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Computational design of PD-L1 small molecule inhibitors for cancer therapy

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Abstract

Drug repurposing opens new avenues in cancer therapy. Drug repurposing, or finding new uses for existing drugs, can substantially reduce drug discovery time and costs. Cheminformatics, genetics, and systems biology advances enable repositioning drugs. Clinical usage of PD-1/PD-L1 blocking has been approved because of its efficacy in improving prognosis in select groups. The PD-1/PD-L1 axis was considered to represent a mechanism for tumour evasion of host tumour antigen-specific T-cell immunity in early preclinical research. The expression of PD-L1 in cancer cells causes T lymphocytes to become exhausted by transmitting a co-inhibitory signal. A better understanding of how PD-L1 is regulated in cancer cells could lead to new therapeutic options. In this view, the study was aimed to repurpose the existing FDA-approved drugs as a potential PD-L1 inhibitor through e-Pharmacophore modelling, molecular docking and dynamic simulation. e-Pharmacophore screening retrieved 324 FDA-approved medications with the fitness score ≥ 1. The top 10-docked FDA candidates were compared with IN-35 (Clinical trial candidate) for its interaction pattern with critical amino acid residues. Mirabegron and Indacaterol exhibited a greater affinity for PD-L1 with docking scores of − 9.213 kcal mol−1 and − 8.023 kcal mol−1, respectively. Mirabegron retain interactions at all three major hotspots in the PD-L1 dimer interface similar to IN-35. MM-GBSA analyses indicated that Mirabegron uses less energy to create a more stable complex and retains all of the inhibitor’s positive interactions found in clinical trial ligand IN-35. Molecular dynamics simulation analysis of the Mirabegron complex showed a similar pattern of deviation in correlation with IN-35, and it retains the interaction with the active key amino acids throughout the simulation time. Our present study has shown Mirabegron as a powerful inhibitor of PD-L1 expression in cancer cells using a drug-repurposing screen.

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Acknowledgements

The authors thank the PSG College of Pharmacy, Peelamedu, Coimbatore for providing computational facility.

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Authors and Affiliations

  1. Department of Pharmacology, School of Pharmacy & Technology Management, SVKM’S NMIMS University, Secunderabad, 500017, India

    Jaikanth Chandrasekaran

  2. Department of Pharmacology, PSG College of Pharmacy, Peelamedu, Coimbatore, 641004, India

    Senthilkumar Elumalai

  3. Department of Chemistry and Biochemistry, Science and Commerce, M S Ramaiah College of Arts, Bengaluru, 560054, India

    Vidya Murugesan

  4. Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India

    Selvaraj Kunjiappan

  5. Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bengaluru, 560054, India

    Parasuraman Pavadai

  6. Department of Pharmaceutical Chemistry, Swamy Vivekanandha College of Pharmacy, Tiruchengodu, 637205, India

    Panneerselvam Theivendren

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Contributions

JC contributed to the study conception and design. JC, PP and SE performed computational simulation and analysed the data. JC, SE, VM and PP wrote the original manuscript. PT and SK proofread the manuscript. All the authors have read and approved the manuscript for submission.

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Correspondence to Jaikanth Chandrasekaran.

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Chandrasekaran, J., Elumalai, S., Murugesan, V. et al. Computational design of PD-L1 small molecule inhibitors for cancer therapy. Mol Divers 27, 1633–1644 (2023). https://doi.org/10.1007/s11030-022-10516-3

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