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AI-Powered Antibody Drug Discovery for Obesity and Cardiometabolic Diseases with Martin Brenner iBio TRANSCRIPT

 
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Manage episode 478239645 series 99915
Content provided by Karen Jagoda. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Karen Jagoda or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Martin Brenner, CEO and Chief Scientific Officer of iBio, is focused on the untapped potential of therapeutic antibodies for obesity and cardiometabolic diseases. Leveraging AI and machine learning, iBio is streamlining the antibody discovery and optimization process and addressing the need for more complex antibody mechanisms of action. Their lead candidate, iBio 600, is an anti-myostatin antibody designed to address the side effects of muscle mass and bone density loss associated with current GLP-1 therapies.

Martin explains, "We can separate this into multiple areas. First of all, there's a predictive model that suggests that there are 5,000 different targets related to disease out there. So, there are 5,000 different possibilities to make medicines. All of the currently approved antibodies target only 92 targets. Even worse, 40% of approved antibodies only target about 10. So you can imagine there's a huge untapped potential of novel targets for which antibodies could be used. The problem is that the technologies must keep up with this to open that novel target space. That is problem number one."

"So, as you know, AI has gotten a little bit of a bad reputation over the last few years, and there was a huge hype about this, and I want to be very clear about this. It takes more than 10,000 steps to make a medicine. At iBio, we enable three of these steps with generative AI. So, that does not make us an AI company. That does not make our molecules AI drugs. What it does is it actually makes it possible for us to create medicines that we couldn't do before. So, the way we use AI at iBio is multiplefold. First, we start our discovery process with the epitope steering engine. You have to imagine that drug targets are massive proteins, and only very small regions on these proteins have a biological function. So you want to get your antibody exactly to those regions that cause a biological function."

#iBio #DrugDiscovery #MedAI #Obesity #GLP1 #CardioMetabolicDiseases #Antibodies #AntibodyTherapies #Myostatin

iBioinc.com

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2297 episodes

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Manage episode 478239645 series 99915
Content provided by Karen Jagoda. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Karen Jagoda or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Martin Brenner, CEO and Chief Scientific Officer of iBio, is focused on the untapped potential of therapeutic antibodies for obesity and cardiometabolic diseases. Leveraging AI and machine learning, iBio is streamlining the antibody discovery and optimization process and addressing the need for more complex antibody mechanisms of action. Their lead candidate, iBio 600, is an anti-myostatin antibody designed to address the side effects of muscle mass and bone density loss associated with current GLP-1 therapies.

Martin explains, "We can separate this into multiple areas. First of all, there's a predictive model that suggests that there are 5,000 different targets related to disease out there. So, there are 5,000 different possibilities to make medicines. All of the currently approved antibodies target only 92 targets. Even worse, 40% of approved antibodies only target about 10. So you can imagine there's a huge untapped potential of novel targets for which antibodies could be used. The problem is that the technologies must keep up with this to open that novel target space. That is problem number one."

"So, as you know, AI has gotten a little bit of a bad reputation over the last few years, and there was a huge hype about this, and I want to be very clear about this. It takes more than 10,000 steps to make a medicine. At iBio, we enable three of these steps with generative AI. So, that does not make us an AI company. That does not make our molecules AI drugs. What it does is it actually makes it possible for us to create medicines that we couldn't do before. So, the way we use AI at iBio is multiplefold. First, we start our discovery process with the epitope steering engine. You have to imagine that drug targets are massive proteins, and only very small regions on these proteins have a biological function. So you want to get your antibody exactly to those regions that cause a biological function."

#iBio #DrugDiscovery #MedAI #Obesity #GLP1 #CardioMetabolicDiseases #Antibodies #AntibodyTherapies #Myostatin

iBioinc.com

Listen to the podcast here

  continue reading

2297 episodes

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