On October 30, 2024 Aitia, a biotech company pioneering the use of causal AI and Digital Twins in drug discovery and development, reported that it is expanding its collaboration with Servier, a global and independent pharmaceutical company, to tackle gliomas, a broad group of brain cancers (Press release, Servier, OCT 30, 2024, View Source [SID1234647550]). This collaboration aims to use Aitia’s breakthrough technology which is powered by millions of data points from patient cancer tissues to find new treatments for this deadly disease.
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Under the terms of the collaboration, Aitia and Servier will work together to discover and, validate novel drug targets and develop novel drug candidates for glioma using Aitia’s Gemini Digital Twins and Servier’s therapeutic discovery and clinical development expertise in oncology. This collaboration will include exploration of the heterogeneous biology of these brain cancers, mechanisms of resistance, and drug response at the individual patient level. In a recent breakthrough in another form of glioma, specifically Grade 2 astrocytoma or oligodendroglioma with a susceptible IDH1 or IDH2 mutation following surgery including biopsy, sub-total resection, or gross total resection in adult and pediatric patients 12 years and older, Servier recently announced approval of the brain cancer drug, VORANIGO, the first approved drug for this disease area in almost 25 years.
Gliomas, tumors that originate in the brain or spinal cord from glial cells, affects more than 190,000 people worldwide annually. The most aggressive form of glioma, glioblastoma, has an average survival time after diagnosis of approximately 15 months, with less than 7% of patients surviving beyond five years, according to the National Brain Tumor Society. Despite standard treatments like surgery, radiation and chemotherapy, glioblastoma has a high rate of recurrence and there are currently no curative therapies.
"We are excited to expand our collaboration with Aitia into gliomas as we continue to prioritize brain cancer after our recent positive results and approval of VORANIGO in the US and in growing number of territories," said Fabien Schmidlin, Global Head of Translational Medicine at Servier. "At Servier, we are committed to advancing therapies for cancer patients with high unmet medical needs, and we believe that integrating Aitia’s Gemini Digital Twins with our expertise in oncology, particularly in targeting molecular mutations, could open new avenues for more effective treatments."
"Glioma is the among the deadliest forms of cancer, and the lack of broadly effective treatments highlights the urgent need for new approaches that leverage the growth of genetic and multi-omic patient data and breakthroughs in AI," said Colin Hill, CEO and co-founder of Aitia. "We believe that our Gemini Digital Twins, along with Servier’s drug discovery and clinical development expertise in hard-to-treat cancers can lead to new breakthrough therapies for this disease."
This collaboration represents the fourth area of research Aitia and Servier are collaborating on. The companies began their partnership in 2022, to advance drug discovery, translational, and clinical development efforts in Multiple Myeloma, then expanding in 2023 to include drug discovery and development for pancreatic cancer, and again in January 2024 to discover patient responders for Servier’s LRRK2 inhibitor in Parkinson’s disease.
Aitia’s approach centers on two key innovations: causal AI and Gemini Digital Twins. Causal AI goes beyond correlative associations to identify which genetic or molecular changes drive diseases and patient response to therapies. Gemini Digital Twins are virtual models that are reverse-engineered from huge quantities of genetic, multi-omic, and clinical data from patients that reveal how genes, proteins, and other molecules interact within cells and tissues to drive clinical outcomes. These Gemini Digital Twins of a specific disease allow researchers to rapidly test billions of experiments such as gene and protein knockdowns or application of a drug candidate in a human patient context versus animal models or cells in petri dishes. These high-throughput "virtual experiments" in patient-derived Gemini Digital Twins leads to discovering and selecting drug candidates that are more likely to succeed in clinical trials in specific patient populations.