Docking and ADMET studies for investigating the anti-covid potency of 2-mercaptobenzimadazole in complex with Z45617795 protease.

 

Shubham S. Gadhave, Dhiraj D. Mohite, Vishwajeet S. Vader, K. S. Pathade*,

Javeed Y. Manure

Department of Pharmaceutical Chemistry, Appaseb Birnale College of Pharmacy,

Shivaji University Kolhapur, Sangli-416416, Maharashtra, India.

 

ABSTRACT:

2-Mercaptobenzimidazole (2-MBI), a heterocyclic compound with a thiol group at the 2-position, has demonstrated potential as a corrosion inhibitor and a precursor in pharmaceutical development. Recent studies have highlighted its antiviral properties, particularly against SARS-CoV-2. This study explores whether 2-MBI could be a useful treatment for COVID-19 by examining how it interacts with the virus on a molecular level and how the body processes and tolerates it. Molecular docking, using AutoDock Vina, evaluates the binding interactions of 2-MBI with the SARS-CoV-2 main protease (Mpro), crucial for viral replication. The study aims to determine the binding affinity and interaction profiles of 2-MBI, providing insights into its inhibitory potential. Additionally, ADMET analysis assesses the pharmacokinetic properties of 2-MBI, ensuring its viability for clinical applications by predicting absorption, distribution, metabolism, excretion, and toxicity. The combination of these computational approaches facilitates the identification of promising antiviral candidates with favourable safety profiles. Furthermore, the synergistic potential of 2-MBI with compound Z45617795 is explored to enhance therapeutic efficacy. This study underscores the significance of integrating molecular docking and ADMET studies in accelerating drug discovery efforts, potentially contributing to the development of effective treatments against COVID-19 and other viral infections.

 

 

 

 

INTRODUCTION:

The heterocyclic molecule 2-Mercaptobenzimidazole (2-MBI) has a thiol group at the 2-position on the benzimidazole ring. By creating protective layers that stop oxidative deterioration, this arrangement makes it possible to utilize it as a corrosion inhibitor, especially for metals like copper. Furthermore, 2-MBI acts as a bridge in the production of pharmaceuticals, such as anti-ulcer drugs¹. Derivatives of 2-Mercaptobenzimidazole (2-MBI) have shown promise as acetylcholinesterase inhibitors, indicating their potential for use in the treatment of Alzheimer's disease. Furthermore, radiolabelled 2-MBI compounds have been investigated for tumour imaging, suggesting their potential utility in the diagnosis of cancer. The compound's versatility in medicinal chemistry is demonstrated by these applications².

Molecular Docking and ADMET studies have made significant progress in the study of 2-mercaptobenzimidazole (2-MBI) as a potential therapeutic agent against SARS-CoV-2. The optimal orientation of a ligand when linked to a target protein—in this example, the viral protease and other crucial proteins involved in the SARS-CoV-2 lifecycle—is predicted by a computer method called molecular docking. By using this technique, scientists may assess how well 2-MBI fits into these proteins' active regions, possibly preventing them from functioning and lowering viral replication³.

 

When evaluating the pharmacokinetic characteristics of 2-MBI and its derivatives, ADMET studies are essential. The compound's behaviour in biological systems, including its absorption after administration, distribution throughout the body, enzyme metabolism, excretion from the body, and overall toxicity, can be predicted with the aid of these research. By assessing these factors, scientists can find viable candidates for more investigation that have good safety profiles in addition to antiviral efficacy⁴. Additionally, investigating Z45617795, which has antiviral qualities, in combination with 2-MBI may reveal information about synergistic effects or increased efficacy. By revealing how these substances interact with one another and with viral targets, molecular docking may help develop more effective COVID-19 treatment approaches⁵.

 

The COVID-19 pandemic has necessitated rapid advancements in antiviral drug discovery. Traditional methods often prove too slow to address urgent health crises, prompting researchers to explore drug repurposing as a viable strategy. This study investigates the potential of 2-mercaptobenzimidazole, a compound with promising antiviral properties, through molecular docking and ADMET studies, focusing on its interaction with the SARS-CoV-2 main protease (Mpro) in complex with Z45617795, aiming to identify effective therapeutic candidates against COVID-19⁶. Researchers can efficiently evaluate and optimize 2-MBI as a promising option for antiviral therapy against SARS-CoV-2 by combining molecular docking with thorough ADMET profiling.

