Der Pharmacia Lettre

Study of N-Arylanthranilic Acids Derivates as the Potential Anti-Inflammatory Agents

Menye Cyrille^1*, Ngabireng Claude Marie² and Kouam F. Simeon^{3 1}Department of Physics, University of Douala, Douala, Cameroon
²Department of Mathematics and Physical Science, University of Yaounde I, Yaounde, Cameroon
³Department of Chemistry, University of Yaounde I, Yaounde, Cameroon
^*Corresponding Author:
Menye Cyrille, Department of Physics, University of Douala, Douala, Cameroon, Tel: +237 697 66 01 59, Email: menyecyrille@yahoo.fr

Received: 02-Jun-2021 Published: 30-Jul-2021 , Citations: Cyrille, Menye, Claude Marie Ngabireng, Simeon Kouam F, Study of N-Arylanthranilic Acids Derivates as the Potential Anti-Inflammatory Agents. Der Pharm Lett 13(2021):-67-78. ,
Copyright: © 2021. Menye Cyrille, et al .This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex [1]. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules. Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an important role in the rational design of drugs [2-8]. Given the biological and pharmaceutical significance of molecular docking, considerable efforts have been directed towards improving the methods used to predict docking. N-arylanthranilic acids belong to the category of NSAIDS. They are amino isosteres of salicylates and are also known as fenemates [9,10]. Important molecules of this class include mefenamic acid, flufenamic acid and meclofenamic acid. Fenemates act by blocking the metabolism of arachidonic acid by the enzyme cyclooxygenase (COX), one of the key enzymes in the arachidonic acid cascade [11,12]. This enzyme bis-oxygenates arachidonic acid move to prostaglandine G2, wich is subsequently degraded to vasoactive and inflammatory mediators such as prostanglandins (PGS), prostacyclin (PGI2), and thromboxane-A2 [13]. Some fenemates also inhibit arachidonic acid lipoxygenase resulting in decreased synthesis of leukotrines, known mediators involved in inflammatory process [14]. Studies suggest that flufenamic and tolfenamic acids suppress proliferation of human peripheral blood lymphocytes by a mechanism; which involves inhibition of Ca2⁺ influx and is not related to inhibition of prostanoid synthesis [15]. There are a good correlation between Minimum Effective Dose (MED), Structural Molecular Fragment (SMF) and N-anthranilic acids [16]. In this study, we are reporting probable binding mechanism of N-arylanthranilic acids analogs with COX by molecular docking. Some of these molecules show a good binding with COX, more than aspirin and has drug-like properties.

Materials and Methods

Preparation of protein structure

The crystal structure of the protein (PDB: 2AW1) has been obtained from RCSB protein Data Bank [17]. All water molecules were removed and on the final stage hydrogen atoms were added to the target protein molecule.

Preparation of ligand structures

All the compounds used for docking study were selected from the literature see figure 1. Chemdraw, chemical intelligent drawing interface was used to construct the structure of the ligands. Using draw mode of chemdraw, the ligands were generated and three dimensional optimization were done and then save in mol file.

Protein ligand interaction using ArgusLab 4.0.1

Argus lab is the electronic structure program that is based on the quantum mechanics, it predicts the potential energies, molecular structures; geometry optimization of the structure, vibration frequencies of coordinates of atoms, bond length, bond angle and reactions pathway [18]. The protein was docked against the obtained one hundred ligand using Arguslab 4.0.1 [19]. Arguslab is used to find the reasonable binding geometries and explore the protein ligand interactions. Docking simulations were performed by selecting “ArgusDock” as the docking engine. The selected residues of the receptor were defined to be a part of the binding site.

A spacing of 0.4A between the grid points was used and an exhaustive search was performed by enabling ‘’ High precision’’ option in docking precision menu, ‘’Dock’’ was chosen as the calculation type, ‘’flexible’’ for ligand and the AScore was used as the scoring function.

At maximum 150 poses were allowed to be analyzed, binding site box size was set to 20 × 20 × 20 angstroms so as to encompass the entire active site.

The AScore function (1), with the parameters read from the AScore.prm file was used to calculate the binding energies of the resulting docked structures. A Score is based on terms taken from the HPScore piece of XScore [20].

