Abstract / Summary
Accurate prediction of time-to-event outcomes is essential for clinical decision-making and model-informed drug development (MIDD). Conventional survival models may inadequately represent complex longitudinal patient trajectories, time-dependent treatments, and evolving biomarker or pharmacokinetic/pharmacodynamic (PK/PD) profiles. Recent advances in machine learning (ML) and deep learning (DL) have enabled dynamic survival models that update individualized risk predictions as new longitudinal information becomes available. This report systematically reviews dynamic survival modeling frameworks, including extensions of Cox proportional hazards (CPH) with time-varying covariates, discrete-time landmarking strategies, random survival forests (RSF), and deep learning approaches such as DeepHit, Dynamic-DeepHit, and neural ordinary differential equations (ODEs). Applications span sepsis detection, cancer prognosis, and neurodegenerative disease progression. Dynamic models generally achieved discrimination comparable to or better than static Cox models when longitudinal data were available; however, calibration was rarely reported, external validation was uncommon, and integration of PK/PD features into dynamic survival frameworks remains underexplored. Integrating exposure-response modeling with dynamic ML/DL may advance precision dosing, support adaptive trial design, and enhance translational relevance in pharmaceutical research. Emerging regulatory frameworks, including ICH M15 and FDA draft guidance on artificial intelligence in drug development, further emphasize clearly defined contexts of use, risk-based model assessment, credibility, and transparent reporting. Dynamic prediction at the intersection of ML/DL, PK/PD, and regulatory science represents a promising path toward clinically deployable models to inform drug development and regulatory decision-making.