How Well Can High-Throughput Screening Tests Results Predict Whether Chemicals Cause Cancer in Mice and Rats?

Abstract

Over the past half century, an enduring intellectual and technical challenge for risk analysts, statisticians, toxicologists, and experts in artificial intelligence, machine-learning and bioinformatics has been to predict in vivo biological responses to realistic exposures, with demonstrably useful accuracy and confidence, from in vitro and chemical structure data. The common goal of many applied research efforts has been to devise and validate algorithms that give trustworthy predictions of whether and by how much realistic exposures to chemicals change probabilities of adverse health responses. This chapter examines recent, promising results suggesting that high-throughput screening (HTS) assay data can be used to predict in vivo classifications of rodent carcinogenicity for certain pesticides. Anticipating the focus on evaluation analytics for assessing the performance of systems, policies, and interventions in Chaps. 9 and 10, it also undertakes an independent reanalysis of the underlying data to determine how well this encouraging claim can be replicated and supported when the same data are analyzed using slightly different methods.