Artificial intelligence (AI) is one of the most discussed technologies across all industries. Life science professionals working in the pharmaceutical industry strive to improve people’s lives tackling incredibly complex diseases. Drug development is often perceived as slow. As the pharma industry looks to improve the drug development process AI promises nothing less than a revolution.

 

Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists?

 

Will AI–designed medicines be safe for people? Have the desired effect on the disease? Meet the rigorous regulatory standards to actually be approved for human use?

 

In this episode, Andreas Busch, Ph.D., Chief Innovation Officer at Absci will answer these questions and shares the value generative-AI is providing drug development today.

 

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Episode Transcript:

00;00;00;00 - 00;00;31;24

Hi, everyone, and welcome to the Latest Dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Artificial Intelligence, AI, is one of the most popular technologies on the planet, and I find it referenced in most, if not all, industries.

 

00;00;31;26 - 00;00;59;16

Those of us working in the pharmaceutical industry strive to improve people's lives. Can AI help scientists develop better medicines faster? Human bodies are incredibly complex. Drug development is slow. Since I've been engaged in drug development, many people, teams, organizations, and companies have been working tirelessly to improve the drug development process, the promise, is nothing more than a revolution for the pharmaceutical industry.

 

00;00;59;19 - 00;01;26;21

The March 8th, 2023 Politico article states “nearly 270 companies are working in AI driven drug discovery”.  Let's start learning more about AI driven drug discovery and discuss if or when the promise of AI will be realized.  Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists?

 

00;01;26;23 - 00;02;02;02

Can AI-designed medicines, be safe for people? Have the desire effect on the disease?  Meet the rigorous regulatory standards to actually be approved for human use? You know, many of these questions can be answered today with my guest, Andreas Busch, Ph.D. Chief Information Officer at Absci.  Andreas brings substantial R&D expertise to Absci’s leadership, a world renowned leader in drug discovery and has led R&D efforts for some of the globe's top pharma companies, including Sanofi, Bayer, and Shire.

 

00;02;02;05 - 00;02;37;05

Andreas’ leadership has resulted in over ten commercial drugs starting from bench to FDA approval, with several more in late stage clinical development. Andreas holds the title of Extraordinary Professor of Pharmacology at the Johann Wolfgang Goethe University in Frankfurt, Germany, where he also received his Ph.D. in pharmacology. Andreas loves, real football a.k.a soccer, enjoys riding his motorcycle through Alps and playing with his beloved dogs Zorro.

 

00;02;37;07 - 00;03;04;28

Welcome, Andreas. Thank you for making the time to speak with me today. Hey, it's a pleasure talking to you Katherine. So, Andreas I have been taught that artificial intelligence, referred to as AI, are computer intelligence programs that can handle real-time problems and help organizations and everyday people achieve their goal. And AI is obviously a topic of discussion these days and getting way more attention with the release of the articles around ChatGPT.

 

00;03;04;28 - 00;03;33;22

Today I'd like to focus our discussion on generative AI, but I thought it would be helpful if you could share with me what's important for me to actually know about this type of AI. I'm glad to talk about it. I guess ChatGPT was certainly a breakthrough in AI and the use of AI for a general population and everybody knows now what AI can do through a GPT.

 

00;03;33;26 - 00;04;07;07

And if you look at generative AI, what we're trying to accomplish simply is to have artificial intelligence supporting us, creating drugs. And as you know, with ChatGPT, you have to give ChatGPT the right prompt in order to get ChatGPT to do the job for you. And this is similar with our generative AI. We need to give the prompt, which is we need to give our models the target, the mechanism we want to work on.

 

00;04;07;10 - 00;04;43;12

And then the model produces for us, in our case for Absci, a de novo designed antibody. So that's fascinating. How long have you been developing this approach with these prompts and these programs and actually been using this at your organization? I mean, Absci is actually a company which started as a cell line development company and realized then that for AI to be very productive, you need a ton of data and you need a ton of very consistent, high quality data.

 

00;04;43;14 - 00;05;14;24

So, these two things have to come together, you know, improvement of AI models, but feeding the AI models with plenty of data. So, the models can get better and better. And we've started really implementing AI for our E.coli expression systems for antibody a bit more than two years ago. And the progress we saw in our generative AI approaches were really very significant, very fast.

 

00;05;14;26 - 00;05;57;16

Already a year ago we were at a stage that we could optimize existing antibodies, so we basically gave the model the information of … look here is a known antibody, …. can you optimize it for affinity, … can you optimize it for immunogenicity and so forth.  And we managed to do that. And just half a year ago, for the first time,  give the model the information of the structure of a protein  that we wanted to address, to produce for us a binding sequence completely de novo or without any idea of an antibody structure before.  I think there was …. really for us …. the breakthrough.

 

00;05;57;19 - 00;06;28;16

And that is something which we have meanwhile even further progressed in the last half year. We extended this approach to more than one binding regions and we are ready now in a situation to address three of the binding regions of an antibody. And we are very, very optimistic that this progress is going to be extremely meaningful and helpful and what we believe disruptive in biologics research in the future.

 

00;06;28;18 - 00;06;49;01

So, this is exciting and extremely fascinating. So, I'm going to go to a statement you made about the data. So, can we talk a little bit about that? So where do these sources of data come from? What types of volume are you talking about? And I guess more importantly, as somebody who has worked with data for many, many years,

 

00;06;49;01 - 00;07;11;00

and one of the things that people will often ask about is ….should you use that data? Is that data appropriate? Is it reliable? Some people use the word quality. So, in order to achieve these impressive results, can you tell us a little bit about, more about, the data that's being used? Where does it come from and all those things?

 

00;07;11;03 - 00;07;36;13

Sure. To make it clear, what we're doing is, once we know the structure of a mechanism we want to address, let's assume whatever a membrane protein like a G protein coupled receptor, whatever you name it, we identify the region to which we want our antibody to bind and we give this information in the structure of this region to the model.

 

00;07;36;14 - 00;08;08;25

The model then delivers to us a number of model hits.  Artificial intelligence generated hits.  Information about what the model thinks the binder should look like.  And what we do then, and that's the very straightforward answer to your question of the quality, is we generate those hits in the laboratory, we express the genes relevant for those binding regions in our expression system.

 

00;08;08;27 - 00;08;42;06

That's a microbial expression system, E coli. And then we simply have a test available called the Ace assay, in which we then validate what is indeed the binding affinity of those calculated binder. So that gives us then immediately an experimental va