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

Area of Science:

• Biomedical Engineering
• Computational Biology
• Nanotechnology

Background:

• Physics-informed machine learning (PIML) is emerging for nanomedicine design.
• Existing reviews cover broader biomedical applications, lacking focus on TME biophysics and nanomedicine.
• The personalization paradox in oncology highlights the need for patient-specific models with limited data.

Purpose of the Study:

• To provide a focused synthesis of PIML at the intersection of TME biophysics and nanomedicine design.
• To articulate a translational roadmap linking PIML methodologies to nanomedicine optimization tasks.
• To introduce a comparative framework evaluating PIML against other approaches for TME applications.

Main Methods:

• Embedding governing physical laws (e.g., Darcy's flow, reaction-diffusion, Navier-Stokes) as soft constraints in PIML models.
• Developing PIML models for predicting nanoparticle transport in heterogeneous TMEs.
• Designing stimulus-responsive nanocarriers and integrating multi-omics/imaging data for personalized therapy.
• Comparing PIML with purely physics-based and data-driven methods.

Main Results:

• PIML models can generate physically plausible, patient-tailored predictions using sparse clinical data.
• PIML demonstrates superior data efficiency and interpretability for TME applications compared to traditional methods.
• Identified key nanomedicine optimization tasks addressable by PIML, including transport prediction and nanocarrier design.

Conclusions:

• PIML offers a powerful solution to the personalization paradox in oncology nanomedicine.
• Significant challenges include data standardization, computational scalability, and regulatory adaptation.
• Future directions include quantum-informed PIML, digital twins for adaptive therapy, and ethical-regulatory frameworks for clinical deployment.