We have teamed up with the journal mAbs to cover their article collection on artificial intelligence and machine learning in antibody development. In this, the first episode of the series, we speak to Charlotte Dean, Professor of Structural Bioinformatics in the Department of Statistics at the University of Oxford, about her paper in the collection: Humatch - fast, gene-specific joint humanization of antibody heavy and light chains.
Charlotte takes us through a critical issue in the development of antibody therapeutics – humanization – and reveals how new AI-based software can improve our solutions to this long-held problem in the field.
Contents
[0:47] Please can you tell us a little bit about yourself and your lab?
[1:16] You authored a paper in the article collection, Artificial Intelligence and Machine Learning in Antibody Development. What was it that attracted you to that collection? And why did you think it was important to contribute to?
[2:36] What other antibody characteristics make for a good drug?
[3:36] Why do we need to humanize antibodies? How hard is it to achieve? How many drugs or potential drug candidates are limited by a lack of humanization?
[5:43] Can you tell us a bit more about Humatch? How does it work and how does it deliver this sort of humanization?
[7:10] Do you have any advice for anyone who might be using Humatch for the first time or is looking to implement it in their own research?
[9:18] Your paper was released at the end of 2024. Have you been able to implement this tool in your own research or seen any particularly exciting applications of this platform in the wider research space?
[10:20] What are your predictions for the impact of AI in this space in the next five years?
[12:20] If there was one thing that you could ask for to help advance the design of antibody therapeutics, what would it be?
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