Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model-Informed Precision Medicine

Clin Pharmacol Ther. 2023 Dec 17. doi: 10.1002/cpt.3153. Online ahead of print.ABSTRACTThe increasing breadth and depth of resolution in biological and clinical data, including -omics and real-world data, requires advanced analytical techniques like artificial intelligence (AI) and machine learning (ML) to fully appreciate the impact of multi-dimensional population variability in intrinsic and extrinsic factors on disease progression and treatment outcomes. Integration of advanced data analytics in Quantitative Pharmacology is crucial for drug-disease knowledge management, enabling precise, efficient and inclusive drug development and utilization - an application we refer to as Model-Informed Precision Medicine. AI/ML enables characterization of the molecular and clinical sources of heterogeneity in disease trajectory, advancing endpoint qualification and biomarker discovery, and informing patient enrichment for Proof-of-Concept studies as well as trial designs for efficient evidence generation incorporating digital twins and virtual control arms. Explainable ML methods are valuable in elucidating predictors of efficacy and safety of pharmacological treatments, thereby informing response monitoring and risk mitigation strategies. In oncology, emerging opportunities exist for development of the next generation of disease models via ML-assisted joint longitudinal modeling of high-dimensional biomarker data such as circulating tumor DNA and radiomics profiles as predictors of su...
Source: Clinical Pharmacology and Therapeutics - Category: Drugs & Pharmacology Authors: Source Type: research