A team of researchers hailing from Harvard and Université de Montréal today launched Epitopes.world, an AI-powered, interactive platform designed to facilitate COVID-19 vaccine development. It’s built atop an algorithm — CAMAP — that generates predictions for potential vaccine targets, enabling researchers to identify which parts of the virus are more likely to be exposed at the surface (epitopes) of infected cells.
Project lead Dr. Tariq Daouda, who worked alongside doctorates in machine learning, immunobiologists, and bioinformaticians to build Epitopes.world, hopes the platform will reduce the time and expense involved in creating vaccine candidates. Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America.
CAMAP, which Daouda developed while obtaining his Ph.D. at the Université de Montréal, was originally applied to cancer immunotherapy. But its aptitude for learning immune system patterns made it an ideal fit for revealing viruses’ weaknesses.
“The COVID-19 pandemic stresses the need to accelerate the design of vaccines and therapies to reduce the human and economic impact of global pandemics,” said Daouda in a statement. “People infected with COVID-19 tend to have [fewer] immune cells, making it difficult to get enough infected cells to study them appropriately in a lab — and because they are so rare, labs are in competition with each other to obtain them.”
Image Credit: BFT
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