Regulatory and Ethical Considerations for AI in Pharma Essentials Training by Tonex
This two-day intensive training explores the regulatory frameworks and ethical dimensions of deploying artificial intelligence (AI) in pharmaceutical applications. It equips participants with practical insights into FDA, EMA, and MHRA compliance requirements, as well as GxP and ALCOA+ standards for trustworthy AI systems. With the rapid expansion of AI/ML tools in drug development, explainability, bias mitigation, and governance become critical. The course also covers Good Machine Learning Practice (GMLP) and landmark case law shaping the pharma AI landscape. Importantly, participants gain clarity on cybersecurity impacts—particularly in ensuring data integrity, model security, and compliance with regulatory cyber protocols.
Audience:
- Regulatory affairs professionals
- Compliance officers
- Digital health strategists
- Clinical operations leads
- Cybersecurity professionals
- Pharma technology consultants
Learning Objectives:
- Understand current FDA, EMA, and MHRA guidance on AI in pharma
- Interpret GxP and ALCOA+ standards in an AI context
- Evaluate risks related to model bias, drift, and explainability
- Apply principles of Good Machine Learning Practice (GMLP)
- Explore auditability and data governance requirements
- Assess the cybersecurity implications of AI integration in pharma
Course Modules:
Module 1: AI Regulation Overview
- Overview of FDA, EMA, MHRA policies
- AI/ML device software function definitions
- Lifecycle oversight in AI/ML tools
- Regulatory sandbox and pilot programs
- Post-market monitoring requirements
- International harmonization efforts
Module 2: GxP and ALCOA+ in AI
- Introduction to GxP compliance principles
- ALCOA+ framework for AI data records
- Data traceability in AI workflows
- AI system validation approaches
- GxP audit readiness for digital tools
- Documentation practices for AI output
Module 3: Ethical Risk Areas
- Bias identification and mitigation strategies
- Transparency and explainability standards
- Managing AI model drift and decay
- Accountability and liability in AI systems
- AI’s impact on informed consent
- Ethical AI assessment frameworks
Module 4: Good Machine Learning Practice
- Key principles of GMLP
- Model training data quality control
- Continuous performance monitoring
- Documentation and change management
- Collaboration between developers and regulators
- Examples of GMLP-aligned practices
Module 5: Legal and Governance Aspects
- AI-specific case law and implications
- Legal precedent in pharma AI compliance
- Data protection and consent regulation
- Liability frameworks for algorithmic decisions
- Corporate governance for AI projects
- Regulatory enforcement and penalties
Module 6: Cybersecurity & Compliance
- Threats to AI model integrity in pharma
- Securing AI pipelines and inference processes
- Cyber-compliance with HIPAA and GDPR
- Incident response in regulated environments
- Authentication and access control for AI systems
- Cyber risk assessment aligned with GxP
Join Tonex for this essential training to strengthen your command of regulatory and ethical frameworks for AI in the pharmaceutical industry. Whether you’re advancing compliance strategies or managing digital transformation, this course offers critical knowledge to lead responsibly in a rapidly evolving landscape. Register now to ensure regulatory alignment and cybersecurity resilience in your AI initiatives.