A Curated Benchmark Database for High-Throughput Mechanistic Pharmacokinetic Prediction

Authors: Jones J, Bachorz RA, Lawless M, Miller DW, Fraczkiewicz R, Lukacova V
Publication: J Pharmacokinet Pharmacodyn
Keywords: admet predictor, benchmark dataset, concentration-time profiles, early drug discovery, high throughput pharmacokinetics, model evaluation, physiologically based pharmacokinetic modeling
Software: ADMET Predictor®

Abstract

The evaluation of a drug candidate’s pharmacokinetic (PK) properties, while critical for the success of a drug, has traditionally been excluded from the early drug development process, in large part due to the cost of assays and the difficulty predicting PK properties from structures. Instead, early drug discovery efforts rely on in vitro assay data as surrogates for the in vivo PK properties. Including downstream PK information at the point of design could expedite drug development, delivering leads with desired PK properties from the earliest stages of development. Several proprietary, commercial, and open-ware platforms have been developed to rapidly predict PK properties, and they are beginning to be incorporated into early drug design. However, evaluating the performance of these “high-throughput” PK prediction platforms has been challenging due to the lack of publicly available, well-curated PK datasets for benchmarking. We assembled a machine-readable database of plasma concentration–time profiles spanning 180 compound–species–route–dose cases, five species, and three routes of administration. The database contains standardized chemical structures, dose and study metadata, digitized concentration-time observations, uncertainty bounds where available, and consistently derived PK endpoints. We demonstrate its use by evaluating high-throughput mechanistic PK workflows parameterized with predicted or measured inputs. Whole-curve error metrics complemented conventional endpoint comparisons and enabled analysis of model limitations, uncertain inputs, and curation errors. The results support concentration–time profiles as an informative basis for reproducible PK benchmarking and show how a shared benchmark can guide model development rather than serve only as a leaderboard. This openly maintained resource is intended to support transparent, reproducible comparison and continued improvement of high-throughput PK prediction methods.

By Jeremy O. Jones, Rafał A. Bachorz, Michael S. Lawless, David W. Miller, Robert Fraczkiewicz, Viera Lukacova