Validation of FDA-Regulated Systems Used in Drug Development & Submissions Using Artificial Intelligence (AI), Machine Learning (ML) & Large Language Models (LLMs), such as ChatGPT

Providing safe and effective drugs and other FDA-regulated products is in the best interests of all those involved in the development, manufacturing, testing, and distribution of these products. You will learn about projects going on in industry and at FDA that take advantage of Artificial Intelligence (AI), Machine Learning (ML) and Large Language Models (LLMs), such as ChatGPT.

With newer technologies such as AI, ML and LLMs, such as ChatGPT in the mix, it means opportunity for greater efficiency and efficacy, but also poses more challenges for companies that develop, test, and support software applications in the life science industries.

In this webinar, you will learn just how AI, ML and LLMs, such as ChatGPT can increase efficiency and effectiveness of software development life cycle (SDLC) activities, enabling the delivery and support of computer solutions and new innovative drugs that will drive industry over the coming years.

This webinar is intended for those working in the FDA-regulated industries, including pharmaceutical, medical device, biological, animal health and tobacco.  Functions that are applicable include research and development, manufacturing, Quality Control, distribution, clinical testing and management, adverse events management and post-marketing surveillance.

You should attend this webinar if you are responsible for planning, executing or managing the development or implementation of any system governed by FDA regulations, or if you are maintaining or supporting such a system. Learn by reviewing industry best practices and knowing where to gather key information to help you move forward with these technologies quickly and in compliance with FDA.

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, 2^nd Edition.
• Understand the Software Validation and Maintenance requirements to better address compliance with software incorporating AI, ML and LLMs, such as ChatGPT.

WHO SHOULD ATTEND

This webinar is intended for those involved in planning, execution and support of computer system validation activities, working in the FDA-regulated industries, including pharmaceutical, medical device, biologics, tobacco and tobacco-related products (e-liquids, e-cigarettes, pouch tobacco, cigars, etc.). Functions that are applicable include research and development, manufacturing, Quality Control, distribution, clinical testing and management, sample labeling, adverse events management and post-marketing surveillance.

Examples of who will benefit from this webinar include:

• Information Technology Analysts
• Information Technology Developers and Testers
• QC/QA Managers and Analysts
• Analytical Chemists
• Compliance and Audit Managers
• Laboratory Managers
• Automation Analysts and Managers
• Manufacturing Specialists and Managers
• Supply Chain Specialists and Managers
• Regulatory Affairs Specialists
• Regulatory Submissions Specialists
• Clinical Data Analysts
• Clinical Data Managers
• Clinical Trial Sponsors
• Computer System Validation Specialists
• GMP Training Specialists
• Business Stakeholders/Subject Matter Experts
• Business System/Application Testers

This webinar will also benefit any vendors and consultants working in the life sciences industry who are involved in computer system implementation, validation and compliance. It will also help those in software development companies who support the life science industries.

TOPIC BACKGROUND

The life science industries, including pharmaceutical, medical device, biotechnology, biological, and tobacco and tobacco-related products continue to embrace new technology to improve delivery of quality products in compliance with FDA. In addition to some trends toward making use of cloud services, Software-as-a-Service (SaaS) solutions, and other technical innovations that have more recently begun to be used more heavily in life science companies.

Artificial Intelligence (AI) and Machine Learning (ML) are beginning to find a presence at these companies. While life science companies tend to lag behind other markets in using these technologies, they are catching up and we are seeing much more activity related to AI use in software applications used to develop, produce, test, and manage life science products with quality and compliance.

As the pace of technological innovation and evolution becomes more intense, there is a critical need for computer system validation, 21 CFR Part 11 (Electronic Records and Electronic Signatures) compliance, and data integrity assurance to continue in environments where artificial intelligence (AI) and machine learning (ML) are becoming prevalent.

FDA became alarmed by the lack of compliance to meet data integrity and Part 11 requirements during the last decade. Out of compliance citations during this period, including Form 483s and Warning Letters have skyrocketed for these key areas of compliance. But why?

Based on discussions with clients and stakeholders at conferences and meetings, it has become more and more obvious that most of the performers in industry are under management pressure to do more work with fewer resources and in less time. This continues to lead performers to seek faster and easier ways to get the work done, and opens the door to more conversation around the use of AI/ML and ChatGPT in software development, testing, and support.

It is time to embark on the AI/ML revolution and continue to deliver quality drugs with compliance to meet the needs of the consumers by putting newer, more innovative, safer, and more effective products in their hands, all of which are key focus areas for FDA.

