A unified view of disease, built from gene expression.
At the core of Agemica is a proprietary AI analysis platform that analyzes gene expression data spanning 34 cancer types, cardiovascular conditions, neurodegenerative diseases, and molecular signatures associated with aging.
The platform combines differential expression analysis, pathway mapping, and drug target identification to connect molecular disruption to actionable intervention points.
- Differential expression analysis identifies genes that are abnormally up- or down-regulated in diseased tissue versus healthy tissue.
- Pathway mapping traces those expression changes back to the mechanisms driving disease biology.
- Drug target identification maps those mechanisms to established drug databases to surface compounds with corrective potential.
- Outputs are ranked therapeutic candidates, each linked to mechanism and scored for selectivity, safety, and predicted efficacy.
Therapies, vaccines, and validation in one system.
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Combination therapies designed for precision and safety.
Agemica identifies overlap points across disease mechanisms and ranks combinations of approved drugs that address multiple pathways while preserving healthy tissue selectivity.
- Higher efficacy potential through multi-point, synergistic pathway coverage.
- Lower side-effect risk by prioritizing compounds with established safety records.
- Reduced resistance and metastasis risk through multi-pathway targeting that is harder to evade.
- Faster translational path because candidates are built from compounds with existing clinical data.
Preventive medicine, designed at the level of mechanism.
The same platform used for active-disease therapies also designs peptide vaccine candidates against molecular drivers identified through pan-disease analysis.
- For elevated-risk individuals, candidates focus on tumor-associated targets identified from pan-cancer signal analysis.
- For adults 40+, aging-oriented candidates target shared mechanisms associated with declining healthspan.
- Each vaccine candidate is constrained by disease relevance and selective expression to direct immune activity where needed.
Computationally rigorous. Experimentally tested.
Validation is structured in two layers: reproduce known standards of care, then advance into new candidate generation under controlled pre-clinical testing.
- In silico validation across 17 cancers includes rediscovery of current standards of care.
- 40 pre-clinically validated candidates, including 8 prioritized candidates spanning 11 cancers.
- 6 drug combinations showing broad ex vivo efficacy across more than 10 cancer types.
- Ongoing pre-clinical validation with global CRO partners in animal-model studies.
Three products. One platform. A measurable extension of healthy life.