UCB enters drug discovery collaboration with Aitia
The collaboration will combine the use of new drug targets for the disease from Aitia's Gemini Digital Twins with UCB’s expertise.
Global biopharmaceutical firm UCB has entered an early drug discovery collaboration with Aitia.
The collaboration is aimed at discovering and validating new drug targets and drug candidates that are linked to clinical endpoints causally in Huntington’s disease, a debilitating genetic disorder.
It will combine the use of new drug targets for Huntington’s disease from Aitia’s Gemini Digital Twins with the expertise of UCB in preclinical model systems and drug research in neurodegeneration for advancing new therapies discovery for Huntington’s disease patients.
UCB executive vice-president and chief scientific officer Dhaval Patel said: “We are pleased to work with Aitia in a bid to unravel the circuity of this debilitating disease.
“We see this collaboration as an investment in next-generation science and technology, allowing us to accelerate our understanding of human pathobiology and explore the potential of developing novel medicines through AI.”
According to the company, Huntington’s disease affects nearly one in 10,000 people across the world.
At present, there is no cure for the disease, with limited treatment options.
Aitia CEO and co-founder Colin Hill said: “At Aitia, our mission is to discover the next generation of breakthrough drugs to improve outcomes for patients where there is a high unmet need.
“We believe that Huntington’s disease is overdue for major disruption and breakthroughs from our Gemini Digital Twins which are created from large quantities of multi-omic patient data and causal AI.
“We’re excited to collaborate closely with UCB to gain deeper insights into the underlying mechanisms of this complex disease and hope to drive major advancements in drug discovery and development faster than ever before.”
The company is exposing the hidden biological mechanisms of disease for developing Digital Twins of disease in immunology, neurodegenerative disorders, and oncology by using the convergence of multi-omic patient data, causal learning and AI and high-performance computing.
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