Predictive models for designing potent tyrosine kinase inhibitors in chronic myeloid leukemia for understanding its molecular mechanism of resistance by molecular docking and dynamics simulations

Derivates of 1,6-dihyadroazaazulenes as inhibitors of tyrosine kinases BCR-ABL1 wild type and mutant T315I: a molecular dynamics approach

The protein tyrosine kinase (PTK) produced by the BCR-ABL1 gene has generated significant interest in the development of inhibitors since the presence of punctual mutations causes resistance to currently approved drugs, mainly the T315I mutation has been the most difficult to address. In this work, derivatives of 1,6-dihydroazaazulenes are studied as possible inhibitors of this PTK in its wild form and the mutant T315I. The recognition of the ligands was explored through molecular docking, and the stability of the complexes and their evolution over time was studied using molecular dynamics (MD) simulations. Our results show that complexes are energetically stable and reside on the ATP binding site in all cases during the MD experiments. Interestingly, a few of our proposed ligands presented greater affinity for T315I, finding more favorable binding free energies (ΔG) than the reference drug axitinib. Furthermore, they may act as inhibitors for both isoforms. Our findings are promising because mutation of T315I does not prevent ligand recognition, as detailed in this work, which is very important to conduct further experimental research.Communicated by Ramaswamy H. Sarma.

Electronic and structural study of T315I mutated form in DFG-out conformation of BCR-ABL inhibitors

In this work, the four main drugs for the treatment of chronic myeloid leukemia were analyzed, being imatinib, dasatinib, nilotinib and ponatinib followed by four derivative molecules of nilotinib and ponatinib. For these derivative molecules, the fluorine atoms were replaced by hydrogen and chlorine atoms in order to shade light to the structural effects on this set of inhibitors. Electronic studies were performed at density functional theory level with the B3LYP functional and 6-311+G(d,p) basis set. The frontier molecular orbitals, gap HOMO-LUMO, and NBO were analyzed and compared to docking studies for mutant T315I tyrosine kinase protein structure code 3IK3, in the DFG-out conformation. Structural similarities were pointed out, such as the presence of groups common to all inhibitors and modifications raised up on new generations of imatinib-based inhibitors. One of them is the trifluoromethyl group present in nilotinib and later included in ponatinib, in addition to the 1-methylpiperazin-1-ium group that is present in imatinib and ponatinib. The frontier molecular orbitals of imatinib and ponatinib are contributing to the same amino acid residues, and the ineffectiveness of imatinib against the T315I mutation was discussed.Communicated by Ramaswamy H. Sarma.

Discovery of Anticancer Hybrid Molecules by Supervised Machine Learning Models and in Vitro Validation in Drug Resistant Chronic Myeloid Leukemia Cells

The concept of hybrid drugs for targeting multiple aberrant pathways of cancer, by combining the key pharmacophores of clinically approved single-targeted drugs, has emerged as a promising approach for overcoming drug-resistance. Here, we report the design of unique hybrid molecules by combining the two pharmacophores of clinically approved BCR-ABL inhibitor (ponatinib) and HDAC inhibitor (vorinostat) and results of in vitro studies in drug-resistant CML cells. Robust 2D-QSAR and 3D-pharmacophore machine learning supervised models were developed for virtual screening of the hybrid molecules based on their predicted BCR-ABL and HDAC inhibitory activity. The developed 2D-QSAR model showed five information rich molecular descriptors while the 3D-pharmacophore model of BCR-ABL showed five different chemical features (hydrogen bond acceptor, donor, hydrophobic group, positive ion group, and aromatic rings) and the HDAC model showed four different chemical features (hydrogen bond acceptor, donor, positive ion group, and aromatic rings) for potent BCR-ABL and HDAC inhibition. Virtual screening of the 16 designed hybrid molecules identified FP7 and FP10 with better potential of inhibitory activity. FP7 was the most effective molecule with predicted IC₅₀ using the BCR-ABL based 2D-QSAR model of 0.005 μM and that of the HDAC model of 0.153 μM, and that using the BCR-ABL based 3D-pharmacophore model was 0.02 μM and that with HDAC model was 0.014 μM. In vitro study (dose-response relationship) of FP7 in wild type and imatinib-resistant CML cell lines harboring Thr315Ile or Tyr253His mutations showed growth inhibitory IC₅₀ values of 0.000 16, 0.0039, and 0.01 μM, respectively. This molecule also showed better biocompatibility when tested in whole blood and in PBMCs as compared to ponatinib or vorinostat.

