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aidrugdevelopment-211119100400.pptx
- 1. Artificial Intelligence in Drug Discovery & Development Dr. Manu Kumar Shetty Associate Professor Department of Pharmacology Maulana Azad Medical College New Delhi
- 2. AI is changing drug discovery Time 40min Why we need AI What are AI Models Advantages Present status & Challenges
- 3. AI/ML in Drug development, Need? “ Evolution is not a force but a process “ High attrition rate Increased expenditure Increase volume of data Increase global regulatory requirement Challenge in timely processing
- 4. Next Generation Drug Development “We’re really at the cusp of delivering huge improvements in drug discovery and development” Shift from traditional model to Next-Gen automated and intelligent model Next Generation Drug Development AI/ML/DL NLP CNN Big Data Analytics
- 6. Role of AI in Drug-Discovery Better compounds going into clinical trials Better target understanding Better conductance of trials Better Post Marketing surveillance
- 7. Drug Develop 1. Target protein properties 2. Ligand/drug properties 3. Target-ligand interaction 4. Drug repurposing 5. Clinical trails 6. Pharmacovigilance
- 8. 1) Target protein understanding The structure of the target protein is vital to design the drug molecule.
- 10. 2. Drug/Ligand properties prediction Quantitative structure–activity relationship (QSAR) -predict physicochemical properties and biological activities based on their known chemical structure
- 13. 3) Drug- Target interaction (DTI) Bioassay Docking Simulation Machine Learning methods
- 18. 4). Drug Repurposing Drug repositioning, drug retasking, drug reprofiling, drug rescuing, drug recycling, drug redirection, and therapeutic switching Process of identifying new therapeutic use(s) for old/existing/available drugs Highly efficient, time saving, low-cost and minimum risk of failure, increases the success rate
- 20. 5). Clinical Trial Half of time and investment 86% of all trials do not meet enrolment timelines 1/3rd of all Phase III trials fail owing to enrolment problems Patient recruitment takes up 1/3rd of duration A 32% failure rate because of patient recruitment problems
- 21. AI models used to enhance patient cohort selection By reducing population heterogeneity Prognostic enrichment Predictive enrichment Electronic phenotyping
- 22. AI techniques used to automatically analyze EMR and clinical trial eligibility databases, find matches between specific patients and recruiting trials, and recommend these matches to doctors and patients Predict the risk of dropout for a specific patient
- 23. 6). Pharmacovigilance Insertion of structured and unstructured content: NLP and ML are used to extract ICSR information in a regulatory compliant manner AI for decision-making: AI may play an important role in predicting the new ADR
- 24. NLP Example 1 - input
- 26. NLP Example 2 - input
- 27. Normalize drug names and dosage units with Spark NLP
- 30. Advantages Improve the quality and accuracy Cost-effective Timely manner Can handle diverse types data formats Transparent data sharing with regulators, prescribers Prediction of new ADR
- 31. Present status
- 34. Challenges Not a monocausal diseases Significant data challenges, such as the scale, growth, diversity, and uncertainty of the data.
- 35. Thank you