IN-SILICO CHARACTERIZATION OF BCR-ABL1 AS A DRUG TARGET IN LEUKEMIA – A REVIEW

Abstract

Background:  Leukemia is a heterogeneous group of hematological malignancies characterized by the uncontrolled proliferation of abnormal leukocytes in the bone marrow and peripheral blood. Chronic myeloid leukemia (CML) and Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) are driven by the BCR-ABL1 fusion oncoprotein, a constitutively active tyrosine kinase produced by the reciprocal translocation t(9;22). Although tyrosine kinase inhibitors (TKIs) such as imatinib have transformed the management of BCR-ABL1-positive leukemia, drug resistance (particularly the T315I “gatekeeper” mutation), adverse effects and high treatment costs remain major clinical challenges.

Objective: The present study was designed to characterize potential inhibitors of the BCR-ABL1 protein using computational (in-silico) approaches,

Methodology: A pure in-silico study design was adopted. The three-dimensional structure of the BCR-ABL1 kinase domain was retrieved from the Protein Data Bank (PDB), and a panel of approximately 30 drug-like compounds was collected from the PubChem database. Protein and ligands were prepared using PyMOL/Discovery Studio, and molecular docking was performed using AutoDock/PyRx to estimate binding energies and protein-ligand interactions. The top-ranked hits were evaluated for their drug-like properties using sift analysis and pkCSM (sift analysis).

Results: These docking results indicated that ponatinib had the best predicted binding score to both wild-type BCR-ABL1 (−11.5 kcal/mol) and T315I mutant (−9.7 kcal/mol), while bosutinib had the least favorable scores (−9.1 and −7.6 kcal/mol, respectively). The mutant had a less negative score than the wild-type target for all six compounds. The greatest score difference was obtained for imatinib (+3.0 kcal/mol) and the smallest for asciminib (+1.4 kcal/mol). Such changes indicate differences in predicted binding based on the docking scoring function and cannot be used to establish drug resistance, clinical effectiveness and inhibitory potency in isolation.

Conclusion: The study confirmed BCR-ABL1 as a validated drug target and showed that ponatinib, asciminib and dasatinib kept the most favourable predicted binding profiles, with ponatinib ranked first against both the wild-type and the T315I-mutated kinase. These three compounds are recommended as lead candidates for future in-vitro and in-vivo validation, and the findings support computational docking as a rapid, low-cost first step towards overcoming tyrosine kinase inhibitor resistance.