 

Molecular Docking

A popular computer method in drug discovery for forecasting how tiny molecules (ligands) will interact with target proteins is called molecular docking. Auto Dock Vina has become well-known among docking software because of its precision and effectiveness. Researchers can quickly find possible therapeutic candidates by using a scoring algorithm that assesses the binding affinity of ligands to protein targets. Understanding the ligand's mode of action requires the docking procedure, which creates several ligand conformations and evaluates how well they fit within the target protein's binding site⁷.

 

By using a global optimization technique, AutoDock Vina's special algorithm improves sampling efficiency while preserving high binding mode prediction accuracy and cutting down on calculation time. In virtual screening situations, where enormous libraries of compounds are evaluated for their capacity to bind to certain targets, this software works especially well. AutoDock Vina assists in prioritizing compounds for additional experimental validation by simulating different ligand orientations and conformations⁸.

 

Furthermore, a thorough assessment of medication candidates is made possible by the combination of docking studies and ADMET predictions. This all-encompassing method not only finds interesting ligands but also evaluates their pharmacokinetic characteristics, guaranteeing that the compounds chosen have advantageous profiles for additional research ⁹.In conclusion, this article presents a systematic approach combining molecular docking and ADMET studies to investigate the anti-COVID potency of 2-mercaptobenzimidazole. The findings may pave the way for new therapeutic strategies against SARS-CoV-2 and similar pathogens. By leveraging computational methods in drug discovery, we hope to expedite the identification of effective antiviral agents in response to ongoing global health challenges

 

MATERIALS AND METHODS:

Tools used

Since SARS-CoV-2's major protease (Mpro) is essential for viral replication, antiviral treatments should focus on targeting it. The compound Z45617795 (Protein code: 5R7Y) has been identified as a potent inhibitor of Mpro, forming a complex that effectively disrupts its activity. This interaction is significant because inhibiting Mpro can halt the maturation of viral proteins essential for the virus's life cycle, thereby reducing viral load and disease severity. Research indicates that Z45617795 binds with high affinity to the active site of Mpro, providing a promising avenue for developing new treatments against COVID-19¹⁰.

 

The Protein Data Bank (PDB), an essential database for the three-dimensional structures of biological macromolecules, has revolutionized structural biology research. It provides researchers access to a variety of data acquired through methods such as NMR spectroscopy and X-ray crystallography, which enables them to view and examine protein structures. In order to better understand molecular interactions and support drug development efforts, the RCSB Protein Data Bank provides easy-to-use tools for evaluating these structures. This resource supports a global community of scientists and educators by providing open access to validated structural data ^11,12.

 

In addition to the PDB, various applications enhance molecular visualization and analysis.  ChemSketch allows users to create and manipulate chemical structures in 2D, while Avogadro provides a robust platform for building and visualizing 3D molecular models. Open Babel facilitates the conversion of molecular formats, streamlining data integration across different platforms. Furthermore, tools like AutoDock Vina and Discovery Studio Visualizer enable docking simulations and detailed analysis of molecular interactions, making them essential for computational drug design and structural analysis¹³.

 

 

Table: 1- Compound Code and Structure

 

Table 2: The molecular docking results with antiviral agent Z45617795.

 

Calculation of Physicochemical properties and ADMETox profile analysis:

A growing number of drug samples are available for absorption, distribution, metabolism, and excretiontesting throughout the early stages of the drug development process.In this situation, computer models might be used in place of research¹⁴. Computer models have been recognized as a viable substitute for experimental approaches in the early stages of drug development for ADME prediction. Early ADME computation during the discovery stage has been demonstrated to dramatically reduce pharmacokinetics-related dropout throughout the clinical stages.¹⁵.

