
assessment of environmental chemicals. However, toxicogenomic dose–response
analyses are typically challenged by limited data, which may result
in significant uncertainties in parameter and benchmark dose (BMD)
estimation. Integrating historical data via prior distribution using
a Bayesian method is a useful but not-well-studied strategy. The objective
of this study is to evaluate the effectiveness of informative priors
in genomic dose–response modeling and BMD estimation. Specifically,
we aim to identify plausible informative priors and evaluate their
effects on BMD estimates at both gene and pathway levels. A general
informative prior and eight time-specific (from 3 h to 29 d) informative
priors for seven commonly used continuous dose–response models
were derived. Results suggest that the derived informative priors
are sensitive to the specific data sets used for elicitation. Real
data-based simulations indicate that BMD estimation with the time-specific
informative priors can achieve increased or equivalent accuracy, significantly
decreased uncertainty, and a slightly enhanced correlation with the
points of departure estimated from apical end points than the counterparts
with noninformative priors. Overall, our study systematically examined
the effects of historical data-based informative priors on BMD estimates,
highlighting the benefits of plausible information priors in advancing
the practice of toxicogenomics.