Exploration of potential EGFR inhibitors: a combination of pharmacophore-based virtual screening, atom-based 3D-QSAR and molecular docking analysis

Pharmacophore modeling, 3D-QSAR, and MD simulation-based overture for the discovery of new potential HDAC1 inhibitors

Histone deacetylases (HDACs) are important epigenetic regulators that modulate the activity of histone and non-histone proteins leading to various cancers. Histone deacetylase 1 (HDAC1) is a member of class 1 HDAC family related to different cancers. However, the nonselective profile of existing HDAC1 inhibitors restricted their clinical utility. Therefore, the identification of new HDAC1 selective inhibitors may be fruitful against cancer therapy. In this present work, a pharmacophore model was built using 60 benzamide-based known HDAC1 selective inhibitors and it was used further to filter the large epigenetic molecular database of small molecules. Further, the 3D-QSAR model was built using the best common pharmacophore hypothesis consisting of higher PLS statistics of R2 of 0.89, Q2 of 0.83, variance ratio (F) of 65.7 and Pearson-r value of 0.94 revealing the model reliability and its high predictive power. The screened hits of the pharmacophore model were then subjected to molecular docking against HDAC1 to identify high-affinity lead molecules. The top 10 hits were ranked from the docking studies using docking scores for lead optimization. The potential hit molecules M1 and M2 identified from the study showed promising interaction during HDAC1 docking and MD simulation studies with acceptable ADME properties. Also, the newly designed lead compounds M11 and M12 may be considered highly potential inhibitors against HDAC1. The 3D-QSAR analysis, conformational requirements, and observations noticed in the MD simulations study will enable the optimization of lead molecules and to design of novel effective, and selective HDAC1 inhibitors in the future.

Pyrrole-based EGFR inhibitors for the treatment of NCSLC: Binding modes and SARs investigations

The treatment of advanced non-small cell lung cancer (NSCLC) has made substantial progress due to the rapid development of small molecule targeted therapy, with dramatically prolonged survival. As an effective drug for the treatment of NSCLC, epidermal growth factor receptor (EGFR) inhibitors are currently experiencing issues like severe adverse events and drug resistance. It is urgent to develop novel types of EGFR inhibitors to overcome the abovementioned limitations. Pyrrole always works well as a probe for the creation of novel medication candidates for hard-to-treat conditions like lung cancer. Although the design, synthesis, and biological assays of pyrrole derivatives have been reported, their inhibitory actions against the receptor tyrosine kinase (RTK) EGFR have not been in-depthly studied. This review highlights the small molecule EGFR inhibitors containing pyrrole heterocyclic pharmacophores in recent years, and the research on their mechanism, biological activity, and structure-activity relationship (SAR).

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.

Inhibition of Oncogenic Kinases: An In Vitro Validated Computational Approach Identified Potential Multi-Target Anticancer Compounds

Tumorigenesis in humans is a multistep progression that imitates genetic changes leading to cell transformation and malignancy. Oncogenic kinases play a central role in cancer progression, rendering them putative targets for the design of anti-cancer drugs. The presented work aims to identify the potential multi-target inhibitors of oncogenic receptor tyrosine kinases (RTKs) and serine/threonine kinases (STKs). For this, chemoinformatics and structure-based virtual screening approaches were combined with an in vitro validation of lead hits on both cancerous and non-cancerous cell lines. A total of 16 different kinase structures were screened against ~739,000 prefiltered compounds using diversity selection, after which the top hits were filtered for promising pharmacokinetic properties. This led to the identification of 12 and 9 compounds against RTKs and STKs, respectively. Molecular dynamics (MD) simulations were carried out to better comprehend the stability of the predicted hit kinase-compound complexes. Two top-ranked compounds against each kinase class were tested in vitro for cytotoxicity, with compound F34 showing the most promising inhibitory activity in HeLa, HepG2, and Vero cell lines with IC50 values of 145.46 μM, 175.48 μM, and 130.52 μM, respectively. Additional docking of F34 against various RTKs was carried out to support potential multi-target inhibition. Together with reliable MD simulations, these results suggest the promising potential of identified multi-target STK and RTK scaffolds for further kinase-specific anti-cancer drug development toward combinatorial therapies.

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.

Structure-based pharmacophore design and virtual screening for novel potential inhibitors of epidermal growth factor receptor as an approach to breast cancer chemotherapy

Cancer cells are described with features of uncontrolled growth, invasion and metastasis. The epidermal growth factor receptor subfamily of tyrosine kinases (EGFR-TK) plays a crucial regulatory role in the control of cellular proliferation and progression of various cancers. Therefore, its inhibition might lead to the discovery of a new generation of anticancer drugs. In the present study, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations were applied to identify potential hits, which exhibited good inhibition on the proliferation of MCF-7 breast cancer cell line and favorable binding interactions on EGFR-TK. Selected compounds were examined for their anticancer activity against the Michigan Cancer Foundation-7 (MCF-7) breast cancer cell line which overexpresses EGFR using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium reduction assay. Compounds 1 and 2, with an isoindoline-1-one core, induced significant inhibition of breast cancer cells proliferation with IC[Formula: see text] values 327 and 370 nM, respectively.

Identification of a Selective G1-Phase Benzimidazolone Inhibitor by a Senescence-Targeted Virtual Screen Using Artificial Neural Networks

Cellular senescence is a barrier to tumorigenesis in normal cells, and tumor cells undergo senescence responses to genotoxic stimuli, which is a potential target phenotype for cancer therapy. However, in this setting, mixed-mode responses are common with apoptosis the dominant effect. Hence, more selective senescence inducers are required. Here we report a machine learning-based in silico screen to identify potential senescence agonists. We built profiles of differentially affected biological process networks from expression data obtained under induced telomere dysfunction conditions in colorectal cancer cells and matched these to a panel of 17 protein targets with confirmatory screening data in PubChem. We trained a neural network using 3517 compounds identified as active or inactive against these targets. The resulting classification model was used to screen a virtual library of ~2M lead-like compounds. One hundred and forty-seven virtual hits were acquired for validation in growth inhibition and senescence-associated β-galactosidase assays. Among the found hits, a benzimidazolone compound, CB-20903630, had low micromolar IC50 for growth inhibition of HCT116 cells and selectively induced senescence-associated β-galactosidase activity in the entire treated cell population without cytotoxicity or apoptosis induction. Growth suppression was mediated by G1 blockade involving increased p21 expression and suppressed cyclin B1, CDK1, and CDC25C. In addition, the compound inhibited growth of multicellular spheroids and caused severe retardation of population kinetics in long-term treatments. Preliminary structure-activity and structure clustering analyses are reported, and expression analysis of CB-20903630 against other cell cycle suppressor compounds suggested a PI3K/AKT-inhibitor-like profile in normal cells, with different pathways affected in cancer cells.