VDP-122 [Assessment]: Ora Biomedical - Healthy Aging Therapies For Next Generation Medicine

One-liner: Ora Biomedical is an early-stage longevity biotechnology company seeking seed funding to move drug discovery programs forward and continue building the world’s largest longevity interventions database using its proprietary WormBot-AI massively high-throughput robotics and AI platform.

Longevity Dealflow WG team

  • Scientific evaluation: [To be completed after Senior Review - Phase 2]

  • Business evaluation: [To be completed after Review - Phase 2]

  • Shepherds: Eleanor Davies, Rhys Anderson

  • Other squad members: Mantas Matjusaitis

  • Sourced by: Eleanor Davies, Max Unfried

Project PI:

  • Dr. Mitchell Lee: CEO & Co-Founder

Simple Summary

Ora Biomedical is a longevity biotechnology company that identifies age-targeting therapeutics to broadly fight disease and extend healthy lifespan for internal development, B2B out-licensing, and partnered research. Our mission: Catalyze the next revolution in health through longevity medicine.

Problem

The challenge of screening small molecules for longevity primarily revolves around the current reliance on target-based screening in cultured cells. While cell-based assays offer a valuable initial glimpse into potential candidates, they often fall short in replicating the complex, multicellular environments and physiological intricacies of a whole organism. Consequently, the results obtained from cell culture may not accurately predict a compound’s impact on the entire biological system, potentially leading to misleading conclusions and a high rate of false positives. In contrast, phenotype-based screening in whole animals present a more informative and comprehensive approach to identifying longevity interventions.

Instead of focusing on particular mechanistic targets, phenotype-based approaches are target agnostic and allow for new aging mechanisms to be identified. By assessing the effects of compounds directly on organismal lifespan and healthspan, these screens provide a holistic understanding of their impact, offering a more robust foundation for identifying promising longevity therapeutics.

Solution

To accelerate small molecule longevity intervention discovery, we built the WormBot-AI. The WormBot-AI robotics and AI platform is designed for high-throughput, phenotype-based drug discovery. Diverging from conventional cell-based assays, WormBot-AI offers a unique advantage by concurrently assessing critical parameters such as lifespan, healthspan, and disease state end-points, thus affording a holistic evaluation of a compound’s effects.

The WormBot-AI platform is the new gold standard in high-throughput phenotype-based analysis.

The WormBot-AI establishes a new benchmark as the premier lifespan-screening platform, characterized by its exceptional scalability and experimental adaptability. The WormBot-AI performs image and video capturing of C. elegans populations from adulthood until death. Each WormBot-AI platform assays up to 144 populations of animals in a single experiment and performs multiple experiments in a single day. In one month, a single WormBot-AI platform can measure health and survival in over 1,000 distinct populations, or 25,000-30,000 animals.

Numerous phenotypes are captured and analyzed using our neural net AI pipeline, including survival, movement, behavior, and changes in disease associated phenotypes. We use sets of age-related phenotypes to create “healthspan clocks” that predict animal age and measure how drug treatment influences healthy aging. Additional features, like optional high-resolution fluorescence measurement, allow mechanistic and target validation to be easily performed using existing fluorescent tagged and other mutant worm strains.

WormBot-AI plays a central role in healthy aging intervention discovery and development towards two discrete commercial pathways. First, lifespan-extending small molecules are tested for age- and disease-related indications that form the foundation for subsequent clinical development of new age-targeting pharmaceuticals that fight disease and age-related physiological decline. Second, it facilitates identifying combinations of lifespan-extending natural products that can be developed for out-licensing to direct-to-consumer companies, providing an accelerated pathway to licensing revenue and recurring royalties from consumer product sales.

Opportunity

Targeting biological aging is the next revolution in health and unlocks a growing $2 trillion global market. Age-targeting interventions broadly fight disease and extend healthy lifespan in humans, companion pets, and other animals. However, few age-targeting compounds and targets currently exist. There are no substantial efforts to look outside of the known “longevity network” to find compounds that surpass current interventions and small molecule combinations that produce additive or synergistic benefits.

As the first age-targeting interventions show efficacy over the next five years, there will be a never-before-seen boom in interest and demand for longevity therapeutics. Powered by WormBot-AI massive phenotypic screening with lifespan and healthspan as primary endpoints, Ora Biomedical is poised to meet this unprecedented demand by identifying and developing the highest quality, most effective longevity therapeutics.

To date, we have screened 2,570 unique treatments and over 185,000 animals. As a comparison, DrugAge is the largest publicly available longevity intervention database and contains data for less than 1,100 different interventions.

Among our most promising early drug hits, we identified an FDA approved compound that extends lifespan and delays disease-associated phenotypes in an Alzheimer’s disease model. This is an off-patent compound that targets a mechanism not previously associated with biological aging. As we progress, this will be our first asset to move into our Drug Repurposing for Rare Disease drug development program. For this development program, we test FDA approved compounds to identify new therapeutics for rare disease. Starting with FDA approved compounds provides an accelerated development pathway because safety profiles for approved drugs already exist. Pursuing rare disease indications provides commercial advantages in the form of tax incentives, waivers, and market exclusivity rights for repurposed drugs.

For our development pipeline, FDA approved compounds that extend lifespan are tested across a panel of rare disease mutant C. elegans models. Drug efficacy identified in disease mutants provides pathways for validation studies in mammal systems. Finally, partnerships are identified with biotech and pharmaceutical companies to out-license assets for further development. We will identify rare disease indications for our first intervention within the next nine months. We then plan to pursue an IP-NFT partnership with VitaDAO to conduct mammalian validation and further develop this asset.