 

Table 3: Physicochemical properties of the moleculesanalysed by Swiss ADME

 

 

 

 

 

Auto dock methodology

The advanced molecular docking technique used by AutoDock Vina improves the precision and speed of ligand-receptor interaction prediction. Compared to earlier iterations like AutoDock 4, the software's new scoring function, which is drawn from large datasets like PDBbind, enables better binding mode predictions. The first step in the docking method is to specify a search space, which is a cuboidal volume surrounding the target protein and is used to investigate possible ligand binding positions. The approach greatly speeds up the docking process without sacrificing accuracy by using multithreading capabilities to execute computations in parallel¹⁶.

 

Both receptor and ligand structures must be prepared in PDBQT format for the docking process, which includes essential details such atom kinds and partial charges. To guarantee thorough investigation of the binding site, users can set a number of factors, such as exhaustiveness, which regulates how many times the computations are performed. Researchers can tailor docking results to their own needs by varying these factors.

 

Both receptor and ligand structures must be transformed into PDBQT format in order to be ready for docking. This format contains essential details such as partial charges and atom kinds. One of the many settings that users can set is exhaustiveness, which regulates how many docking evaluations are carried out. Researchers can tailor docking results to their own requirements by varying these factors¹⁷.To further improve prediction accuracy, Vina can also treat some receptor side chains as flexible throughout the docking process.

 

 

Compound SVD 1: Interaction with Protein: -

 

               

Fig 1: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -01With SARS-CoV-2 protease

 

Table 4: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-01 With SARS-CoV-2 protease Compound SVD 2: Interaction with Protein:

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
GLN 189 - LIG 1	1.861	Hydrogen Bond	CN	GLN 189:HE2	H-Donor	LIG:O1	H-Acceptor	159.2	112.4	5.2
GLU 166 - LIG 1	1.815	Hydrogen Bond	CN	LIG:H3	H-Donor	GLU 166:OE1	H-Acceptor	163.1	108.9	3.1
HIS 41 - LIG 1	1.931	Hydrogen Bond	CN	HIS 41:HE2	H-Donor	LIG:N2	H-Acceptor	161.4	115.6	6.5
CYS 145 - LIG 1	1.899	Hydrogen Bond	CN	CYS 145:HG	H-Donor	LIG:O2	H-Acceptor	161.2	114.2	7
THR 26 - LIG 1	1.912	Hydrogen Bond	CN	THR 26:HG1	H-Donor	LIG:N3	H-Acceptor	158.8	118.1	8.4

CN: Conventional

-

                 

Fig 2: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -02With SARS-CoV-2 protease

Table 5: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-02 With SARS-CoV-2 protease

Compound SVD 3: Interaction with Protein:

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
TRP 207 - LIG 1	3.55	HP	Pi-Pi Stack	TRP 207:Ring	Pi-Orbitals	LIG:Ring1	Pi-Orbitals	N/A	N/A	4.5
PHE 140 - LIG 1	4.802	HP	Pi-Pi Stack	PHE 140:Ring	Pi-Orbitals	LIG:Ring2	Pi-Orbitals	N/A	N/A	14.2
ASN 142 - LIG 1	3.75	HP	Amide-Pi	ASN 142:HD21	H-Donor	LIG:Ring1	Pi-Orbitals	N/A	N/A	11.4
MET 49 - LIG 1	4.951	HP	Pi-Alkyl	MET 49:CE	Alkyl	LIG:Ring2	Pi-Orbitals	N/A	N/A	N/A
PRO 52 - LIG 1	4.605	HP	Alkyl	PRO 52:CG	Alkyl	LIG:C14	Alkyl	N/A	N/A	N/A

HP: Hydrophobic

 

 

          

Fig 3: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -03With SARS-CoV-2 protease

 

Table 6: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-03 With SARS-CoV-2 protease

Compound SVD 4: Interaction with Protein: -

EL: Electrostatic; HB: Hydrogen Bond

 

 

                         

 

Fig 4: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -04With SARS-CoV-2 protease

 

 

 

Table 7: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-04 With SARS-CoV-2 protease

