Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development - PubMed
8 hours ago
- #machine learning
- #PBPK modeling
- #QSAR models
- This review explores how machine learning (ML) improves physiologically based pharmacokinetic (PBPK) modeling by enhancing quantitative structure-activity relationship (QSAR) predictions for drug ADME properties.
- Traditional PBPK models are limited by unreliable input parameters for new compounds, but ML-enhanced QSAR models predict these parameters directly from chemical structures, expanding PBPK applicability.
- Recent advances in ML-powered QSAR lead to better predictions of parameters like partition coefficients and clearance rates, improving PBPK simulation accuracy when integrated as inputs.
- This integration supports drug discovery and development decisions, from candidate selection to safety assessment, potentially reducing pharmacokinetic-related failures.
- Challenges include limited chemical diversity in training data, model interpretability, and regulatory considerations for ML-derived parameters in PBPK submissions.