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Computational Drug Design of Novel Small Molecule Inhibitors for Therapy in Pancreatic Ductal Adenocarcinoma
  • Aryansh Shrivastava
Aryansh Shrivastava
Washington High School

Corresponding Author:sendtoaryansh@gmail.com

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Today, pancreatic ductal adenocarcinoma, the most common form of pancreatic cancer, is one of the deadliest cancer types and remains largely unresolved. Through my research, I engineer a class of novel small molecule ligands to cure the disease by inhibiting its central carcinogenic pathway, taking the unique approach of applying the framework of targeted drug therapy and leveraging computer-aided techniques.
The target molecule for which drug design is employed is xCT, a protein embedded in the ductal cell membrane that enables cancer cells to survive without a nutrient supply and kills normal cells of the pancreas. All viable drug candidate ligands must be engineered to have a complementary structure and biochemistry to the binding site of xCT.
Starting with some known ligands of xCT to first identify the binding site and binding mode, I conduct guided substituent recombination based on the analysis of hydrophobicity and Coulombic surfaces as well as intermolecular interactions through computational docking simulations. In doing so, I employ industry-grade softwares including Chimera (to visualize 3D structures), Avogadro (to minimize free energies and infer 3D chemical structures), ChemDraw (to draw 2D chemical structures), AutoDockTools (to visualize the location of the binding site), AutoDock Vina (to perform docking simulations), and the SeaWulf computational cluster (for intensive, high accuracy computations).
A total of 1461 novel results are tabulated, and the top two drug candidates among them are found to be CID 136204070 and 135564873, the best one among them CID 135564873. These can be used by drug industries to create new targeted drug therapies for pancreatic cancer, increasing the survival rate of the disease and saving countless lives through proactive treatment despite diagnostic delays. I've computationally ensured the absorption, distribution, metabolism, excretion, and toxicity results for the final drug candidates, and all of the relevant quantities are computationally verified through well-defined biochemistry lab procedures for the final ligand molecules and found to be very reliable for drug candidacy.
I did this project through the Simons Summer Research Program at Stony Brook University as a Simons Fellow and paid research intern, under the guidance of Distinguished Professor Iwao Ojima and student mentors Adam Taouil and Frank Wang.