The second of our two initial drug development programs focus on creating new natural product combinations for direct-to-consumer products. Healthy aging natural product combinations backed by the highest quality science are in great demand in nutraceutical, skincare/cosmeceutical, and animal health products. Development of natural product and generally recognized as safe (GRAS) compound combinations that promote healthy aging provides an accelerated pathway to market because they are not intended to treat particular disease states and do not require FDA approval. We have had early conversations with skincare companies interested in licensing out our proprietary natural product combinations.

To develop new healthy aging natural product/GRAS compound combinations, we will use the WormBot-AI to screen an existing natural product library. We will test compounds separately, then test 2- and 4-way combinations of lifespan-extending compounds to identify the best lifespan-extending combinations. For skincare/cosmeceutical formulation development, natural product combinations will be tested in human tissue culture to identify those that increase collagen and extracellular matrix component expression while decreasing expression of inflammation and senescence-associated genes. Further nonclinical testing will be performed by partnered skincare/cosmeceutical companies. We are seeking funding through an NIH SBIR grant for natural product combination development. Phase I of this grant will provide $300K for library screening, combination development, and expression analysis. Phase II (which provides up to $2 million in funding) will focus on measuring senescence-associated phenotypes in cultured cells and nonclinical human testing. We expect to have out-licensed natural product combinations on the market generating royalty revenue by 2025.

The overarching goal of our large-scale drug screening efforts is to test one-million interventions for increased healthy lifespan. The Million-Molecule Challenge will transform longevity medicine by accelerating drug discovery by decades. With a fleet of 50 WormBot-AI platforms, we will complete this transformative goal within five years. The resulting database will be the world’s largest longevity interventions database and be of the highest quality and experimental consistency. In the database will be hundreds of novel single and combination lifespan extending interventions for commercialization and new mechanistic targets of biological aging. We will leverage the unmatched size and quality of our database to use generative AI techniques to further identify novel interventions, combinations, and healthy aging biological mechanisms. Our database and unmatched AI discovery will set Ora Biomedical apart as the source for longevity therapeutics. Novel longevity interventions, targets, and generative AI capacity create incredible value for this database. We are currently seeking federal grant funding to complete the Million-Molecule Challenge from the newly formed Advanced Research Projects Agency for Health (ARPA-H) agency.


The Ora Biomedical Million-Molecule Database: A Billion-dollar value proposition in five years.

Relevance to longevity

This project is profoundly relevant to the field of longevity as it represents a pioneering effort to catalyze breakthroughs in aging and develop interventions to extend both lifespan and healthspan. The Million-Molecule Challenge is a moonshot approach aiming to rapidly advance the discovery of longevity interventions while simultaneously constructing the world’s most extensive longevity interventions database. Employing a formidable 50-WormBot drug discovery army, this project seeks to test a staggering one-million interventions, systematically evaluating their impact on increasing lifespan and enhancing healthspan in C.elegans. By undertaking an unprecedentedly large exploration of potential interventions, this project holds the promise of unveiling novel combinatorial strategies to promote healthy aging and ultimately improve the quality of life for countless individuals.

IP Roadmap and Experimental plan​


Note: Ora Biomedical holds the exclusive commercial use rights for the WormBot robotics platform and neural net AI phenotyping and data analysis software granted by the University of Washington.

Budget

Stage 1: 6 months (IP generation)

  • Salaries: $275,000
  • Reagents: $62,000
  • Equipment: $20,000
  • Overhead/Operations: $40,000

Subtotal: $397,000

Stage 2: 12 months (IP validation)

  • Salaries: $650,000
  • Reagents: $200,000
  • Equipment: $50,000
  • Overhead/Operations: $100,000
  • Patent filings: $25,000
  • Medicinal Chem: $25,000

Subtotal: $1,050,000

Total: $1,447,000

Financing and VitaDAO Funding Terms

Ora Biomedical is raising a $1.5M Seed Series at a $15M post-money valuation with a total committed cash of $800K. The round is led by Sabey Corporation, a strategic investor seeking to advance healthcare innovation. The round will close by the end of October 2023. Prior to the Seed Series, Ora Biomedical raised $395K in pre-Seed SAFE financing at a $10M post-money valuation led by Sabey Corporation/Optispan, three angel investors, and an angel syndicate.

Ora Biomedical is seeking a $75K of funding from VitaDAO and welcomes conversations from other interested investors from within the VitaDAO community.

As a part of the million molecule challenge, Ora Biomedical has agreed to utilise the IP-NFT/IPT infrastructure to collaborate with VitaDAO on mutually agreed longevity/disease specific therapeutic candidate programs derived from the WormBotAI platform under the principal budgeting scheme above. VitaDAO will utilize the IP-NFT/IPT framework to finance, develop and commercialize multiple potential IP assets and R&D data in parallel allowing both parties to de-risk multiple assets at scale with a non-dilutive asset based funding mechanism.

Team

Leadership

Mitchell Lee, PhD (CEO and Co-Founder)
Dr. Mitchell Lee, CEO of Ora Biomedical, researches healthy aging, natural genetic variation and how it impacts longevity intervention efficacy, and age-related disease. He has received science communication awards, NIH grants, and a Howard Hughes Medical Institute Gilliam Fellowship. Dr. Lee is a successful scientific leader, with over 45 mentored researchers and varied leadership experience in scientific societies, like the American Aging Association. Dr. Lee holds a PhD in Experimental Pathology, degrees in Biology and Philosophy, and a Biotechnology Project Management certificate.

