Learn how machine learning can improve the productivity of life science research and help to optimise manufacturing processes.
Through a case study of a project that is applying a novel ML-powered digital tool, this webinar explores how machine learning (ML) can extract added value from manufacturing process data to improve productivity in the development of new therapeutics. Targets include cutting interpretation time and achieving improved yields, thus reducing development costs and facilitating manufacturing scale-up. Oligonucleotides are a major emerging new class of medicines, offering the potential to treat a range of common and rare genetic diseases. However, broader clinical application is limited by challenges in oligonucleotide synthesis and sustainable manufacturing. Intellegens and CPI, in collaboration with industry partners, are applying ML to this challenge, enabling rapid, best-in-class prediction of potential impurities and characterisation of oligonucleotides.
Original broadcast: Tuesday, 26th March, 2024
Speakers:
- Claire MacLeod is Grand Challenge Lead at CPI, leading projects relating to oligonucleotide manufacturing.
- Mike Webb is a former Vice-President of API Chemistry & Analysis at GSK, now an independent consultant to the pharma industry.
- Emma Green is an experienced biotech executive with an extensive track record in global clinical affairs, clinical development and operations.
- Tom Whitehead is Head of Machine Learning at Intellegens, leading the team that applies ML expertise to diverse projects in life science, chemicals, and related sectors.
