Drug Discovery and Design: Kappa-Opioid Receptors Antagonists Inhibitors Affinity
Board Location: #105
Discipline: Chemistry and Chemical Sciences
Subcategory: Chemistry (not Biochemistry)
Session: 1
Brenda Davila - Miami Dade College
The opioid crisis presents a concerning public health challenge in the United States, characterized by the devastating consequences of opioid addiction. It is crucial to address this crisis by the development of selective antagonists targeting the kappa-opioid receptor (KOR). These antagonists, characterized by their capacity to bind to KOR and inhibit its activity, hold the potential to reduce the addictive properties of opioids and reduce the associated risk of overdose. By utilizing molecular modeling simulations via AutoDock, this study has undertaken rigorous investigations into potential ligands that exhibit the requisite binding affinity to the KOR binding pocket, with a specific focus on achieving a binding energy exceeding -9 kcal/mol. Promising compounds, most notably the JDTic ligand, have been identified as displaying a high affinity for the KOR binding site. Numerous ligands have demonstrated robust binding energies coupledwith lower binding constants, potentially reducing required dosages fortherapeutic benefit. However, judicious ligand selection remainsimperative during drug synthesis, as some possess characteristics thatcould inadvertently lead to highly addictive compounds. Ourparamount objective remains the creation of a solution to the opioidcrisis rather than engendering substances more potent than existingpharmaceuticals. Through the synergy of rigorous research
Funder Acknowledgement(s): The original research summarized in the following was supported, in part, byU.S. Department of Education grant awards: P120A200007 (STEM AISLE),P031C210035 (STEM PACTS), P031C210028 (STEM SMART); and in partby the National Science Foundation grant award: 1832436 (Building CapacityHispanic Student Success from 2-year to 4-year Institutions through CUREs).In addition, I want to give a special thank you to my Mentor and Professor Dr.Dinesh Vidhani for his guidance and computational chemistry knowledge.
Faculty Advisor: Dr. Dinesh Vidhani, dvidhani@mdc.edu
Role: My contribution to this research was critical, I first converted each ligand from SMILES format into PDBQT format (Protein Data Bank, Partial Charge) and then I started the process of screening and docking over 400 molecules. Each docking was conducted through the computational chemistry software Autodock, an automated docking tool that predicts small molecule interactions bind to a receptor in a 3D structure format. Each molecule screened took over 1o minutes. After obtaining the data from each doing, I analyzed and compared the protein-ligand interactions from each molecule. by using its Binding affinity constant and its similarity to JDTic. Then, I worked on obtaining the Drug-likeness and medical chemistry properties from the best-performing molecules.