Matt Kaeberlein, PhD (Chair, Board of Directors and Co-Founder)
Dr. Matt Kaeberlein, CEO of Optispan and a former University of Washington School of Medicine professor, is a groundbreaking leader in aging research who focuses on aging mechanisms and interventions to enhance healthspan for humans and pets. Dr. Kaeberlein has authored 200+ scientific papers and received awards like the Vincent Cristofalo Rising Star in Aging Research Award. Throughout his career, Dr. Kaeberlein has led several aging research institutes, training programs, and scientific societies.

Brian Kennedy, PhD (Co-Founder)
Dr. Brian Kennedy, internationally acclaimed for his foundational discoveries in biological aging, directs the Centre for Healthy Longevity at the National University Health System in Singapore. Dr. Kennedy is the former CEO of the Buck Institute for Research on Aging and involved in biotech companies. Dr. Kennedy also serves as a Co-Editor-In-Chief at Aging Cell. Dr. Kennedy is a thought-leader in aging and played a pivotal role in popularizing the geroscience concept which connects biological aging to age-associated and other disease states.

Ben Blue, PhD (CTO and Co-Founder)
Dr. Benjamin Blue, Ora Biomedical’s Chief Technical Officer, specializes in laboratory automation and technology development to facilitate scientific advances. Dr. Blue is the architect of Ora Biomedical’s neural net AI phenotyping and data analysis software. Dr. Blue has a broad background creating and optimizing nematode-based robotics and automation tools. Dr. Blue earned a PhD in Molecular Medicine and Mechanism of Disease from the University of Washington School of Medicine after completing his B.S. in Biochemistry from the University of Oregon.

Jason Pitt, PhD (WormBot Inventor and Co-Founder)
Dr. Pitt, a former University of Washington scientist, is the creator of the WormBot robotics platform. Dr. Pitt’s research spans biology, software, and hardware engineering, with a large focus on understanding the effects of low oxygen on C. elegans aging. He has successfully competed for grants from the NIH and NASA. Dr. Pitt earned his PhD degree in Molecular and Cellular Biology from the University of Washington.

Jan Gruber, PhD (Co-Founder)
Dr. Jan Gruber holds joint appointments at Yale-NUS and NUS Yong Loo Lin School of Medicine. He is the founder of the The C. elegans Ageing Laboratory at National University Singapore Centre of Life Science and researches aging mechanisms like mitochondrial dysfunction and macromolecular damage accumulation. Dr. Gruber is an expert in combinatorial drug analysis to identify new lifespan and age-associated disease therapeutics. He has over 60 published research papers and holds degrees from RWTH Aachen University, the University of Cambridge, and the University of Oxford.

Key Collaborators

Due to the long-standing leadership efforts of Drs. Lee, Kaeberlein, and Kennedy within the field, the Ora Biomedical team is incredibly well-networked among biologists of aging and age-associated disease. Below is a list of current research and resource sharing collaborators:

  • Dr. Jessica Young, University of Washington School of Medicine
  • Dr. Alaattin Kaya, Virginia Commonwealth University
  • Dr. Andrey Parkhitko, University of Pittsburgh
  • Dr. Brian Kraemer, VA Puget Sound Health Care System
  • Dr. Alessandro Bitto, University of Washington School of Medicine

#[quote=“gweisha, post:1, topic:1444, full:true”]
One-liner: Ora Biomedical is an early-stage longevity biotechnology company seeking seed funding to move drug discovery programs forward and continue building the world’s largest longevity interventions database using its proprietary WormBot-AI massively high-throughput robotics and AI platform.

Longevity Dealflow WG team

  • Senior Reviewers: 2 scientists, 1 pharma professional, 1 VC, 1 biotech entrepreneur

  • Shepherds: Eleanor Davies, Rhys Anderson

  • Other squad members: Mantas Matjusaitis

  • Sourced by: Eleanor Davies, Max Unfried

Project PI:

  • Dr. Mitchell Lee: CEO & Co-Founder

Simple Summary

Ora Biomedical is a longevity biotechnology company that identifies age-targeting therapeutics to broadly fight disease and extend healthy lifespan for internal development, B2B out-licensing, and partnered research. Our mission: Catalyze the next revolution in health through longevity medicine.

Problem

The challenge of screening small molecules for longevity primarily revolves around the current reliance on target-based screening in cultured cells. While cell-based assays offer a valuable initial glimpse into potential candidates, they often fall short in replicating the complex, multicellular environments and physiological intricacies of a whole organism. Consequently, the results obtained from cell culture may not accurately predict a compound’s impact on the entire biological system, potentially leading to misleading conclusions and a high rate of false positives. In contrast, phenotype-based screening in whole animals present a more informative and comprehensive approach to identifying longevity interventions.

Instead of focusing on particular mechanistic targets, phenotype-based approaches are target agnostic and allow for new aging mechanisms to be identified. By assessing the effects of compounds directly on organismal lifespan and healthspan, these screens provide a holistic understanding of their impact, offering a more robust foundation for identifying promising longevity therapeutics.

Solution

To accelerate small molecule longevity intervention discovery, we built the WormBot-AI. The WormBot-AI robotics and AI platform is designed for high-throughput, phenotype-based drug discovery. Diverging from conventional cell-based assays, WormBot-AI offers a unique advantage by concurrently assessing critical parameters such as lifespan, healthspan, and disease state end-points, thus affording a holistic evaluation of a compound’s effects.

The WormBot-AI platform is the new gold standard in high-throughput phenotype-based analysis.

