Title:A Virtual Screening Approach for the Identification of High Affinity Small Molecules Targeting BCR-ABL1 Inhibitors for the Treatment of Chronic Myeloid Leukemia
Volume: 17
Issue: 26
Author(s): Saphy Sharda, Palash Sarmandal, Shirisha Cherukommu, Kiran Dindhoria, Manisha Yadav, Srinivas Bandaru, Anudeep Sharma, Aditi Sakhi, Tanmay Vyas, Tajamul Hussain, Anuraj Nayarisseri*Sanjeev Kumar Singh*
Affiliation:
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu,India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu,India
Keywords:
Chronic Myeloid Leukemia, BCR-ABL, BCR-ABL1 inhibitors, Virtual screening methods, Kinase, ADMET,
Leukemia.
Abstract: CML originates due to reciprocal translocation in Philadelphia chromosome leading to the
formation of fusion product BCR-ABL which constitutively activates tyrosine kinase signaling pathways
eventually leading to abnormal proliferation of granulocytic cells. As a therapeutic strategy,
BCR-ABL inhibitors have been clinically approved which terminates its phosphorylation activity and
retards cancer progression. However, a number of patients develop resistance to inhibitors which demand
for the discovery of new inhibitors. Given the drawbacks of present inhibitors, by high throughput
virtual screening approaches, present study pursues to identify high affinity compounds targeting
BCR-ABL1 anticipated to have safer pharmacological profiles. Five established BCR-ABL inhibitors
formed the query compounds for identification of structurally similar compounds by Tanimoto coefficient
based linear fingerprint search with a threshold of 95% against PubChemdatabase. Assisted by
MolDock algorithm all compounds were docked against BCR-ABL protein in order to retrieve high affinity
compounds. The parents and similars were further tested for their ADMET propertiesand bioactivity.
Rebastinib formed higher affinity inhibitor than rest of the four established compound investigated
in the study. Interestingly, Rebastinib similar compound with Pubchem ID: 67254402 was also
shown to have highest affinity than other similars including the similars of respective five parents. In
terms of ADMET properties Pubchem ID: 67254402 had appreciable ADMET profile and bioactivity.
However, Rebastinib still stood as the best inhibitor in terms of binding affinity and ADMET properties
than Pubchem ID: 67254402. Nevertheless, owing to the similar pharmacological properties with
Rebastinib, Pubchem ID: 67254402 can be expected to form potential BCR-ABL inhibitor.
