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UNC team hits bullseye with ‘magic bullet’
August 2019
by Lori Lesko  |  Email the author
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CHAPEL HILL, N.C.—University of North Carolina (UNC) Eshelman School of Pharmacy primary researcher Yanguang (Carter) Cao and his multidisciplinary team have used a bioluminescence resonance energy transfer (BRET)-based platform to non-invasively quantify antibody target occupancy in living systems—thus edging one step closer to personalized medicine.
 
“In our present study, first published in May 2019 in iScience, we developed a BRET-based system to non-invasively quantify antibody researchers to directly visualize when and where antibodies interact with their targets in the body,” Cao explains. “These findings may be valuable for designing more effective therapeutic antibodies, and more importantly, help identify mechanisms of resistance to many antibody-based therapeutics.”
 
Entitled “A Bioluminescence Resonance Energy Transfer-Based Approach for Determining Antibody-Receptor Occupancy In Vivo”, the iScience article states: “Elucidating receptor occupancy (RO) of monoclonal antibodies (mAbs) is a crucial step in characterizing the therapeutic efficacy of mAbs. However, the in-vivo assessment of RO, particularly within peripheral tissues, is greatly limited by current technologies.”
 
Antibody-based drugs are now the fastest growing class of therapeutic agents. In 2018, seven out of 10 best-selling drugs were antibody-based drugs. Monoclonal antibodies (mAbs) are often regarded as “magic bullets” toward the treatment of an array of human diseases, the UNC study states. These therapeutic mAbs are engineered to specifically bind their cognate antigens with high affinities and have been deployed for neutralizing pathologic factors, blocking cellular signaling and stimulating immune functions.
 
Therapeutic mAbs have shown great promise in cancer treatments, given their therapeutically desirable characteristics of long plasma half-lives, researchers state. To date, over 30 mAbs have been approved for treatment of various types of cancers, including hematologic malignancies and solid tumors.
 
Elucidating the RO of a given antibody is extremely critical to characterize its therapeutic potential and define the optimal dosing regimens, Cao notes. However, eliciting RO “remains a daunting task.”
 
“Our research team is focused on developing novel imaging technology, bundled with many computational modeling approaches, to elucidate the factors that limit the efficacy and therapeutic potential of antibody-based immunotherapy,” he says. “Many therapeutic strategies could be developed based on our findings to fully release the potential of antibody-based immunotherapy.”
 
As noted by the study’s authors, “We developed a BRET-based system that leverages the large signal:noise ratio and stringent energy donor-acceptor distance dependency to measure antibody RO in a highly selective and temporal fashion. This versatile and minimally invasive system enables longitudinal monitoring of the in-vivo antibody-receptor engagement over several days.”
 
This UNC methodology could also save hundreds of hours and thousands of dollars trying to match a particular treatment to a particular patient.
 
Cao hopes his team’s findings provide future researchers with direct evidence on the binding kinetics between antibodies and their targets, and the ability to evaluate the pathophysiological and biophysical factors that could potentially restrict effective target engagement.
 
According to Cao, “The promise of this BRET-imaging based technology is quite high. Although more investigations are warranted in translating this technology from preclinical to clinical settings, this approach has the potential to enable us to find out many patient-associated factors that limit antibody target engagement and treatment efficacy—and then develop strategies to further improve treatment outcome for each patient.”
 
The UNC’s researchers’ findings also help identify mechanisms of resistance to many antibody-based therapeutics, he added. The technology “developed in our study, from antibody-antigen binding perspective, could identify many mechanisms as to why some patients are not responding to a treatment.”
 
Cao tells DDNews that this method is “one step closer to personalized medicine, absolutely. As much benefit as antibody-based therapies deliver to cancer patients, the high fraction of resistance has become the greatest challenge of antibody-based immunotherapy.”
 
The research team is now in the process of developing a computational model to quantitatively evaluate many pathophysiological factors that could explain incomplete target binding and strategize ways to improve, he adds.
 
“A great progress has been made,” Cao says. “Our recent findings, using a computational modeling approach, provide strong evidence to support ‘target binding heterogeneity’ inside solid tumors.
 
“More effective immunotherapy could be developed based on our findings,” he concludes. “The research environment provided by UNC Eshelman School of Pharmacy is critical for this collaborative research effort.”
 
Code: E081909

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