ABSTRACT In this study, we developed a hybrid machine‐learning (ML) and physics‐informed ML (PIML) framework to predict and optimize the toxicity of oxaliplatin‐loaded nanocomposites. Using a curated dataset of 70 formulations, we integrated physicochemical descriptors with mechanistic features derived from drug release kinetics, cellular uptake, and oxidative stress pathways. The PIML approach improved predictive performance (R 2 = 0.81; AUC = 0.88) and enhanced interpretability by linking toxicity outcomes to physical parameters such as uptake rate and reactive oxygen species (ROS) generation. Optimization analyses of the current dataset suggested nanocarriers with moderate particle sizes (80–150 nm), mildly negative surface charges (−20 to −10 mV), and controlled drug release (24–72 h), trends associated with reduced predicted off‐target effects. Retrospective comparison with a small set of published in vitro studies suggested potential toxicity reductions of up to ∼70% for formulations adjusted according to these trends. However, these are preliminary, dataset‐dependent observations from a limited corpus and not validated design guidelines. Prospective validation with new formulations and independent datasets is required. While this study establishes a reproducible, physics‐guided framework for rational nanocarrier design in anticancer therapy, it is limited to in vitro and retrospective analyses. Key translational gaps, including in vivo pharmacokinetics, biodistribution, and toxicology, must be addressed for clinical relevance.
Rahdar et al. (Sun,) studied this question.