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Validity : 28th Jun'25 to 08th Jul'25
AI in Drug Development
- The Drug Discovery / Development Process - 5 Key Steps and AI
- Key FDA Concerns
- Discovery and Development
- Pre-clinical and Clinical Research
- FDA Review
- Post-Market Surveillance
- Patient-focused Development
Overview of the webinar
The US FDA has announced steps toward a new regulatory policy and framework specifically tailored to promote the development of safe and effective drugs using advanced artificial intelligence / machine learning algorithms by the regulated industry. Artificial intelligence algorithms are software that can learn from and act on data. These types of algorithms are already being used on a limited but growing scale by industry to aid in screening for diseases and to provide treatment recommendations.
The recent FDA authorizations of medical devices and their drug development policy statements indicate these technologies are viewed as a harbinger of progress that the FDA expects to see in the five basic elements of drug development:
- Discovery and development
- Preclinical research
- Clinical research
- FDA review, and FDA post-marketing safety monitoring
AI production software validation has some new requirements as well. The Agency plans to apply their current authorities in new ways to keep up with the rapid pace of innovation and ensure the safety of these drugs. This webinar will evaluate these stated FDA policy shifts as it applies to drug discovery and development.
Who should attend?
- Top management
- R&D
- Engineering
- Marketing
- Regulatory Affairs
- Clinical
- QA
- Manufacturing
- Operations
- Staff
Why should you attend?
The use of Generative AI in the Drug discovery and Development Process. The US FDA's encouragement of AI technology in pharma development and clinicals - their risk-based framework. Generative AI is a type of artificial intelligence (AI) that attempts to match or surpass human thinking abilities across a wide range of large data tasks. The FDA is adapting to the use of AI in medical products, and has recently issued policy statements on an advanced form of AI in pharma development, looking to the future. One of these operates without pre-set commands or training data, solving problems and creating rules as it navigates the virtual world. In artificial intelligence (AI), creating adaptable systems that learn independently is a key goal. It is enabled to adapt and solve problems in new situations without needing explicit programming for each task. While FDA specifically mentions this capability, their statements indicate their thinking in general on AI in drug discovery and development, as well as a willingness to work with their regulated industry partners in expanding use of generative AI into all appropriate areas of pharmaceutical development, production, and post-market monitoring.
Faculty - Mr.John E. Lincoln
John E. Lincoln, is Principal of J. E. Lincoln and Associates LLC, a consulting company with over 40 years experience in U.S. FDA-regulated industries, 27 of which are as an independent consultant. John has worked with companies from start-up to Fortune 100, in the U.S., Mexico, Canada, France, Germany, Sweden, China and Taiwan. He specializes in quality assurance, regulatory affairs, QMS problem remediation and FDA responses, new / changed product 510(k)s, process / product / equipment including QMS and software validations, ISO 14971 product risk management files / reports, Design Control / Design History Files, Technical Files, CAPA systems and analysis. He's held positions in Manufacturing Engineering, QA, QAE, Regulatory Affairs, to the level of Director and VP (R&D). In addition, John has prior experience in military, government, electronics, and aerospace. He has ptublished numerous articles in peer reviewed journals, conducted workshops and webinars worldwide on CAPA, 510(k)s, risk analysis / management, FDA / GMP audits, validation, root cause analysis, and others. He writes a recurring column for the Journal of Validation Technology. John is a graduate of UCLA.