Recently, the France-based WIDGeT consortium emerged from a collaboration between Sanofi, WhiteLab Genomics, the TaRGeT Laboratory at Nantes University, and Institut Imagine. Scientists and companies in this consortium hope to speed up the development of gene therapies based on adeno-associated viruses (AAVs) for rare diseases by combining next-generation AAV vectors and artificial intelligence (AI).
“Gene therapies present some of the most promising avenues in modern medicine, yet they come with significant challenges that must be overcome,” says David Del Bourgo, CEO and co-founder of WhiteLab Genomics. “Key among these is the need to minimize the dosage of gene therapies administered to patients while concurrently devising more targeted delivery vectors.” To address these challenges, Del Bourgo points out the need for learning more about the immune response to AAVs and enhancing manufacturing processes, including the development of versatile AAV-producing cell lines.
“The WIDGeT initiative aims to catalyze advances in gene therapy through several strategic objectives, including drafting novel AAV vectors capable of effectively targeting and transducing traditionally challenging cell types, such as microglia and kidney cells,” Del Bourgo says. Part of this will depend on the “enhancement of WhiteLab Genomics’ machine learning algorithms, which are critical in predicting the productivity and stability of AAV capsids,” Del Bourgo adds. “These advanced algorithms will inform the selection of optimal AAV-vector candidates and improve upstream-processing conditions for more efficient AAV-vector production.”
Achieving these objectives will require the diverse capabilities of the groups behind the WIDGeT consortium. Del Bourgo says that these capabilities “range from AI expertise to develop a rational, guided approach for a specific cell type to expertise from a pharmaceutical partner, which will be necessary to advance new molecules through clinical validation in order to eventually launch them on the market.”
Those objectives span a wide range of challenges in improving AAV-based gene therapies, and the process will depend on ongoing financial support. As Del Bourgo concludes: “Ultimately, what is required are significant investments to bring AI-designed molecules into clinical phases, along with robust models and bioproduction processes.”
