GNS Healthcare, a precision medicine company leveraging artificial intelligence to model individual patients’ response to drug treatment, have unveiled the first in silico multiple myeloma patient. Called Gemini, the in silico multiple myeloma patient is a computer model of disease progression and drug response at the individual patient level.
The company says Gemini has multiple applications for precision medicine including discovering markers of response and nonresponse for clinical trial design, predicting optimal combination therapies, and running head-to-head in silico trials. Clinical applications could include providing information to switch patients to more effective treatments and optimizing treatment sequencing.
“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 in a press release. “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.”
Gemini leverages broad datasets of molecular, genomic, and clinical and include the most common drug types used to treat multiple myeloma such as proteasome inhibitors, IMIDs, corticosteroids, alkylating agents, anti-SLAMF7, anti-CD38, among others. It delivers information on the impact of these drugs including progression-free survival and overall survival. Results of previous in silico studies of in silico multiple myeloma patients wer presented recently at the the American Society of Hematology (ASH) Annual Meeting and recently published in Leukemia.
Gemini is the culmination of nearly 10 years of research and development efforts between GNS and multiple pharmaceutical companies, academic medical centers and the Multiple Myeloma Research Foundation (MMRF).
“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.
