Insilico Medicine launches AI initiative to develop longevity vaccines
· News-MedicalInsilico Medicine, a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, today announced Longevity Vaccines, a research initiative developing treatments that direct a patient's own immune cells to eliminate the earliest cellular drivers of age-related disease. This initiative expands the company's dual-purpose, aging-oriented discovery strategy from small molecules into RNA-encoded, in vivo cell engineering.
Alex Zhavoronkov, PhD, Founder and co-CEO of Insilico MedicineWe built Insilico to treat aging and disease together, and to prove that AI can design medicines that deliver real patient impact. Longevity Vaccines initiative extends that same rigor to preventive medicine—delivering programmable, single-dose therapies that clear the root-cause cells of age-related disease, starting with the immune system itself."
Targeting the culprit cells at the outset
Many aging-related diseases are initiated and sustained by discrete cell populations with well-studied surface markers. Examples include senescent cells that secrete pro-inflammatory proteins (the senescence-associated secretory phenotype, or SASP), activated fibroblasts that drive fibrosis, and autoreactive lymphocytes that erode self-tolerance.
Rather than treating downstream pathology, Longevity Vaccines are designed to eliminate these initiating cell populations within a preventive window and on a transient basis. The approach builds on a growing body of peer-reviewed research showing that engineered T-cells can clear such cells and deliver durable functional benefits from a single therapeutic course.
A programmable, self-limiting chassis
The initiative utilizes a single, programmable delivery modality: a short-lived genetic instruction, encoded in circular mRNA (cmRNA) and packaged within a targeted lipid nanoparticle (LNP). Delivered inside the body, it arms a patient's own T cells to recognize and remove specific target cell populations.
Circular mRNA resists degradation to support durable yet self-limiting expression, while the targeted LNP ensures selective delivery.
An AI engine for early, safe target selection
Selecting antigens for preventive medicine demands an exceptionally high bar: targets must appear at the earliest stages of pathogenesis while offering a wide therapeutic window. Insilico addresses this using its end-to-end Pharma.AI platform, which spans multi-omics target discovery (PandaOmics), generative biologics for de novo binder design, generative chemistry (Chemistry42) for ionizable lipids and delivery systems, and AI-assisted clinical trial design (inClinico). This integrated platform ranks candidate surface antigens by evidence strength, expression timing, tissue specificity against a whole-body surface atlas, deliverability, and optimal construct design. Candidates are then validated in automated laboratories, where experimental readouts continuously feed back to refine target rankings and optimize molecular designs. Furthermore, Insilico's aging foundation models and Virtual Aging Cell supply the temporal, age-conditioned context required to pinpoint the earliest divergent cell states in a disease trajectory.
Built on a validated, aging-first discovery engine
The initiative rests on Insilico's experience in itsRentosertib (ISM001-055)—a first-in-class TNIK inhibitor whose target was nominated by AI for its role across multiple hallmarks of aging and whose molecule was fully AI-designed—advanced from target identification to preclinical candidate in just 18 months. It recently demonstrated lung capacity restoration in a randomized Phase IIa trial for idiopathic pulmonary fibrosis (IPF) and has entered Phase III development.
Based on the experience obtained in the Rentosertib program, Insilico has established its dual-purpose strategy of discovering therapeutics that tackle both specific clinical indications and fundamental biological aging processes. Longevity Vaccines carries this strategy into a preventive, cell-clearing modality.
Initial indications
The initiative will first focus on the rejuvenation of the aging immune system, targeting the accumulation of senescent lymphocytes that drive immunosenescence, reduced vaccine responsiveness, and the rising susceptibility to infection and cancer with age. Additional indications under evaluation share defined initiating cell populations reachable by the same chassis, such as metabolic dysfunction and membranous nephropathy.
Recently, Insilico reported total revenue of approximately $106 million in the first half of 2026, a 287% year-over-year increase, and achieved its first profitable half-year since listing, with an adjusted net profit exceeding $51 million. This milestone was driven by a series of out-licensing, co-development, and R&D collaborations with global partners, including Eli Lilly, Servier, Takeda, SK Biopharmaceuticals, Qilu Pharmaceutical, Hygtia Therapeutics, CMS, and Tenacia. As of the latest practicable date, the total contract value of transactions announced by Insilico in 2026 reached approximately $7.3 billion, pushing the cumulative contract value of its major collaborations since 2021 approximately $11 billion.
On the AI-driven R&D front, Insilico nominated nine development candidates within eight months of 2026 as of late August, setting a new company record for annual pipeline productivity and achieving eight clinical milestones across its proprietary and co-developed programs. Leading this progress is Rentosertib (ISM001-055), the world's first drug candidate discovered and developed using generative AI, which has advanced to a Phase III trial evaluating for idiopathic pulmonary fibrosis (IPF). In addition, Insilico Medicine launched a comprehensive set of benchmarks that allow foundation models to be evaluated in all tasks needed for drug discovery and launched state of the art (SOTA) foundation models outperforming other models and even internal tools in benchmarks. Using this new capability Insilico hopes to expand and accelerate longevity drug discovery and development both in terms of scale, therapeutic modalities, and indications.
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