Pharmacophore development for identification of anti-lung cancer drugs

“Lung malignant neoplastic disease” is a correct medical term, but it is more commonly known as “lung cancer.”

Lung cancer is a particularly deadly and common type of cancer. It is treated with chemotherapy, radiation therapy, and surgery, depending on the type of lung cancer and the stage of the disease.

There is a need to develop new anti-lung cancer drugs with fewer side effects and improved efficacy, focusing on the drugs used for chemotherapy and their associated side effects. Pharmacophore modeling proves to be a very helpful tool in designing and developing new lead compounds.

In this paper, the pharmacophore of 10 new anti-lung cancer compounds has been identified and validated for the first time.

Using LigandScout, the pharmacophore characteristics were predicted, and 3D pharmacophores were extracted via the VMD package. A data set was collected from literature and the proposed model was applied to the data set, verifying their similar activity to that of the most active compounds. Therefore, they could be recommended for further studies.

Key words: Pharmacophore, anti-lung cancer drugs, computer-aided drug design, LigandScout, VMD.

Introduction:

Lung cancer is known to have a high mortality rate among both males and females, taking more lives each year compared to colon, prostate, ovarian, and breast cancers (1). Lung cancer is classified into two main types: Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC), of which NSCLC accounts for approximately 80% of 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). Surgical resection is not applicable when first diagnosed, as NSCLC is usually in an advanced stage. Patients may have a possibility of prolonging survival with chemotherapy (4).

Chemotherapy for advanced NSCLC is often considered overly toxic. However, meta-analyses have demonstrated that compared with supportive care, chemotherapy results in a slight improvement in survival in patients with advanced NSCLC (5).

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 individual agents, although for maximum benefit, they need to be used in combination with other drugs or therapeutic agents. Initial candidate chemicals or “leads” are often recognized and tested for individual 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 must be further optimized and assessed to characterize their pharmacokinetic and pharmacodynamic properties and apparent toxic effects.

Clinical evaluation is performed by trials in humans to identify a maximum tolerated dose, define severe toxic effects, and estimate bioactivity. These trials 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’s task is to interpret the binding of anatomically varied molecules at a common receptor site.

To generate common characteristic pharmacophore from the set of compounds active for a certain receptor, the features necessary for binding receptor in a generalized manner (8). The understanding of the common properties of binding groups is critical for determining the type of inhibitor binding the target.

Pharmacophore theoretical modeling is really convenient for achieving this end. The surface of the cell is where the ligand-receptor and receptor-receptor interactions occur. The procedure undergoes sequential degrees of activity, starting ab initio from the cell surface and then moving towards the intracellular signaling tracts, so gene expression corresponds to cellular responses.

The 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 tract and therefore leads to the formation of a tumor (9).

In our research, a 3D pharmacophore model was developed to advance the discovery of precise and effective EGFR inhibitors for the treatment of non-small cell lung cancer. The compounds used in this study have been characterized as reported in the mentioned documents. 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 deducing the 3D structure from structural data of ligand complexes more quickly and clearly in a wholly automated and expedient manner. It offers a flawless workflow both for ligand- and structure-based pharmacophore modeling (10).

LigandScout is thought to be an indispensable software tool for structure-based drug design. It is not only good for carrying out analysis of binding sites but also for alliance based on pharmacophore and the designing of shared characteristic pharmacophores. LigandScout runs freely on all common operating systems.

To date, a number of successful application examples have been carried out and published (11).

The very important and the very first step in pharmacophore modeling 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 in the form of PDB files were imported into LigandScout and converted into matching 3D pharmacophore constructions:

Cisplatin, Pemetrexed, Docetaxel, Bevacizumab, Vinblastine, Carboplatin, Gemcitabine, Crizotinib, Gefitinib, Paclitaxel, Vinorelbine, Erlotinib Hydrochloride.

2D constructions of selected information set of anti-non-small-cell lung malignant neoplastic disease. The pharmacophoric characteristics include H-bond giver, H-bond acceptor, hydrophobic, aromatic, positively and negatively ionizable groups (16).

The pharmacophore for each compound was generated, and the distances among the pharmacophoric characteristics were calculated using the VMD package.

VMD is designed not only for modeling, visualization, and analysis of biological systems such as proteins, nucleic acids, and 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 characteristics. A number of application examples have been published to date (17).

Once the pharmacophore of all the compounds was identified, the ligand was then superimposed so that the pharmacophore elements overlap, and a common template, i.e., the pharmacophore model, was identified. The preparation set, consisting of four compounds, was collected from the 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-cell lung malignant neoplastic disease.

Results and Discussion

Pharmacophore analysis is considered an essential part of drug design. The pharmacophore generated by LigandScout for the selected information set of anti-non-small-cell lung malignant neoplastic disease showed three main characteristics, i.e., H-bond acceptor (blue vectors), H-bond giver (blue vectors), and aromatic rings (yellow domains).

The pharmacophoric characteristics for each compound. The pharmacophores of all the compounds were then matched, and a unique pharmacophore was identified after a detailed analysis.

Overall, the representative pharmacophoric characteristics for each compound. Resembling characteristics were identified after analyzing the pharmacophore of all compounds generated by LigandScout. Then the similar characteristics of all the compounds were superimposed and merged into a single pharmacophore. The uniquely identified pharmacophoric characteristics.

Our commonly featured pharmacophore predicted for three compounds of anti non-small cell lung malignant neoplastic disease is based on three HBAs, six HBDs, and four aromatic centers. The distance triangle measured between the common pharmacophore characteristics of each compound utilizing VMD. The distance ranges from minimum to maximum and has been measured between the HBA and HBD, HBA and aromatic ring, and HBD and aromatic ring.

A preparation set of three compounds was collected from the literature, i.e., MethylNonanoate, MMDA, and Flavopirido (18). The generated 3D pharmacophore model was applied to the preparation set, whereby their enhanced and similar activity to that of the standard compounds was validated and verified. This further confirmed our observation and proposals for a pharmacophore model as it corresponds to the predicted pharmacophore.

Distance ranges among pharmacophoric characteristics in predicted pharmacophore

To support the suggested pharmacophore model, the distance was estimated. The predicted distance of the preparation set and the standard drugs respectively.

This shows the close resemblance of Flavopiridol with that of standard drugs, thereby validating that the compound shows high correlation with the predicted pharmacophore triangle and hence has similar activity.

Conclusion

The pharmacophore model is a very handy tool for new lead compound discovery and development. In this study, pharmacophore models were built for fresh drugs of non-small cell lung malignant neoplastic disease, and pharmacophoric characteristics were predicted.

A 3D pharmacophore has been generated for non-small cell lung malignant neoplastic disease. A triangle of three different categories has been selected for the pharmacophore, and Hydrogen bond Acceptor, Hydrogen bond Donor, and Hydrophobic character of standard drugs have been filtered out as cardinal pharmacophoric characteristics.

The generated model was applied to the preparation set and it has been validated and proposed that Flavopiridol shows similar enhanced activity as that of standard drugs. Hence it could be used for further studies. Furthermore, pharmacophore-based docking will be used for virtual screening and designing of some fresh drugs for non-small cell lung malignant neoplastic disease in continuation of this work.

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