Pharmacophore development for identification of anti-lung cancer drugs Essay

Lung pubic louse is one particular caseful of malignant neoplastic disease that is more deadly and vulgar than any other(a). Lung pubic louse is treated with chemotherapy, radiation therapy and surgery depending on the type of lung senscer and the stage of the disease. Focusing on the drugs use for chemotherapy and their associated side effects, there is a need to endeavor and develop new anti-lung crabby person drugs with lesser side effects and improved efficacy. Pharmacophore type proves to be a very helpful tool serving in the designing and development of new lead escalates. In this paper, pharmacophore of 10 novel anti-lung cancer fluxs has been identified and validated for the first time.Using LigandScout the pharmacophore gasconades were predicted and 3D pharmacophore possess been extracted via VMD softw be. A readiness practise data was roll up from literature and the proposed moulding was applied to the training set whereby confirmatory and verifying their meterized activity as that of the most active unites. Therefore they could be recommended for further studies. place words Pharmacophore, anti-lung cancer drugs, Computer help drug designing, LigandScout, VMD INTRODUCTIONLung cancer is known to have a high fatality rate among males and females and takes more lives each year as compared to colon, prostate, ovarian and breast cancers (1).Lung cancer is classified into both main types c every uply Small Cell Lung crab louse (SCLC) and Non-Small Cell Lung crabby person (NSCLC) of which NSCLC accounts for to the highest degree 80% cases and SCLC accounts for 10-15% among all other types of lung cancers (2).Non-small cellular telephone lung cancer (NSCLC) is a worldwide leading cause of oddment (3). The surgical resections are not applicable when first diagnosed as NSCLC is usually in an travel stage. The diligent may have a possibility of prolonging extract with chemotherapy (4). Chemotherapy for advanced NSCLC is often consi dered excessively toxic. However, meta-analyses have demonstrate that as compared with collateral care, chemotherapy results in a small improvement in survival in patients with advanced NSCLC (5).*Corresponding author. Emaildrhamidjinnah.edu.pkAbbreviations HBA, hydrogen- adherence acceptor,HBD, hydrogen-bond donor, NSCLC, Non-small cell lung cancer, SCLC, Small Cell Lung Cancer, EGFR Epidermal Growth Factor Receptor.Drugs developed for cancer are star agents although for the maximum advantage they need to be utilise in recipe with other drugs or therapeutic agents. Initial candidate chemicals or leads, are often recognized and tested for single agents that change cancer-cell pro life storyration or prolong survival. This led to the identification of most of the clinically active cancer drugs used today. Specific leads then must be further optimized and assessed to characterize their pharmacokinetic and pharmacodynamic properties and evident toxic effects. Clinical evaluation i s performed by trails in humans to identify a maximum tolerated dose, define severe toxic effects, and envision bioactivity. These trails are time consuming and expensive (6).Pharmacophore is the initial step towards spirit the interaction between a receptor and a ligand. Pharmacophore was often postulated as the essence of the structure-activity knowledge they had gained(7).Todays researcher task is to understand the binding of anatomically varied molecules at a common receptor site. To generate common feature pharmacophore from the set of sharpens active for certain receptor, the characteristics necessary for binding receptor in a generalized way(8). The understanding of the common properties of binding group is vital for the determination of the type of inhibitor binding the target.Pharmacophore model is very well-provided for attaining this goal. Surface of the cell are the regions where the ligand-receptor and receptor-receptor interaction occur. The process undergo Sequen tial levels of activity starts initiallyfrom the cell surface and then moves towards the intracellular signaling pathways, then gene transcription which corresponds to cellular responses. Epidermal produce factor out receptor (EGFR) was initially identified as an abnormally activated or mutated form which leads to a number of