NuMedii, Inc. announced the formation of a research collaboration with Johns Hopkins School of Medicine with the goal of discovering new targets and therapeutic options for pancreatitis. The collaboration brings together deep clinical expertise with NuMedii’s Artificial Intelligence Drug Discovery (AIDD) technology, which will enable the discovery of novel targets and precision drug candidates for both acute and chronic forms of the disease.
Under the research collaboration, NuMedii will work with one of the world’s leading experts in pancreatitis and pancreatic disease, P. Jay Pasricha, MBBS, MD, Professor of Medicine, Johns Hopkins School of Medicine and Director of the Johns Hopkins Center for Neurogastroenterology at Johns Hopkins Bayview Medical Center.
“Patients with chronic pancreatitis have a higher risk of pancreatic cancer, a horrible disease with a significant unmet medical need. Treatments for acute pancreatitis can potentially be used to prevent the progression to chronic disease and reduce the risk of pancreatic cancer especially among patients with genetic predisposition to such transformation. We are excited to work with Dr. Pasricha and his research team to uncover innovative treatment targets and advance innovative therapies that could help patients and the physicians who treat them,” said Gini Deshpande, Ph.D., chief executive officer, NuMedii.
Acute pancreatitis is one of the most frequent gastrointestinal causes of hospital admission in the U.S. and worldwide. Chronic pancreatitis, although lower in incidence, significantly reduces patients’ quality of life. Patients with the chronic form of the disease have a 20-fold increased risk of pancreatic cancer, which is associated with a high mortality rate and one of the top five causes of death from cancer. There are currently no effective treatment options for acute pancreatitis and the burden of pancreatic disorders is expected to increase over time.
NuMedii’s AIDD technology employs deep learnings of human biology consisting of hundreds of millions of structured molecular, pharmacological and clinical data points that the company has curated and harmonized. The company couples these data with proprietary machine learning and network-based algorithms to discover and advance precise, effective new drug candidates, as well as biomarkers predictive of efficacy for subsets of patients, in a broad spectrum of therapeutic areas including inflammation, cancer and orphan diseases.