The WormBot-AI establishes a new benchmark as the premier lifespan-screening platform, characterized by its exceptional scalability and experimental adaptability. The WormBot-AI performs image and video capturing of C. elegans populations from adulthood until death. Each WormBot-AI platform assays up to 144 populations of animals in a single experiment and performs multiple experiments in a single day. In one month, a single WormBot-AI platform can measure health and survival in over 1,000 distinct populations, or 25,000-30,000 animals.

Numerous phenotypes are captured and analyzed using our neural net AI pipeline, including survival, movement, behavior, and changes in disease associated phenotypes. We use sets of age-related phenotypes to create “healthspan clocks” that predict animal age and measure how drug treatment influences healthy aging. Additional features, like optional high-resolution fluorescence measurement, allow mechanistic and target validation to be easily performed using existing fluorescent tagged and other mutant worm strains.

WormBot-AI plays a central role in healthy aging intervention discovery and development towards two discrete commercial pathways. First, lifespan-extending small molecules are tested for age- and disease-related indications that form the foundation for subsequent clinical development of new age-targeting pharmaceuticals that fight disease and age-related physiological decline. Second, it facilitates identifying combinations of lifespan-extending natural products that can be developed for out-licensing to direct-to-consumer companies, providing an accelerated pathway to licensing revenue and recurring royalties from consumer product sales.

Opportunity

Targeting biological aging is the next revolution in health and unlocks a growing $2 trillion global market. Age-targeting interventions broadly fight disease and extend healthy lifespan in humans, companion pets, and other animals. However, few age-targeting compounds and targets currently exist. There are no substantial efforts to look outside of the known “longevity network” to find compounds that surpass current interventions and small molecule combinations that produce additive or synergistic benefits.

As the first age-targeting interventions show efficacy over the next five years, there will be a never-before-seen boom in interest and demand for longevity therapeutics. Powered by WormBot-AI massive phenotypic screening with lifespan and healthspan as primary endpoints, Ora Biomedical is poised to meet this unprecedented demand by identifying and developing the highest quality, most effective longevity therapeutics.

To date, we have screened 2,570 unique treatments and over 185,000 animals. As a comparison, DrugAge is the largest publicly available longevity intervention database and contains data for less than 1,100 different interventions.

Among our most promising early drug hits, we identified an FDA approved compound that extends lifespan and delays disease-associated phenotypes in an Alzheimer’s disease model. This is an off-patent compound that targets a mechanism not previously associated with biological aging. As we progress, this will be our first asset to move into our Drug Repurposing for Rare Disease drug development program. For this development program, we test FDA approved compounds to identify new therapeutics for rare disease. Starting with FDA approved compounds provides an accelerated development pathway because safety profiles for approved drugs already exist. Pursuing rare disease indications provides commercial advantages in the form of tax incentives, waivers, and market exclusivity rights for repurposed drugs.

For our development pipeline, FDA approved compounds that extend lifespan are tested across a panel of rare disease mutant C. elegans models. Drug efficacy identified in disease mutants provides pathways for validation studies in mammal systems. Finally, partnerships are identified with biotech and pharmaceutical companies to out-license assets for further development. We will identify rare disease indications for our first intervention within the next nine months. We then plan to pursue an IP-NFT partnership with VitaDAO to conduct mammalian validation and further develop this asset.

The second of our two initial drug development programs focus on creating new natural product combinations for direct-to-consumer products. Healthy aging natural product combinations backed by the highest quality science are in great demand in nutraceutical, skincare/cosmeceutical, and animal health products. Development of natural product and generally recognized as safe (GRAS) compound combinations that promote healthy aging provides an accelerated pathway to market because they are not intended to treat particular disease states and do not require FDA approval. We have had early conversations with skincare companies interested in licensing out our proprietary natural product combinations.

To develop new healthy aging natural product/GRAS compound combinations, we will use the WormBot-AI to screen an existing natural product library. We will test compounds separately, then test 2- and 4-way combinations of lifespan-extending compounds to identify the best lifespan-extending combinations. For skincare/cosmeceutical formulation development, natural product combinations will be tested in human tissue culture to identify those that increase collagen and extracellular matrix component expression while decreasing expression of inflammation and senescence-associated genes. Further nonclinical testing will be performed by partnered skincare/cosmeceutical companies. We are seeking funding through an NIH SBIR grant for natural product combination development. Phase I of this grant will provide $300K for library screening, combination development, and expression analysis. Phase II (which provides up to $2 million in funding) will focus on measuring senescence-associated phenotypes in cultured cells and nonclinical human testing. We expect to have out-licensed natural product combinations on the market generating royalty revenue by 2025.

The overarching goal of our large-scale drug screening efforts is to test one-million interventions for increased healthy lifespan. The Million-Molecule Challenge will transform longevity medicine by accelerating drug discovery by decades. With a fleet of 50 WormBot-AI platforms, we will complete this transformative goal within five years. The resulting database will be the world’s largest longevity interventions database and be of the highest quality and experimental consistency. In the database will be hundreds of novel single and combination lifespan extending interventions for commercialization and new mechanistic targets of biological aging. We will leverage the unmatched size and quality of our database to use generative AI techniques to further identify novel interventions, combinations, and healthy aging biological mechanisms. Our database and unmatched AI discovery will set Ora Biomedical apart as the source for longevity therapeutics. Novel longevity interventions, targets, and generative AI capacity create incredible value for this database. We are currently seeking federal grant funding to complete the Million-Molecule Challenge from the newly formed Advanced Research Projects Agency for Health (ARPA-H) agency.


The Ora Biomedical Million-Molecule Database: A Billion-dollar value proposition in five years.

