InSilico AI-Developed Drug Lowers Human "Biological Age"
Analysis of Phase 2a Clinical Trial for Rencocetib
Improvements Observed in Biological Age and Lung Function
A signal indicating a decrease in biological age has been detected in humans after administering a new drug candidate discovered and designed using generative artificial intelligence (AI). Across all six different proteomic aging clocks, a consistent reduction in biological age was observed, and lung function—which typically declines with aging—also showed improvement.
According to the bio industry on September 9, InSilico Medicine announced in the international journal 'Nature Biotechnology' on September 7 the results of a collaborative analysis of phase 2a clinical data for Rencocetib with researchers from Harvard Medical School, Stanford University, the Broad Institute, RWTH Aachen University, Peking University, and Westlake University.
The research team analyzed longitudinal Olink proteomic data collected from 42 patients who participated in the 12-week phase 2a clinical trial of Rencocetib. Using six internationally developed proteomic aging clocks to assess biological age, they found that the predicted biological age decreased after administration of Rencocetib in all six models.
Changes in lung function were also observed. Forced vital capacity (FVC), a major lung function indicator that declines with age, showed dose-dependent improvement compared to the placebo group.
This research is noteworthy in that it clinically evaluated changes in biological aging in a new drug candidate whose disease-related targets were identified and molecular structure was designed using AI, rather than repurposing existing drugs such as rapamycin or metformin for aging treatment.
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Based on these results, the research team proposed an approach that incorporates measurement of aging biomarkers into conventional clinical trials. It is seen as demonstrating the potential to clinically evaluate changes in biological aging resulting from administration of new drugs within clinical trials.
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