Computational screening, ensemble docking and pharmacophore analysis of potential gefitinib analogues against epidermal growth factor receptor

Pharmacophore-based virtual screening approaches to identify novel molecular candidates against EGFR through comprehensive computational approaches and in-vitro studies

Alterations to the EGFR (epidermal growth factor receptor) gene, which primarily occur in the axon 18-21 position, have been linked to a variety of cancers, including ovarian, breast, colon, and lung cancer. The use of TK inhibitors (gefitinib, erlotinib, lapatinib, and afatinib) and monoclonal antibodies (cetuximab, panitumumab, and matuzumab) in the treatment of advanced-stage cancer is very common. These drugs are becoming less effective in EGFR targeted cancer treatment and developing resistance to cancer cell eradication, which sometimes necessitates stopping treatment due to the side effects. One in silico study has been conducted to identify EGFR antagonists using other compounds, databases without providing the toxicity profile, comparative analyses, or morphological cell death pattern. The goal of our study was to identify potential lead compounds, and we identified seven compounds based on the docking score and four compounds that were chosen for our study, utilizing toxicity analysis. Molecular docking, virtual screening, dynamic simulation, and in-vitro screening indicated that these compounds' effects were superior to those of already marketed medication (gefitinib). The four compounds obtained, ZINC96937394, ZINC14611940, ZINC103239230, and ZINC96933670, demonstrated improved binding affinity (-9.9 kcal/mol, -9.6 kcal/mol, -9.5 kcal/mol, and -9.2 kcal/mol, respectively), interaction stability, and a lower toxicity profile. In silico toxicity analysis showed that our compounds have a lower toxicity profile and a higher LD50 value. At the same time, a selected compound, i.e., ZINC103239230, was revealed to attach to a particular active site and bind more tightly to the protein, as well as show better in-vitro results when compared to our selected gefitinib medication. MTT assay, gene expression analysis (BAX, BCL-2, and β-catenin), apoptosis analysis, TEM, cell cycle assay, ELISA, and cell migration assays were conducted to perform the cell death analysis of lung cancer and breast cancer, compared to the marketed product. The MTT assay exhibited 80% cell death for 75 µM and 100µM; however, flow cytometry analysis with the IC50 value demonstrated that the selected compound induced higher apoptosis in MCF-7 (30.8%) than in A549.

Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth

Dysregulated epidermal growth factor receptor (EGFR) expression is frequently observed in non-small cell lung cancer (NSCLC) growth and metastasis. Despite recent successes in the development of tyrosine kinase inhibitors (TKIs), inevitable resistance to TKIs has led to urgent calls for novel EGFR inhibitors. Herein, we report a rational workflow used to identify novel EGFR-TKIs by combining hybrid ligand- and structure-based pharmacophore models. Three types of models were developed in this workflow, including 3D QSAR-, common feature-, and structure-based EGFR-TK domain-containing pharmacophores. A National Cancer Institute (NCI) compound dataset was adopted for multiple-stage pharmacophore-based virtual screening (PBVS) of various pharmacophore models. The six top-scoring compounds were identified through the PBVS pipeline coupled with molecular docking. Among these compounds, NSC609077 exerted a significant inhibitory effect on EGFR activity in gefitinib-resistant H1975 cells, as determined by an enzyme-linked immunosorbent assay (ELISA). Further investigations showed that NSC609077 inhibited the anchorage-dependent growth and migration of lung cancer cells. Furthermore, NSC609077 exerted a suppressive effect on the EGFR/PI3K/AKT pathway in H1975 cells. In conclusion, these findings suggest that hybrid virtual screening may accelerate the development of targeted drugs for lung cancer treatment.

Computational Approaches for the Design of (Mutant-)Selective Tyrosine Kinase Inhibitors: State-of-the-Art and Future Prospects

Kinases remain one of the major attractive therapeutic targets for a large number of indications such as cancer, rheumatoid arthritis, cardiac failure and many others. Design and development of kinase inhibitors (ATP-competitive, allosteric or covalent) is a clinically validated and successful strategy in the pharmaceutical industry. The perks come with limitations, particularly the development of resistance to highly potent and selective inhibitors. When this happens, the cycle needs to be repeated, i.e., the design and development of kinase inhibitors active against the mutated forms. The complexity of tumor milieu makes it awfully difficult for these molecularly-targeted therapies to work. Every year newer and better versions of these agents are introduced in the clinic. Several computational approaches such as structure-, ligand-based or hybrid ones continue to live up to their potential in discovering novel kinase inhibitors. New schools of thought in this area continue to emerge, e.g., development of dual-target kinase inhibitors. But there are fundamental issues with this approach. It is indeed difficult to selectively optimize binding at two entirely different or related kinases. In addition to the conventional strategies, modern technologies (machine learning, deep learning, artificial intelligence, etc.) started yielding the results and building success stories. Computational tools invariably played a critical role in catalysing the phenomenal progress in kinase drug discovery field. The present review summarized the progress in utilizing computational methods and tools for discovering (mutant-)selective tyrosine kinase inhibitor drugs in the last three years (2017-2019). Representative investigations have been discussed, while others are merely listed. The author believes that the enthusiastic reader will be inspired to dig out the cited literature extensively to appreciate the progress made so far and the future prospects of the field.

