AI & Antibodies mini-series | An artificial approach to humanization
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Who is Charlotte Dean and what does her Oxford Protein Informatics Group focus on?
Hello and welcome to Talking Techniques, the show that brings you the latest from the life sciences, straight from the people exploring them. I'm your host, Biotonique Senior Editor Tristan Free, and today as part of a special series of podcasts covering the ongoing article collection on artificial intelligence and machine learning and antibody development in the journal MABS, I'm joined by Charlotte Dean, Professor of Structural Bioinformatics in the Department of Statistics at the University of Oxford. Charlotte's paper, Humatch, Fast Gene-Specific Joint Humanisation of Antibody Heavy and Light Chains, details a novel approach to humanised synthetic antibodies, and we'll be diving into the whys and wherefores of that paper today.
Charlotte, it's lovely to have you on the show.
It's really nice to be here, thanks.
So first off, please can you tell us a little bit about yourself and your lab?
So my lab is obviously in Oxford, as you said, in the Department of Statistics. We call ourselves the Oxford Protein Informatics Group. There is about 40 of us now who work across small molecule and sort of large molecule, sort of antibody and TCR discovery, all from a computational sense, writing tools and techniques. And I sort of exploring that space to work out the best ways to really understand those types of molecules and how you'd make better drugs from them.
Perfect. And so you authored a paper in the article collection. And again, that title is 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?
I think, I mean, we are at a kind of tipping point, I think, in particular in terms of the development of biologics. So antibodies would be a really good example of that, where computational techniques are moving from the bolt-on on the side or the interesting thing we might do or the thing we might play with to something which is fundamentally changing the way that we do discovery and speeding it up and really giving us opportunities to get to really good molecules much faster, really good antibodies. And I think the exciting part for me is this concept of antibody development. So it's not just finding a binder. So I see a lot of papers that are about just finding binders to things. And actually, whenever I have a conversation about a drug, I point out that finding a binder in antibody land is probably one of the easiest things to do.
I know it's not that easy, but we have experiments where we do that. What's really hard is to find a binder that is also a good drug. So all the other properties you need around it. And So I was really keen to have a paper and talk about the facts, the kinds of things you can do with artificial intelligence and machine learning to help you with all the other properties. I mean, in this case, it was only one of them, but the humanness of an antibody in our case here.
Okay, fantastic. And so obviously the binder being the part of the antibody that's binding to your target, what are those other aspects of the antibody that make it then a good drug?
So in my case, what I was looking to implement was to make an antibody human, because it's a fairly obvious statement, but if you inject someone or put someone on drip with a protein which is not human, the very first thing that your body does is your own immune system comes and gets rid of it in some way.
Why did Charlotte contribute to the mAbs AI & Machine‑Learning article collection?
So best case scenario, that makes it not a very effective drug because your own body is rejecting it and destroying it. Worst case scenario, you have some very big reaction to it, which is not very good for you. Not a good thing when somebody is sick. So making an antibody human so that you won't have that type of reaction to it is really important if they're going to be effective drugs. There are lots of other properties you can talk about that you also want to optimize there, like being able to not aggregate, for example. You don't want these things to aggregate. You want them to be highly expressive, all sorts of other things.
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Chapters
8 chapters
1
Who is Charlotte Dean and what does her Oxford Protein Informatics Group focus on?
0:03–3:03
2
Why did Charlotte contribute to the mAbs AI & Machine‑Learning article collection?
3:03–5:20
3
What antibody characteristics beyond binding make a good therapeutic drug?
5:20–6:34
4
Why is antibody humanisation essential and how many candidates are limited by it?
6:34–8:41
5
How does the Humatch tool perform fast, gene‑specific joint humanisation of heavy and light chains?
8:41–11:04
6
What practical tips does Charlotte give for first‑time users of Humatch?
11:04–13:13
7
What impact does Charlotte predict AI will have on antibody development over the next five years?
13:13–14:38
8
If she could wish for one thing to advance antibody design, what data does Charlotte need?
14:38–15:09
Speakers
1 identifiedMore from Talking Techniques
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