Deep-Learning: An Emerging Tool to Support Model-Informed Drug Development.

Traditional Model-Informed Drug Development (MIDD) primarily relies on hypothesis-driven (HD) models based on biological, physiological, and physicochemical principles. While these mechanistic models have been instrumental in drug development and regulatory decision-making, they may be limited in capturing complex nonlinear relationships. Deep learning (DL) provides a complementary, data-driven approach capable of learning these relationships directly from data without requiring predefined model structures or mechanistic assumptions. In this work, the DL methodology was implemented using an artificial neural network, supported by the universal approximation theorem, which states that a feed-forward neural network with a sufficient number of neurons can approximate any continuous function to arbitrary accuracy. Two independent datasets were used to compare the performance of DL and HD, with the objective of evaluating whether DL can complement, rather than replace, conventional mechanistic modeling. To assess the robustness and generalizability of the DL model, train/test validation and bootstrap analyses were performed together with a comprehensive set of diagnostic evaluations. These included assessments of prediction stability and precision, residual analyses based on the distribution of conditional weighted residuals, goodness-of-fit diagnostics, and visual predictive checks. Collectively, these analyses demonstrated that the DL model exhibited robust predictive performance and reliability comparable to established HD approaches while requiring substantially fewer a priori assumptions regarding the underlying system. These findings indicate that mechanistic HD models and data-driven DL models are complementary. Integrating both approaches within the MIDD framework has the potential to leverage the biological interpretability of mechanistic models together with the predictive flexibility of deep learning.