The constitutive BCR–ABL1 active protein fusion has been identified as the main cause of chronic myeloid leukemia. The emergence of T334I and D381N point mutations in BCR–ABL1 confer drug resistance. Recent experimental studies show a synergistic effect in suppressing this resistance when Nilotinib and Asciminib are co-administered to target both the catalytic and allosteric binding site of BCR–ABL1 oncoprotein, respectively. However, the structural mechanism by which this synergistic effect occurs has not been clearly elucidated. To obtain insight into the observed synergistic effect, molecular dynamics simulations have been employed to investigate the inhibitory mechanism as well as the structural dynamics that characterize this effect. Structural dynamic analyses indicate that the synergistic binding effect results in a more compact and stable protein conformation. In addition, binding free energy calculation suggests a dominant energy effect of nilotinib during co-administration. van der Waals energy interactions were observed to be the main energy component driving this synergistic effect. Furthermore, per-residue energy decomposition analysis identified Glu481, Ser453, Ala452, Tyr454, Phe401, Asp400, Met337, Phe336, Ile334, And Val275 as key residues that contribute largely to the synergistic effect. The findings highlighted in this study provide a molecular understanding of the dynamics and mechanisms that mediate the synergistic inhibition in BCR–ABL1 protein in chronic myeloid leukemia treatment.
This is a preview of subscription content, log in to check access.
The authors acknowledge the College of Health Science, the University of KwaZulu-Natal for financial support and Centre of High-Performance Computing (CHPC) Cape Town, RSA, for computational resources (http://www.chpc.ac.za).
Nagar B, Bornmann WG, Pellicena P et al (2002) Crystal structures of the kinase domain of c-Abl in complex with the small molecule inhibitors PD173955 and imatinib (STI-571). Cancer Res 62:4236–4243Google Scholar
Schindler T, Bornmann W, Pellicena P et al (2000) Structural mechanism for STI-571 inhibition of abelson tyrosine kinase. Science 289:1938–1942CrossRefGoogle Scholar
Fava C, Morotti A, Dogliotti I et al (2015) Update on emerging treatments for chronic myeloid leukemia. Expert Opin Emerg Drugs 20:183–196CrossRefGoogle Scholar
Miura M (2015) Therapeutic drug monitoring of imatinib, nilotinib, and dasatinib for patients with chronic myeloid leukemia. Biol Pharm Bull 38:645–654CrossRefGoogle Scholar
Roskoski R Jr (2016) Classification of small molecule protein kinase inhibitors based upon the structures of their drug-enzyme complexes. Pharmacol Res 103:26–48CrossRefGoogle Scholar
Shah NP, Nicoll JM, Nagar B et al (2002) Multiple BCR–ABL kinase domain mutations confer polyclonal resistance to the tyrosine kinase inhibitor imatinib (STI571) in chronic phase and blast crisis chronic myeloid leukemia. Cancer Cell 2:117–125CrossRefGoogle Scholar
Wylie A, Schoepfer J, Berellini G et al (2014) ABL001, a potent allosteric inhibitor of BCR–ABL, prevents emergence of resistant disease when administered in combination with nilotinib in an in vivo murine model of chronic myeloid leukemia. Blood 124:398–398Google Scholar
Eadie LN, Saunders VA, Leclercq TM et al (2015) The allosteric inhibitor ABL001 is susceptible to resistance in vitro mediated by overexpression of the drug efflux transporters ABCB1 and ABCG2. Blood 126:4841–4841Google Scholar
Ottmann OG, Alimena G, DeAngelo DJ et al (2015) ABL001, a potent, allosteric inhibitor of BCR–ABL, exhibits safety and promising single- agent activity in a phase I study of patients with CML with failure of prior TKI therapy. Blood 126:138–138Google Scholar
Hughes TP, Goh Y-T, Ottmann OG et al (2016) Expanded phase 1 Study of ABL001, a potent, allosteric inhibitor of BCR–ABL, reveals significant and durable responses in patients with CML-chronic phase with failure of prior TKI therapy. Blood 128:625–625CrossRefGoogle Scholar
Sprenger KG, Jaeger VW, Pfaendtner J (2015) The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids. J Phys Chem B 119:5882–5895CrossRefGoogle Scholar
Shao Y, Molnar LF, Jung Y et al (2006) Advances in methods and algorithms in a modern quantum chemistry program package. Phys Chem Chem Phys 8:3172–3191CrossRefGoogle Scholar
Richmond TJ (1984) Solvent accessible surface area and excluded volume in proteins. Analytical equations for overlapping spheres and implications for the hydrophobic effect. J Mol Biol 178:63–89CrossRefGoogle Scholar