Discovery of Eriodictyol as Putative Exportin-1 Inhibitor for Non-small Cell Lung Cancer Therapy (original) (raw)
Related papers
2020
Aim: To establish, through molecular modelling, safe and clinically acceptable putative antagonists of E571K-mutated exportin-1 among the bioactive compounds in various parts of Juglans mandshurica. Methods: The bioactive compounds were subjected to compendium of druglikeness and lead-likeness filter workflows prior to docking of the resultant compounds into E571K exportin-1 active site using PyRx AutoDock vina to establish their binding affinity and interaction profile. The evolutionary algorithm of Osiris property explorer DataWarrior software as well as lead-likeness filter were employed for generation of novel non-promiscuous analogues of the lead compound with better putative selectivity and clinical acceptability as E571K Exportin-1 antagonists.Results: The findings of this study present taxifolin as the putatively effective and lead-like E571K Exportin-1 inhibitor with high potential of qualifying for clinical evaluation but is associated with high promiscuity tendency in hig...
Bioinformation, 2016
Non-small cell lung cancer (NSCLC) is the most dominating and lethal type of lung cancer triggering more than 1.3 million deaths per year. The most effective line of treatment against NSCLC is to target epidermal growth factor receptor (EGFR) activating mutation. The present study aims to identify the novel anti-lung cancer compounds form nature against EGFR 696-1022 T790M by using in silico approaches. A library of 419 compounds from several natural resources was subjected to pre-screen through machine learning model using Random Forest classifier resulting 63 screened molecules with active potential. These molecules were further screened by molecular docking against the active site of EGFR 696-1022 T790M protein using AutoDock Vina followed by rescoring using X-Score. As a result 4 compounds were finally screened namely Granulatimide, Danorubicin, Penicinoline and Austocystin D with lowest binding energy which were -6.5 kcal/mol, -6.1 kcal/mol, -6.3 kcal/mol and -7.1 kcal/mol resp...
The Density Functional Theory (DFT) method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with different degrees of cytotoxicity against the human hepatocellular carcinoma HepG2 line. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to select the most important descriptors related to anticancer activity. The significant molecular descriptors related to the compounds with anticancer activity were the ALOGPS_log, Mor29m, IC5 and GAP energy. The Pearson correlation between
Chemistry Africa, 2023
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was confirmed as the causative virus of COVID-19 disease, which is currently a worldwide pandemic. Efavirenz, a non-nucleoside reverse transcriptase inhibitor (NNRTI), is one of the most potent chemical compounds proposed to treat COVID-19 infection. We, therefore, performed virtual screening on FDA approved drugs that are similar to the efavirenz moiety. Subsequently, the compounds were subjected to screening by analyzing their drug-likeness, such as Lipinski's rule of five and ADMET properties. Molecular docking study revealed that Met165, His41, His163, and Phe140 were important interacting residues for COVID-19 main protease receptor-ligand interaction. Five top-ranked compounds, podophyllotoxin, oxacillin, lovastatin, simvastatin, and gefitinib, were selected by virtual screening and docking studies. The highest occupied molecular (HOMO) orbital, lowest unoccupied molecular orbital (LUMO) and energy gap values was calculated using density functional theory (DFT). The results of the study showed that lovastatin and simvastatin might be considered as lead compounds for further development for COVID-19 main protease inhibitors.
Docking Studies reveal Phytochemicals as the long searched Anticancer Drugs for Breast Cancer
Natural products including phytochemicals have been recently proposed as tumor suppressors. In this paper, docking study is presented to use these phytochemicals for their prospective role in cancers including breast and prostate cancer. The most common type of cancer in women all over the world is breast cancer. The breast cells including cancerous breast cells have receptors for binding with estrogen and progesterone to stimulate a growth response. This crucial property has been exploited to investigate binding properties of phytochemicals with these receptors to generate an antagonist response in order to resolve uncontrolled cancerous growth. The most commonly used breast cancer drugs mainly work against the effects of estrogen on these cells. In this context groups of different set of phytochemicals (3-IMG-Glucosinolates, Anthocyanins, Apigenin, Carnosol, Daidzein, Genistein, Isoflavones and Quercetin) were taken and docked into the active site of Human estrogen receptor (PDB ID: 2IOK). In this study, based on molecular docking, potential phytochemicals have successfully been identified which may be used as anticancer drugs against breast cancer. These studies based on binding energy, docking energy, drug likeness and other relevant scores show that Daidzein, Genistein and Quercetin could be the potential lead molecule for the inhibition of signals potent for Human breast cancer and Leu346, Leu384, Leu387, Phe404 and Leu525 are the most important residues for potential drug targets. This paper is the initial step towards a rational design of novel selective and potent Human estrogen inhibitors for the treatment of cancer.
