Abstract
Due to challenges with historical data and the diversity of assay formats, in silico models for safety-related endpoints are often based on discretized data instead of the data on a natural continuous scale. Models for discretized endpoints have limitations in usage and interpretation that can impact compound design. Here, we present a consistent data inference approach, exemplified on two data sets of Ether-à-go-go-Related Gene (hERG) K+ inhibition data, for dose-response and screening experiments that are generally applicable for in vitro assays. hERG inhibition has been associated with severe cardiac effects and is one of the more prominent safety targets assessed in drug development, using a wide array of in vitro and in silico screening methods. In this study, the IC(50) for hERG inhibition is estimated from diverse historical proprietary data. The IC(50) derived from a two-point proprietary screening data set demonstrated high correlation (R = 0.98, MAE = 0.08) with IC(50s) derived from six-point dose-response curves. Similar IC(50) estimation accuracy was obtained on a public thallium flux assay data set (R = 0.90, MAE = 0.2). The IC(50) data were used to develop a robust quantitative model. The model's MAE (0.47) and R(2) (0.46) were on par with literature statistics and approached assay reproducibility. Using a continuous model has high value for pharmaceutical projects, as it enables rank ordering of compounds and evaluation of compounds against project-specific inhibition thresholds. This data inference approach can be widely applicable to assays with quantitative readouts and has the potential to impact experimental design and improve model performance, interpretation, and acceptance across many standard safety endpoints.