Researchers at the University of Michigan have unveiled a groundbreaking breakthrough in the fight against diseases like Parkinson’s, Alzheimer’s and colorectal cancer. Their innovative machine-learning algorithms promise to supercharge therapeutic antibodies, making them more effective in targeting disease while avoiding unwanted interactions.
Antibodies, often hailed as our immune system’s front-line warriors, can sometimes backfire when they mistakenly bind with non-target molecules or even with each other. This can hinder their ability to combat diseases effectively. However, the University of Michigan’s new algorithms are set to change that.
These algorithms act as detectives, pinpointing problematic areas in antibodies that are prone to binding to the wrong molecules. By identifying these troublesome spots, researchers can then modify the antibodies to correct the issue without introducing new complications.
Peter Tessier, who holds the prestigious Albert M. Mattocks Professorship in Pharmaceutical Sciences at U-M, conveyed his enthusiasm for these models, stating that these models possess great utility as they can be applied to existing antibodies, emerging antibodies in the developmental phase, and antibodies yet to be synthesized.
Antibodies work by binding to specific molecules known as antigens on disease-causing agents, such as the spike protein on the COVID-19 virus. Once attached, antibodies either neutralize the harmful agents directly or send signals to the body’s immune cells to do so.
However, antibodies designed to bind very strongly and quickly to their intended targets can inadvertently latch onto non-antigen molecules, rendering them ineffective against the disease. Moreover, they may also bind to antibodies of the same type, leading to the formation of thick solutions that are challenging to administer as drugs.
Tessier further stated that the ideal antibodies should do three things at once and these are bind tightly to what they are supposed to, repel each other and ignore other things in the body.