BioAge Labs CEO & co-founder brings extensive knowledge and experience in early stage drug discovery strategy
Known Medicine is pleased to announce it has appointed Kristen Fortney, PhD, as an advisor. Dr. Fortney is the CEO and co-founder of BioAge Labs, a clinical stage drug discovery company in Richmond, CA. BioAge utilizes longitudinal patient data and artificial intelligence (AI) to identify the causes of age-related diseases.
“Dr. Fortney brings her personal experience as the CEO of a clinical stage, platform-based drug discovery company to our advisory board. We are thrilled to welcome her to our team in this capacity and value her insights as we utilize our platform and technology for asset in-licensing and the generation of new drugs to improve the lives of cancer patients,” said Andrea Mazzocchi, PhD, CEO and co-founder of Known Medicine.
Dr. Fortney received her PhD in medical biophysics from the University of Toronto, followed by postdoctoral training at Stanford University where she was a fellow of the Ellison Medical Foundation and American Federation for Aging Research. Her work over the lifetime of her career has focused on computational drug discovery, biomarkers of aging, and the genetics of exceptional human longevity. Founded in 2015, BioAge Labs couples genomic data with machine learning to measure human aging and to accelerate drug discovery. Under her leadership, BioAge has raised $127M from Andreessen Horowitz, Kaiser Foundation Hospitals, and others. BioAge currently has two assets in clinical trials and will begin a third in 2022, which highlights the success of their approach to accelerate drug discovery and provide patients with new therapies.
“Known Medicine is taking an innovative, patient-first approach to create a high-throughput platform and dataset. I’m excited to bring my expertise evaluating and developing drugs informed by data-driven investigations to Known Medicine,” said Kristen Fortney, PhD, CEO and co-founder of BioAge Labs.
Known Medicine generates thousands of 3-dimensional (3D) micro-tumors from a single patient sample. Each micro-tumor is tested with hundreds of drugs in parallel on their platform while observing how the patient-specific cancer cells respond. Their machine learning-based analysis relies on high-content images of these 3D micro-tumors to better understand why cells respond to one treatment over another.Functional outcomes are combined with high dimensional -omics datasets to identify the best patients for existing drugs, and the best new drug candidates.
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