Relevance to longevity

This project is profoundly relevant to the field of longevity as it represents a pioneering effort to catalyze breakthroughs in aging and develop interventions to extend both lifespan and healthspan. The Million-Molecule Challenge is a moonshot approach aiming to rapidly advance the discovery of longevity interventions while simultaneously constructing the world’s most extensive longevity interventions database. Employing a formidable 50-WormBot drug discovery army, this project seeks to test a staggering one-million interventions, systematically evaluating their impact on increasing lifespan and enhancing healthspan in C.elegans. By undertaking an unprecedentedly large exploration of potential interventions, this project holds the promise of unveiling novel combinatorial strategies to promote healthy aging and ultimately improve the quality of life for countless individuals.

IP Roadmap and Experimental plan​


Note: Ora Biomedical holds the exclusive commercial use rights for the WormBot robotics platform and neural net AI phenotyping and data analysis software granted by the University of Washington.

Budget

Stage 1: 6 months (IP generation)

  • Salaries: $275,000
  • Reagents: $62,000
  • Equipment: $20,000
  • Overhead/Operations: $40,000

Subtotal: $397,000

Stage 2: 12 months (IP validation)

  • Salaries: $650,000
  • Reagents: $200,000
  • Equipment: $50,000
  • Overhead/Operations: $100,000
  • Patent filings: $25,000
  • Medicinal Chem: $25,000

Subtotal: $1,050,000

Total: $1,447,000

Financing and VitaDAO Funding Terms

Ora Biomedical is raising a $1.5M Seed Series at a $15M post-money valuation with a total committed cash of $800K. The round is led by Sabey Corporation, a strategic investor seeking to advance healthcare innovation. The round will close by the end of October 2023. Prior to the Seed Series, Ora Biomedical raised $395K in pre-Seed SAFE financing at a $10M post-money valuation led by Sabey Corporation/Optispan, three angel investors, and an angel syndicate.

Ora Biomedical is seeking a $75K of funding from VitaDAO and welcomes conversations from other interested investors from within the VitaDAO community.

As a part of the million molecule challenge, Ora Biomedical has agreed to utilise the IP-NFT/IPT infrastructure to collaborate with VitaDAO on mutually agreed longevity/disease specific therapeutic candidate programs derived from the WormBotAI platform under the principal budgeting scheme above. VitaDAO will utilize the IP-NFT/IPT framework to finance, develop and commercialize multiple potential IP assets and R&D data in parallel allowing both parties to de-risk multiple assets at scale with a non-dilutive asset based funding mechanism.

Team

Leadership

Mitchell Lee, PhD (CEO and Co-Founder)
Dr. Mitchell Lee, CEO of Ora Biomedical, researches healthy aging, natural genetic variation and how it impacts longevity intervention efficacy, and age-related disease. He has received science communication awards, NIH grants, and a Howard Hughes Medical Institute Gilliam Fellowship. Dr. Lee is a successful scientific leader, with over 45 mentored researchers and varied leadership experience in scientific societies, like the American Aging Association. Dr. Lee holds a PhD in Experimental Pathology, degrees in Biology and Philosophy, and a Biotechnology Project Management certificate.

Matt Kaeberlein, PhD (Chair, Board of Directors and Co-Founder)
Dr. Matt Kaeberlein, CEO of Optispan and a former University of Washington School of Medicine professor, is a groundbreaking leader in aging research who focuses on aging mechanisms and interventions to enhance healthspan for humans and pets. Dr. Kaeberlein has authored 200+ scientific papers and received awards like the Vincent Cristofalo Rising Star in Aging Research Award. Throughout his career, Dr. Kaeberlein has led several aging research institutes, training programs, and scientific societies.

Brian Kennedy, PhD (Co-Founder)
Dr. Brian Kennedy, internationally acclaimed for his foundational discoveries in biological aging, directs the Centre for Healthy Longevity at the National University Health System in Singapore. Dr. Kennedy is the former CEO of the Buck Institute for Research on Aging and involved in biotech companies. Dr. Kennedy also serves as a Co-Editor-In-Chief at Aging Cell. Dr. Kennedy is a thought-leader in aging and played a pivotal role in popularizing the geroscience concept which connects biological aging to age-associated and other disease states.

Ben Blue, PhD (CTO and Co-Founder)
Dr. Benjamin Blue, Ora Biomedical’s Chief Technical Officer, specializes in laboratory automation and technology development to facilitate scientific advances. Dr. Blue is the architect of Ora Biomedical’s neural net AI phenotyping and data analysis software. Dr. Blue has a broad background creating and optimizing nematode-based robotics and automation tools. Dr. Blue earned a PhD in Molecular Medicine and Mechanism of Disease from the University of Washington School of Medicine after completing his B.S. in Biochemistry from the University of Oregon.

Jason Pitt, PhD (WormBot Inventor and Co-Founder)
Dr. Pitt, a former University of Washington scientist, is the creator of the WormBot robotics platform. Dr. Pitt’s research spans biology, software, and hardware engineering, with a large focus on understanding the effects of low oxygen on C. elegans aging. He has successfully competed for grants from the NIH and NASA. Dr. Pitt earned his PhD degree in Molecular and Cellular Biology from the University of Washington.

Jan Gruber, PhD (Co-Founder)
Dr. Jan Gruber holds joint appointments at Yale-NUS and NUS Yong Loo Lin School of Medicine. He is the founder of the The C. elegans Ageing Laboratory at National University Singapore Centre of Life Science and researches aging mechanisms like mitochondrial dysfunction and macromolecular damage accumulation. Dr. Gruber is an expert in combinatorial drug analysis to identify new lifespan and age-associated disease therapeutics. He has over 60 published research papers and holds degrees from RWTH Aachen University, the University of Cambridge, and the University of Oxford.