Speaking of the FDA, they are currently trying to modernize their systems and use of data. FDA announced all agency’s centers must fully integrate generative AI into work by the end of June 2025. The intent is to reduce non-productive busy-work that has historically consumed the review process. Drug submission review can often take many months, but there is an opportunity to reduce this timeframe and allow expert reviewers to focus on the more complex cases.

Systems supporting drug manufacturing and quality testing using AI are starting to be used in life science companies. This means that the company’s quality management system (QMS) must ensure consistent production and control of manufacturing and quality systems, and involves routine inspections and audits.

The Verifying Accurate Leading-Edge Development Act, or Valid Act is pending, and will codify the “firm-based” approach to regulation. FDA will oversee methods used for technology development and validate reliability rather than decoupling the AI product’s construction. By ensuring robust systems are in place, FDA can enhance overall safety and effectiveness of drugs produced.

Rapid cycles of innovation inherent in products due to constant modification based on new information available pose challenges. ChatGPT from OpenAI has demonstrated substantial semantic medical knowledge and the ability to perform work that will accelerate the drug approval process.

Large Language Models (LLMs), such as ChatGPT trained on vast datasets embody the ultimate black box in the realm of FDA regulation. They are nonlinear and high-dimensional, making it difficult to trace specific inputs to outputs. A risk is they may return wrong answers when trained on unreliable datasets. Under a firm-based regulation approach by FDA, innovators can bring certain new products to market more efficiently.

ChatGPT offers the ability to summarize text for drug labeling documents. It is also potentially viable for summarizing clinical dialogue. This is critical to telemedicine, which has grown rapidly in recent years, aiming to enhance medical efficiency and reduce workload. This means efficient and accurate medical dialogue summarization algorithms are needed to condense lengthy conversations into shorter versions, focused on relevant medical facts.

AI-driven approaches have also helped identify potential risks more efficiently and can accelerate toxicological research. ChatGPT can automate specific literature screening to enhance FDA-regulated drug products. Other areas of research, including nephrotoxicity and cardiotoxicity are also being worked on.

LabelComp AI is useful for updating adverse reactions in drug labeling based on a wealth of data that is vast and routinely updated. Uses include post-marketing surveillance, where AI can play a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse populations.

LLMs will boost efficiency, but input data must be quality-checked. Industry must develop adequate standards and controls, evaluating AI algorithm models under the specific intended use of a drug. Ultimately, industry will be able to identify new drug candidates and plan, execute and analyze data from clinical trials.

In June 2025, FDA announced plans to use AI to speed new drug approvals. Elsa is a tool that may enhance FDA review of safety data, summarize reports and flag facilities needing inspection. Built within a high-security GovCloud environment, it offers a secure platform for FDA staff to access internal documents while ensuring information remains within the agency. Clinical protocol reviews could be considerably shortened.

Learn more about how FDA and life science companies are using AI and ChatGPT. This is expected to improve efficiency while enabling FDA and companies to run more smoothly. This is critical at a time when we are faced with a rising demand for healthcare, expansion of specialty medicines and physician shortages. Leveraging comprehensive data systems will lead to greater efficiency in diagnosis and treatment planning, and integration of patient data facilitates personalized medicine approaches for more effective interventions.

But this webinar doesn’t stop there!

We’ll provide an overview of computer system validation, including the draft guidance from FDA on Computer Software Assurance (CSA), and the latest GAMP®5, 2^nd Edition that aligns with CSA. We’ll walk you through the Software Validation and Maintenance approach that will bring clarity to what FDA is looking for.

Course Director: CAROLYN TROIANO

Carolyn Troiano has more than 30 years of experience in computer system validation in the pharmaceutical, medical device, animal health, tobacco and other FDA-regulated industries. She is currently an independent consultant, advising companies on computer system validation and large-scale IT system implementation projects. During her career, Carolyn worked directly, or on a consulting basis, for many of the larger pharmaceutical companies in the US and Europe. She developed validation programs and strategies back in the mid-1980s, when the first FDA guidebook was published on the subject, and collaborated with FDA and other industry representatives on 21 CFR Part 11, the FDA’s electronic record/electronic signature regulation. Carolyn has participated in industry conferences. She is currently active in the PMI, AITP, and RichTech, and volunteers for the PMI’s Educational Fund as a project management instructor for non-profit organizations.