Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review

Abstract

Machine learning applications in preclinical drug development have been focused on automated covariate selection in pharmacometric modeling, and high-throughput screening processes early in drug discovery. While inherent drug property prediction has made significant improvements in the last decade, fusing early target-based drug discovery methods to preclinical stage pharmacokinetic (PK) property predictions has been limited. This scoping review investigates the current state of PK property prediction of small molecules in drug discovery using machine learning methods and a combination of machine learning and mechanistic models. We identified major obstacles hindering the development of superior prediction models for small molecule behavior in biological systems. These encompass data accessibility, quantity, and quality, architectural constraints such as poor interpretability and model inherent assumptions, and the lack of robust evaluation and uncertainty assessment methods. To mitigate data-related constraints, we advocate for the use of collaborative federated learning frameworks. Furthermore, we propose leveraging the pattern recognition capabilities of deep learning models in conjunction with the biological interpretability provided by mechanistic approaches to strike an optimal balance between accuracy and biological explainability guided by the intended application of the prediction model. Addressing these limitations will advance reliable modeling pipelines and enable effective extrapolation to novel chemical space, additional species, and emerging drug development scenarios.