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Dennis Liotta, PhD, DScThe Emory Institute for Drug Development and the Department of Chemistry, Emory University, Atlanta, GA 30322 USA
Hosted by Jennifer Golden & Medicinal Chemistry Core
The chemokine receptor, CXCR4, is a seven transmembrane, G-protein coupled receptor whose only endogenous ligand is the protein, CXCL12 (also known as SDF-1). These receptors play a crucial role in a variety of important normal and aberrant physiological processes, including chemotaxis, metastasis, angiogenesis and tumor growth. Since it is highly overexpressed in more than 23 hematopoietic and solid cancers, compounds that can modulate selective components of the CXCR4 signaling pathways, including those associated with cellular proliferation and survival, have become popular targets for therapeutic development. Especially active targets in this area include hematopoietic stem cell mobilization and chemosensitization. In addition, since the CXCR4 receptor serves as one of the host co-receptors responsible for mediating HIV-1 entry into immune cells, compounds that block entry could potentially be used as HIV therapeutics. Unfortunately, the complexities associated with the multiple CXCR4 signaling pathways have created a major challenge for identifying compounds that are both efficacious and possess a safety profile suitable for dosing patients every day of their lives.
My lab has discovered a series of CXCR4 modulators that, depending on their binding orientations, can act as either potent antagonists or inverse agonists of CXCR4 cell surface receptors. In addition, we have discovered a series of dual tropic antagonists with excellent safety profiles that simultaneously block HIV entry through both CXCR4 and CCR5. In this presentation I will discuss some of the opportunities and challenges associated with the development of these series.
Finally, I will also describe a chemoinformatics/machine learning protocol, called FRESH, that dramatically increases the ease of quality candidate identification. To date, novel compounds, identified by FRESH and subsequently synthesized, were consistently found to have nanomolar or sub-nanomolar activity for several dissimilar proteins. In addition, we have used similar cheminformatics and machine learning protocols to explore the susceptibility of molecules to mutation-induced loss of potency for HIV reverse transcriptase. These results strongly suggest that artificial intelligence will have great utility in the design of biologically active molecules.