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Structure-based optimization of tyrosine kinase inhibitors: a molecular docking study

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Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

The most common pharmacologic approaches to inhibiting EGFR have been to develop small-molecule inhibitors which exert their effects at the intracellular portion of the receptor to prevent tyrosine kinase phosphorylation and subsequent activation of signal transduction pathways. A non-covalent molecular docking study was carried-out between 119 NCI anticancer compounds with receptor tyrosine kinase domain from epidermal growth factor receptor (PDB ID: 1M14), out of which 11 compounds had binding energy calculated with monte Carlo algorithm in ICM-pro Molsoft < − 25.25 kcal/mol. was found to have highest binding affinity with a reported value of − 32.832 kcal/mol, while Asaley had the overall least value (− 1.977 kcal/mol). The binding energy of all the compounds were found to be greatly influenced by the number and type of hydrogen bond interaction present between the ligands and the receptor except in few instances involving 5-flourouracil and mitomycin. A detailed description of the interactions of EGFR inhibitors developed can assist in the design of a more potent, more specific cytostatic drugs that can arrest tumor growth and cause tumor regression.

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Acknowledgements

The authors would like to thank Andrew Orry, for granting us the license for ICM-Pro molsoft program we adopted for this docking study.

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Authors and Affiliations

  1. Department of Chemistry, Ahmadu Bello University (ABU), Zaria, Kaduna State, Nigeria

    David Ebuka Arthur, Adamu Uzairu, Paul Mamza, Stephen Eyije Abechi & Gideon Adamu Shallangwa

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  1. David Ebuka Arthur
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  2. Adamu Uzairu
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  3. Paul Mamza
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  4. Stephen Eyije Abechi
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  5. Gideon Adamu Shallangwa
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Correspondence to David Ebuka Arthur.

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Arthur, D.E., Uzairu, A., Mamza, P. et al. Structure-based optimization of tyrosine kinase inhibitors: a molecular docking study. Netw Model Anal Health Inform Bioinforma 7, 9 (2018). https://doi.org/10.1007/s13721-018-0170-4

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  • DOI: https://doi.org/10.1007/s13721-018-0170-4

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