Medications, such as chemotherapy, are often limited by their tendency to be detrimental to healthy cells as an unintended side effect. Now research in the November 18th issue of Cell Press's Biophysical Journal offers a new computational model that can help investigators design ways to direct drugs to their specific targets.
A major problem with many cancer drugs is the harmful effects they can have on normal cells. Similarly, treatments for a variety of other diseases can have side effects by acting on cells that are not meant to be targeted. Researchers have tried to overcome this by linking drugs to antibodies, for example by linking a chemotherapy agent to an antibody that specifically attaches to cancer cells to create a fusion protein. However, it can be difficult to do so without compromising the medication's effectiveness because the drug has to be able to reach its targeted cell receptor at the same time as the antibody binds to its own target on the cell.
Investigators have now developed a computational model to help design the most effective way to link an antibody to a therapeutic drug to create such a fusion protein. The model takes into consideration how the length of an antibody linker will affect a drug's ability to interact with its target and uses this information as well as other parameters to model and predict how a particular fusion protein will look geometrically, how it will act when applied to cells, and what concentration is optimal.
"The importance of this finding is that it has the potential to allow us to predict the behavior of fusion proteins without having to physically make them, eliminating unsuccessful candidates for drug design using modeling and thereby saving time and effort," explains senior author Dr. Pamela Silver, of Harvard Medical School and the Wyss Institute for Biologically Inspired Engineering at Harvard University. Therefore, accurately modeling the behavior associated with a given design for a fusion protein could allow researchers to move away from a screening-based, guess-and-check method to one that is based on rational design. "Using modeling as a first step of validation will allow us to determine which constructs are likely to be promising and focus our efforts on the best candidates for testing," says lead author Dr. Avi Robinson-Mosher, of the Wyss Institute.
A major problem with many cancer drugs is the harmful effects they can have on normal cells. Similarly, treatments for a variety of other diseases can have side effects by acting on cells that are not meant to be targeted. Researchers have tried to overcome this by linking drugs to antibodies, for example by linking a chemotherapy agent to an antibody that specifically attaches to cancer cells to create a fusion protein. However, it can be difficult to do so without compromising the medication's effectiveness because the drug has to be able to reach its targeted cell receptor at the same time as the antibody binds to its own target on the cell.
Investigators have now developed a computational model to help design the most effective way to link an antibody to a therapeutic drug to create such a fusion protein. The model takes into consideration how the length of an antibody linker will affect a drug's ability to interact with its target and uses this information as well as other parameters to model and predict how a particular fusion protein will look geometrically, how it will act when applied to cells, and what concentration is optimal.
"The importance of this finding is that it has the potential to allow us to predict the behavior of fusion proteins without having to physically make them, eliminating unsuccessful candidates for drug design using modeling and thereby saving time and effort," explains senior author Dr. Pamela Silver, of Harvard Medical School and the Wyss Institute for Biologically Inspired Engineering at Harvard University. Therefore, accurately modeling the behavior associated with a given design for a fusion protein could allow researchers to move away from a screening-based, guess-and-check method to one that is based on rational design. "Using modeling as a first step of validation will allow us to determine which constructs are likely to be promising and focus our efforts on the best candidates for testing," says lead author Dr. Avi Robinson-Mosher, of the Wyss Institute.
The scientists verified their approach by creating various geometric combinations of antibody fragments and therapeutic molecules with linkers of different lengths, then successfully demonstrated that each fusion protein's effectiveness could be predicted through their model.
"This study is important because it provides a roadmap for how to design more selective, less toxic chemotherapies," adds Dr. Leslie Loew, Director of the R. D. Berlin Center for Cell Analysis and Modeling at the University of Connecticut Health Center and Editor in Chief of Biophysical Journal. "It is especially impressive that these researchers not only developed very elegant computational models, but then went to the lab to verify the predictions with experiments."
Robinson-Mosher et al. Designing cell targeted therapeutic proteins reveals the interplay between domain connectivity and cell binding. Biophys J. 2014;107(10):2456–2466 DOI: http://dx.doi.org/10.1016/j.bpj.2014.10.007 [Abstract]
"This study is important because it provides a roadmap for how to design more selective, less toxic chemotherapies," adds Dr. Leslie Loew, Director of the R. D. Berlin Center for Cell Analysis and Modeling at the University of Connecticut Health Center and Editor in Chief of Biophysical Journal. "It is especially impressive that these researchers not only developed very elegant computational models, but then went to the lab to verify the predictions with experiments."
Robinson-Mosher et al. Designing cell targeted therapeutic proteins reveals the interplay between domain connectivity and cell binding. Biophys J. 2014;107(10):2456–2466 DOI: http://dx.doi.org/10.1016/j.bpj.2014.10.007 [Abstract]
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