Physics-informed machine learning for tumor microenvironment-responsive nanomedicine: Recent updates

Physics-informed machine learning (PIML) is rapidly emerging as a transformative paradigm for designing tumor microenvironment (TME)-responsive nanomedicines. While existing reviews have surveyed PIML in broader biomedical contexts, this work provides a focused, critical synthesis specifically at the intersection of PIML, multiscale TME biophysics, and clinically actionable nanomedicine design. We argue that the unique value of PIML lies not merely in combining physics with data, but in its capacity to resolve the "personalization paradox" in oncology: the tension between the need for patient-specific models and the scarcity of patient-specific data. By embedding governing physical laws-such as Darcy's flow, reaction-diffusion kinetics, and Navier-Stokes equations-as soft constraints, PIML models can generate physically plausible, patient-tailored predictions even with sparse clinical inputs. This review uniquely articulates a translational roadmap that systematically links fundamental PIML methodologies to concrete nanomedicine optimization tasks: predicting nanoparticle transport in heterogeneous TMEs, designing stimulus-responsive nanocarriers, and integrating multi-omics/imaging for personalized therapy. We further introduce a novel comparative framework evaluating PIML against purely physics-based and purely data-driven approaches, highlighting its superior data efficiency and interpretability for TME applications. However, significant challenges remain, including data standardization, computational scalability, and regulatory adaptation. Looking forward, we identify under-explored yet high-impact frontiers, such as quantum-informed PIML for molecular-scale nanocarrier design, real-time adaptive nanomedicine guided by patient digital twins, and the ethical-regulatory frameworks needed for clinical deployment. By synthesizing cross-disciplinary insights and proposing a clear path from bench to bedside, this review aims to not only summarize the state-of-the-art but also to catalyze the next generation of intelligent, patient-centric cancer nanotherapeutics.