London Futurists
Pioneering AI drug development, with Alex Zhavoronkov
This episode discusses progress at Insilico Medicine, the AI drug development company founded by our guest, longevity pioneer Alex Zhavoronkov.
1.20 In Feb 2022, Insilico got an IPF drug into phase 1 clinical trials: a first for a wholly AI-developed drug
1.50 Insilico is now well-funded; its software is widely used in the pharma industry
2.30 How drug development works. First you create a hypothesis about what causes a disease
4.00 Pandaomics is Insilico’s software to generate hypotheses. It combines 20+ AI models, and huge public data repositories
6.00 This first phase is usually done in academia. It usually costs $ billions to develop a hypothesis. 95% of them fail
6.50 The second phase is developing a molecule which might treat the disease
7.15 This is the job of Insilico’s Chemistry 42 platform
7.30 The classical approach is to test thousands of molecules to see if they bind to the target protein
7.50 AI, by contrast, is able to "imagine" a novel molecule which might bind to it
8.00 You then test 10-15 molecules which have the desired characteristics
8.20 This is done with a variety of genetic algorithms, Generative Adversarial Networks (GANs), and some Transformer networks
8.35 Insilico has a “zoo” of 40 validated models
10.40 Given the ten-fold improvement, why hasn’t the whole drug industry adopted this process?
10.50 They do all have AI groups and they are trying to change, but they are huge companies, and it takes time
11.50 Is it better to invent new molecules, or re-purpose old drugs, which are already known to be safe in humans?
13.00 You can’t gain IP with re-purposed drugs: either somebody else “owns” them, or they are already generic
15.00 The IPF drug was identified during aging research, using aging clocks, and a deep neural net trained on longitudinal data
17.10 The third phase is where Insilico’s other platform, InClinico, comes into play
17.35 InClinico predicts the results of phase 2 (clinical efficacy) trials
18.15 InClinico is trained on massive data sets about previous trials
19.40 InClinico is actually Insilico’s oldest system. Its value has only been ascertained now that some drugs have made it all the way through the pipeline
22.05 A major pharma company asked Insilico to predict the outcome of ten of its trials
22.30 Nine of these ten trials were predicted correctly
23.00 But the company decided that adopting this methodology would be too much of an upheaval; it was unwilling to rely on outsiders so heavily
24.15 Hedge funds and banks have no such qualms
24.25 Insilico is doing pilots for their investments in biotech startups
26.30 Alex is from Latvia originally, studied in Canada, started his career in the US, but Insilico was established in Hong Kong. Why?
27.00 Chinese CROs, Contract Research Organisations, enable you to do research without having your own wetlab
28.00 Like Apple, Insilico designs in the US and does operations in China. You can also do clinical studies there
28.45 They needed their own people inside those CROs, so had to be co-located
29.10 Hong Kong
With a new episode every Wed morning, the Climate Confident podcast is weekly podcast...
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Inspiring ComputingThe Inspiring Computing podcast is where computing meets the real world. This podcast...
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This episode discusses progress at Insilico Medicine, the AI drug development company founded by our guest, longevity pioneer Alex Zhavoronkov.
1.20 In Feb 2022, Insilico got an IPF drug into phase 1 clinical trials: a first for a wholly AI-developed drug
1.50 Insilico is now well-funded; its software is widely used in the pharma industry
2.30 How drug development works. First you create a hypothesis about what causes a disease
4.00 Pandaomics is Insilico’s software to generate hypotheses. It combines 20+ AI models, and huge public data repositories
6.00 This first phase is usually done in academia. It usually costs $ billions to develop a hypothesis. 95% of them fail
6.50 The second phase is developing a molecule which might treat the disease
7.15 This is the job of Insilico’s Chemistry 42 platform
7.30 The classical approach is to test thousands of molecules to see if they bind to the target protein
7.50 AI, by contrast, is able to "imagine" a novel molecule which might bind to it
8.00 You then test 10-15 molecules which have the desired characteristics
8.20 This is done with a variety of genetic algorithms, Generative Adversarial Networks (GANs), and some Transformer networks
8.35 Insilico has a “zoo” of 40 validated models
10.40 Given the ten-fold improvement, why hasn’t the whole drug industry adopted this process?
10.50 They do all have AI groups and they are trying to change, but they are huge companies, and it takes time
11.50 Is it better to invent new molecules, or re-purpose old drugs, which are already known to be safe in humans?
13.00 You can’t gain IP with re-purposed drugs: either somebody else “owns” them, or they are already generic
15.00 The IPF drug was identified during aging research, using aging clocks, and a deep neural net trained on longitudinal data
17.10 The third phase is where Insilico’s other platform, InClinico, comes into play
17.35 InClinico predicts the results of phase 2 (clinical efficacy) trials
18.15 InClinico is trained on massive data sets about previous trials
19.40 InClinico is actually Insilico’s oldest system. Its value has only been ascertained now that some drugs have made it all the way through the pipeline
22.05 A major pharma company asked Insilico to predict the outcome of ten of its trials
22.30 Nine of these ten trials were predicted correctly
23.00 But the company decided that adopting this methodology would be too much of an upheaval; it was unwilling to rely on outsiders so heavily
24.15 Hedge funds and banks have no such qualms
24.25 Insilico is doing pilots for their investments in biotech startups
26.30 Alex is from Latvia originally, studied in Canada, started his career in the US, but Insilico was established in Hong Kong. Why?
27.00 Chinese CROs, Contract Research Organisations, enable you to do research without having your own wetlab
28.00 Like Apple, Insilico designs in the US and does operations in China. You can also do clinical studies there
28.45 They needed their own people inside those CROs, so had to be co-located
29.10 Hong Kong
With a new episode every Wed morning, the Climate Confident podcast is weekly podcast...
Listen on: Apple Podcasts Spotify
Inspiring ComputingThe Inspiring Computing podcast is where computing meets the real world. This podcast...
Listen on: Apple Podcasts Spotify