(Press-News.org) Cambridge, MA - February 11, 2020 - Dyno Therapeutics, a biotech company applying artificial intelligence (AI) to gene therapy, today announced a publication in Nature Biotechnology that demonstrates the use of artificial intelligence to generate an unprecedented diversity of adeno-associated virus (AAV) capsids towards identifying functional variants capable of evading the immune system, a factor that is critical to enabling all patients to benefit from gene therapies. The research was conducted in collaboration with Google Research, Harvard's Wyss Institute for Biologically Inspired Engineering and the Harvard Medical School laboratory of George M. Church, Ph.D., a Dyno scientific co-founder. The publication is entitled "Deep diversification of an AAV capsid protein by machine learning."
It is estimated that up to 50-70% of the human population have pre-existing immunity to natural forms of the AAV vectors currently being using to deliver gene therapies. This immunity renders a large portion of patients ineligible to receive gene therapies which rely upon these capsids as the vector for delivery. Overcoming the challenge of pre-existing immunity to AAV vectors is therefore a major goal for the gene therapy field.
"The approach described in the Nature Biotechnology paper opens a radically new frontier in capsid design. Our study clearly demonstrates the potential of machine learning to guide the design of diverse and functional sequence variants, far beyond what exists in nature," said Eric Kelsic, Ph.D., Dyno's CEO and co-founder. "We continue to expand and apply the power of artificial intelligence to design vectors that can not only overcome the problem of pre-existing immunity but also address the need for more effective and selective tissue targeting. At Dyno, we are making rapid progress to design novel AAV vectors that overcome the limitations of current vectors, improving treatments for more patients and expanding the number of diseases treatable with gene therapies."
The Nature Biotechnology paper describes the rapid production of a large library of distinct AAV capsid variants designed by machine learning models. Nearly 60% of the variants produced were determined to be viable, a significant increase over the typical yield of END
Artificial intelligence generates new diversity of AAV capsids for broader gene therapy
Approach rapidly generates the functional diversity needed to evade neutralization by the immune system, allowing more patients to benefit from gene therapies
2021-02-11
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[Press-News.org] Artificial intelligence generates new diversity of AAV capsids for broader gene therapyApproach rapidly generates the functional diversity needed to evade neutralization by the immune system, allowing more patients to benefit from gene therapies