A transformative innovation GeminiTM, the in silico patient, will drive better drug development, clinical trial design and generation of real-world evidence in multiple myeloma
GNS Healthcare, an AI-driven precision medicine company, today announced the launch of GeminiTM, the in silico multiple myeloma patient. The in silico patient is a highly accurate computer model of disease progression and drug response at the individual patient level. Clinical development applications include discovering markers of response/nonresponse for clinical trial design, predicting optimal combination therapies, and running head-to-head in silico trials. Market access applications include generating evidence for line of therapy switching and optimizing treatment sequencing.
Drawing from large quantities of molecular, genomic, and clinical data, this in silico patient represents a culmination of almost a decade of research and development in collaboration with several biopharmaceutical companies, academic medical centers, and the Multiple Myeloma Research Foundation (MMRF). GNS and MMRF recently announced a five-year collaboration which seeks to answer key questions for multiple myeloma patients. GeminiTM includes the drug mechanisms most commonly used to treat multiple myeloma – such as proteasome inhibitors, IMIDs, corticosteroids, alkylating agents, anti-SLAMF7, anti-CD38, and others, connecting the impact of these drugs to clinical endpoints including progression free survival (PFS) and overall survival (OS). Previous results from the in silico multiple myeloma patient have been presented at the American Society of Hematology (ASH) Annual Meeting and recently published in Leukemia.
“Over the past decade there have been a dozen treatments approved for multiple myeloma but there is still a lack of evidence to ensure patients receive optimal treatments in first line and subsequent lines of therapy,” said GNS Chairman and CEO, Colin Hill. “Creating Gemini, the in silico patient, allows us to break the bottleneck of understanding what treatments work for which patients, driving better clinical trial design, generating real-world evidence for market positioning and ultimately creating better outcomes for patients.”
“We are reaching a tipping point where patient data is becoming rich and multi-layered enough to power AI models that can help predict patient response at the individual level. This announcement represents a true step forward in personalizing cancer treatment,” said Dr. Ravi Parikh, an Oncologist and instructor of Medical Ethics and Health Policy in the Perelman School of Medicine at the University of Pennsylvania.
To support the future formation of in silico patients, GNS recently convened an in silico patient advisory board to guide development and commercialization strategy. The in silico patient for multiple myeloma represents the first of several poised to expand the world’s understanding of causal response to therapeutics across a range of diseases within oncology, immunology, and neurology.
More information about GeminiTM, the in silico patient for multiple myeloma, can be found on the GNS website: https://www.aitiabio.com/in-silico-patient/.
Editor’s Note: Parikh receives financial compensation as an advisor for GNS Healthcare.
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About GNS Healthcare
GNS Healthcare is an AI-driven precision medicine company developing in silico patients from real world and clinical data to reveal the complex system of interactions underlying disease progression and drug response. In silico patients simulate drug response at the individual patient level to precisely match therapeutics to patients and rapidly discover key insights across drug discovery, clinical development, commercialization, and payor markets. GNS REFS™ causal AI technology integrates and transforms a wide-variety of patient data types into in silico patients across oncology, auto-immune diseases, neurology, and cardio-metabolic diseases. GNS partners with the world’s leading biopharmaceutical companies and health plans and has validated its science and technology in over 50 peer-reviewed papers and abstracts.
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