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Pharmacophore development for identification of anti-lung cancer drugs Essay

Lung cancer is one particular type of cancer that is more deadly and common than any other. Lung cancer is treated with chemotherapy, radiation therapy and surgery depending on the type of lung cancer and the stage of the disease. Focusing on the drugs used for chemotherapy and their associated side effects, there is a need to design and develop new anti-lung cancer drugs with lesser side effects and improved efficacy. Pharmacophore model proves to be a very helpful tool serving in the designing and development of new lead compounds. In this paper, pharmacophore of 10 novel anti-lung cancer compounds has been identified and validated for the first time.

Using LigandScout the pharmacophore features were predicted and 3D pharmacophore have been extracted via VMD software. A training set data was collected from literature and the proposed model was applied to the training set whereby validating and verifying their similar activity as that of the most active compounds. Therefore they could be recommended for further studies.

Key words: Pharmacophore, anti-lung cancer drugs, Computer aided drug designing, LigandScout, VMD INTRODUCTION
Lung 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 two main types namely Small Cell Lung Cancer (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-small cell lung cancer (NSCLC) is a worldwide leading cause of death (3). The surgical resections are not applicable when first diagnosed as NSCLC is usually in an advanced stage. The patient may have a possibility of prolonging 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).

*Corresponding author. Email:[email protected]
Abbreviations: HBA, hydrogen-bond 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 single agents although for the maximum advantage they need to be used 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 proliferation 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 is performed by trails in humans to identify a maximum tolerated dose, define 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 receptor and a ligand. Pharmacophore was often postulated as the “essence” of the structure-activity knowledge they had gained(7).Today’s researcher task is to interpret the binding 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 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 convenient for attaining this goal. Surface of the cell are the regions where the ligand-receptor and receptor-receptor interaction occur. The process undergo Sequential levels of activity starts initially from the cell surface and then moves towards the intracellular signaling pathways, then gene transcription which corresponds to cellular responses. Epidermal growth factor 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 treatment 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 METHODS
The work was initiated using LigandScout software. LigandScout is a tool for deriving the 3D from structural data of ligand complexes more speedily and evidently in a completely automated and expedient way. It offers flawless workflow both from ligand and structure based pharmacophore modeling (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 shared feature pharmacophores. LigandScout runs freely on all common operating systems.

Till date a number of successful application examples have been carried out and standpublished (11).

The very important and the very first step in pharmacophore model generation is the selection of data set compounds. A number of  drugs 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 name: cisplatin)( 12),carboplatin, Taxotere(generic name: docetaxel), Gemzar(generic name: gemcitabine) ,Taxol(generic name: paclitaxel) , Almita(generic name: pemetrexed), Avastin(generic

name:
Bevacizumab), Xalkori(generic name:
Crizotinib),
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 below ( Figure 1) in the form of Pdb files were imported into LigandScout and converted into corresponding 3D pharmacophore structures.

Cisplatin

Pemetrexed

Docetaxel

Bevacizumab

Viblastine

Carboplatin

Gemcitabine

Crizotinib

Gefitinib

Paclitaxel

Vinorelbine

Erlotinib
Hydrochloride

Figure 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 each compound was generated and the

distances among the pharmacophoric 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 view more general molecules, as VMD can read standard 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 being generated for non small lung cancer.

RESULTS AND DISCUSSION
Pharmacophore analysis is considered as an fundamental part of drug design. The pharmacophore generated by LigandScout for the selected data set of anti non small cell lung cancer showed three main features i-e H-bond acceptor(blue vectors), H-bond donor(blue vectors) and aromatic rings(yellow spheres).The representative pharacophores of each compound are shown in Figures 2,3,4 and 5

Figure 2. A pharmacophore of Pemetrexed (Alimta®)

The pharmacophoric features for each compound on the whole are shown in Table 1.The pharmacophores of all the compounds were then matched and a unique pharmacophore was identified after a detailed analysis.

Figure 3 . A pharmacophore of Bevacizumab

Figure 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 Gefitinib
Our 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 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 compound
Compounds

H-Bond Donor

H-Bond Acceptor

Aromatic Centre

Paclitaxel

+

+

+

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 compound

Compound

H-Bond Donor

H-Bond
Acceptor

Aromatic Centre

Paclitaxel

4

6

2

Pemetrexed

3

6

3

Bevacizumab

2

3

1

Carboplatin

0

3

0

Crizotinib

2

4

3

Erlotinib Hydrocholride

2

6

3

Gefitinib

2

6

4

Gemcitabine

3

7

2

Methotrexate

3

9

3

Table 3. Uniquely identified pharmacophoric features of compounds

Compound
Bevacizumab
Pemetrexed
Gefitinib

H-Bond
Donor
2
3

H-Bond
Acceptor
3
6

2

6

Aromatic
Centre
1
3
4

A 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 Compounds

Acceptor Aromatic Ring

Aromatic Ring Donor

Donor Acceptor

Gefitinib

7.10

4.76

6.97

Pemetrexed

7.03

4.15

5.85

Bevacizumab

8.14

4.29

6.36

Figure 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 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 Model

Acceptor Aromatic Ring

Aromatic Ring Donor

Donor Acceptor

MMDA

5.99

5.52

5.95

Flavopiridol

7.01

4.04, 4

6.18

MethyNonanoate

4.01

7.60

2.24

Table 6. The 3D pharmacophoric distance triangle of the training set and the standard drugs respectively Model

Standard Drugs

Training Set

Acceptor Aromatic Ring

7.37-8.84

7.01-8.96

Aromatic Ring Donor

4.39-4.89

4.04-4.62

Donor Acceptor

6.18-6.97

6.18-6.64

CONCLUSION
The pharmacophore model is a very 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 novel drugs for non small lung cancer in continuation of this work.

ACKNOWLEDGEMENTS
We owe special thanks to Dr. Hamid Rashid, Ms. Saima Kalsoom , Faculty Mohammad Ali Jinnah University, Islamabad for support and supervision in the research work. REFERENCES

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