Deep Learning for Predicting Nanoparticle Pharmacokinetics & Biodistribution

To equip participants with foundational and applied knowledge of how deep learning models predict nanoparticle PK and biodistribution to support smarter nanocarrier design.

Participants will:

1. Understand nanoparticle PK, ADME, and biodistribution principles.
2. Explore data types influencing nanoparticle fate in vivo.
3. Learn the basics of deep learning architectures (ANN, CNN, RNN) used in biomedical prediction tasks.
4. Understand descriptor engineering for nanoparticle datasets.
5. Analyze how deep models assist in forecasting circulation half-life, organ accumulation, and clearance.
6. Discuss real-world challenges and future directions in AI-driven nanomedicine.

Day 1 — Foundations of Nanoparticle PK & 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

Day 2 — Deep Learning Models for Nano-PK Prediction

• 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

Day 3 — Model Interpretation, Optimization & Translation to Nanocarrier Design

• 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

By the end, participants will be able to:

• Explain how nanoparticle properties influence PK and biodistribution.
• Identify key descriptors required to build AI-ready nanoparticle datasets.
• Understand the workflow of deep learning models for PK prediction.
• Interpret model predictions and assess their relevance in nanoparticle design.
• Recognize how AI accelerates preclinical evaluation and supports translational nanomedicine.

• UG & PG students in Biotechnology, Nanotechnology, Biomedical Sciences, Pharmacy, Materials Science
• PhD scholars in nanomedicine, AI/ML, drug delivery, cancer biology
• Academicians looking to integrate AI and nanotechnology
• Industry professionals from pharma, biotech, advanced therapeutics, and computational R&D