Artificial Intelligence and Machine Learning in the Pharmaceutical Industry

Artificial intelligence (AI) and machine learning (ML) have become widely used in various industries, including the pharmaceutical industry. These technologies are already making an impact on drug development and manufacturing. However, they also raise unique regulatory challenges that need to be addressed.

For instance, if a ML algorithm can autonomously modify a manufacturing process to improve efficiency, how can the drug manufacturer ensure that the updated process remains compliant with current Good Manufacturing Practices (CGMP)? Similarly, if AI is used to identify potential candidates for a drug trial, how do we address any biases in the underlying data that influences AI’s decision-making?

The Food and Drug Administration (FDA) has recognized these challenges and is actively seeking input from stakeholders in the pharmaceutical industry. They released a discussion paper to gather insights on how to approach the regulatory landscape for AI and ML in drug development and manufacturing.

AI and ML are branches of computer science, statistics, and engineering that use algorithms or models to perform tasks and make predictions. ML is a subset of AI that allows models to be developed through data analysis without explicit programming.

AI and ML have several potential applications in drug development and manufacturing. They can create “digital twins” of individuals to predict their reactions to drugs before actual usage. AI can optimize manufacturing processes by continuously monitoring sensor data for changes that signal the need for maintenance. It can also be used for quality control inspections and to prevent supply chain disruptions by forecasting product demand.

To guide their regulatory approach to AI and ML, the FDA has outlined three overarching principles:

1. Human-led governance, accountability, and transparency: Trustworthy AI and ML systems require transparency and documentation to ensure accountability. FDA’s approach aligns with their handling of AI and ML in the medical device industry, where software changes by AI/ML are closely monitored and periodically updated to the FDA.

2. Quality, reliability, and representativeness of data: FDA is concerned about potential biases in the data underlying AI and ML processes. A future regulatory framework is likely to require documentation and explanation of bias management in the drug development process.

3. Model development, performance, monitoring, and validation: Regular monitoring and documentation are critical to ensure explainability, reliability, and verifiability of AI and ML models. However, the specific requirements may vary based on the complexity of the models.

In order to gather relevant insights, the FDA has specifically asked for feedback from pharmaceutical industry stakeholders on various topics, including:

– Ensuring accountability, transparency, and trustworthiness of complex AI and ML systems.– Preventing amplification of errors and biases in data sources and safeguarding patient data privacy.– Ensuring data integrity, quality, and security when using cloud applications to store manufacturing data.– Storing regulatory compliance data in a manner that allows retrieval and analysis to support decision-making.– Complying with regulatory obligations when ML algorithms change and adapt processes based on real-time data.

Industry participants are encouraged to provide their feedback to the FDA by August 9, 2023, in order to shape the regulatory approach to AI and ML in the pharmaceutical industry. This collaboration between stakeholders and the FDA is crucial to address the evolving challenges and ensure the safe and effective use of these technologies in drug development and manufacturing.