International Journal of Pharmaceutical Sciences and Drug Research, 2020
Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype that lacks hormonal receptors. This reduces the therapeutic options for TNBC patients creating more focus on chemotherapy. Drug resistance has posed as a major hurdle in treating TNBC patients. Deregulation of drug transporter proteins is one of major factors that cause resistance to chemotherapeutic drugs. In this study, ABCC6 a drug transporter protein that is found dysregulated in several resistant cancer cells has been docked with natural compounds or phytochemicals with known anti-cancer activities. Subtrifloralactone G, a withanolide extracted from Deprea subtriflora is found to show highest binding energy with ABCC6 protein. Molecular dynamics simulations further prove the stability of the ABCC6 protein- Subtrifloralactone G ligand complex. ADMET analysis shows that phytochemical Subtrifloralactone G can be used as an anti-cancer therapeutic drug in treating resistant cancer cells. The study mainly fo...
Structure-Based In Silico Investigation of Agonists for Proteins Involved in Breast Cancer
Evidence-Based Complementary and Alternative Medicine, 2022
Cancer is recognized as one of the main causes of mortality worldwide by the World Health Organization. The high cost of currently available cancer therapy and certain limitations of current treatment make it necessary to search for novel, cost-effective, and efficient methods of cancer treatment. Therefore, in the current investigation, sixty-two compounds from five medicinal plants (Tinospora cordifolia, Ocimum tenuiflorum, Podophyllum hexandrum, Andrographis paniculata, and Beta vulgaris) and two proteins that are associated with breast cancer, i.e., HER4/ErbB4 kinase and ERα were selected. Selected compounds were screened using Lipinski’s rule, which resulted in eighteen molecules being ruled out. The remaining forty-four compounds were then taken forward for docking studies followed by molecular dynamics studies of the best screened complexes. Results showed that isocolumbin, isopropylideneandrographolide, and 14-acetylandrographolide were potential lead compounds against the s...
2024
Breast cancer (BC) is the most commonly diagnosed cancer in women around the world. Several genetic mutations tend to induce the risk of BC progression. SPDEF (Sam pointed domain containing ETS transcription factor) is a prostate-derived ETS factor that maintains homeostasis, differentiation of epithelial tissues, and heritable alterations in cancer. Plant secondary metabolites like flavonoids, terpenoids, and alkaloids have shown anticarcinogenic effect in several literatures. Therefore, in this study the SPDEF protein was used as potential breast cancer therapeutic target. Pharmacokinetic properties (ADMET), drug-likeness, and molecular docking of the fifteen phytoconstituents were assessed against SPDEF protein by various in silico approaches. The results showed that genistein, 2-hydroxychalcone, ajoene, and allicin had no toxicity. As per toxicological endpoints prediction study, the median lethal dosage (LD50) values vary from 159 to 3919 mg/Kg. All of the phytoconstituents derivatives taken into account in this investigation, are projected to be good candidates for P-glycoprotein (p-gp). Using in silico methods, the fifteen phytoconstituents identified from the different plants were predicted for their inhibitory actions against SPDEF protein, suggesting their breast cancer therapeutic potential. Silibinin, codonolactone and genistein have showed the lowest binding energy (-7.7,-6.1 and-6.1 kcal/mol) respectively and predicted to have the best inhibitory effect against SPDEF protein. We selected these three phytoconstituents for molecular dynamic simulation at 200 ns. In a comparison analysis, the Silibinin-receptor complex structure qualifies for the maximum parameters. The predictions about the pharmacokinetic properties of these phytoconstituents would form the basis for future in vivo, and in vitro experiments to identify the most appropriate therapeutic compound.
Molecular modeling method has been used for modeling a new molecule for Breast and colorectal cancer using Topotecan, a drug that’s already designed. This drug is drawn using HYPERCHEM and its R group is modified by replacing different functional groups like OH, CCl2OH, CF2OH, CH2CH2CH3, CH2CH3, CH3, Cl, F, H, and NH2, etc in its place. Molecules designed as such are optimized using different algorithms and their affinity is checked with the protein. The binding free energy of the protein is calculated by performing docking process. The docking process is done with the help of GOLD software. The molecule with minimum binding energy will have the maximum binding affinity. From the results obtained it’s clear that ligand “2(CCl2OH)”has the maximum binding affinity and this molecule is determined as the best lead molecule targets computationally. The calculated binding affinities between inhibitors 1,2,3,4,5,6, 7,8,9,10 are compared. The calculated binding affinities of the inhibitors indicate that inhibitor “2” (CCl2OH) would be expected to be better inhibitor than lead inhibitor 1,3,4,5,6,7,8,9 and 10. Inhibitor “2’’ predicted to be the most potent inhibitor of TOPOTECAN inhibitor as compared to all the other inhibitors considered in this study. For all the cases the minimization results provided qualitative agreement with experimental results. Therefore, this approach could be very useful for screening a larger set of compounds prior to synthesis accordingly; there is a need for methods that enable rapid assessment of large number of structurally unrelated molecules in a reasonably accurate manner. Energy components calculated by performing molecular mechanics calculations both in explicit solvent and complex states are sufficient to estimate the relative binding free energy differences between inhibitors qualitatively.