BioAge platform

Identifying key drug targets

The BioAge platform identifies key drug targets that will impact aging. The company’s proprietary human aging cohorts have blood samples collected up to 45 years ago, with participant -omics data that is tied to extensive medical follow-up records including detailed future healthspan, lifespan, and disease outcomes.

We have built a systems biology and AI platform that leverages these rich datasets to identify the molecular drivers of age-related pathology. Our pipeline of therapies targeting these key pathways will address the significant unmet medical needs of an aging population.


The World Has Changed

The New Age of Healthcare

The world has changed, and so has the way technology is integrated with healthcare. The current pandemic has reinforced the belief that through the use of AI and machine learning technologies, will we be able to predict, detect, and diagnose healthcare conditions rapidly and accurately. There is a need to build better, accurate & reliable technologies that help us make a symbiotic leap in health-tech.


Curing the Incurable

Jack Kreindler & Parker Moss

The current status quo for the treatment of cancer is based on old data … which is not even captured from the vast majority of patients.  Cancers are also heterogeneous and rapidly evolving diseases. How can we shift cancer research and treatment to one of real world, continuous learning, enabled through layers of human and artificial intelligence, not buried deep in journals but available in real-time within the next generation of clinical user interfaces? 

At Exponential Medicine 2018, Dr. Jack Kreindler led an exploration of the topic with Parker Moss. Kreindler and Parker are helping build the United Kingdom’s SMART Grid for Cancer (S.ystems M.edicine and A.I with next gen R.andomized T.rials) to not only treat but truly study patients with currently incurable disease, to accelerate learning and democratize cures.


Aging clocks: AI-based biomarkers of aging


First published in 2016, predictors of chronological and biological age developed using deep learning are rapidly gaining popularity in the aging research community. These deep aging clocks [that have been developed using artificial intelligence (AI)] can be used in a broad range of applications in the pharmaceutical industry, spanning target identification, drug discovery, data economics, and synthetic patient data generation.

Recent advances in machine learning, coupled with increases in computational power and availability of the large publicly available datasets, have led to a renaissance in AI. These advances have generated substantial investment and hype, and many data scientists and companies are exploiting the surge in AI hype for promotional purposes. This has sown confusion in the market and triggered criticism from scientists working in the pharmaceutical industry, where approval in clinical trials is the ultimate measure of success.

Most of the credible advances in the field have been in deep learning and reinforcement learning. Since 2013, deep learning systems have surpassed human performance in multiple applications, including strategy games as well as image and text recognition. In healthcare, deep learning systems outperformed human dermatologists, ophthalmologists, and radiologists in various tasks. Deep learning also demonstrated significant improvement over conventional machine learning methods in biomedical data analysis.

Biomarkers of aging

During this same period of deep learning progress, aging research has also experienced a renaissance, and new breakthroughs are rapidly emerging. Multiple data types can be used to predict age and associate the prediction with mortality, disease, general well-being, or other biological processes including methylation, gene expression, microbiome, and imaging data.

Since the publication of the first multitissue methylation aging clock by Steven Horvath in 2013, multiple methylation aging clocks and applications of these clocks in humans and mice were developed. Even though these clocks were developed using traditional machine learning approaches – notably linear regression with regularization and the use of a limited number of samples – the results suggest that gradual changes during aging can be tracked using various data types with reasonable accuracy.

FULL TEXT: Trends in Pharmacological Sciences