– Cystinosis is a rare genetic disease affecting 1 in 100,000-200,000 live births and poses a lifelong risk to those affected. Cystinosis slowly destroys the body’s organs including the kidneys, eyes, thyroid, muscles, liver, pancreas, gonads, and brain.
– There are currently no curative treatments for cystinosis.
– The Mechanisms of Inherited Kidney Disorders (MIKADO) group at the University of Zurich (UZH) and Insilico Medicine will be working together to identify, rank, and annotate novel drug targets to identify potential therapeutics for cystinosis.
NEW YORK and ZURICH, March 10, 2022 /PRNewswire/ — The MIKADO group, a translational team at the UZH focused on generating evidence-driven insights to understand and treat rare inherited kidney diseases, and Insilico Medicine, an end-to-end Artificial Intelligence (AI)-driven drug discovery company, today announced a research and development collaboration designed to accelerate the discovery of transformative novel therapeutics for cystinosis. The duration of the initial research collaboration will be one year.
Specifically, the MIKADO group will leverage its multi-omics databank obtained from preclinical models and cystinosis-based cell models by using Insilico’s comprehensive novel target discovery AI platform PandaOmics to identify cellular and molecular pathways that drive life-threatening complications in cystinosis patients. The MIKADO group will analyse disease-relevant targetable pathways and utilize Insilico’s proprietary algorithms to generate libraries of small molecule compounds that are expected to be validated in preclinical models and cystinosis cell systems using disease-relevant screening technologies developed by the MIKADO group at the UZH.
“I am thrilled by the collaboration between MIKADO and Insilico. With the power of artificial intelligence-driven, systems biology-based drug discovery, we intend to accelerate the analysis and discovery of drug targets, with the goal of bringing novel breakthrough medicines to cystinosis patients while decreasing costs and increasing probabilities of success,” said Olivier Devuyst, MD, Ph.D., head of MIKADO group at the UZH.
“Cystinosis is a commonly neglected disease with large unmet need. We are pleased to partner with MIKADO at the UZH combining the best of PandaOmics target discovery AI and human intelligence for the potential benefit of cystinosis patients worldwide,” said Alex Zhavoronkov Ph.D., CEO of Insilico Medicine.
Using innovative preclinical models and disease-relevant phenotype screening, we aim to validate novel drug targets for diseases which no cure has been found. Ultimately, we want to improve the quality of life for those affected by the disease and bring tangible hope to thousands of cystinosis patients around the world,” said Alessandro Luciani Ph.D., senior scientist and team leader at MIKADO.
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Cystinosis—one of a family of approximately 70 rare inborn diseases of the metabolism known as lysosomal storage diseases that collectively affect 1 in 5,000 live births—is caused by inactivating mutations in the CTNS gene encoding the proton-driven transporter cystinosin, which exports cystine from the lysosome. Its functional loss leads cystine to accumulate within the lysosome of tissues across the body, culminating in severe multiorgan dysfunctions that affect primarily the brain, eyes, liver, muscles, pancreas, and kidneys. Fanconi syndrome is the first manifestation of cystinosis, usually presenting within the first year of life and characterized by the early and severe dysfunction of the kidney proximal tubule, most often complicated by chronic kidney disease and life-threatening manifestations. In their second to third decade of life, patients with cystinosis can also develop hypothyroidism, hypogonadism, diabetes, myopathy, and deterioration of fine vision and decline of the central nervous system. Beyond supportive care, the only available FDA-approved strategy to counteract cystine storage is the oral administration of cysteamine, which allows cystine to exit from the lysosomes. However, cysteamine treatment is hampered by side effects and poor tolerance, and it does not prevent nor treat PT dysfunction and kidney disease. Therefore, there is an urgent need to develop novel therapeutics for this devastating disease.
About Insilico Medicine
Insilico Medicine, an end-to-end artificial intelligence (AI)-driven drug discovery company, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques to discover novel targets and to design novel molecular structures with desired properties. Insilico Medicine is delivering breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system (CNS) diseases and aging-related diseases.
About MIKADO group
The group “Mechanisms of Inherited Kidney Disorders” led by Prof. Dr. med. Olivier Devuyst and Dr. Alessandro Luciani is investigating the fundamental mechanisms that cause inherited kidney disorders affecting the epithelial cells lining the kidney tubule, leading to chronic kidney disease (CKD) ¾ one of the fastest growing disease worldwide and a major public health burden. Combining human genetic studies with innovative model organisms (e.g., rat and zebrafish) and physiologically relevant cellular systems, and integrating them with OMICs technologies and cutting-edge screening assays, we are applying the knowledge gained from fundamental studies of epithelial cell biology to develop disease biomarkers and novel therapeutics that might potentially improve the care for patients with genetic disorders affecting the kidney and other organs. The MIKADO group has established a zebrafish platform to perform high-throughput and high-content screens for kidney disease therapeutics, and a biochemical profiling platform adapted to microsamples (high-throughput screens coupled to clinical studies or metabolic studies in model organisms).
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