Scientists have developed a new cancer treatment inspired by natural antibodies found in camels and llamas. The new treatment is highly selective in blocking action of faulty enzymes essential in tissue regeneration.
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.
But when a tumour grows, certain MMPs are produced in excess that allows cancer to spread in other parts of body.
This treatment using antibodies of camels and llamas was developed by Xin Ge, an assistant professor at the University of California, Riverside in the US and his colleagues. They 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 creation of these human antibodies was inspired by antibodies found naturally in the camelid family of animals, which includes camels and llamas.
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.
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.
"Clinical trial failures have taught us that selective, rather than broad-based, inhibitors are required for successful MMP therapies, but achieving this selectivity with small-molecule inhibitors is exceedingly difficult because of the incredible conservation among MMP family members.
"As a result, broad-spectrum inhibitors have failed in clinical trials due to their low overall efficacy and side effects," Ge said.
(With IANS Inputs)
