In silico PK predictions in Drug Discovery: Benchmarking of Strategies to Integrate Machine Learning with Empiric and Mechanistic PK modelling

A successful drug needs to combine several properties including high potency and good pharmacokinetic (PK) properties to sustain efficacious plasma concentration over time. To estimate required doses for preclinical animal efficacy models or for the clinics, in vivo PK studies need to be conducted. While the prediction of ADME properties of compounds using Machine Learning (ML) models based on chemical structures is well established in drug discovery, the prediction of complete plasma concentration-time profiles has only recently gained attention. In this study, we systematically compare various approaches that integrate ML models with mechanistic PK models to predict PK profiles in rats after i.v. administration prior to synthesis. More specifically, we compare a standard noncompartmental analysis (NCA) based approach (prediction of CL and V _ss ), a pure ML approach (non-mechanistic PK description), a compartmental modeling approach, and a physiologically based pharmacokinetic (PBPK) approach. Our study based on internal preclinical data shows that the latter three approaches yield PK profile predictions of comparable accuracy (evaluated as geometric mean fold errors for each profile) across a large test set (>1000 small molecules). In summary, we demonstrate the improved ability to prioritize drug candidates with desirable PK properties prior to synthesis with ML predictions.