Current developments in antibody specificity testing
Welcome to Specificity Insights, our blog about current developments in antibody specificity testing! Over the coming months, we’ll be releasing a multi-part series about specificity testing with the Membrane Proteome Array (MPA) and our journey to qualify the MPA as a Drug Development Tool (DDT) through the FDA’s ISTAND program.
Specificity testing is ultimately a patient safety question: does this antibody bind only what it’s supposed to? Answering it requires a library that represents the human proteins a biotherapeutic will encounter in vivo, in their biologically relevant conformations. The Membrane Proteome Array (MPA) library was purpose-built to do exactly that.
Off-target binding doesn’t always cause harm—but when it goes undetected, the consequences can be serious. Published case studies document off-target interactions that caused adverse events in clinical trials, halted drug development programs, and may explain the known safety profiles of approved therapeutics.
In this article: How common is off-target binding, really? Does polyspecificity persist into late-stage clinical development? Why can’t you predict off-targets from sequence analysis alone?
In 2021, we began working with the FDA to qualify the MPA as a Drug Development Tool through the ISTAND program. While the FDA already accepts MPA data, this qualification process validates the scientific rigor behind the platform and helps formalize the shift toward more predictive, human-relevant specificity testing methods.
Even though TCR is widely used and has long been recommended by the FDA, it has a long and much-discussed list of limitations. There is a clear need for a shift to newer specificity testing approaches that are quantitative, objective, validated, and better correlated with patient safety outcomes.
Even though monoclonal antibodies are prized for their specificity, 1 in 3 lead candidates exhibit off-target binding. Because off-target interactions are so common and their safety consequences so large, we need accurate methods for identifying them.
