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AI Applications in CMC: Process, Analytics & Lifecycle

Available for Group Training

Apply AI across CMC workflows to optimise processes, enhance analytics and support data-driven lifecycle decisions

This course is currently not scheduled however it can be delivered for your team. To register your interest, complete the form below

We Can Deliver This Course for Your Team

 

No scheduled dates for this course right now — but we can deliver it exclusively for your team.

 

Follow the link below and complete the short form to receive a tailored course outline and clear, transparent pricing. Share your focus areas, objectives, timelines, and group size, and we’ll come back to you with a draft programme, estimated investment, and recommended next steps.

Learn more about the course by toggling through the tabs below. Scroll down to view the agenda, trainer info and who should attend.

Course Overview

 

Once the foundations for AI are in place, the next challenge is applying it effectively across CMC workflows. This course focuses on how AI is being used in practice to optimise process development, enhance analytical performance, and support lifecycle decision-making.

 

Participants will explore real-world applications of AI across key CMC domains, including upstream and downstream process optimisation, analytical method development, stability modelling, comparability and complex modalities. The course demonstrates how AI can be used to improve efficiency, reduce variability, and generate deeper insight from complex datasets.

 

Rather than focusing on theory or infrastructure, this programme is designed for technical teams looking to understand how AI can be embedded into scientific workflows. Through case studies and practical examples, participants will gain a clear understanding of where AI adds value, how it is applied, and what considerations are required to use it effectively in regulated environments.

 

By the end of the course, attendees will be equipped to identify, assess and prioritise AI opportunities within their own CMC activities, supporting more data-driven and efficient development and lifecycle management.

 

Learn more about how we deliver live online training.

Key Learning Objectives

 

  • Identify how AI can be applied across key CMC domains, including process development and analytical sciences
  • Understand how AI supports upstream and downstream optimisation, scale-up and tech transfer
  • Apply AI concepts to analytical method development, optimisation and validation
  • Evaluate the use of AI in stability modelling and data-driven specification setting
  • Understand how AI supports comparability, lifecycle management and change control
  • Recognise data integrity, validation and model risk considerations for AI systems
  • Explore the role of AI in complex modalities, including biologics and advanced therapies
  • Identify practical opportunities to apply AI within their own workflows

Who Should Attend?

 

This course is designed for technical professionals working within CMC, manufacturing and analytical functions who want to understand how AI can be applied in their day-to-day work.

Process Development & Manufacturing

  • Process Development Scientists, MSAT, Bioprocess Engineers

Analytical & QC

  • Analytical Development Scientists, QC Scientists, Method Development Teams

CMC Lifecycle & Regulatory

  • CMC Regulatory Professionals, Comparability and Lifecycle Specialists

Advanced Modalities

  • Scientists working in biologics, cell and gene therapies, and complex products

 

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

AI for Process Development

  • ML for upstream optimisation: media, feed strategies, cell line selection
  • AI in downstream development: chromatography and filtration prediction
  • Digital twins for scale-up: modelling bench-to-GMP transitions
  • Predictive models for tech transfer: site-to-site variability
  • AI-enhanced QbD: design space exploration, CPP/CQA relationships
  • Lifecycle AI: continuous process verification with ML
  • Case studies: Bioreactor digital twin; ML-driven resin screening

 

AI for Analytical Sciences

  • AI-assisted method selection: predictive models for technique suitability
  • Automated method development: self-optimising HPLC/MS conditions
  • AI in qualification and verification: automated system suitability
  • Validation considerations for AI-enabled analytical methods
  • Platform methods enhanced with AI vs. custom AI-driven methods
  • Case study: AI-assisted method development—selection through validation

 

AI for Stability & Specification Setting

  • Predictive stability modelling: accelerated-to-real-time correlation
  • AI for HCP analysis: pattern recognition in complex datasets
  • Aggregate prediction: ML models for HMW formation kinetics
  • Potency assay optimisation: reducing variability through ML
  • Data-driven specification setting: statistical justification with AI
  • Case study: ML-predicted shelf-life from accelerated data

 

AI for Comparability & Data Integrity

  • AI-assisted comparability protocols: automated statistical comparisons
  • Managing AI through development changes: model versioning, retraining triggers
  • Data integrity for AI systems: audit trails, reproducibility, transparency
  • AI-specific risks: model drift, bias, hallucination, black-box concerns
  • Validation of AI systems: bridging GAMP and emerging AI frameworks
  • Case study: Comparability study with AI-generated trending

 

AI for Complex Modalities

  • CQA prediction for complex molecules: multi-target optimisation
  • Expanded analytical strategies: handling larger attribute sets with AI
  • Reference standard characterisation for complex modalities
  • Navigating compounded regulatory uncertainty: AI + novel modality
  • Structure-function modelling: AI for developability assessment
  • Case study: AI developability assessment for bispecific antibodies

Discover more CMC courses and Pharmaceutical Training courses

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AI Applications in CMC: Process, Analytics & Lifecycle

Available for Group Training

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