In-Silico Study of Ethambutol and Its Derivatives On 7nt7 Receptor for Anti-Tuberculosis Activity

Dr. Dinesh Kawade, Shriya Bagh, Shikha Shahu, Yashika Bhattad, Sejal Kshirsagar,

doi:10.5281/zenodo.15260816

Research Paper | Open Access
Volume 03 | Issue 04 | Article Id IJPS/250304308

In-Silico Study of Ethambutol and Its Derivatives On 7nt7 Receptor for Anti-Tuberculosis Activity
Dr. Dinesh Kawade* Shriya Bagh Shikha Shahu Yashika Bhattad Sejal Kshirsagar
Priyadarshini J L College of Pharmacy, Electronic Zone Building, MIDC, Hingna Road, Nagpur-440016, India.

Abstract

The present study was undertaken to evaluate the binding potential and molecular interactions of ethambutol, an established first-line anti-tubercular drug, using molecular docking techniques. Tuberculosis (TB) remains a major global health challenge, particularly with the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis. Ethambutol, known to inhibit arabinosyl transferases involved in cell wall biosynthesis, was evaluated in this project for its interaction with the enzyme decaprenyl phosphoryl-?-D-ribose 2?-epimerase (DprE1), which plays a critical role in the synthesis of the mycobacterial cell wall. Using Auto Dock software, the docking analysis revealed that ethambutol binds efficiently within the active site of the DprE1 protein, forming multiple interactions, including hydrogen bonds with key amino acid residues such as Tyr60, Gly117, and His132. The binding energy was calculated to be -5.2 kcal/mol, indicating a moderately strong and stable interaction between the ligand and the target protein. Such interactions may inhibit the enzymatic activity of DprE1, thereby supporting the pharmacological action of ethambutol in disrupting the integrity of the bacterial cell wall. The molecular docking results obtained from this study align with the known pharmacodynamic properties of ethambutol and provide computational validation of its mechanism of action. Furthermore, these findings highlight the importance of target-based drug discovery and the relevance of molecular docking studies in understanding drug-target interactions at the molecular level. In addition to enhancing our understanding of ethambutol’s mode of action, this study also demonstrates the utility of molecular modeling tools in modern pharmaceutical research. Such computational techniques can be effectively employed to screen potential drug candidates, predict their binding affinities, and design new analogs with improved efficacy and specificity. To conclude, the docking study of ethambutol with the DprE1 protein has successfully illustrated a strong theoretical basis for its inhibitory action, thereby reinforcing its therapeutic relevance. This project lays a foundation for further in vitro and in vivo studies, which are essential to validate these findings and to explore possible structural modifications of ethambutol or the design of new anti-TB agents with similar or enhanced activity. As the burden of tuberculosis continues to threaten global health, studies like this contribute to the ongoing efforts in anti- tubercular drug research and development.

Keywords

Ethambutol, 7NT7 Receptor, Antituberculosis activity.

Introduction

Drug Discovery

Drug discovery is a process which aims at identifying a compound therapeutically useful in curing and treating disease. This process involves the identification of candidates, synthesis, characterization, validation, optimization, screening and assays for therapeutic efficacy. Once a compound has shown its significance in these investigations, it will initiate the process of drug development earlier to clinical trials. New drug development process must continue through several stages in order to make a medicine that is safe, effective, and has approved all regulatory requirements. Drug discovery is the process through which potential new medicines are identified. It involves a wide range of scientific disciplines, including biology, chemistry and pharmacology. Drug discovery is the process by which new candidate medications are discovered. Historically, drugs were discovered through identifying the active ingredient from traditional remedies or by serendipitous discovery. Later chemical libraries of synthetic small molecules, natural products or extracts were screened in intact cells or whole organisms to identify substances that have a desirable therapeutic effect in a process known as classical pharmacology. Modern drug discovery involves the identification of screening hits, medicinal chemistry and optimization of those hits to increase the affinity, selectivity (to reduce the potential of side effects), efficacy/potency, metabolic stability (to increase the half-life), and oral bioavailability. Once a compound that fulfils all of these requirements has been identified, it will begin the process of drug development prior to clinical trials. One or more of these steps may, but not necessarily, involve computer-aided drug design.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-19.png" target="_blank">
<img alt="Stages Of Drug Discovery and Development Process.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-19.png" width="150">
</a>
Fig No.1- Stages Of Drug Discovery and Development Process

B. Process Chemistry

Process chemistry is the arm of pharmaceutical chemistry concerned with the development and optimization of a synthetic scheme and pilot plant procedure to manufacture compounds for the drug development phase. Process chemistry is distinguished from medicinal chemistry, which is the arm of pharmaceutical chemistry tasked with designing and synthesizing molecules on a small scale in the early drug discovery phase. Medicinal chemists are largely concerned with synthesizing a large number of compounds as quickly as possible from easily tuneable chemical building blocks (usually for SAR studies). For the synthesis of substances chosen to go from research or discovery to a broader scale, process chemistry involves the development of practically sound, safe, and economical procedures. Process chemistry advances the active chemical compound's production process to enable more reliable production of the compound in bigger quantities. A process chemistry technique may be used to speed up any chemical reaction that takes a lot of stages and time.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-18.png" target="_blank">
<img alt="Process Chemistry.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-18.png" width="150">
</a>
Fig No.2- Process Chemistry