other abnormalities in the signaling pathway and hence leads to the formation of tumor (9).In our research, a 3D pharmacophore model was developed in order to promote the discovery of precise and effective EGFR inhibitor for the discourse of non-small cell lung cancer. The compounds used in this study have been characterized as reported in reference papers. In order to correlate experimental and computational studies we used their bioactivity data.MATERIALS AND METHODSThe work was initiated using LigandScout software. LigandScout is a tool for derivation the 3D from structural data of ligand complexes more speedily and evidently in a completely alter and e xpedient way. It offers flawless workflow both from ligand and structure based pharmacophore modeling (10). LigandScout is thought to be an demand software tool for structure based drug designing, it is not only beneficial for carrying out abridgment of binding sites but also for alignment based on pharmacophore and the designing of shared feature pharmacophores. LigandScout runs freely on all common operating systems.Tilldateanumberofsuccessfulapplications programmeexampleshavebeencarried out and standpublished (11).The very important and the very first step in pharmacophore model extension is the selection of data set compounds.Anumberofdrugs have been reported that are in or so way related to, or used in the treatment of Non-Small Cell Lung Cancer which include Platinol(generic name cisplatin)( 12),carboplatin, Taxotere(generic name docetaxel), Gemzar(generic name gemcitabine) ,Taxol(generic name paclitaxel) , Almita(generic name pemetrexed), Avastin(genericnameBevacizumab), Xalkori(generic nameCrizotinib),Navelbine(generic name vinorelbine , Iressa(generic name Gefitinib) and Terceva(generic name Erlotinib) (13)( 14)( 15).The two dimensional (2D) chemical structures of the compounds were drawn using ChemDraw Ultra (8.0) and the structures were saved as .Pdb files. Subsequently the 2D structures as set upn below ( Figure 1) in the form of Pdb files were imported into LigandScout and converted into corresponding 3D pharmacophore structures.CisplatinPemetrexedDocetaxelBevacizumabViblastineCarboplatinGemcitabineCrizotinibGefitinibPaclitaxelVinorelbineErlotinibHydrochlorideFigure 1. 2D structures of selected data set of anti non small lung cancer The pharmacophoric features include H-bond donor, H-bond acceptor, Hydrophobic, redolent, positively and negatively ionizable groups (16).The pharmacophore for each compound was generated and thedistances among the pharmacophoric features were calculated using VMD software. VMD is designed not only for modeling, v isualization, and analysis of biological systems such as proteins, nucleic acids, lipide bilayer assemblies but it may also be used to view more general molecules, as VMD can read commonplace Protein Data Bank (PDB) files and display the contained structure with their features. A number of application examples have been published to date (17). Once the pharmacophore of all the compounds were identified, the ligand was then super imposed so the pharmacophore elements overlap and a common template i-e the pharmacophore model is identified. The training set consisting of four compounds was collected from literature and it was found that the groups show enhanced and similar activity as that of the most active compounds based on the 3D pharmacophore organism generated for non small lung cancer.RESULTS AND DISCUSSIONPharmacophore analysis is considered as an fundamental part of drug design. The pharmacophore generated by LigandScout for the selected data set of antinon small cell lung ca ncer showed trio main features i-e H-bond acceptor(blue vectors), H-bond donor(blue vectors) and redolent(p) rings(yellow spheres).The representative pharacophores of each compound are shown in Figures 2,3,4 and 5Figure 2. A pharmacophore of Pemetrexed (Alimta)The pharmacophoric features for each compound on the whole are shown in fudge 1.The pharmacophores of all the compounds were then matched and a unique pharmacophore was identified after a detail analysis.Figure 3 . A pharmacophore of BevacizumabFigure 4 . A pharmacophore of Gemcitabine (Gemzar)On the whole, the representative pharmacophoric features for each compound are shown in Table 2.Resembling features were identified after analyzing the