Hacking the Complex System of Aging (Peter Fedichev, GERO)

This week, we welcomes Peter Fedichev, an entrepreneur and scientist with over 20 years of experience in academic research and biotech business who has co-founded three biotech companies. He’s currently the co-founder & CEO of GERO, a longevity startup on a mission to hack aging. 

In this episode, Chris and Peter discuss GERO’s goal to accelerate our understanding of aging and create a therapy that will significantly extend a healthy human lifespan. First, they talk about the relationship between physics and biotech, and from there, the conversation moves to the importance of resilience in human and animal aging. Finally, Peter walks us through GERO’s drug discovery approach, how we can ‘hack’ complex dynamic systems and aging using AI, and shares his optimism about the future of aging research.

The Finer Details of This Episode: 

Discussing complex systems and agingUnderstanding slower aging in some animalsResilience and dynamic stabilityThe dynamic frailty indicatorModels of premature aging and slow agingGERO’s drug discovery approach 

Quotes: 

“What we can do and what I think is very good to learn how to do in biology is to understand those universal properties that do not depend on fine details of life histories.”

“Obviously aging, that is a very slow process—so slow that almost everything averages out and people that are living under different conditions with totally different life histories are still living more or less the same long life.”

“It's easier to rejuvenate an animal which doesn't have any resilience because resilience means the ability to get back to the norm after the intervention. If you are resilient, either a bad effect like smoking or a good effect as your future aging drug will be small, and the more resilient you are, the smaller is the effect.”

“If you can only increase your lifespan once you're already unstable, the overall effect of such interventions from lifespan will be, unfortunately, incremental and limited.”

“It looks like our progress in chronic diseases is very slow. It's very slow because even though genome is cheaper, we have all genetic therapies, all kinds of new therapeutic modalities, everything, but for reasons that we need to understand, it's very hard to do drugs against chronic diseases in humans.”

“I think by bringing these ideas from neuroscience like your company is doing, like our company, like all our communities are doing, I think we will find ways to educate them. And who knows, maybe in five years, one of the major pharmas will start doing drugs against aging, using the techniques and the experience that we will help them to create; I think we're very close to this tipping point in the industry.”

Links: 

Email questions, comments, and feedback to [email protected]

Translating Aging on Twitter: @bioagepodcast

BIOAGE Labs Website BIOAGELabs.com

BIOAGE Labs Twitter @bioagelabs

BIOAGE Labs LinkedIn

Peter Fedichev on LinkedIn

GERO.AI

“Unsupervised learning of aging principles from longitudinal data” Avchaciov, K., Antoch, M.P., Andrianova, E.L. et al. Unsupervised learning of aging principles...

This week, we welcomes Peter Fedichev, an entrepreneur and scientist with over 20 years of experience in academic research and biotech business who has co-founded three biotech companies. He’s currently the co-founder & CEO of GERO, a longevity startup on a mission to hack aging. 

In this episode, Chris and Peter discuss GERO’s goal to accelerate our understanding of aging and create a therapy that will significantly extend a healthy human lifespan. First, they talk about the relationship between physics and biotech, and from there, the conversation moves to the importance of resilience in human and animal aging. Finally, Peter walks us through GERO’s drug discovery approach, how we can ‘hack’ complex dynamic systems and aging using AI, and shares his optimism about the future of aging research.

The Finer Details of This Episode: 

Discussing complex systems and agingUnderstanding slower aging in some animalsResilience and dynamic stabilityThe dynamic frailty indicatorModels of premature aging and slow agingGERO’s drug discovery approach 

Quotes: 

“What we can do and what I think is very good to learn how to do in biology is to understand those universal properties that do not depend on fine details of life histories.”

“Obviously aging, that is a very slow process—so slow that almost everything averages out and people that are living under different conditions with totally different life histories are still living more or less the same long life.”

“It's easier to rejuvenate an animal which doesn't have any resilience because resilience means the ability to get back to the norm after the intervention. If you are resilient, either a bad effect like smoking or a good effect as your future aging drug will be small, and the more resilient you are, the smaller is the effect.”

“If you can only increase your lifespan once you're already unstable, the overall effect of such interventions from lifespan will be, unfortunately, incremental and limited.”

“It looks like our progress in chronic diseases is very slow. It's very slow because even though genome is cheaper, we have all genetic therapies, all kinds of new therapeutic modalities, everything, but for reasons that we need to understand, it's very hard to do drugs against chronic diseases in humans.”

“I think by bringing these ideas from neuroscience like your company is doing, like our company, like all our communities are doing, I think we will find ways to educate them. And who knows, maybe in five years, one of the major pharmas will start doing drugs against aging, using the techniques and the experience that we will help them to create; I think we're very close to this tipping point in the industry.”

Links: 

Email questions, comments, and feedback to [email protected]

Translating Aging on Twitter: @bioagepodcast

BIOAGE Labs Website BIOAGELabs.com

BIOAGE Labs Twitter @bioagelabs

BIOAGE Labs LinkedIn

Peter Fedichev on LinkedIn

GERO.AI

“Unsupervised learning of aging principles from longitudinal data” Avchaciov, K., Antoch, M.P., Andrianova, E.L. et al. Unsupervised learning of aging principles from longitudinal data. Nat Commun 13, 6529 (2022). https://doi.org/10.1038/s41467-022-34051-9