C. Computer aided drug design (CADD)

Computer aided drug design (CADD) is an evolving cascade of research area encompassing many facets. Computer-aided drug design (CADD) is an exciting and diverse discipline where various aspects of applied and basic research merge and stimulate each other. The theoretical basis of CADD involves quantum mechanics and molecular modelling studies like structure- based drug design; ligand-based drug design; database searching and binding affinity based on the knowledge of a biological target. Computer aided drug design (CADD) provides several tools and techniques that help in various stages of drug design thus reducing the cost of research and development time of the drug. Drug discovery and developing a new medicine is a long, complex, costly and highly risky process that has few peers in the commercial world. This is why computer-aided drug design (CADD) approaches are being widely used in the pharmaceutical industry to accelerate the process. The cost benefit of using computational tools in the lead optimization phase of drug development is substantial. The cost and time invested by the pharmacological research laboratories are heavy during the various phases of drug discovery, starting from therapeutic target identification (2,3).

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-17.png" target="_blank">
<img alt="Computer Aided Drug Design.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-17.png" width="150">
</a>
Fig No.3- Computer Aided Drug Design (CADD)

D. Molecular Docking

Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. The goal of ligand-protein docking is to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure. Successful docking methods search high-dimensional spaces effectively and use a scoring function that correctly ranks candidate dockings. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. The setting up of the input structures for the docking is just as important as the docking itself, and analysing the results of stochastic search methods can sometimes be unclear. This chapter discusses the background and theory of molecular docking software, and covers the usage of some of the most-cited docking software. (4) Molecular docking can demonstrate the feasibility of any biochemical reaction as it is carried out before experimental part of any investigation. There are some areas, where molecular docking has revolutionized the findings. In particular, interaction between small molecules (ligand) and protein target (may be an enzyme) may predict the activation or inhibition of enzyme. Such type of information may provide a raw material for the rational drug designing.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-16.png" target="_blank">
<img alt="Molecular Docking.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-16.png" width="150">
</a>
Fig No.4-Molecular Docking

Molecular Docking Steps

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-15.png" target="_blank">
<img alt="Steps of Molecular Docking.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-15.png" width="150">
</a>
Fig No.5- Steps of Molecular Docking

Molecular Docking is the process in which the intermolecular interaction between 2 molecules was studied in In-silico. In this process, the Macromolecule is the protein receptor. The micro molecule is the Ligand molecule which can be acted as an inhibitor. So, the Docking process involves the following steps:

Step I – Preparation of protein: Three dimensional structure of the Protein should be retrieved from Protein data bank (PDB); afterward the retrieved structure should be pre- processed. This should admit removal of the water molecules from the cavity, stabilizing the charges, filling the missing residues, generation of the side chains etc. according to the parameters available.

Step II – Active site prediction: After the preparation of protein, the active site of protein should be predicted. The receptor might possess lots of active sites merely the one of the concern should be picked out. Mostly the water molecules and hetero atoms are removed if present (5,6).

Step III – Preparation of ligand: Ligands can be retrieved from several databases such as ZINC, Pub Chem or can be sketched applying Chem sketch tool. While picking out the ligand, the LIPINSKY’S RULE OF 5 should be utilized. Lipinski rule of 5 assists in discerning amongst non-drug like and drug like candidates. It promises high chance of success or failure due to drug likeness for molecules abiding by with 2 or more than of the complying rules. For choice of a ligand allowing to the Lipinsky’s Rule:

(1) Less than five hydrogen bond donors

(2) Less than ten hydrogen bond acceptors

(3) Molecular mass less than 500 Da

(4) High lipophilicity (expressed as Log P not over (5) Molar refractivity should be between 40-130

Step IV - Docking: Ligand is docked against the protein and the interactions are analysed. The scoring function gives score on the basis of best docked ligand complex is picked out.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-14.png" target="_blank">
<img alt="The computer aided drug design and discovery.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-14.png" width="150">
</a>
Fig No.6- The computer aided drug design and discovery (CADD) procedure

E. Tuberculosis

Tuberculosis, caused by Mycobacterium tuberculosis, is the most common infectious disease caused by a single bacterium, and it is estimated that two billion people or one-third of the world’s population have been infected by M. tuberculosis. More than eight million new cases of active TB disease each year result in two million deaths annually, mostly in developing countries. (7,8) Tuberculosis treatment can take 4 to 9 months to be complete by combination of first-line drugs such as isoniazid, rifampin, rifapentine, ethambutol, and pyrazinamide. Due to a long treatment process, drug-resistant bacteria can emerge if the drugs are not taken properly. The treatment for MDR-TB requires a combination of second- line drugs, which can cause unwanted side effects, longer treatment time, as well as a higher treatment cost relative to first-line drugs (up to 100 times, 2012). According to the rapid growth of MDR-TB and the fatality of the disease, a new effective drug/treatment for TB is urgently needed.