Key Collaborators

Due to the long-standing leadership efforts of Drs. Lee, Kaeberlein, and Kennedy within the field, the Ora Biomedical team is incredibly well-networked among biologists of aging and age-associated disease. Below is a list of current research and resource sharing collaborators:

  • Dr. Jessica Young, University of Washington School of Medicine
  • Dr. Alaattin Kaya, Virginia Commonwealth University
  • Dr. Andrey Parkhitko, University of Pittsburgh
  • Dr. Brian Kraemer, VA Puget Sound Health Care System
  • Dr. Alessandro Bitto, University of Washington School of Medicine

Slide deck

Slide Deck: link to deck non-confidential deck

Highlights

  • Exclusive commercial use rights for automated WormBot-AI platform
  • Building world’s largest and highest quality longevity intervention database (screening 1M small molecules in C. elegans)
  • New lifespan-extending interventions and aging targets already identified
  • Accelerated pathways to revenue with early focus on natural product combination development and FDA drug repurposing for rare diseases
  • First provisional patents in development

Risks

  • Compounds that extend lifespan in worms may not work in other models, human cells, or clinical trials
  • Prior use of C. elegans robotics precludes patent protection for WormBot platform
  • Small drug screens in C.elegans have been conducted in the past

Press/Bibliography

  • Agree
  • Revisions Requested [Details in Comments]
  • Disagree
0 voters

[/quote]

Press/Bibliography

  • Agree
  • Revisions Requested [Details in Comments]
  • Disagree
0 voters

The grantsmanship on this one is A+, which bodes well for the SBIR application. Other strengths include strong mission relevance and the willingness to use the IP-NFT.

But there are no preliminary data.

And the premise is ‘let’s screen a ton of worms against a bunch of drugs in a high throughput assay but we’ll rename the analysis “AI” to cash in on a buzzword’ and hope we get some good hits. The screen for a cool phenotype, hand the hits to postdoc to characterize the phenotype and hope it turns into a Nature paper does work. Sometimes. It also burns through a lot of postdocs and characterization data. And many fail.

I thought most drug libraries people screened these days started at 10,000 to 30,000 compounds. The ‘million drug’ screen is great marketing, but at 2570 compounds tested so far, it’s not clear the company can sustain the 4000 compounds tested per year per system.

How many hits have been identified? How many already-known hits were found? Did it miss any expected hits? What validation data exist for identified hits? Are the 2570 “unique treatments” screened a random batch (eg we can estimate hit rate), or are there selection biases in them? Is the analysis ‘when do the worms stop moving?’, or is the platform able to measure intermediate phenotypes, fecundity, behavioral/foraging changes? How do you rule out effects of the compounds on the bacteria that the worms are fed?

1 Like

Thanks for the comments and opportunity to respond. For anyone who is wondering “is Matt really involved”. Matt is deeply involved and passionate about making this happen. The new company I’m leading (Optispan) is co-located with Ora in adjoining space, and I’m over at Ora every day working with these guys. I am personally and profoundly invested in making this happen because I truly believe this is among the most important initiatives in this field (the Dog Aging Project being another one).

For those who were in Dublin and saw my talk, you’ll appreciate that the premise is not “let’s screen a ton of worms”. I believe that talk will be posted online soon. The premise behind Ora and why I pushed to build this is that intervention discovery in the field has stagnated (this is data) and we need to start looking outside the lamppost if we want to do better than rapamycin and CR (this is my opinion). The Million Molecule Challenge is an opportunity to change the game in longevity by identifying interventions with effect sizes much larger than what we currently have. Personally, I’m not satisfied with rapamycin even though I like to talk about it a lot :slight_smile:

I don’t quite understand the dismissive tone or comments about post-docs. There are no post-docs involved here and the system does combine robotics with AI (sorry if that’s a buzzword) to enable quantitative analysis of longevity interventions at a scale that has never before been possible. The quantitative part is important here.
This is not a sloppy drug screen in an academic lab where only one hit is validated and followed up to publish a paper. This is rigorous quantitative analysis with built in replication for 1,000,000 unique interventions. The system is automated, removing human bias, and the raw data are stored as time-stamped digital images that can be re-analyzed and verified and seed future computational research. Ora will find interventions more effective than anything we currently have in the non-genetic space. What percentage of these will translate through to mammals and eventually humans? That’s impossible to know for certain, but history tells us that some will, and probably those with the largest effect size will be most likely to work in mammals. Fortunately, we have rapidly improving tools to quickly determine this in mice in order to prioritize.

It’s also worth stating that lifespan and healthspan metrics are not only a “cool phenotype”, they are the most important phenotype if you are interested in longevity. Too often people try to game the system and fail because they aren’t looking for what matters. We want interventions that have big effects on lifespan and healthspan. Period, full stop.

I showed two types of preliminary data in my talk in Dublin. In the first, Ora screened an FDA library alone and in combination with metformin across a transgenic line expressing Abeta. The takehomes are that several hits were identified and combinations (drug X + metformin) often yield interesting interactions including additivity, synergy, and anti-synergy. Combinatorial interactions are essentially a black box in the field and Ora is going to blow the lid off that box.

The second set of preliminary data I showed is the drug analysis (FDA approved + a few experimental mTOR inhibitors) for lifespan in wild type worms. The data were blinded of course. The takehome there is that we already have an mTOR inhibitor that works better than rapamycin.