All the compound in the dataset were docked into the active site of our protein, using the same protocol. After completion of docking, the docked protein (protein-ligand complex) was analyzed to investigate the type of interactions [21].

The docking poses saved for each compound were ranked according to their dock score function. The pose having the highest dock score was selected for further analysis.

Prediction activity spectra for substances (pass)

This computer system can predict biological activity based on structural formula of a chemical compound. The PASS approach is based on the suggestion, Activity=Function (Structure).Thus, “comparing” structure of a new substance with that of the standard biologically active substances, it is possible to find out whether a new substance has a particular effect or not. PASS estimates the probabilities of a particular substances belonging to the active and inactive sub-sets from the SAR Base (Structure-Activity Relationships Base) [22]. The result of prediction is returned in the form of a table containing the list of biological activity with the appropriate probability values (i.e) the values defining the likelihood for a given activity type to be either revealed PASS Activity (Pa probability of presence of anti-inflammatory activity) or not revealed PASS Inactivity (Pi probability of absence of anti-inflammatory activity) for each activity type from the predicted biological activity spectrum. Their values vary from 0.000 to 1.000. Only those activity types for which Pa>Pi are considered possible. Usual interpretation of prediction results is based on the Pa values. If Pa>0.7 the chance to find the activity in experiment is high, but in many cases the compound may occur to be the close analogue of known pharmaceutical agents. If 0.5<Pa<0.7 the chance to find the activity in experiment is less, but the compound is not so similar to known pharmaceutical agents. If Pa<0.5 the chance to find the activity in experiment is even more less, but if it will be confirmed the compound might occur to be a New Chemical Entity.

ADME/Toxicity testing

ADME (absorption, distribution, metabolism, and excretion) determines drug like activity of the ligand molecules based on Lipinski Rule of 5 [23,24]. Lipinski’rule states that, in general, an orally active drug has no more than one violation of the following criteria:

No more than 6 hydrogen bond donors (the total number of nitrogen or oxygen-hydrogen bonds)

No more than 12 hydrogen bond acceptors (all nitrogen or oxygen atoms)

A molecular mass less than 600 Daltons

An octanol-water partition coefficient that does not exceed 6

The polar surface area less than 150.

A dataset

Our dataset possesses of 100 molecules of N-arylanthranilic acids, one molecule of aspirin and target protein 2AW1 (Figures 1-3).

The experimental activity A was calculated from the Minimal Effective Dose (MED mg/kg body) by formula (2). In the literature, the molecules with a value of biological activity less than 3.20 are considered to be inactive molecules and the compounds with a value of biological activity greater than or equal to 3.20 are considered as active molecules [25,26] (Table 1).

Table 1: A dataset of 100 N-arylanthranilic acids with their experimental activities.

Acknowledgements

Part of this work was supported by international foundation of science ?no. F/4893-1? and the third world academy science ? No.10.004RG/CHE/AF/AC-I ?.SFK also thank the Humboldt Foundation for equipment.

Conclusion

We have reviewed the scoring functions currently used for protein–ligand interactions in molecular docking with arguslab. We have also described the computer system PASS (Prediction of Activity Spectra for Substances).The results and discussions made above lead to the conclusion that a serie of the 100 molecules of anthranilic acids have an anti-inflammatory properties. This has proven in a study using molecular docking and Prediction Activity Spectra for Substances (PASS). For the study using molecular docking, 100 N-anthranilic acids and aspirin were used, 56 out of 100 molecules show a good binding interaction with the target protein (2AW1). For Computer system PASS (Prediction of Activity Spectra for Substances), the same 100 N-anthranilic acids and aspirin were used, for Pa>Pi, 73 out of 100 molecules possess a probability to be anti-inflammatory. The results of our present study can be useful for the design and development of novel compounds having better inhibitory activity against several type of inflammation.

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Citation: Cyrille, Menye, Claude Marie Ngabireng, Simeon Kouam F, Study of N-Arylanthranilic Acids Derivates as the Potential Anti-Inflammatory Agents. Der Pharm Lett 13(2021):-67-78.

Copyright: © 2021. Menye Cyrille, et al .This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.