Signs and Symptoms

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-13.png" target="_blank">
<img alt="Signs and Symptoms.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-13.png" width="150">
</a>
The main symptoms of variants and stages of tuberculosis are given, with many symptoms overlapping with other variants, while others are more (but not entirely) specific for certain variants. Multiple variants may be present simultaneously. Tuberculosis may infect any part of the body, but most commonly occurs in the lungs (known as pulmonary tuberculosis). Extrapulmonary TB occurs when tuberculosis develops outside of the lungs, although extrapulmonary TB may coexist with pulmonary TB. General signs and symptoms include fever, chills, night sweats, loss of appetite, weight loss, and fatigue. Significant nail clubbing may also occur.

Pulmonary

If a tuberculosis infection does become active, it most commonly involves the lungs (in about 90% of cases). Symptoms may include chest pain and a prolonged cough producing sputum. About 25% of people may not have any symptoms (i.e., they remain asymptomatic). Occasionally, people may cough up blood in small amounts, and in very rare cases, the infection may erode into the pulmonary artery or a Rasmussen's aneurysm, resulting in massive bleeding. Tuberculosis may become a chronic illness and cause extensive scarring in the upper lobes of the lungs. The upper lung lobes are more frequently affected by tuberculosis than the lower ones. The reason for this difference is not clear. It may be due to either better air flow, or poor lymph drainage within the upper lungs.

Extrapulmonary

In 15-20% of active cases, the infection spreads outside the lungs, causing other kinds of TB. These are collectively denoted as extrapulmonary tuberculosis. Extrapulmonary TB occurs more commonly in people with a weakened immune system and young children. In those with HIV, this occurs in more than 50% of cases. Notable extrapulmonary infection sites include the pleura (in tuberculous pleurisy), the central nervous system (in tuberculous meningitis), the lymphatic system (in scrofula of the neck), the genitourinary system (in urogenital tuberculosis), and the bones and joints (in Pott disease of the spine), among others. A potentially more serious, widespread form of TB is called "disseminated tuberculosis"; it is also known as miliary tuberculosis. Miliary TB currently makes up about 10% of extrapulmonary cases.

Symptoms of Extrapulmonary

• Cough with or without sputum production.

• Haemoptysis (coughing up blood).

• Chest pain.

• Loss of appetite.

• Fatigue.

• Night sweats.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-12.png" target="_blank">
<img alt="Tuberculosis Spread from Person to Person.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-12.png" width="150">
</a>
Fig No.8- Tuberculosis Spread from Person to Person

Fig No.9-Symptoms of Tuberculosis

Drug Profile: Ethambutol

Drug Name: Ethambutol
IUPAC Name: (2S,2'S)-2,2'-(ethane-1,2-diyldiimino) bis-butan-1-ol
Molecular Formula: C10H24N2O2

Molecular Structure:

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-11.png" target="_blank">
<img alt="Molecular Structure.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-11.png" width="150">
</a>
5. Molecular Weight: 204.31 g/mol

6. Drug Category: Anti-tubercular agent

7. Mechanism of Action: Inhibits arabinosyl transferase, thereby disrupting the synthesis of the mycobacterial cell wall (arabinogalactan and lipoarabinomannan).

8. Target Receptor: 7NT7 protein receptor (putative model receptor for evaluating anti-TB activity through molecular docking).

9. Binding Mechanism (in silico): Hydrogen bonding and van der Waals interactions with the active site residues of 7NT7.

10. Pharmacokinetics & ADMET:

• Absorption: Good oral bioavailability, high GI absorption

• Distribution: Widely distributed (lungs, kidneys)

• Metabolism: Partially hepatic

• Excretion: Primarily renal

• No CYP450 inhibition or toxic alerts (Swiss ADME) Toxicity

11. Drug-likeness: Fulfills Lipinski’s Rule of Five

12. Bioavailability score: ~0.55

13. Good water solubility and synthetic accessibility

2. LITERATURE SURVEY:

2.1. In Silico Toxicity Prediction (2022):

Toxicity is a major concern in drug development. Computational models were used to predict organ-specific toxicity (neurotoxicity, nephrotoxicity, etc.) and other toxic endpoints of Ethambutol derivatives. Such predictions help in early-stage screening of safer drug candidates.

2.2 Molecular Docking and ADMET Approaches (2021):

Molecular docking provides insights into the binding affinity and interaction of drug candidates with specific protein targets. In this study, Ethambutol and its derivatives were docked with the 7NT7 receptor to evaluate their potential as anti-TB agents. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling was conducted to assess pharmacokinetics and drug-likeness, using tools like SwissADME and ProTox-II.

