Deep Learning for Predicting Nanoparticle Pharmacokinetics & Biodistribution

• What determines nanoparticle fate in vivo?
  • Absorption, distribution, metabolism, excretion (ADME)
• Key parameters:
  • Circulation half-life, opsonization, RES uptake, tumor accumulation
• Influence of nanoparticle size, charge, surface chemistry, shape
• Understanding EPR effect and biological barriers
• Overview of real PK datasets and experimental designs

• Why deep learning for nanomedicine?
• Types of deep learning architectures used:
  • ANN for descriptor-based predictions
  • CNN for structured physicochemical matrices
  • RNN/LSTM for time-series PK curves
• Nano descriptor engineering:
  • Hydrodynamic size, zeta potential, coating density, ligand type
• Input data formats: experimental PK curves, in vivo organ accumulation, time-series uptake
• Conceptual Python demonstration of a DL workflow

• Using model predictions to guide nanoparticle design
• Interpreting DL outputs: SHAP values, feature importance, sensitivity analysis
• Optimization of nanoparticle properties using AI predictions
• Translational challenges: variability, animal-to-human prediction gaps, regulatory acceptance
• Future directions:
  • Digital twins for nanomedicine
  • In silico trials
  • Personalized nanoparticle design

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