pharmacophore of all compounds generated by LigandScout. Then the similar features of all the compounds were superimposed and merged into single pharmacophore. The uniquely identified pharmacophoric features are shown in Table 3.Figure 5. A pharmacophore of GefitinibOur common feature d pharmacophore predicted for three compound of anti non small lung cancer is based on three HBAs, six HBDs and four aromatic centers. The distance trilateral measured between the common pharmacophore features of each compound using VMD is shown in Table 4.The distance ranges from minimum to maximum and have measured between the HBA and HBD,HBA and aromatic ring and HBD and aromatic ring.Table 1. Pharmacophoric features of each compoundCompoundsH-Bond DonorH-Bond Acceptor smelling(p) CentrePaclitaxel+++Pemetrexed+++Bevacizumab+++Carboplatin+++Crizotinib+++Erlotinib Hydrocholride+++Gefitinib+++Gemcitabine+++Methotrexate+++The distances among the common pharmacophoric features between the predicted pharmacophore are shown in Figure 6. The distances between aromatic ring and HBD range from 4.15-4.80, between aromatic rings to HBA range from 7.03-8.66 and between HBA to HBD range from 5.85-6.97. Table 2. Pharmacophoric features of each compoundCompoundH-Bond DonorH-BondAcceptorAromatic CentrePaclitaxel462Pemetrexed363Bevacizumab231Carboplatin030Crizotinib243Erlotinib Hydrocholride263Gefitinib264Gemcitabine372Methotrexate393Table 3. Uniquely identified pharmacophoric features of compoundsCompoundBevacizumabPemetrexedGefitinibH-BondDonor23H-BondAcceptor3626AromaticCentre134A training set of three compounds was collected from literature i-e MethyNonanoate, MMDA, Flavopirido(18).The generated 3D pharmacophore model was applied to the training set whereby validating and verifying their enhanced and similar activity as that of the standard compounds shown in Table 5. This further confirmed our observation and proposals for a pharmacophore model as it corresponds to the predicted pharmacophore.Table 4.Pharmacophoric triangle distances of each uniquely identified compounds CompoundsAcceptor Aromatic goAromatic Ring DonorDonor AcceptorGefitinib7.104.766.97Pemetrexed7.034.155.85Bevacizumab8.144.296.36Figure 6. Distance ranges among pharmacophoric features in predicted pha rmacophore To support the suggested pharmacophore model , distance was estimated. The predicted distance of the training set and the standard drugs respectively are shown in Table 6.This table shows the close resemblance of Flavopiridol with that of standard drugs whereby validating that the compound shows high correlation with the predicted pharmacophoric triangle hence having similar activity.Table 5. The distance triangle for compounds of the training set ModelAcceptor Aromatic RingAromatic Ring DonorDonor AcceptorMMDA5.995.525.95Flavopiridol7.014.04, 46.18MethyNonanoate4.017.602.24Table 6. The 3D pharmacophoric distance triangle of the training set and the standard drugs respectively Model meter DrugsTraining SetAcceptor Aromatic Ring7.37-8.847.01-8.96Aromatic Ring Donor4.39-4.894.04-4.62Donor Acceptor6.18-6.976.18-6.64CONCLUSIONThe pharmacophore model is a very handy tool for new lead compounds discovery and development. In this study pharmacophore models were reinforced for no vel drugs of non small lung cancer, pharmacophoric features were predicted and 3D pharmacophore has been generated for non small lung cancer. A triangle of three different classes has been selected for pharmacophore and Hydrogen bond Acceptor, Hydrogen bond Donor and Hydrophobic character of standard drugs have been filtered out as delineate pharmacophoric feature.The generated model was applied to the training set and it has been validated and proposed that Flavopiridol shows similar enhanced activity as that of standard drugs, hence could be used for further studies. Moreover Pharmachopore based docking will be used for virtual screening and designing of some noveldrugsfornonsmalllungcancerincontinuationofthiswork.ACKNOWLEDGEMENTSWe owe special thanks to Dr. Hamid Rashid, Ms. Saima Kalsoom , Faculty Mohammad Ali Jinnah University, Islamabad for support and supervision in the research work. REFERENCES1.Thomas L, Doyle LA, Edelman MJ. Lung cancer in women emerging differences in ep idemiology, biology, and therapy. 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