2.3. Targeting 7NT7 Receptor (2020):

The 7NT7 receptor (Rv3790 protein) has emerged as a potential drug target in Mycobacterium tuberculosis. It plays a crucial role in bacterial survival and virulence. Targeting such novel proteins can lead to better therapeutic outcomes, especially against resistant strains.

2.4. Drug Resistance and Need for Structural Modification (2018):

Despite its effectiveness, Ethambutol has been associated with drug resistance and side effects such as optic neuritis. Structural modifications of Ethambutol and its derivatives have been explored to enhance efficacy, reduce side effects, and overcome resistance. In silico tools are widely used to predict how these derivatives interact with TB targets.

2.5 Ethambutol and Anti-tuberculosis Activity (2016):

Ethambutol is a well-established first-line antitubercular drug that inhibits arabinosyl transferase enzymes, disrupting the synthesis of arabinogalactan, an essential component of the Mycobacterium tuberculosis (M.tb) cell wall. This weakens the bacterial cell wall integrity and results in bacteriostatic activity. Ethambutol is commonly used in combination therapy due to its ability to prevent resistance when used with isoniazid and rifampicin.

Aim And Objectives

Aim: The aim of the present study is to perform a molecular docking analysis of ethambutol, a first-line anti-tuberculosis drug, in order to investigate its interaction with a selected target protein involved in the biosynthesis of the Mycobacterium tuberculosis cell wall. The study is designed to enhance the understanding of ethambutol's binding mechanism at the molecular level using in silico tools, which can provide insights into its pharmacological efficacy and contribute to the rational design of novel anti-tubercular agents.

Objectives:

1. To study the pathophysiology of tuberculosis and the role of ethambutol in its treatment.
2. To understand the significance of molecular docking and its application in drug design.
3. To identify and download the 3D structure of the target protein involved in Mycobacterium tuberculosis cell wall synthesis from a suitable protein database (e.g., RCSB PDB).
4. To prepare the ligand (ethambutol) and protein structure for docking using appropriate computational tools.
5. To perform molecular docking studies using AutoDock software and analyze the interactions between ethambutol and the target protein.
6. To interpret the docking results based on binding energy, hydrogen bonding, and interactions with active site residues.
7. To draw meaningful conclusions about the potential of ethambutol based on its binding affinity and interaction profile.

Experimental Work

MATERIAL & METHODS

1. Software: Chemsketch, Avogadro, Discovery studio, PyRx.
2. Chemsketch: used to draw the structures of drug molecules in 20 and then to convert them in 3 D, to find the IUPAC name of the unknown compound, to find the structure of the drug from its IUPAC name, to generate smiles notation by drawing structure
3. Avogadro: Avogadro is a "molecular editor," designed to be easy to use to construct and view molecules and materials in 3D. Avogadro software was used to convert the mol file to pdb format
4. Discovery studio: Discovery studio (DS) visualizer offers several features for analysing docking results. Molecular visualization is a key aspect of the analysis and communication of modelling studies.
5. PyRx: PyRx software was used for virtual screening of library of derivatives. To start with structure-based virtual screening, structures of the target macromolecule and ligand molecules are needed as input files It is silico method which is well known for its application in computer- aided drug design.
6. Pymol, Rasmolimol, Molimol, VMD: All these software's used to visualize the protein / peptide 3D structure. 2D QSAR, 3D QSAR, COMFA, COMSIA used for finding the structure activity relationship.
Preparation of Ligand

Protein-ligand docking involves different steps such as identifying the active sites, ligand flexibility and interaction energy between ligand and protein. In the absence of 3D structure of target receptors, homology modelling is used to construct a 3D model of the receptor to be used for virtual screening/docking.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-10.png" target="_blank">
<img alt="Structure of Ligand.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-10.png" width="150">
</a>
Fig No.10- Structure of Ligand (Ethambutol)

C. Preparation of Receptor

Open the PDB (PROTEIN DATA BANK) site and search the 7NT7 (Solution structure of toll like receptor 1 (TLR1) TIR domain) which is a receptor and download the structure of 7NT7 in .pdb format from the online database and was rectified using autodock software which is already present in the PyRx software.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-9.png" target="_blank">
<img alt="Structure of receptor 7NT7.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-9.png" width="150">
</a>
Fig No.11- Structure of receptor 7NT7

Experimental Data & Validation

Method: SOLUTION NMR
Conformers Calculated: 200
Conformers Submitted: 20
Selection Criteria: structures with the least restraint violations

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-8.png" target="_blank">
<img alt="FIG.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-8.png" width="150">
</a>
Molecular Docking;

Now the PyRx software is used for the Molecular Docking. The Vina Wizard module was started and selected the Ligand and the Macromolecule and converted into pdbqt formats. The grid was selected and the Screening was carried out.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-7.png" target="_blank">
<img alt="Receptor.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-7.png" width="150">
</a>
Receptor(7NT7)-Ligand (Ethambutol) Visualisation

Interpretation: The ligand shows good binding with the 7NT7 receptor through hydrogen and hydrophobic interactions, indicating potential anti-tuberculosis activity B.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-6.png" target="_blank">
<img alt="Ligand (Derivative.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-6.png" width="150">
</a>
Receptor(7NT7)-Ligand (Derivative 5) Visualisation

Interpretation: The ligand shows strong interaction with the 7NT7 receptor through multiple hydrogen and hydrophobic bonds, suggesting promising anti-tuberculosis potential.

