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Ligand-based virtual screening, consensus molecular docking, multi-target analysis and comprehensive ADMET profiling and MD stimulation to find out noteworthy tyrosine kinase inhibitor with better efficacy and accuracy

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Abstract

Inhibition of BCR–ABL tyrosine kinase plays a crucial role in the management of chronic myelogenous leukemia (CML). The suppression of CML is getting harder because of a distinct pattern of resistance. Developing new types of ABL tyrosine kinase inhibitors along with ABL2, CSF1R, KIT, LCK, PDGFRA, and PDGFRB inhibitors is the main objective of this study that may overcome the drug resistance issue. The current study has been conducted using a kinase database containing 177,000 bioactive molecules, the top 135 molecules were selected with the best docking score and subjected to comprehensive ADMET profiling, multi-target analysis. Based on consensus molecular docking score (AutoDock, Chimera, Achilles, and Mcule), 22 molecules have been screened out which later undertaken for ADME/T profiling. After profiling of ADME/T data, selected molecules subjected to docking with multiple targets. Finally, molecular dynamics simulations had performed to screen the binding accuracy of the four lead molecules with ABL1. MD simulations of the desired complex (ABL1, ABL2, CSF1R, KIT, LCK, PDGFRA, and PDGFRB, among them ABL1 was the prime target) performed and found that PCID 10181160 and PCID 72724706 are the most promising inhibitors comparing to imatinib. These lead molecules are the potential CML inhibitors that could resolve the resistance pattern. Further chemical synthesis, wet lab analysis, and experimental validation deserve the utmost attention.

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Acknowledgements

Support to CBRL from Achilles blind docking server, OpenEye Scientific Software Inc. (Santa Fe, NM, USA) gratefully acknowledged. The research work had planned and conducted by AR, NHN & SCR. NQ and RM approved the research workflow, and proofread the article.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Computational Biology Research Lab, Department of Pharmacy, Jagannath University, Dhaka, 1100, Bangladesh

    Arifur Rahman, Nazmul Hasan Naheed, Sabreena Chowdhury Raka & A. Z. M. Ruhul Momen

  2. FDCC Testing and Research Lab, Department of Pharmacy, Jagannath University, 9-10, Chittaranjan Ave, Dhaka, 1100, Bangladesh

    Arifur Rahman

  3. Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh

    Sabreena Chowdhury Raka

  4. Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, 1000, Bangladesh

    Nazmul Qais

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  1. Arifur Rahman
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  2. Nazmul Hasan Naheed
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Correspondence to Arifur Rahman.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Arifur Rahman has no conflict of interest. Nazmul Hasan Naheed has no conflict of interest. Sabreena Chowdhury Raka has no conflict of interest. Nazmul Qais has no conflict of interest. A. Z. M. Ruhul Momen has no conflict of interest.

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Rahman, A., Naheed, N.H., Raka, S.C. et al. Ligand-based virtual screening, consensus molecular docking, multi-target analysis and comprehensive ADMET profiling and MD stimulation to find out noteworthy tyrosine kinase inhibitor with better efficacy and accuracy. ADV TRADIT MED (ADTM) 20, 645–661 (2020). https://doi.org/10.1007/s13596-019-00406-9

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