EP 161: How large language models can help build immunotherapies with Michelle Teng of Etcembly Ltd.

0:00 Intro to The Genetics Podcast
01:00 Welcome to Michelle
01:35 Explaining immunotherapy and its evolution over the past decade
04:10 Current insights on immunotherapy responders and the underlying factors driving varied individual responses
05:50 The latest generation of T-cell receptor therapies
08:53 The origin of the Long Term Survivor Study, its purpose and how it informs discovery of new T-cell receptor therapies
12:32 How Etcembly is characterising T-cells and antibodies in survivor profiles
15:00 Using machine learning to understand the immune system
18:44 The complexity of Human Leukocyte Antigen (HLA) and how it relates to differences in T-cell receptor biology
22:35 T-cell repertoires in Long Term Survivor Study participants
26:06 Training LLMs in immune system biology, data and more
27:54 Michelle’s work at Immunocore and how she’s applied her knowledge to grow Etcembly
33:02 Setting up a new company at the crossroads of the Covid-19 pandemic and the inception of LLMs and AlphaFold
35:54 Current bottlenecks in pre-clinical immunotherapy development
37:23 Michelle’s eldest daughter’s experience with an ultra-rare genetic disease and the founding of SynaptixBio
43:34 Utilising biobanks and registries to better understand ultra-rare disease presentation
47:05 The power and importance of patient parent groups for developing rare disease treatments
48:48 Closing remarks
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