Molecular interaction study of novel indoline derivatives with EGFR-kinase domain using multiple computational analysis

Epidermal growth factor receptors are constitutively overexpressed in breast cancer cells, which in turn stimulate many downstream signaling pathways that are involved in many carcinogenic processes. This makes EGFR a striking target for cancer therapy. This study focuses on the EGFR kinase domain inactivation by novel synthesized indoline derivatives. The compounds used are N-(2-hydroxy-5-nitrophenyl (4'-methyl phenyl) methyl) indoline (HNPMI), alkylaminophenols - 2-((3,4-Dihydroquinolin-1(2H)-yl) (p-tolyl) methyl) phenol (THTMP) and 2-((1, 2, 3, 4-Tetrahydroquinolin-1-yl) (4 methoxyphenyl) methyl) phenol (THMPP). To get a clear insight into the molecular interaction of EGFR and the three compounds, we have used ADME/Tox prediction, Flexible docking analysis followed by MM/GB-SA, QM/MM analysis, E-pharmacophore mapping of the ligands and Molecular dynamic simulation of protein-ligand complexes. All three compounds showed good ADME/Tox properties obeying the rules of drug-likeliness and showed high human oral absorption. Molecular docking was performed with the compounds and EGFR using Glide Flexible docking mode. This showed that the HNPMI was best among the three compounds and had interactions with key residue Lys 721. The protein-ligand complexes were stable when simulated for 100 ns using Desmond software. The interactions were further substantiated using QM/MM analysis and MM-GB/SA analysis in which HNPMI was scored as the best molecule. All the analyses were carried out with a reference molecule-Gefitinib which is a known standard inhibitor of EGFR. Thus, the study elucidates the potential role of the indoline derivatives as an anti-cancer agent against breast cancer by effectively inhibiting EGFR.Communicated by Ramaswamy H. Sarma.

Mst1 promotes mitochondrial dysfunction and apoptosis in oxidative stress-induced rheumatoid arthritis synoviocytes

In this study, we investigated the role of macrophage stimulating 1 (Mst1) and the AMPK-Sirt1 signaling pathway in the oxidative stress-induced mitochondrial dysfunction and apoptosis seen in rheumatoid arthritis-related fibroblast-like synoviocytes (RA-FLSs). Mst1 mRNA and protein expression was significantly higher in hydrogen peroxide (H₂O₂)-treated RA-FLSs than untreated controls. H₂O₂ treatment induced the mitochondrial apoptotic pathway by activating caspase3/9 and Bax in the RA-FLSs. Moreover, H₂O₂ treatment significantly reduced mitochondrial membrane potential and mitochondrial state-3 and state-4 respiration, but increased reactive oxygen species (ROS). Mst1 silencing significantly reduced oxidative stress-induced mitochondrial dysfunction and apoptosis in RA-FLSs. Sirt1 expression was significantly reduced in the H₂O₂-treated RA-FLSs, but was higher in the H₂O₂-treated Mst1-silenced RA-FLSs. Pretreatment with selisistat (Sirt1-specific inhibitor) or compound C (AMPK antagonist) significantly reduced the viability and mitochondrial function in H₂O₂-treated Mst1-silenced RA-FLSs by inhibiting Sirt1 function or Sirt1 expression, respectively. These findings demonstrate that oxidative stress-related upregulation and activation of Mst1 promotes mitochondrial dysfunction and apoptosis in RA-FLSs by inhibiting the AMPK-Sirt1 signaling pathway. This suggests the Mst1-AMPK-Sirt1 axis is a potential target for RA therapy.

In silico insights into the identification of potential novel angiogenic inhibitors against human VEGFR-2: a new SAR-based hierarchical clustering approach

In this study, binding efficiency of new pyrrolopyrimidine structural analogs against human vascular endothelial growth factor receptor-2 (VEGFR-2) were elucidated using integrated in silico methods. Optimized high-resolution model of VEGFR-2 was generated and adopted for structure-based virtual screening approaches. Pyrrolopyrimidine inhibitor (CP15) associated compounds were screened from PubChem database and subjected to virtual screening and comparative docking methods against the receptor ligand-binding domain. Accordingly, high efficient compounds were clustered with similarity indices through PubChem structure cluster module using single-linkage algorithm. Moreover, pharmacokinetics including drug metabolism activities of high-binding leads under investigation was portrayed using ADMET and similarity ensemble analysis. Optimal energy orientations of the selected protein model have been shown to be reliable, and highly recommended for screening and docking studies. Docking and clustering strategies were shown that nineteen candidates as most effective binders for VEGFR-2 than CP15, and are grouped as three classes. Lys⁸⁶⁸, Glu⁸⁸⁵, Cys⁹¹⁹, His¹⁰²⁶, Arg¹⁰²⁷, Asp¹⁰⁴⁶, and Gly¹⁰⁴⁸ residues were predicted as novel hotspot residues, and participate in H-bonds, π-cation, π-stacking, halogen bonds, and salt-bridges formation with ligands. These additional bonds are contributing extent stability that holds the receptor structure at flexible state, this make difficult to any further conformational changes for evoking angiogenic signals. The ADMET and similarity ensemble analysis results were strongly indicated that thirteen candidates as best ligands for angiogenesis targets. Altogether, these findings indicate potential angiogenic templates and their binding levels with VEGFR-2; sorted viewpoints could be useful as a promising way to describe potential angiogenesis inhibitors with related molecular targets.

Computational Approaches Towards Kinases as Attractive Targets for Anticancer Drug Discovery and Development

BACKGROUND

One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development.

METHODS

Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets.

RESULTS

Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer.

CONCLUSION

Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.