Pharmacophore development for identification of anti-lung cancer drugs Essay

Lung crab louse is one particular fictional character of crabby person that is to a greater extent deadly and coarse than any other. Lung genus malignant neoplastic disease is treated with chemotherapy, radiation therapy and mental process depending on the type of lung cancer and the stage of the disease. Foc development on the drugs employ for chemotherapy and their associated side effects, there is a take on to design and develop sore anti-lung cancer drugs with lesser side effects and improved efficacy. Pharmacophore copy proves to be a very helpful tool serving in the purpose and development of new lead compounds. In this paper, pharmacophore of 10 novel anti-lung cancer compounds has been identify and validated for the first time.Using LigandSc disclose the pharmacophore features were predicted and 3D pharmacophore fix been extracted via VMD softw ar. A cooking set entropy was equanimous from literature and the proposed model was employ to the training set whereb y clear and verifying their homogeneous activity as that of the most(prenominal) alert compounds. Therefore they could be recommended for further studies.Key rowing Pharmacophore, anti-lung cancer drugs, Computer aided drug designing, LigandScout, VMD INTRODUCTIONLung cancer is cognise to read a high fatality rate among males and females and takes more lives for from whole(prenominal) one one year as compared to colon, prostate, ovarian and breast cancers (1).Lung cancer is classified into both main types anatomyly Small Cell Lung crabby person (SCLC) and Non-Small Cell Lung Cancer (NSCLC) of which NSCLC accounts for about 80% cases and SCLC accounts for 10-15% among all other types of lung cancers (2).Non- down(p) cubicle lung cancer (NSCLC) is a worldwide leading cause of death (3). The surgical resections are non applicable when first diagnosed as NSCLC is usually in an advanced stage. The tolerant may have a possibility of confirming survival with chemotherapy (4). Chemotherapy for advanced NSCLC is often considered excessively toxic. However, meta-analyses have demonstrated that as compared with supportive care, chemotherapy results in a small improvement in survival in patients with advanced NSCLC (5).* alike author. Emaildrhamidjinnah.edu.pkAbbreviations HBA, hydrogen- tie down acceptor,HBD, hydrogen-bond donor, NSCLC, Non-small electric cell lung cancer, SCLC, Small Cell Lung Cancer, EGFR Epidermal Growth figure Receptor. doses developed for cancer are single agents although for the level best advantage they need to be utilise in recipe with other drugs or sanative agents. Initial candidate chemicals or leads, are often recognized and well-tried for single agents that change cancer-cell proliferation or prolong survival. This led to the identification of most of the clinically active cancer drugs utilise today. Specific leads then must be further optimized and assessed to characterize their pharmacokinetic and pharmacodynamic properties and evident toxic effects. Clinical military rating is performed by trails in humans to identify a maximum tolerated dose, ascertain severe toxic effects, and estimate bioactivity. These trails are time consuming and expensive (6).Pharmacophore is the initial step towards understanding the interaction between a sense organ and a ligand. Pharmacophore was often postulated as the essence of the structure-activity knowledge they had gained(7).Todays investigator task is to interpret the backrest of anatomically varied molecules at a common receptor site. To generate common feature pharmacophore from the set of compounds active for certain receptor, the characteristics necessary for binding receptor in a reason 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 convenient for attaining this goal. Surface of the cell are the regions where the ligand-recep tor and receptor-receptor interaction occur. The process undergo Sequential levels of activity starts initiallyfrom the cell surface and then moves towards the intracellular signaling pathways, then gene arranging which corresponds to cellular responses. Epidermal growth factor receptor (EGFR) was initially identify as an abnormally activated or mutated form which leads to a arrive of other abnormalities in the signaling pathway and therefrom leads to the formation of tumour (9).In our research, a 3D pharmacophore model was developed in collection to promote the discovery of precise and effective EGFR inhibitor for the treatment of non-small cell lung cancer. The compounds employ in this study have been characterized as reported in reference guide papers. In order to correlate experimental and computational studies we used their bioactivity data.MATERIALS AND METHODSThe work was initiated victimisation LigandScout software. LigandScout is a tool for deriving the 3D from str uctural data of ligand complexes more speedily and evidently in a completely automated and expedient way. It offers flawless workflow both from ligand and structure ground pharmacophore mildew (10). LigandScout is thought to be an essential software tool for structure based drug designing, it is not only beneficial for carrying out analysis of binding sites but also for alignment based on pharmacophore and the designing of share feature pharmacophores. LigandScout runs freely on all common operating systems. tilldateanumberofsuccessfulapplicationexampleshavebeencarried out and standpublished (11).The very important and the very first step in pharmacophore model generation is the selection of data set compounds.Anumberofdrugs have been reported that are in some way related to, or used in the treatment of Non-Small Cell Lung Cancer which include Platinol(generic distinguish cisplatin)( 12),carboplatin, Taxotere(generic name docetaxel), Gemzar(generic name gemcitabine) ,Taxol(gene ric 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 shown beneath ( 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, aromatic, positively and negatively ionizable groups (16).The pharmacophore for individually compound was generated and thedistances among the ph armacophoric features were calculated using VMD software. VMD is designed not only for modeling, visualization, and analysis of biological systems such as proteins, nucleic acids, lipid bilayer assemblies but it may also be used to outlook more general molecules, as VMD can read prototype Protein selective information 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 impose so the pharmacophore elements overlap and a common template i-e the pharmacophore model is identified. The training set consisting of foursome compounds was collected from literature and it was found that the groups show intensify and similar activity as that of the most active compounds based on the 3D pharmacophore being generated for non small lung cancer.RESULTS AND DISCUSSIONPharmacophore analysis is considered as an key part o f drug design. The pharmacophore generated by LigandScout for the selected data set of antinon small cell lung cancer showed three main features i-e H-bond acceptor(blue vectors), H-bond donor(blue vectors) and aromatic screams(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 dining table 1.The pharmacophores of all the compounds were then matched and a unique pharmacophore was identified after a small 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 dodge 2.Resembling features were identified after analyzing the pharmacophore of all compounds generated by LigandScout. Then the similar features of all the compounds were layered and merged into single pharmacophore. The uniquel y identified pharmacophoric features are shown in Table 3.Figure 5. A pharmacophore of GefitinibOur common featured pharmacophore predicted for three compound of anti non small lung cancer is based on three HBAs, six HBDs and four aromatic centers. The distance triangle measured between the common pharmacophore features of each compound using VMD is shown in Table 4.The distance plys 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 AcceptorAromatic rivetPaclitaxel+++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 f rom 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 commonplace 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 striationAromatic reflect DonorDonor AcceptorGefitinib7.104.76 6.97Pemetrexed7.034.155.85Bevacizumab8.144.296.36Figure 6. Distance ranges among pharmacophoric features in predicted pharmacophore 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 semblance 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 typeAcceptor 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 ModelStandard 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 v ery handy tool for new lead compounds discovery and development. In this study pharmacophore models were built for novel 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 key 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, capital of Pakistan for support and super vision in the research work. REFERENCES1.Thomas L, Doyle LA, Edelman MJ. Lung cancer in women emerging differences in epidemiology, biology, and therapy. 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