Computational Pharmaceutics

Computational pharmaceutics is a multidisciplinary field that integrates pharmaceutical sciences with computational techniques to optimize drug formulation, design, and delivery. Here are the key topics that are covered in the computational pharmaceutics workshop that focus on enhancing drug discovery, formulation, and delivery efficiency while reducing costs and time:

1. 1. Molecular Docking and Drug Design: Simulating how drug molecules bind to target receptors to predict efficacy and optimize interactions.
   2. Pharmacokinetic Modeling (PK) and Pharmacodynamic (PD) Simulations: Using mathematical models to simulate drug absorption, distribution, metabolism, and elimination (ADME), as well as its biological effects.
   3. Quantum Mechanics and Molecular Mechanics (QM/MM): Studying the interactions of drugs at the atomic level to predict molecular behavior and optimize compound properties.
   4. In Silico Toxicology: Predicting the toxicity of new drugs using computational models, reducing reliance on animal testing.
   5. Nanomedicine and Drug Delivery Modeling: Simulating nanocarrier systems for targeted drug delivery, optimizing drug release profiles and therapeutic efficacy.
   6. Predictive Modeling for Formulation Stability: Using machine learning and simulations to predict the stability, solubility, and shelf life of drug formulations.
   7. Virtual Screening: Employing algorithms to scan libraries of compounds for potential drug candidates.
   8. PBPK (Physiologically Based Pharmacokinetic) Modeling: Predicting how drugs behave in the body by simulating interactions with various biological systems.
   9. Artificial Intelligence in Drug Formulation: Leveraging AI/ML to optimize drug formulations, predict patient-specific responses, and enhance drug design.
   10. Lattice-Boltzmann and Computational Fluid Dynamics (CFD): Simulating fluid flow in drug delivery systems and processes such as spray drying, mixing, or tablet coating.