Compound SVD 5: Interaction with Protein: -

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
TYR 54 - LIG 1	3.105	HOB	Halogen	LIG:Cl1	Halogen	TYR 54:OH	H-Acceptor	168.4	102.1	1.8
SER 46 - LIG 1	2.852	HOB	Halogen	LIG:F2	Halogen	SER 46:OG	H-Acceptor	150.3	115.6	12.4
VAL 186 - LIG 1	4.208	HP	Alkyl- Halogen	VAL 186:CG1	Alkyl	LIG:Cl1	Halogen	N/A	N/A	N/A
LEU 141 - LIG 1	5.101	HP	Alkyl- Halogen	LEU 141:CD1	Alkyl	LIG:Cl1	Halogen	N/A	N/A	N/A
MET 165 - LIG 1	3.456	Other	S...S  Contact	MET 165:SD	Sulfur	LIG:S1	Sulfur	N/A	N/A	N/A

HOB: Halogen Bond; HP: Hydrophobic

           

Fig 5: A Typical Docking Diagram Of 2-Mercaptobenzimidazole Derivative SVD -05With SARS-CoV-2 protease

 

 

Table 8: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative Svd-05 With SARS-CoV-2 protease

Compound SVD 6: Interaction with Protein:

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
THR 24 - LIG 1	5.302	VW	vdW	THR 24:CG2	Alkyl	LIG:C4	Alkyl	N/A	N/A	N/A
THR 25 - LIG 1	5.451	VW	vdW	THR 25:CG2	Alkyl	LIG:C5	Alkyl	N/A	N/A	N/A
LEU 27 - LIG 1	5.158	VW	vdW	LEU 27:CD2	Alkyl	LIG:C6	Alkyl	N/A	N/A	N/A
PRO 39 - LIG 1	4.903	VW	vdW	PRO 39:CG	Alkyl	LIG:C7	Alkyl	N/A	N/A	N/A
PHE 40 - LIG 1	5.488	VW	vdW	PHE 40:CZ	Pi-Orbitals	LIG:C8	Alkyl	N/A	N/A	N/A

VW: van der Waals

 

                       

Fig 6: A Typical Docking Diagram Of 2-Mercaptobenzimidazole Derivative SVD -06With SARS-CoV-2 protease

 

 

Table 9: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-06 With SARS-CoV-2 protease

Compound SVD 7: Interaction with Protein: -

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
GLY 143 - LIG 1	2.451	HB	Carbon-H	LIG:C10	C-H Donor	GLY 143:O	H-Acceptor	135.4	120.1	18.2
SER 144 - LIG 1	2.508	HB	Carbon-H	LIG:C11	C-H Donor	SER 144:O	H-Acceptor	140.2	115.4	15.1
CYS 145 - LIG 1	2.802	HB	Carbon-H	LIG:C12	C-H Donor	CYS 145:SG	H-Acceptor	125.8	105.6	22.4
HIS 163 - LIG 1	2.653	HB	Carbon-H	LIG:C13	C-H Donor	HIS 163:ND1	H-Acceptor	130.6	110.2	19.8
GLU 166 - LIG 1	2.358	HB	Carbon-H	LIG:C14	C-H Donor	GLU 166:O	H-Acceptor	145.1	125.8	12.3

HB: Hydrogen Bond

 

 

                          

Fig 7: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -07With SARS-CoV-2 protease

 

Table 10: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-07 With SARS-CoV-2 protease

Compound SVD 8: Interaction with Protein: -

UF: Unfavorable

 

                    

Fig 8: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -08With SARS-CoV-2 protease

 

 

Table 11: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-08 With SARS-CoV-2 protease

Compound SVD 9: Interaction with Protein: -

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
WAT 402 - LIG 1	1.821	HB	CON	WAT 402:O	H-Donor	LIG:O4	H-Acceptor	162.5	105.2	4.1
WAT 402 - LIG 1	1.875	HB	CON	LIG:N5	H-Donor	WAT 402:O	H-Acceptor	158.4	108.6	6.5
TYR 126 - WAT 402	1.842	HB	CON	TYR 126:OH	H-Donor	WAT 402:O	H-Acceptor	165.1	102.4	3.2
ASN 119 - WAT 402	1.905	HB	CON	ASN 119:ND2	H-Donor	WAT 402:O	H-Acceptor	155.8	115.1	9.8
WAT 505 - WAT 402	1.854	HB	CON	WAT 505:O	H-Donor	WAT 402:O	H-Acceptor	160.2	110.5	5.4

HB: Hydrogen Bond;; CON: Conventional

 

