Key Study Highlights:
Demonstrates how dual-purpose therapeutic targets may address both hepatocellular carcinoma progression and cellular senescence, supporting emerging strategies that link disease treatment with aging biology
Identifies PRPF19 and MAPK9 as targets that suppress tumor cell proliferation while reducing senescence-associated signaling in relevant cellular models
Provides evidence of senomorphic activity, reducing harmful senescence-associated secretory phenotype (SASP) signaling without marked cytotoxicity
Illustrates the effectiveness of integrating AI-driven target discovery, multi-omic human datasets, and experimental validation to prioritize biologically relevant and translationally promising targets
Reinforces Insilico's broader AI-guided discovery approach for uncovering shared mechanisms across disease and aging
Insilico Medicine ("Insilico", HKEX:03696) today announced the publication of new research in npj Aging describing an AI-aided strategy to identify therapeutic targets that may address both hepatocellular carcinoma (HCC) biology and cellular senescence in chronic liver disease. Dual-purpose disease and aging targets are gaining increasing attention as a strategy to address active pathology while simultaneously modifying the biological processes that contribute to long-term disease risk. By targeting molecular mechanisms shared between chronic disease and cellular aging, such approaches aim to deliver near-term therapeutic benefits while potentially improving durability of response and reducing disease recurrence. This evidence suggests that inhibiting senescence, particularly treatment-induced senescence and associated inflammatory signaling, may complement anti-cancer therapies and help limit tumor-promoting microenvironment effects.
Cellular senescence is a stress-induced state in which cells permanently stop dividing but remain metabolically active and secrete pro-inflammatory and tissue-remodeling factors collectively referred to as the senescence-associated secretory phenotype (SASP). Accumulation of senescent cells has been implicated in aging, chronic disease, and cancer progression, making senescence-associated pathways an increasingly attractive target for therapeutic intervention.
The study, titled " AI-aided identification of dual-purpose therapeutic targets PRPF19 and MAPK9 in hepatocellular carcinoma and cellular senescence " leverages Insilico's AI-driven target discovery platform PandaOmics to prioritize candidate genes supported by multi-omic human data, followed by experimental validation.
PandaOmics performed an AI-powered meta-analysis across 11 transcriptomic and methylomic datasets comprising 1,047 HCC samples and 563 adjacent normal samples. Candidate targets were prioritized using multiple AI-derived scores and filters, including expression consistency, druggability, safety, and novelty. From the top 200 prioritized targets (100 high-confidence and 100 novel), the authors intersected the results with cellular senescence-associated gene sets compiled from CellAge, Gene Ontology, and Reactome, ultimately identifying 27 high-confidence and 8 novel senescence-linked HCC candidates.
In vitro experiments reported in the study demonstrated that knockdown of PRPF19 or MAPK9 suppressed HCC cell proliferation. In parallel, suppression of PRPF19 or MAPK9 reduced cellular senescence in doxorubicin-treated hepatic stellate cells, supporting their proposed dual-purpose potential.
PRPF19 knockdown reduced SA-β-Gal–positive cells in senescent hepatic stellate cells and was associated with downregulation of multiple SASP factors, consistent with a senomorphic effect, attenuation of harmful senescence-associated signaling without inducing broad cytotoxicity or cell elimination.
Similarly, MAPK9 knockdown reduced SA-β-Gal–positive cells without a significant decrease in total cell count, suggesting anti-senescence activity without marked cytotoxicity. The authors further report that depletion of MAPK3, MAPK9, and RPS6KA3 reduced proliferation of HCC cell lines,with MAPK9 uniquely exhibiting a senomorphic profile consistent with selective modulation of senescence rather than generalized cell loss.
Beyond identifying PRPF19 and MAPK9 as promising dual-purpose targets in HCC and cellular senescence, this work illustrates the effectiveness of Insilico's integrated discovery strategy. By combining AI-driven target prioritization from PandaOmics, large-scale multi-omic human datasets, and experimental validation, the approach enables efficient identification of targets with a high probability of biological relevance and translational potential. This study adds to Insilico's growing body of research demonstrating how AI-guided discovery can uncover shared mechanisms across disease and aging contexts, supporting the development of therapies designed to address both immediate disease biology and longer-term drivers of health decline.
By integrating advanced AI and automation technologies, Insilico has significantly improved the efficiency of early-stage drug development in real-world practices, setting a benchmark for AI-driven drug discovery. Whereas traditional early-stage drug discovery typically requires 2.5 to 4 years, more than 20 of Insilico's internal programs initiated between 2021 and 2024 achieved PCC nomination in just 12 to 18 months on average, with only about 60–200 molecules synthesized and tested per program.
Reference
Ren, C. et al. AI-aided identification of dual-purpose therapeutic targets PRPF19 and MAPK9 in hepatocellular carcinoma and cellular senescence. npj Aging (2025). https://www.nature.com/articles/s41514-025-00294-1
About Insilico Medicine
Insilico Medicine is a pioneering global biotechnology company dedicated to integrating artificial intelligence and automation technologies to accelerate drug discovery, drive innovation in the life sciences, and extend health longevity to people on the planet. The company was listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025, under the stock code 03696.HK.