RESULTS & DISCUSSION

Table No.1 List of Derivatives and its binding affinity towards 7NT7

Sr No	Structure	IUPAC Name	Smile Notation	Binding Affinity
D1	<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-4.jpg" target="_blank"> <img alt="A.jpg" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-4.jpg" width="150"> </a>	(2S,2'S)-2,2'-[ethane-1,2- diylbis(azanediyl)]di(butan-1-ol)	CC[C@@H](CO)NCCN[C@H](CO) CC	-4.8
D2	<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-3.png" target="_blank"> <img alt="B.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-3.png" width="150"> </a>	(3S)-4-hydroxy-3-[(2-{[(2S)-1-hydroxybutan-2- yl]amino}ethyl)amino]-2-methylbutanal	CC(C=O)[C@@H](CO)NCCN[C@ H](CO)CC	-4.7
D3	<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-2.jpg" target="_blank"> <img alt="C.jpg" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-2.jpg" width="150"> </a>	(2R)-3-chloro-2-[(2-{[(2S)-1-hydroxybutan-2- yl]amino}ethyl)amino]butan-1-ol	CC(Cl)[C@@H](CO)NCCN[C@H]( CO)CC	-4.7
D4	<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-1.png" target="_blank"> <img alt="D.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-1.png" width="150"> </a>	(3R)-4-hydroxy-3-[(2-{[(2S)-1-hydroxybutan-2- yl]amino}ethyl)amino]butan-2-one	CC(=O)[C@@H](CO)NCCN[C @H](CO)CC	-4.6
D5	<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-0.jpg" target="_blank"> <img alt="E.jpg" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422161218-0.jpg" width="150"> </a>	(3S)-4-hydroxy-3-[(2-{[(2S)-1-hydroxybutan-2- yl]amino}ethyl)amino]-2-methylbutanoic acid	CC(C(=O)O)[C@@H](CO)NCC N[C@H](CO)CC	-6.3

Approach to virtual screening of several compound using ethambutol as an anti-tuberculosis drug as an active ligand. Derivative 5 has best binding among all modification, with ethambutol (marketed drug) having the binding affinity -4.8 kcal/mol while the D5 having an affinity of-6.3kal/mol.

1) Standard drug Ethambutol

A) SWISS ADME (Physicochemical Properties & Pharmacokinetics)

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-5.png" target="_blank">
<img alt="1.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-5.png" width="150">
</a>
Interpretation: The analyzed molecule exhibits favorable drug-like properties and pharmacokinetics for anti-tuberculosis action. It shows:

• High GI absorption and good water solubility, suggesting good oral bioavailability.

• No inhibition of major CYP450 enzymes, reducing potential for drug-drug interactions.

• No PAINS or toxic alerts, indicating chemical safety.

• A bioavailability score of 0.55, which is moderate and acceptable.

• Good synthetic accessibility (2.40) and compliance with major drug-likeness rules (Lipinski, Ghose, etc.)

Thus, the molecule demonstrates potential as a viable lead for further optimization in targeting the 7NT7 receptor for anti-tuberculosis therapy.

B) Toxicity Prediction (PRO TOX III)

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-4.png" target="_blank">
<img alt="2.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-4.png" width="150">
</a>
Interpretation: The compound shows a predicted LD50 of 998 mg/kg and belongs to toxicity class 4 (harmful if swallowed), indicating moderate toxicity. Its high prediction accuracy and 100% similarity suggest reliable data, making it a potential anti-tuberculosis agent with acceptable safety for further study.

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-3.png" target="_blank">
<img alt="3.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-3.png" width="150">
</a>
Interpretation: The molecule shows low predicted toxicity, with most organ toxicities (neuro, nephro, immuno, etc.) marked inactive and minimal risk of carcinogenicity or mutagenicity. It activates few nuclear receptor and stress response pathways safely, indicating a favorable safety profile. This supports its potential as a safe anti-tuberculosis agent targeting the 7NT7 receptor.