AFAIK, nothing that is a solid true positive in worms has failed to be detected in Ora’s analyses so far, but let’s be honest, there aren’t that many solid true positives in worms. The literature is a mess because a lot of sloppy work gets published. Rapamycin, metformin, AKG, rifampicin, etc. all consistently test positive on the WormBot. In this regard, I think it’s also worth appreciating that we aren’t interested in things that have a small effect (20%) - we’re interested in things that have huge effects (think 200%). The scale of what Ora can do means we can tolerate some false negatives and the likelihood of a false negative diminishes with effect size.

The comment about “it’s not clear the company can sustain the 4000 compounds tested per year per system” is unwarranted. The company has quantitatively assessed more compounds than the entire rest of the field combined in the last 6 months (actually more than the entire DrugAge database) on a shoestring budget, using 5 WormBots and three full-time employees, only one of whom is doing the actual worm work. Come on. When we scale to 50 WormBots things are gonna get interesting really fast.

How do we get to 1,000,000? Combinations. I think everyone agrees we need to look at combinations, but nobody else has a good solution for how to do it. Why 1,000,000? Because we can, and it’s a eye-catching number. Will we stop at 1,000,000? I don’t think so. That’s the beauty of this. Once the factory (or army - I like “army of robots to cure aging”) is built, ongoing costs become much reduced. This means opportunities to continue pushing the envelope or to expand the phenotypes of interest (e.g. disease models, behavioral models, etc.). In this regard, it’s worth mentioning that even though we aren’t looking for things like antihelminthics or behavior modifying drugs, these will come out of the longevity screen. Very short lifespan = potential novel antihelminthic. And the AI algorithms can be retooled to find things that modify all sorts of behavioral phenotypes.

The other more detailed questions about the bacteria, movement, fecundity, statistics, etc. are all reasonable questions, but also things that have been solved a long time ago in my lab and elsewhere. We know what we’re doing and have done rigorous work in this area for a long time. I’d be happy to jump on a call with the group and answer any and all questions in as much detail as people want, but I also hope at some point you can accept the track record and experience here.

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‘Trust me bro’ is not an argument I buy in science.

Lots of brilliant scientists have had lots of ideas that failed. Team is close to a gateway criterion for me-- is the expertise there to do it? If yes, move forward. If not, major red flag. If this was your SBIR that I was scoring, Investigator would be an easy 1 or 2. I would expect environment to be a 1, too. Approach and Innovation would be different stories. Overall Impact would be close to whatever I gave for Approach.

This proposal sounds like the standard approach to postdocs in high profile labs that’s been used for the last 20+ years. Create a screen to select for a specific phenotype (in your case the software is detecting worm movement as a proxy for healthspan). Use the screen to pull out hits for that phenotype. Validate the top hits. Go after the most exciting hit or hits to get mechanism. Leverage the screen to get a postdoc grant. Publish in Nature. Get faculty position and spend career digging through the other hits/working on that mechanism.

I acknowledge that this approach works sometimes. But there are a ton of screens that fizzle out and go nowhere, and that’s not necessarily the postdoc’s fault. It’s also something anybody can do, and is limited by access to the compounds. That’s why preliminary and feasibility data are key for these kinds of screens.

My experience is that imprecise language + overconfidence = worse prelim and feasibility data than we assume. Case in point is the math on compound screening:

The math is even worse than I assumed because I thought you did all 2750 on 1 wormbot:

2750 compounds / 5 wormbots /0.5 year = 1100 compounds /wormbot/year
50 wormbots x 1100 compounds /wormbot/year = 55,000 compounds/year.
1000000 combos/55000 compounds/year = 18.18 years.

You need to be running 4000 compounds/wormbot/year to hit your goal, and you’re nowhere close to that level of output with the wormbots you have. How do you get to the needed level of output? If you scale with no loss of efficiency (and that’s a big if), you’ll test 275,000 compounds in 5 years. So this proposal sounds over-ambitious. If you’re overambitious about the math, easy leap to assume you’re overambitious about the harder parts of the project, too.

Is this 2 hits? 5 hits? out of 100 compounds? out of 1000? All 2750? What is the magnitude of effect in these hits? Is your cutoff 200% for lifespan increases? Restoration to wild type levels? This leaves aside hit validity due to the risks/challenges with Abeta in general, much less doing it in worms.

How much better? How was this hit confirmed?

Dare I even ask about concentrations of the compounds screened?

What is your estimation of the hit rate with the compounds you’ve screened already?

But you’ve only been using WormBot for 6 months… Are they being done with Wormbots?

On one hand, giant screens may pull something out. On the other hand, people have been doing big screens for the last 30 years or so, including with worms. So I’m skeptical that your approach fixes the problem of ‘stagnated intervention discovery’ until I see data and feasibility suggesting it might.

From a big picture approach, I don’t see one drug, or even 2-3 drugs together, giving us a 2x for human age. They may be part of getting us there, but I don’t think they’ll be enough by themselves.

As I said, we’d be happy to jump on a call and discuss technical details with the team if that’s a genuine concern. I don’t think you actually believe that Ora isn’t going to find a bunch of novel longevity- extending drugs, several of which work much better than anything currently out there. I’m confident that some of those will also work in mice and dogs and humans. I can’t prove that yet because we haven’t done it. If you fundamentally don’t believe in the conservation of longevity mechanisms, then you won’t believe this and we’re stuck.

It also seems obvious that there are multiple paths toward monetization of novel longevity interventions once they’ve been validated pre-clinically in mice. Two to three years from now, Ora will be positioned exceptionally well to capitalize on this. Ora will own the database with by then 200,000+ longevity intervention experiments with at least a couple validated in aged mice to work better than rapamycin. The database itself will likely be worth multiples of the capital invested by that point.

