AI in Drug Development: FDA Compliance, Validation, and Data Integrity

Overview:

Artificial Intelligence is no longer sitting at the edge of drug development. It is moving into the work that supports how companies discover candidates, analyze data, assess safety, prepare submissions, review literature, summarize clinical and regulatory information, monitor adverse events, support labeling updates, and manage manufacturing and quality data.

That shift creates a difficult question for pharmaceutical, biotechnology, biologics, and other FDA-regulated companies: when AI helps produce or interpret information that may influence safety, effectiveness, quality, or a regulatory submission, how do you prove that the system can be trusted?

FDA has already seen a sharp rise in submissions that include AI components, and the agency’s current thinking is moving toward a risk-based credibility framework built around intended use, context of use, data quality, model performance, documentation, and lifecycle control. At the same time, FDA is expanding its own internal use of AI to support scientific review, safety assessment, label comparison, inspection targeting, and agency workflows.

This leaves many industry teams in a challenging position. They are being asked to move faster, use larger datasets, reduce manual review burden, and explore AI-enabled tools — but they still need to satisfy expectations for computer system validation, 21 CFR Part 11, data integrity, audit trails, electronic records and signatures, vendor oversight, change control, security, and inspection readiness.

The risk is not simply that AI gives a wrong answer. The deeper risk is that a company cannot explain how the tool was used, what data it relied on, whether the output was verified, how the model’s performance was assessed, whether the system remained in a validated state, or whether the documentation supports the decisions made.

This webinar will help attendees understand how AI, Machine Learning, Large Language Models, and tools such as ChatGPT are being used in drug development and related regulated operations. Carolyn Troiano will walk through the practical compliance questions that arise when AI supports drug development, quality, manufacturing, clinical, safety, labeling, documentation, or submission-related work.

The session will connect AI adoption to FDA expectations for risk-based validation, Computer Software Assurance, GAMP®5, 21 CFR Part 11, data integrity, software validation and maintenance, and ongoing system control. Attendees will gain a clearer view of where professionals often go wrong, what FDA is focusing on, and how to approach AI-supported systems in a way that protects product quality, patient safety, data reliability, and regulatory confidence.

Areas covered in the session:

• Learn about how AI increasing in use in the life sciences industries, and how companies are leading the way to delivering more effective, safer, and more beneficial drugs as a result.
• Learn about the potential risks and challenges related to AI, ML and LLMs, such as ChatGPT.
• Learn about the challenges and vulnerabilities facing industry today, and how these new technologies can provide steps forward.
• Learn about FDA’s considerations for adapting its review process for AI-enabled software used to manufacture and quality test drugs that have the ability to evolve rapidly in response to new data, sometimes in ways difficult to foresee
• Learn how and under what circumstances drug products relying on AI are regulated by FDA.
• Learn about the potential impact and risk threatening data, processes, products, and ultimately patients based on these.
• Understand how to ensure benefits of drugs outweigh risks.
• Understand how FDA, Congress, technology developers, and health care industry must work together to forge this new path and lead to a deeper and broader application of AI in operational processes in today’s FDA-regulated companies.
• Understand current industry best practices and recommendations for improving compliance of drugs that leverage AI, ML and LLMs, such as ChatGPT in operational processes.
• Learn about industry best practices for implementing, validating, meeting FDA Part 11 and data integrity requirements, as AI applications improve operational efficiency and effectiveness in the process.
• Learn about the FDA’s Computer Software Assurance (CSA) draft guidance and how it aligns with GAMP®5, 2nd Edition.
• Understand the Software Validation and Maintenance requirements to better address compliance with software incorporating AI, ML and LLMs, such as ChatGPT.
• Q&A

Attendees will receive the following handouts:

• AI in Drug Development Use-Case Risk Triage Worksheet
• AI Model Credibility & Validation Evidence Checklist

Why Should You Attend?

AI is beginning to influence how drug companies screen literature, analyze data, support clinical and safety work, prepare regulatory documents, update labeling, monitor adverse events, and manage manufacturing and quality information. That creates real opportunity, but it also raises a serious question: can the AI-supported output be trusted, verified, documented, and defended when it supports regulated work?

Many teams are moving quickly into AI, ML, LLMs, and tools such as ChatGPT without fully understanding where validation, Part 11, data integrity, audit trails, intended use, vendor oversight, change control, and lifecycle management come into play. The problem is rarely just the tool itself. The bigger risk is using AI in a way that leaves the company unable to explain the data, the model, the output, or the decision it supported.

This webinar will help you understand where AI fits into drug development and related FDA-regulated operations, where professionals often misjudge the compliance risk, and how to approach AI-supported systems with stronger control and clearer documentation. Carolyn Troiano will connect the practical use of AI in drug development with FDA expectations for validation, Computer Software Assurance, GAMP®5, 21 CFR Part 11, data integrity, and inspection readiness.

Who Will Benefit?

This webinar is designed for professionals responsible for AI-supported drug development systems, regulated data, validation, quality, submissions, and FDA compliance.

Those include:

• Regulatory Affairs Professionals
• Regulatory Submission Professionals
• Drug Development Leaders
• Clinical Development Professionals
• Clinical Operations Professionals
• Clinical Data Management Professionals
• Pharmacovigilance Professionals
• Drug Safety Professionals
• Medical Writing Professionals
• Labeling Professionals
• Quality Assurance Professionals
• Quality Control Professionals
• CSV Professionals
• Computer System Validation Managers
• Computer Software Assurance Professionals
• GxP Compliance Professionals
• 21 CFR Part 11 Compliance Professionals
• Data Integrity Professionals
• Validation Engineers
• Validation Managers
• Software Quality Assurance Professionals
• IT Quality Professionals
• Life Sciences IT Managers
• Digital Transformation Leaders in Pharma and Biotech
• AI Governance Professionals in Life Sciences
• Data Science Leaders in Regulated Environments
• Manufacturing Quality Professionals
• Manufacturing Systems Professionals
• Laboratory Systems Managers
• LIMS Administrators
• Regulatory Operations Professionals
• Clinical Systems Managers
• Quality Systems Managers
• Document Control Professionals
• Audit and Inspection Readiness Professionals
• Vendor Qualification and Supplier Quality Professionals
• Compliance Officers in FDA-Regulated Companies
• Pharmaceutical Professionals
• Biotechnology Professionals
• Biologics Professionals
• Contract Research Organization Professionals
• Contract Manufacturing Organization Professionals