Lipid Nanoparticles for RNA & Drug Delivery: Formulation, Characterization & AI Optimization

Aim

Workshop Objectives

Workshop Structure

📅 Day 1: LNP Formulation, Lipid Chemistry & RNA/Drug Encapsulation

Focus: Understand LNP composition, formulation principles, and mechanisms of RNA and drug delivery.

Topics Covered

• Fundamentals of lipid nanoparticles and nanomedicine
• LNP architecture and roles of ionizable lipids, phospholipids, cholesterol, and PEG-lipids
• Lipid physicochemical properties and formulation design
• RNA and small-molecule drug encapsulation
• Lipid–cargo interactions and nanoparticle formation
• Cellular uptake, endosomal escape, and intracellular delivery
• Formulation parameters affecting particle size, PDI, encapsulation, and stability
• Applications in mRNA, siRNA, gene delivery, vaccines, and therapeutic drug delivery

🛠️ Hands-on

Explore formulation datasets, compare lipid compositions, calculate basic formulation descriptors, and examine relationships between composition and delivery performance.

Workflow:
Lipid Composition → Formulation → Encapsulation → Particle Properties → Delivery Performance

🧰 Tools Covered

PubChem, RDKit, Python, pandas, NumPy, Google Colab

📅 Day 2: LNP Characterization & Formulation-Performance Analysis

Focus: Interpret physicochemical and biological data to evaluate LNP quality and delivery performance.

Topics Covered

• Particle-size and size-distribution analysis
• Polydispersity index (PDI) and colloidal stability
• Zeta potential and surface properties
• RNA/drug encapsulation efficiency and loading
• Morphology and structural characterization concepts
• Stability and storage-related considerations
• Cargo release and delivery-performance assessment
• Biological response and cellular uptake measurements
• Formulation–property–performance relationships
• Data quality, normalization, and comparative formulation analysis

🛠️ Hands-on

Analyse curated LNP characterization datasets, compare formulations, visualize particle properties, and identify formulation factors associated with delivery performance.

Workflow:
LNP Formulation → Characterization → Data Analysis → Performance Comparison → Key Formulation Factors

🧰 Tools Covered

Python, pandas, NumPy, matplotlib, Plotly, Google Colab, curated LNP datasets

📅 Day 3: AI-Based LNP Optimization & Candidate Prioritization

Focus: Apply machine learning and explainable AI to predict and optimize LNP formulation performance.

Topics Covered

• LNP dataset curation and feature engineering
• Molecular and formulation descriptors
• Data preprocessing, normalization, and exploratory analysis
• Prediction of particle size, PDI, encapsulation efficiency, and delivery outcomes
• Random Forest and XGBoost modelling
• Model training, validation, and cross-validation
• Model evaluation using R², MAE, RMSE, and classification metrics
• Feature importance and SHAP-based explainable AI
• Virtual screening of LNP formulations
• Multi-parameter and multi-objective optimization
• Candidate ranking and formulation prioritization
• Experimental validation, scalability, and translational considerations

🛠️ Hands-on

Build an ML-based LNP performance model, identify important formulation variables, screen candidate formulations, and generate an AI-prioritized formulation shortlist.

Workflow:
LNP Dataset → Feature Engineering → ML Prediction → XAI → Optimization → Candidate Ranking → Validation

🧰 Tools Covered

Python, RDKit, scikit-learn, XGBoost, SHAP, pandas, Plotly, Google Colab

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure