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Pharma Tech Outlook | Friday, October 07, 2022
GNS's MM Digital Twin utilizes the rich multi-modal patient data provided by MMRF to construct a clear, accurate, and quantitative picture of complicated biological mechanisms, thereby exposing new circuitry of human disease.
FREMONT, CA: "This collaboration furthers Servier's goal to accelerate its drug discovery and clinical development efforts through AI and biosimulation and other digital initiatives. We believe GNS' Digital Twin and unique causal AI models will help our research and early development group improve our understanding of MM disease biology and, ultimately, transform our drug discovery and clinical development process". says Claude Bertrand, EVP of Research and Development at Servier.
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GNS is one of the leaders in using "Digital Twins" and Causal AI & biosimulation technology for biopharmaceutical drug discovery and development, and Servier, a multinational pharmaceutical company, partners to advance drug discovery, translational, and clinical development efforts in Multiple Myeloma (MM).
"We are delighted to collaborate with Servier to help advance research in MM and leverage our Gemini Digital Twin models to help uncover novel insights and reveal the hidden biological circuity of this disease. Working together with the outstanding Servier team, we hope to make a difference in the lives of patients with MM." says Joe Donahue, Chief Business Officer at GNS.
This project will initially utilize the GNS Gemini Digital Twin for Multiple Myeloma (MM) that was created with the rich clinico-genomic data from the Multiple Myeloma Research Foundation's (MMRF) CoMMpass study. The collaboration will address essential questions about how multiple myeloma (MM) progresses in distinct patient subpopulations, such as identifying the most synergistic pathways for new combination therapies. It will help Servier gain insights that can advance its translational and clinical development efforts.
Ultimately, the MM Digital Twins will drastically alter how Pharma R&D is conducted by accelerating the identification of new targets and target combinations and biomarkers and enabling the in silico design of more effective clinical trials.
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