Matrix metalloproteinases (MMPs) are a group of 26 closely related proteinases (enzymes that break down other proteins) that are essential in regeneration of tissues and other normal cellular processes.
However, when a tumour grows, certain MMPs are over-produced, allowing cancer cells to spread to other parts of the body.
Xin Ge, an assistant professor at the University of California, Riverside in the US and colleagues developed therapeutic monoclonal antibodies that are highly selective to MMPs, meaning they can bind to a specific MMP and block its activity without affecting other MMP family members.
The results could lead to new treatments - not only for a variety of cancers, but also other diseases that arise from faulty proteinases, such as Alzheimer's, asthma, multiple sclerosis and arthritis, researchers said.
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For more than 20 years, scientists have been developing drugs that block faulty MMPs in order to stop cancers from starting and spreading.
However, clinical trials on a variety of promising small molecules have failed - largely because they lack the specificity needed to target faulty MMPs while still allowing "good" MMPs to perform their regular cellular duties.
"As a result, broad-spectrum inhibitors have failed in clinical trials due to their low overall efficacy and side effects," Ge said.
Monoclonal antibodies, with their large and inherently more specific binding sites, have been touted as an alternative to small molecules.
However, until now, scientists have struggled to develop MMP-blocking antibodies due to the incompatibility between their binding sites.
"Both human antibodies and MMPs have concave - or buried - binding sites, making interactions between them almost impossible. They simply won't stick together," Ge said.
The team chemically synthesised billions of variants of human antibodies with convex loops found in camelids. In testing them, they identified dozens that are highly effective at blocking MMPs and reducing the spread of cancer in laboratory models.
The research was published in the journal PNAS.