Re: the preliminary data - of course the study in the Abeta strain is subject to caveats and 2-3x there doesn’t necessarily mean 2-3x in wild type worms. That was never the point. The point was to start to understand what the interaction map looks like. It’s a proof-of-principle to show that we can perform combinatorial experiments at the 1000s of interactions level rather than 3-4 per study, which is what the field is limited to at the moment.

The Ora team has been been building Wormbots, improving the technology, and screening simultaneously all while building a fledgling company. Obviously, testing of interventions has not been going at max throughput or capacity and 3 guys still outperformed the rest of the field combined and quantitatively tested more interventions than are contained in the entire DrugAge database. If that doesn’t impress you and help you believe the team can get the job done, then I’m not sure what would.

Tech dev has been one of the things the team has been working on simultaneously with everything else, which has allowed us to develop a plate-swapping system that enables a single WormBot to measure 144 x 6 simultaneous lifespan experiments. Let’s say it takes 8 weeks to do a flight, which is about twice as long as it actually takes, that’s 5616 per year. We can get in the weeds and talk about whether it’s even necessary to do the experiment until the very end - for screening purposes you already know the answer after ~20 days, but I like having the quantitative data. Regardless, Ora could, of course, build more devices (50 is the current plan) if necessary to hit our goals. Per device costs are not a limiting factor here. This is scalable as far as you want to take it. Again 1,000,000 is just a big number. There’s nothing magical about it.

Nobody knows what will double human lifespan. I would agree with you that I don’t think 2 or 3 of the currently known longevity drugs will double human lifespan. I also know that we’ve only explored a tiny fraction of the intervention space and even that tiny fraction is dominated by qualitative loss-of-function genetic studies, so who knows what we’ll find if we stop looking under the lamppost. What I do know is that you won’t catch any fish if you don’t go fishing, and you won’t catch the big fish if you fish from the dock.

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Why would I be mentioning something as risk or problem if it wasn’t a genuine concern? Not a big fan of calls, though.

I am agnostic on this at the moment, but the lack of concrete data pushes me more to the ‘no’ side.
Most of my questions are aimed at getting a better sense of this.

I don’t know how many hits you will get. Of those hits, I don’t know how many will be already known. Of the remaining hits, I don’t know how many will fail primary validation. Of the survivors, I don’t know how many will translate to other preclinical models. Without data, I lean to ‘worse answers than I might think’.

High-throughput screens for worms are not novel. Nor do I believe for a second that only 2000 -3000 compounds have been screened in worms by the entire field. One database like DrugAge is not the be-all, end-all of drug screening in worms.

That means it is likely that others have tried many of these compounds you will be testing, and they failed. So will one more screen do the trick? I don’t know. It depends on the quality of the screen, which is not possible to assess with the information given. Right now, you’ve given ‘one TOR inhibitor that does better than rapamycin’. But it’s not even clear how much better. What is convincing: data.

This sounds like you haven’t considered any challenges with scaling the system up, which suggests they will be a surprise when they happen.

You’re the one promising 1 million, as a grandiose number to catch attention and to tease a $1B valuation. 200k would have been more practical. So that part looks oversold, and makes the rest of the proposal also look oversold. The way to evaluate if it’s all sales: data, plus plans for pitfalls and alternative approaches.

For me, the weaknesses on this proposal (lack of data) dampens enthusiasm, and outweighs the strengths.

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I’m sorry you feel that way, but of course that’s your perogative. I’ll simply restate that Ora has preliminary data including quantitative lifespan assessment for longevity interventions already exceeding that contained in DrugAge. Additionally, extensive preliminary data was presented in Fig 1 of the Million Molecule Challenge paper linked above and here again (The million-molecule challenge: a moonshot project to rapidly advance longevity intervention discovery - PubMed). I’m sure Mitch can arrange to have additional data provided for anyone who seriously doubts our credibility. I will also restate my willingness to jump on a call with anyone in the group who would find that valuable. Thanks for considering.

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Hello and thank you for your engagement. Preliminary data can be found in the paper Matt referenced below. Here are a couple recent hits:


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The AI that we use is computer vision for animal tracking and phenotype calling (surivival, movement, behavior, and others). We also use AI to create “healthspan clocks” based off of this data that predict animal age based on this phenotypic data. As the database grows, we will be leveraging generative AI techniques to predict new interventions, targets, and intervention combinations.

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Regarding throughput, our current techniques and capacity per bot allow us to test over 500 interventions per bot per day (depending on replication). An experiment runs for about a month. So, we can test about 6,000 interventions per bot per year. With 50 bots, that’s 300,000 interventions per year. We are growing the team and WormBot fleet over the next two years to meet that throughput. With three years at that throughput combined with past progress, we will surpass one-million interventions within five years.

Importantly, it takes a larger team to accomplish the throughput we will achieve. This plays to the strengths of the current team as we are all experienced managing large team science research. The Kaeberlein Lab created a playbook for these approaches some time ago.

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I’ve known Matt and Mitch for awhile and am big supporters of their cautious and creative approach to Geroscience. I think WormBot and Ora’s approach provides an excellent way to test combinations of gerotherapeutics (in different categories) to have a synergistic effect on longevity. They have a great value proposition: discovery biology coupled with rapid derisking of compounds.

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Thanks for posting some preliminary data and additional information about throughput.

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Thanks for this expansive and clarifying comments @mkaeberlein @mlee33 and also for the great questions @bowtiedshrike!! Personally very excited about this effort!

Thank you everyone for your thoughtful comments. The deal squad and Ora team will decide on next steps

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super excited! @gweisha

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