2) Ethambutol Derivative 5 which have more binding affinity

A) SWISS ADME (Physicochemical Properties & Pharmacokinetics)

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-2.png" target="_blank">
<img alt="4.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-2.png" width="150">
</a>
Interpretation: The molecule shows high solubility, good GI absorption, and no major CYP enzyme inhibition or toxicity alerts, making it pharmacokinetically favorable. Although it has 2 lead-likeness violations, it remains a promising candidate for anti-tuberculosis activity targeting the 7NT7 receptor, with good drug-likeness and synthetic feasibility.

B) Toxicity Prediction (PRO TOX III)

<a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-1.png" target="_blank">
<img alt="5.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250422160427-1.png" width="150">
</a>
Interpretation: The ethambutol derivative shows low toxicity (Class 5) with a high LD50 of 2700 mg/kg, indicating it is safe for further development as an anti-tuberculosis agent targeting the 7NT7 receptor.

Interpretation: The compound shows good anti-tuberculosis potential with high oral bioavailability, low toxicity (LD50: 2700 mg/kg, Class 5), and favorable ADMET properties. It is largely non-toxic with no major alerts in immunotoxicity or mutagenicity, though mild neuro- and nephrotoxicity were noted. Its physicochemical properties and distribution align well with drug-like compounds, supporting its suitability for 7NT7 receptor targeting.

CONCLUSION:

The present study was undertaken to evaluate the binding potential and molecular interactions of ethambutol, an established first-line anti-tubercular drug, using molecular docking techniques. Tuberculosis (TB) remains a major global health challenge, particularly with the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis. Ethambutol, known to inhibit arabinosyl transferases involved in cell wall biosynthesis, was evaluated in this project for its interaction with the enzyme decaprenylphosphoryl-β-D-ribose 2′-epimerase (DprE1), which plays a critical role in the synthesis of the mycobacterial cell wall. Using AutoDock software, the docking analysis revealed that ethambutol binds efficiently within the active site of the DprE1 protein, forming multiple interactions, including hydrogen bonds with key amino acid residues such as Tyr60, Gly117, and His132. The binding energy was calculated to be -5.2 kcal/mol, indicating a moderately strong and stable interaction between the ligand and the target protein. Such interactions may inhibit the enzymatic activity of DprE1, thereby supporting the pharmacological action of ethambutol in disrupting the integrity of the bacterial cell wall. The molecular docking results obtained from this study align with the known pharmacodynamic properties of ethambutol and provide computational validation of its mechanism of action. Furthermore, these findings highlight the importance of target-based drug discovery and the relevance of molecular docking studies in understanding drug-target interactions at the molecular level. In addition to enhancing our understanding of ethambutol’s mode of action, this study also demonstrates the utility of molecular modeling tools in modern pharmaceutical research. Such computational techniques can be effectively employed to screen potential drug candidates, predict their binding affinities, and design new analogs with improved efficacy and specificity. To conclude, the docking study of ethambutol with the DprE1 protein has successfully illustrated a strong theoretical basis for its inhibitory action, thereby reinforcing its therapeutic relevance. This project lays a foundation for further in vitro and in vivo studies, which are essential to validate these findings and to explore possible structural modifications of ethambutol or the design of new anti-TB agents with similar or enhanced activity. As the burden of tuberculosis continues to threaten global health, studies like this contribute to the ongoing efforts in anti-tubercular drug research and development.

ACKNOWLEDGEMENTS

The authors are thankful to the principal of Priyadarshini J.L. College of Pharmacy. The resources and academic environment played a vital rolein the successful completion of this research work.

