GNS, the leader in the use of "Digital Twins" and Causal AI & biosimulation technology for biopharmaceutical drug discovery and development, and Servier, a global pharmaceutical group, today announced a collaboration to advance drug discovery, translational, and clinical development efforts in Multiple Myeloma (MM).
"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".
Claude Bertrand, EVP of Research and Development at Servier
"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," said Joe Donahue, Chief Business Officer at GNS. "Working together with the outstanding Servier team, we hope to make a difference in the lives of patients with MM."
The collaboration will address key questions about how MM progresses in different patient subpopulations, including discovering what are the most synergistic pathways for new combination therapies and will help Servier gain insights that can advance their translational and clinical development efforts. This work will initially leverage the GNS Gemini Digital Twin for MM developed using the rich clinico-genomic data from the Multiple Myeloma Research Foundation CoMMpass study.
GNS' MM Digital Twin leverages the rich patient multi-modal data generated by MMRF to create a transparent, accurate, quantified representation of complex biological mechanisms, revealing new circuitry of human disease. By conducting biosimulations, which are essentially computational experiments, one can discover new drug targets and disease mechanisms, and can also determine which drug candidates are more effective than others and why certain molecular profiles of patients lead to better and more durable response. Ultimately, the MM Digital Twins will radically change how Pharma R&D is conducted by helping to accelerate the identification of new targets and target combinations, new biomarkers, and enabling the in silico design of better trials.
About Servier Group
Servier is a global pharmaceutical group governed by a Foundation. With a strong international presence in 150 countries and a total revenue of 4.7 billion euros in 2021, Servier employs 21,800 people worldwide. Servier is an independent group that invests over 20% of its brand-name revenue in Research and Development every year. To accelerate therapeutic innovation for the benefit of patients, the Group is committed to open and collaborative innovation with academic partners, pharmaceutical groups, and biotech companies. It also integrates the patient's voice at the heart of its activities.
A leader in cardiology, the ambition of the Servier Group is to become a renowned and innovative player in oncology. Its growth is based on a sustained commitment to cardiovascular and metabolic diseases, oncology, neuroscience and immuno-inflammatory diseases. To promote access to healthcare for all, the Servier Group also offers a range of quality generic drugs covering most pathologies.
About GNS
GNS is the leader in the application of Causal AI and simulation technology to discover and validate novel drug targets, simulate clinical trials, and help pharmaceutical and biotech companies discover and develop new medicines faster. GNS' patented AI uncovers new insights from multi-omics and real-world data leading to the discovery and prioritization of novel biological targets, and more efficient clinical trials with patients who are likely to respond to therapies. The Gemini Digital Twin models across oncology, auto-immune diseases, and neurology enable the simulation of disease progression and drug response at the individual patient level to simulate clinical trials in diverse patient cohorts.