(l-r) Ariel Tennenhouse, Lev Khmelnitsky, Prof. Sarel Fleishman and Razi Khalaila
Israeli scientists, in yet another breakthrough for the country, say they have developed a new algorithm that may greatly speed up the process of engineering therapeutic antibodies. Researchers from the Weizmann Institute of Science say that their algorithm, called CUMAb, offers a “much faster and cheaper way of adapting animal antibodies to make them safe for humans.”
Antibodies are Y-shaped proteins produced by the immune system in response to the presence of foreign substances, such as bacteria, viruses, and toxins. They are also known as immunoglobulins (Igs). Antibodies bind to specific antigens (foreign substances) and help to neutralize them, which helps to protect the body from infection and disease.
“Humanizing an antibody so it can fight disease in our bodies without provoking an immune response can be tricky,” says team leader Prof. Sarel Fleishman of Weizmann’s Biomolecular Sciences Department. He explains that the standard approach has been to select a human antibody that most closely resembles the animal version, then replace the animal segments with human ones while retaining the bits that act against disease. But because different parts of the antibody are interconnected, these replacements can make the antibody unstable and ineffective.
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“It’s like altering the internal walls of a house without taking its foundations into account,” explains graduate student and lead author Ariel Tennenhouse.
Fleishman’s lab is a protein design center, whose members develop computer programs that improve proteins or generate new ones. Since it’s been known for decades that humanizing animal antibodies based on their similarity to human ones often results in unstable molecules, Tennenhouse hypothesized that stability might be a more reliable criterion for humanization. To test this, he developed the CUMAb, an algorithm for computational humanization of antibodies.
The algorithm computed a whopping 20,000 humanized variants of a single antibody and predicted the structural stability of each, selecting the best ones for testing. After a great deal of work the scientists found the one that works.
CUMAb is a powerful new tool that could significantly accelerate the design of new antibody-based drugs, as well as lower development costs, the scientists boast.
Fleishman and his team have turned the algorithm into a web server that any academic can use.
“This is the first time that a method has shown such broad success in this critical biomedical engineering problem. It is quite likely to become a key element in accelerating the transition from therapeutic candidate molecules to real-world drugs,” says Fleishman.
The new approach to humanizing antibodies might be applied to other issues in therapeutic antibody engineering. For example, many antibody formulations tend to be highly viscous, and they have to be diluted before use. These are then administered through infusion, and it can take hours to get enough of the antibody into the bloodstream. With a computer program that yields a number of viable antibodies, researchers might be able to choose some that require less dilution or have other properties that make them easier or more comfortable to administer, improving outcomes for patients.
“We hope that CUMAb, now accessible online, will empower researchers and professionals to develop antibodies more swiftly and accurately,” says Tennenhouse.
