Machine learning method to design better antibody drugs
2021
ETH Zurich, Basel, Switzerland
The researchers have created a machine learning method that supports the optimization phase of antibody drugs, potentially helping to develop more effective therapeutics. The standard antibody optimization approach allows the identification of the best antibody from a group of a few thousand. The researchers are now using machine learning to increase the initial set of antibodies to be tested to several million. They provided the proof-of-concept for their new method using antibody cancer drug Herceptin, which has been on the market for 20 years. After screening more than 70 million antibody DNA sequences, the scientists characterized 55 unique antibody variants, some of which bound better to the target protein and one variant was even better tolerated in the body than Herceptin itself. The scientists are now applying their artificial intelligence method to optimize antibody drugs that are in clinical development.
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Sai T. Reddy
Added on: 10-21-2021
[1] https://www.nature.com/articles/s41551-021-00699-9[2] https://www.drugtargetreview.com/news/89324/machine-learning-method-to-design-better-antibody-drugs-developed/?utm_source=Email+marketing&utm_medium=email&utm_campaign=DTR+-+Industry+Insight+-+Cell+Signaling+-+Proteomics+%26+Antibodies+-+10.09.21&utm_term=Discover+more+on+proteomics+and+antibodies+in+our+industry+insight+&utm_content=https%3a%2f%2femails.drugtargetreview.com%2frussellpublishinglz%2f&gator_td=Hh4ennvKSMIlbRgkgoQfXJmtZ1bfCSAqp0vkozkH1Po4Bho%2f5X96Q3v0vU8HyVkDwzf8z60DXdl7NNgeDBHXUX8nyRJhRBkCWtmPDisDtRqGYVwRbVcf8H%2fkmubbbWpABOLg5VVFR6MIXm7g3ji%2bAhfCLXPu2dvN3IVpir6fJ0saGvb90Ip0bzbkCP8wH%2bfKxQVyinUtEfoVEpQET5hGiGtT3n0vgKcC2XL290xhNGUOmgU4ModTDGBCiBO3QvXj7IbpJNNl68DMl3Us11x5HA%3d%3d
