Key result
Continuous in silico modeling improves hERG inhibition prediction using extrapolated two-point screening data.
Why the study?
In silico safety models often rely on discretized data due to historical data challenges and assay diversity, which limits usage and interpretation during compound design.
Does an expanded continuous data inference approach from two-point screening improve the performance of in silico hERG inhibition models compared to categorical discretization?
Does an expanded continuous data inference approach from two-point screening improve the performance of in silico hERG inhibition models compared to categorical discretization?
Effect estimate: R = 0.98, MAE = 0.08
Estimating continuous hERG IC50 values from limited screening data improves predictive in silico toxicology models, allowing better rank-ordering of compounds during early drug discovery.
May aid preclinical hERG screening efficiency; leaves open impact on clinical arrhythmia risk assessment.
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 IC50 for hERG inhibition is estimated from diverse historical proprietary data. The IC50 derived from a two-point proprietary screening data set demonstrated high correlation (R = 0.98, MAE = 0.08) with IC50s derived from six-point dose–response curves. Similar IC50 estimation accuracy was obtained on a public thallium flux assay data set (R = 0.90, MAE = 0.2). The IC50 data were used to develop a robust quantitative model. The model’s MAE (0.47) and R2 (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.
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Melnikov et al. (2022) studied In vitro hERG safety assessment (n=4,829). Continuous data inference from two-point screening (in silico model) vs. Categorical data models based on discretized endpoints was evaluated on Correlation of extrapolated two-point IC50 with six-point dose-response curves (R = 0.98, MAE = 0.08). The continuous in silico model utilizing IC50 values extrapolated from two-point screening data predicted hERG inhibition with an R2 of 0.46 and MAE of 0.47, enabling improved rank ordering of compounds.
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