Kristen Fortney. Image/BioAge
BY JONATHAN SMITH
The development of longevity treatments is hampered by a lack of biomarkers and validated drug targets. BioAge’s co-founder and CEO, Kristen Fortney, explains how the firm is enlisting machine learning (ML), artificial intelligence (AI) and biobanks to fill in the gaps.
The quest for human longevity treatments is attracting big cash in the biotech industry. One of the most impressive investments entering the field was the $3 billion launch of the U.S. anti-aging company Altos Labs in January 2022.
However, efforts to extend our healthy lifespan are dogged by a lack of clear biomarkers that correlate with the aging process. There are a set of observed hallmarks of aging, such as the breakdown of cells, a lack of stem cells in tissues and unstable DNA, but the search for reliable drug targets to slow the aging process is difficult.
In 2020, for example, the U.S. company Unity Biotechnology hit a major setback when its lead drug targeting aged, or “senescent,” cells failed to treat the age-related condition osteoarthritis in a phase 2 trial.
To overcome the challenges of developing longevity treatments, BioAge was launched in the U.S. in 2015. The firm raised a $90 million Series C round in late 2020 to finance the development of small molecule drug candidates for age-related conditions including anemia, muscle atrophy and COVID-19. BioAge also paired up with Age Lab AS in August 2022 to tap into the latter firm’s biobank — containing tissue samples collected from healthy humans over many years.
In an interview, BioAge’s co-founder and CEO, Kristen Fortney, told us how ML and AI in addition to biobank sampling can fuel the search for drug targets in longevity treatment.
Continue reading… “AI and biobanks could open the way to longevity treatments”