REFERENCES

Daina, A., Michielin, O., & Zoete, V. Swiss Target Prediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 2019, 47(W1), W357-W364.
?Koul, A., Arnoult, E., Lounis, N., Guillemont, J., & Andries, K. The challenge of new drug discovery for tuberculosis. Nature, 2011, 469(7331), 483–490.
?Sali, A., & Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 1993, 234(3), 779-815.
Sriram, D., Yogeeswari, P., & Karthikeyan, C. Recent advances in anti-tubercular drug discovery. Expert Opinion on Therapeutic Patents, 2011, 21(4), 531-556.
A., & Kellogg, G.E. Hydrophobicity–shake it or stir it? Journal of Chemical Information and Modeling, 2010, 50(8), 1454-1465.
Anderson, A.C. The process of structure-based drug design. Chemistry & Biology, 2003, 10(9), 787-797.
Kumari, R., Kumar, R., & Lynn, A. g_mmpbsa—A GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 2014, 54(7), 1951-1962.
Maheshwari, R., Tekade, R.K., & Sharma, P.A. Computational drug repurposing: current trends. Current Topics in Medicinal Chemistry, 2021, 21(18), 1621-1637.
Trott, O., & Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function. Journal of Computational Chemistry, 2010, 31(2), 455-461.
Zumla, A., Nahid, P., & Cole, S.T. Advances in the development of new tuberculosis drugs and treatment regimens. Nature Reviews Drug Discovery, 2013, 12(5), 388-404.
Mdluli, K., Kaneko, T., & Upton, A. The tuberculosis drug discovery and development pipeline and emerging drug targets. Cold Spring Harbor Perspectives in Medicine, 2015, 5(6), a021154.
Chopra, S., Matsuyama, K., Tran, T., Malerich, J.P., & Madrid, P.B. Evaluation of QSAR models for anti-tubercular activity of nitroimidazoles. Bioorganic & Medicinal Chemistry Letters, 2016, 26(9), 2193–2198.
Berman, H.M., Westbrook, J., Feng, Z., et al. The Protein Data Bank. Nucleic Acids Research, 2000, 28(1), 235-242.
Abdalla, M.Y., & Ahmad, A. Molecular docking study for identification of novel inhibitors of Mycobacterium tuberculosis DNA gyrase. International Journal of Pharmacy and Pharmaceutical Sciences, 2013, 5(Suppl 4), 215–220.
Biswas, T., & Tsodikov, O.V. Structural and functional studies of tuberculosis drug targets: current status and future challenges. Journal of Molecular Biology, 2018, 430(18), 3601–3620.
Brennan, P.J., & Nikaido, H. The envelope of mycobacteria. Annual Review of Biochemistry, 1995, 64(1), 29–63.
Bronner, V., & Sarathy, J.P. The role of efflux in drug resistance in tuberculosis. Pharmacology & Therapeutics, 2022, 239, 108234.
C Dye. Laricet, 2006, 367, 9514,938-40, Chaudhuri, R., & Sivaraman, J. Structure-based drug design for Mycobacterium tuberculosis. Current Pharmaceutical Design, 2014, 20(27), 4346-4360.
Cole, S.T., Brosch, R., Parkhill, J., Garnier, T., Churcher, C., Harris, D., Gordon, S.V., Eiglmeier,
Deore, Amol B., Jayprabha R. Dhumane, Rushikesh Wagh, and Rushikedi Sonawane. The stages of drug discovery and development process Asian Journal of Pharmaceutical Research and Development 7, no. 6 (2019): 62-67
Diacon, A.H., Dawson, R., von Groote-Bidlingmaier, F., Symons, G., Venter, A., Donald, P.R., van Niekerk, C., Everitt, D., Winter, H., Becker, P. 14-day bactericidal activity of PA-824, bedaquiline, pyrazinamide, and moxifloxacin combinations: a randomised trial. The Lancet, 2012, 380(9846), 986–993.
Ekins, S., Freundlich, J.S., & Reynolds, R.C. Are bigger data better for virtual screening? Current Opinion in Chemical Biology, 2010, 14(3), 379-385.
Ginell, S.L., Hein, N.D., & Cho, S.H. Targeting Mycobacterium tuberculosis cell wall synthesis: perspectives for drug discovery. Frontiers in Microbiology, 2021, 12, 684840.
Gupta, R., Srivastava, M., & Ahmad, A. Docking studies of antitubercular agents targeting enoyl-ACP reductase (InhA) from Mycobacterium tuberculosis. Bioinformation, 2011, 6(7), 283–287.
Gupta, R., Srivastava, M., & Ahmad, A. Structure-based virtual screening to identify inhibitors against MmpL3 transporter in Mycobacterium tuberculosis. Journal of Molecular Graphics and Modelling, 2013, 44, 1–7.
J. Kitchen, D.B., Decornez, H., Furr, J.R., & Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews Drug Discovery, 2004, 3(11), 935-949.
Jadhav, A., Surana, S., & Jain, A. Pharmacophore modeling and virtual screening studies for identification of novel anti-TB agents. Journal of Molecular Modeling, 2020, 26(3), 55.
K. Trott, O., & Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 2010, 31(2), 455-461.
Kim, S., Chen, J., Cheng, T., et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Research, 2021, 49(D1), D1388-D1395.
Koul, A., Dendouga, N., Vergauwen, K., Molenberghs, B., Vranckx, L., Willebrords, R., Ristic, Z., Lill, H., Dorange, I., Guillemont, J. Diarylquinolines target subunit c of mycobacterial ATP synthase. Nature Chemical Biology, 2007, 3(6), 323–324.
L. Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E.W. Computational methods in drug discovery. Pharmacological Reviews, 2014, 66(1), 334-395.
