RESEARCH

Research in the LaPlante Lab is centered around discovering drugs, practical tools and interesting chemical properties. Our goal is to explore the initial and essential steps of drug discovery: finding the starting chemical matter  for designing future drugs that target diseases. We are developing new breakthrough technologies that involve fragment-based lead discovery (FBLD) for finding these drug seeds.

 FBLD involves the screening of small libraries of low molecular-weight compounds called ”fragments” to search for new binders/inhibitors. These inhibitors can then be employed as scaffolds from which appendages are systematically added to improve potency and to “build a drug". However,  there are serious issues which limit the practical application of FBLD currently.  

The LaPlante Lab aims to overcome these limitations and expediate the discovery of quality starting chemical-matter for any drug discovery program. We enable this by working with our team's extensive experience in the pharmaceutical industry and our highly motivated students to evaluate and re-engineer each step on the FBLD process. We do this by implementing a synergistic complement of biology, biophysics and chemistry to overcome historic barriers. Currently, hardware and software automation is being introduced at keys steps, and new medicinal chemistry and affinity NMR Kd cycles are being implemented to facilitate the transformation of binders to drug leads.     

Developing and implementing these strategies requires a network of people, expertise and technologies. This is being achieved via the establishment of the new Drug Discovery Network (DDN). Teams of researchers from the University Quebec INRS-IAF, Harvard Medical School, Broad Institute, NMX and Key Organics are developing new, well-curated fragment libraries ready for high-throughput screening purposes. New, NMR-based target-screening strategies have been introduced and successfully applied with many biotechnology companies located in Boston, Montreal and San Francisco to target diseases. The complexities of analyzing massive datasets are being simplified by the design of in-house software.