Course Outline

Course Information

• The course begins at the time stated below
• The course is broken up into modules outlined below
• There will be breaks between modules

Day 1

Introduction to AI

• What is AI? Machine learning, deep learning, generative AI—a taxonomy
• Brief history: why the sudden acceleration (data, compute, algorithms)
• Types of AI applications: prediction, classification, generation, optimisation
• Why AI is touching everything in pharma now
• Setting realistic expectations: what AI can and cannot do

AI Readiness & Digital Infrastructure

The foundational capabilities required before implementing AI applications.

Data Harmonisation

• Why CMC data is messy: siloed systems, legacy formats, inconsistent naming
• What ML-ready data looks like: structure, labelling, metadata
• Data lakes, governance frameworks, and FAIR principles
• Case study: Cross-site data harmonisation for ML model training

Lab Automation & Instrument Integration

• The automation spectrum: scripting to robotic work cells
• Instrument communication protocols and data capture
• ELN connectivity and structured data output
• Case study: Automated analytical data capture pipeline

RAG & Enterprise Knowledge Systems

• What is RAG (retrieval-augmented generation)?
• Semantic search across documents, SOPs, and historical submissions
• Building queryable institutional knowledge
• Case study: AI-powered search across regulatory correspondence

AI for Companies vs. Contract Organisations

Different business models require different AI implementation approaches.

• Data ownership and permissions: whose data is it?
• The temptation to learn across clients—and the risks
• Platform vs. bespoke: internal efficiency tools vs. client-facing services
• Confidentiality in the age of LLMs: what happens when client data meets AI?
• Competitive positioning: AI as a differentiator for CDMOs
• Case study: CDMO AI implementation—what worked, what was off-limits

Regulatory Landscape for AI in Pharma

Understanding regulatory constraints before diving into applications.

• Regulatory philosophy: risk-based thinking across jurisdictions
• US/FDA: January 2025 draft guidance and the 7-step credibility framework
• EU: AI Act timeline (August 2025–2027), Annex 22 (GMP), AI literacy requirements
• APAC/Singapore: HSA SaMD guidelines, AI Verify Toolkit, regional convergence
• FDA–EMA joint principles for AI in drug development (January 2026)
• Practical implications: documentation expectations and regulatory engagement
• Note: This module provides an informed overview, not regulatory consulting