Towards combating antibiotic resistance by exploring the quantitative structure-activity relationship of NDM-1 inhibitors

Authors

  • Tianshi Yu Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand https://orcid.org/0000-0003-2381-2856
  • Aijaz Ahmad Malik Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand https://orcid.org/0000-0001-5132-1574
  • Nuttapat Anuwongcharoen Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand https://orcid.org/0000-0001-5361-5314
  • Warawan Eiamphungporn Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand https://orcid.org/0000-0003-4698-3236
  • Chanin Nantasenamat Streamlit Open Source, Snowflake Inc., USA. E-Mail: hellodataprofessor@gmail.com https://orcid.org/0000-0003-1040-663X
  • Theeraphon Piacham Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand. E-mail: theeraphon.pia@mahidol.ac.th https://orcid.org/0000-0001-8975-9520

DOI:

https://doi.org/10.17179/excli2022-5380

Keywords:

antibiotic resistance, beta-lactamase, NDM-1, QSAR, drug discovery, data science

Abstract

The emergence of New Delhi metallo-beta-lactamase-1 (NDM-1) has conferred enteric bacteria resistance to almost all beta-lactam antibiotics. Its capability of horizontal transfer through plasmids, amongst humans, animal reservoirs and the environment, has added up to the totality of antimicrobial resistance control, animal husbandry and food safety. Thus far, there have been no effective drugs for neutralizing NDM-1. This study explores the structure-activity relationship of NDM-1 inhibitors. IC50 values of NDM-1 inhibitors were compiled from both the ChEMBL database and literature. After curation, a final set of 686 inhibitors were used for machine learning model building using the random forest algorithm against 12 sets of molecular fingerprints. Benchmark results indicated that the KlekotaRothCount fingerprint provided the best overall performance with an accuracy of 0.978 and 0.778 for the training and testing set, respectively. Model interpretation revealed that nitrogen-containing features (KRFPC 4080, KRFPC 3882, KRFPC 677, KRFPC 3608, KRFPC 3750, KRFPC 4287 and KRFPC 3943), sulfur-containing substructures (KRFPC 2855 and KRFPC 4843), aromatic features (KRFPC 1566, KRFPC 1564, KRFPC 1642, KRFPC 3608, KRFPC 4287 and KRFPC 3943), carbonyl features (KRFPC 1193 and KRFPC 3025), aliphatic features (KRFPC 2975, KRFPC 297, KRFPC 3224 and KRFPC 669) are features contributing to NDM-1 inhibitory activity. It is anticipated that findings from this study would help facilitate the drug discovery of NDM-1 inhibitors by providing guidelines for further lead optimization.

Additional Files

Published

2022-11-16

How to Cite

Yu, T., Ahmad Malik, A., Anuwongcharoen, N., Eiamphungporn, W., Nantasenamat, C., & Piacham, T. (2022). Towards combating antibiotic resistance by exploring the quantitative structure-activity relationship of NDM-1 inhibitors. EXCLI Journal, 21, 1331–1351. https://doi.org/10.17179/excli2022-5380

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