Computer-aided drug discovery of c-Abl kinase inhibitors from plant compounds against chronic myeloid leukemia

Abstract

Chronic myeloid leukemia (CML) is a hematological malignancy characterized by the neoplastic transformation of hematopoietic stem cells, driven by the Philadelphia (Ph) chromosome resulting from a translocation between chromosomes 9 and 22. This Ph chromosome harbors the breakpoint cluster region (BCR) and the Abelson (ABL) oncogene (BCR-ABL1) which have a constitutive tyrosine kinase activity. However, the tyrosine kinase activity of BCR-ABL1 have been identified as a key player in CML initiation and maintenance through c-Abl kinase. Despite advancements in tyrosine kinase inhibitors, challenges such as efficacy, safety concerns, and recurring drug resistance persist. This study aims to discover potential c-Abl kinase inhibitors from plant compounds with anti-leukemic properties, employing drug-likeness assessment, molecular docking, in silico pharmacokinetics (ADMET) screening, density function theory (DFT), and molecular dynamics simulations (MDS). Out of 58 screened compounds for drug-likeness, 44 were docked against c-Abl kinase. The top hit compound (isovitexin) and nilotinib (control drug) were subjected to rigorous analyses, including ADMET profiling, DFT evaluation, and MDS for 100 ns. Isovitexin demonstrated a notable binding affinity (-15.492 kcal/mol), closely comparable to nilotinib (-16.826 kcal/mol), showcasing a similar binding pose and superior structural stability and reactivity. While these findings suggest isovitexin as a potential c-Abl kinase inhibitor, further validation through urgent in vitro and in vivo experiments is imperative. This research holds promise for providing an alternative avenue to address existing CML treatment and management challenges.

Communicated by Ramaswamy H. Sarma

Keywords:

• Chronic myeloid leukemia
• anti-leukemic plants
• plant compounds
• c-Abl kinase
• tyrosine kinase inhibitors

Acknowledgments

The authors thank the University of Groningen Peregrine for providing computational resources to run MD simulations. All authors acknowledge their respective institutions for providing an enabling environment for learning and research. Also, all authors are thankful to Dr. Tariq, the Head of the National Data Management Office, Saudi Arabia for his ongoing support. The authors are thankful to Shubham Srivastava, Central University of Rajasthan, Ajmer for providing help for computational study.

Authors’ contributions

MMA and HIU: Conceptualization, Software. NK, NAK, ZO, MK Alshammari, MK Alghazwni, and ROB: Methodology, Data curation, Writing-Original draft preparation. NAK, AMA, ROB, and OMA: Visualization, Investigation. HIU and MMA: Supervision. NK, NAK, AMA, ZAA and ROB: Software, Validation. ZAA, ZO, MK Alshammari, MK Alghazwni, MMA, and OMA: Writing- Reviewing and Editing. All the authors read and approved the final version for submission.

Disclosure statement

The authors declare no known competing financial interests or no personal relationships that could have appeared to influence the work reported in this paper.

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Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

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Funding

The author(s) reported there is no funding associated with the work featured in this article.