Predicting the Molecular Mechanism of EGFR Domain II Dimer Binding Interface by Machine Learning to Identify Potent Small Molecule Inhibitor for Treatment of Cancer

Epidermal growth factor receptor (EGFR) is a transmembrane druggable target controlling cellular differentiation, proliferation, migration, survival and invasion. EGFR activation mainly occurs by its homo/hetro dimerization molecular phenomenon leading to tumor development and invasion. Several tyrosine kinase based inhibitors were discovered as potent anti-cancer drugs. However, mutations in its kinase domain confer resistance to most of these drugs. To overcome this drug resistance, development of small molecule inhibitors disrupting the EGFR Domain II dimer binding by machine learning methods are promising. Based on this insight, a structure-based drug repurposing strategy was adopted to repurpose the existing FDA approved drugs in blocking the EGFR Domain II mediated dimerization. We identified five best repurposed drug molecules showing good binding affinity at its key arm-cavity dimer interface residues by different machine learning methods. The molecular mechanisms of action of these repurposed drugs were computationally validated by molecular electrostatics potential mapping, point mutations at the dimer arm-cavity binding interface, molecular docking and receptor interaction studies. The present machine learning strategy thus forms the basis of identifying potent and putative small molecule drugs for the treatment of different types of cancer.

Theoretical calculations of molecular descriptors for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against gastric cancer cell line (MGC-803): DFT, QSAR and docking approaches

This work used quantum chemical method via DFT to calculate molecular descriptors for the development of QSAR model to predict bioactivity (IC50- 50% inhibition concentration) of the selected 1, 2, 3-triazole-pyrimidine derivatives against receptor (human gastric cancer cell line, MGC-803). The selected molecular parameters were obtained by B3LYP/6-31G∗∗. QSAR model linked the molecular parameters of the studied compounds to their cytotoxicity and reproduced their observed bioactivities against MGC-803. The calculated IC50 tailored the observed IC50 and greater than standard compound, 5-fluorouracil, suggesting that the developed QSAR model reproduced the observed bioactivity. Statistical analyses (including R2, CV. R2 andR a 2 gave 0.950, 0.970 and 0.844 respectively) revealed a very good fitness. Molecular docking studies revealed the hydrogen bonding with the amino acid residues in the binding site, as well as ligand conformations which are essential feature for ligand-receptor interactions. Therefore, the methods used in this study are veritable tools that can be employed in pharmacological and medicinal chemistry researches in designing better drugs with improve potency.

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

Modeling the Antileukemia Activity of Ellipticine-Related Compounds: QSAR and Molecular Docking Study

The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski's rules and have a structure analogous to ellipticine, was performed using the quantitative structure activity relationship (QSAR) technique, followed by a detailed docking study on three different proteins significantly involved in this disease (PDB IDs: SYK, PI3K and BTK). As a result, a model with only four descriptors (HOMO, softness, AC1RABAMBID, and TS1KFABMID) was found to be robust enough for prediction of the antileukemia activity of the compounds studied in this work, with an R2 of 0.899 and Q2 of 0.730. A favorable interaction between the compounds and their target proteins was found in all cases; in particular, compounds 9 and 22 showed high activity and binding free energy values of around -10 kcal/mol. Theses compounds were evaluated in detail based on their molecular structure, and some modifications are suggested herein to enhance their biological activity. In particular, compounds 22_1, 22_2, 9_1, and 9_2 are indicated as possible new, potent ellipticine derivatives to be synthesized and biologically tested.