Navid Pourzardosht1,2, Zahra Sadat Hashemi3, Maysam Mard-Soltani4, Abolfazl Jahangiri5, Mohammad Reza Rahbar6, Alireza Zakeri7, Ebrahim Mirzajani1,2, Saeed Khalili7. 1. Cellular and Molecular Research Center, Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran. 2. Biochemistry Department, Guilan University of Medical Sciences, Rasht, Iran. 3. ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran. 4. Department of Clinical Biochemistry, Faculty of Medical Sciences, Dezful University of Medical Sciences, Dezful, Iran. 5. Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. 6. Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. 7. Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran.
Abstract
PURPOSE: The interaction between PD-L1 on tumor cells and the programmed death 1 (PD1) on immune cells helps them to escape the immune system elimination. Therefore, developing therapeutic agents to block this interaction has garnered a lot of attention as a therapeutic approach. In the present study, we have tried to screen for an inhibitory compound to inhibit the interaction between the PD1/PD-L1 molecules. METHODS: In this regard, the structure of PD-L1 and its inhibitor were prepared and employed to generate an e-Pharmacophore model. A library of approved compounds was prepared and toxicity analysis using Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictor was performed. The built e-Pharmacophore model was validated and used to screen the prepared compound library. Ligand docking and binding energy calculation were performed on the screened ligands. RESULTS: A seven-feature e-Pharmacophore model was generated using the PD-L1 complex. All of the compounds within the library passed the ADMET criteria. Performing the virtual screening, only 79 compounds have survived the criteria to fit four pharmacophoric features. The compound with the highest binding energy was the liothyronine (T3). CONCLUSION: The ability of T3 in PD1/PD-L1 checkpoint blockade along with its potential in T4 reduction could be a desirable combination in cancer treatment. These abilities of T3 could be used to restore the ability of the immune system to eliminate tumor cells.
PURPOSE: The interaction between PD-L1 on tumor cells and the programmed death 1 (PD1) on immune cells helps them to escape the immune system elimination. Therefore, developing therapeutic agents to block this interaction has garnered a lot of attention as a therapeutic approach. In the present study, we have tried to screen for an inhibitory compound to inhibit the interaction between the PD1/PD-L1 molecules. METHODS: In this regard, the structure of PD-L1 and its inhibitor were prepared and employed to generate an e-Pharmacophore model. A library of approved compounds was prepared and toxicity analysis using Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictor was performed. The built e-Pharmacophore model was validated and used to screen the prepared compound library. Ligand docking and binding energy calculation were performed on the screened ligands. RESULTS: A seven-feature e-Pharmacophore model was generated using the PD-L1 complex. All of the compounds within the library passed the ADMET criteria. Performing the virtual screening, only 79 compounds have survived the criteria to fit four pharmacophoric features. The compound with the highest binding energy was the liothyronine (T3). CONCLUSION: The ability of T3 in PD1/PD-L1 checkpoint blockade along with its potential in T4 reduction could be a desirable combination in cancer treatment. These abilities of T3 could be used to restore the ability of the immune system to eliminate tumor cells.
Entities:
Keywords:
In silico; PD-L1; immune checkpoint blockade; liothyronine; virtual screening