GNS Healthcare accelerates the discovery and development of drugs to improve patient outcomes and significantly reduce total cost of patient care. Our causal AI technology integrates and transforms a wide variety of patient data types into Gemini – The in silico Patient™, which reveals the complex system of interactions underlying disease progression and drug response. The in silico patients enable the simulation of drug response at the individual patient level across oncology, auto-immune, neurology, and cardio-metabolic disease in partnership with the world’s largest biopharma companies and health plans.
The in silico Patient™
Gemini – The in silico Patient™, simulates disease progression and drug response at the individualized patient level. Powered by large clinico-genomic patient data and REFS causal AI and simulation technology, Gemini reconstructs the complex molecular mechanisms driving outcomes. Gemini – The in silico Patient™ enables better clinical trial design, including identification of responder/non-responder subpopulations and underlying mechanisms, in addition to generating evidence for line of therapy changes and treatment sequence optimization. Gemini is also being leveraged to discover novel disease mechanisms and drug targets, leading to the discovery of new drugs across various diseases.
How GNS Partners with Pharma and Payers
Novel Simulation for Discovery and Translational Research
Run in silico simulations to determine intervention effects as well as reveal biomarkers of disease progression and drug response.
Faster, Better Designed Clinical Trials
Select optimal patient subpopulations for drugs and targets in development. Discover and evaluate the most promising hypotheses before study design, reducing expensive trial and error.
Accelerated Market Access
Generate evidence of comparative effectiveness at later stages of drug development to support value-based contracts, support line of therapy positioning and label expansion.
Advanced Payer Analytics
Apply AI-drive models across lines of business to better predict rising patient risk and understand which patients are least likely to benefit from interventions or treatments.