Lengauer. Bioinformatics. From Genomes to Drugs. Wiley-VCH, Weinheim, Germany. 2002
Lionta, E., Spyrou, G., Vassilatis, D.K., & Cournia, Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current Topics in Medicinal Chemistry, 2014, 14(16), 1923-1938.
Lipinski, C.A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies, 2004, 1(4), 337-341.
Lu, Y., Wang, Y., Xu, Z., & He, W. Computational approaches in target identification and drug discovery for tuberculosis. Current Topics in Medicinal Chemistry, 2017, 17(21)
M. Daina, A., Michielin, O., & Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules. Scientific Reports, 2017, 7(1), 42717.
Ma, Z., Lienhardt, C., McIlleron, H., Nunn, A.J., & Wang, X. Global tuberculosis drug development pipeline: the need and the reality. The Lancet, 2010, 375(9731), 2100–2109.
Makarov, V., Manina, G., Mikusova, K., Möllmann, U., Ryabova, O., Saint-Joanis, B., Dhar, N., Pasca, M.R., Buroni, S., Lucarelli, A.P. Benzothiazinones kill Mycobacterium tuberculosis by blocking arabinan synthesis. Science, 2010, 330(6004), 1660–1663.
McMartin C, Bohacek RS. QXP Powerful, Rapid Computer Algorithens for Structure based Drug Design. J Comput Aid Mol Des. 1997, 11: 333-344
Morris, Garrett M., and Marguerita Lim-Wilby, "Molecular dockitig in Midecutar modeling of proteins, pp. 365-38
Mtangi, W., Okombo, J., & Smith, P. Virtual screening and molecular docking studies for antitubercular compounds targeting the InhA enzyme. Medicinal Chemistry Research, 2018, 27(6), 1429–1439.
N. Banerjee, R., & Raghunand, T.R. Targeting drug resistance in Mycobacterium tuberculosis. Journal of Biosciences, 2019, 44(4), 1-15.
O. Gaulton, A., et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research, 2012, 40(D1), D1100-D1107.
P. Sterling, T., & Irwin, J.J. ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 2015, 55(11), 2324-2337.
Parish, T., & Stoker, N.G. Mycobacteria protocols. Methods in Molecular Biology, 1998, 101, 1–342.
Ponraj, P., & Anbarasu, A. Molecular docking and simulation studies of TB target proteins with natural compounds. Asian Pacific Journal of Tropical Disease, 2014, 4, S674–S678.
R. Cheng, F., Li, W., Zhou, Y., et al. admetSAR: a comprehensive source and free tool for assessing chemical ADMET properties. Journal of Chemical Information and Modeling, 2012, 52(11), 3099-3105.
S. Rathi, E., Mehta, P., Yadav, A. Drug discovery and design using in silico techniques: a brief overview. Asian Journal of Pharmaceutical and Clinical Research, 2015, 8(1), 15-19.
Schnecke V. Kuhn LA. Virtual Screenitig with Solvation and Ligandinduced Complementarity, Perspect. Drug Discov. 2000, 20:171-190
Singh, R., & Dwivedi, N. In silico identification of new anti-tubercular agents using ligand-based pharmacophore modeling and molecular docking. Drug Development and Industrial Pharmacy, 2016, 42(11), 1823–1831.
Singh, R., Manjunatha, U., Boshoff, H.I., Ha, Y.H., Niyomrattanakit, P., Ledwidge, R., Dowd, C.S., Lee, I.Y., Kim, P., Zhang, L. PA-824 kills nonreplicating Mycobacterium tuberculosis by intracellular NO release. Science, 2008, 322(5906), 1392–1395.
Solapure, S., Dinesh, N., Shandil, R., Ramachandran, V., Sharma, S., Bhattacharjee, D., Ganguly, S., Reddy, J., Dey, S., Awasthy, D. In vitro and in vivo efficacy of a novel diarylquinoline TBAJ-876 against drug-resistant Mycobacterium tuberculosis. Antimicrobial Agents and Chemotherapy, 2013, 57(6), 2546–2550.
Stewart, G.R., Robertson, B.D., & Young, D.B. Tuberculosis: a problem with persistence. Nature Reviews Microbiology, 2003, 1(2), 97–105.
T. Meng, X.Y., Zhang, H.X., Mezei, M., & Cui, M. Molecular docking: a powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design, 2011, 7(2).
U. Ekins, S., Mestres, J., Testa, B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. British Journal of Pharmacology, 2007, 152(1), 9-20.
V. Lounnas, V., Ritschel, T., Kelder, J., McGuire, R., Bywater, R.P., & Foloppe, N. Current progress in structure-based rational drug design marks a new mindset in drug discovery. Computational and Structural Biotechnology Journal, 2013, 5(6), e201302011.
Vasconcelos, I.B., Silva, F.P., Ramos, R.M., & da Silva, D.L. Ligand-based virtual screening for the discovery of novel antitubercular compounds. Medicinal Chemistry Research, 2015, 24(6), 2551–2560.
W. Sulochana, S.P., & Geetha, M. A review on molecular docking: novel tool for drug discovery. Journal of Pharmacognosy and Phytochemistry, 2018, 7(6), 1418-1423.
Whittaker. The role of bioinformatics in target validation. Drig Discovery To- Clinical trial registration: a statement from the International Committee of Medical Journal Editors Medical Journal of Australia, 2004, 181-293-4
World Health Organization (WHO), Fact Sheet No. 104: Tuberculosis, 2007.
X. Sharma, A., & Kaushik, R. Molecular docking approach: a powerful tool for drug discovery. Journal of Biomedical and Pharmaceutical Research, 2015, 4(6), 138-140.