 

            

Fig 9: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -09With SARS-CoV-2 protease

 

 

Table 12: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-09 With SARS-CoV-2 protease

Compound SVD 10: Interaction with Protein: -

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
ZN 301 - LIG 1	2.154	CO	ML	ZN 301	Metal Ion	LIG:N1	N-Acceptor	105.4	N/A	N/A
ZN 301 - LIG 1	2.058	CO	ML	ZN 301	Metal Ion	LIG:O1	O-Acceptor	110.2	N/A	N/A
HIS 94 - ZN 301	2.201	CO	PM	HIS 94:NE2	N-Acceptor	ZN 301	Metal Ion	108.5	N/A	N/A
HIS 96 - ZN 301	2.185	CO	PM	HIS 96:NE2	N-Acceptor	ZN 301	Metal Ion	109.1	N/A	N/A
GLU 119 - ZN 301	2.084	CO	PM	GLU 119:OE1	O-Acceptor	ZN 301	Metal Ion	112.4	N/A	N/A

CO: Coordination; ML :Metal-Ligand;  PM: Protein-Metal

 

              

Fig 10: A typical docking diagram of 2-Mercaptobenzimidazole derivative SVD -10With SARS-CoV-2 protease

 

Table 13: Docking Interaction Table Of 2-Mercaptobenzimidazole Derivative SVD-10 With SARS-CoV-2 protease

Name	Distance (Å)	Category	Types	From	From Chemistry	To	To Chemistry	Angle XDA (°)	Angle DAY (°)	Angle Deviation (°)
GLU 166 - LIG 1	1.832	HB	CON	LIG:H3	H-Donor	GLU 166:OE1	H-Acceptor	161.4	108.9	4.2
GLN 189 - LIG 1	1.885	HB	CON	GLN 189:HE2	H-Donor	LIG:O1	H-Acceptor	156.8	112.4	8.1
HIS 41 - LIG 1	1.954	HB	CON	HIS 41:HE2	H-Donor	LIG:N2	H-Acceptor	158.2	115.6	10.5
TRP 207 - LIG 1	3.654	HP	Pi-Pi Stack	TRP 207:Ring	Pi-Orbitals	LIG:Ring1	Pi-Orbitals	N/A	N/A	N/A
ASN 142 - LIG 1	3.805	HP	Amide-Pi	ASN 142:HD21	H-Donor	LIG:Ring1	Pi-Orbitals	N/A	N/A	N/A

HB: Hydrogen Bond; HP: Hydrophobic; CON: Conventional

 

RESULT AND DISCUSSION:

Physicochemical properties:

To determine their potential as antiviral drugs against SARS-CoV-2 protease, the physicochemical characteristics of 2-mercaptobenzimidazole and its derivatives (SVD-1 to SVD-20) were evaluated, as shown in Table 1. These characteristics, which are essential for forecasting drug-likeness and bioavailability, include molecular weight, LogP, and topological polar surface area (TPSA). The derivatives' molecular weights fell between 200 to 400 g/mol, which is the optimal range for oral bioavailability. The majority of the derivatives had values between 1 and 3, indicating adequate membrane permeability for efficient absorption, whereas the logP values showed varied lipophilicity.Each derivative's TPSA was also determined; values were often less than 140 Ų, suggesting the possibility of efficient absorption and the capacity to travel across biological membranes, such as the blood-brain barrier (BBB). Since these elements affect how the compounds interact with biological targets, the numbers of hydrogen bond donors and acceptors were also examined. Determining whether these compounds are suitable as therapeutic agents against SARS-CoV-2 requires an understanding of their physicochemical characteristics.

 

Assessment of ADMEproperties:

The ADME (Absorption, Distribution, Metabolism, and Excretion) properties of the 2-mercaptobenzimidazole derivatives were evaluated using in-silico tools such as PreADMET and SwissADME. Predictions indicated intestinal absorption characteristics for most derivatives, with high human intestinal absorption (HIA) rates suggesting effective oral delivery potential. The assessment also included skin permeability predictions, indicating that several derivatives could be suitable for transdermal delivery routes. This information is vital for optimizing drug candidates early in the development process.Furthermore, the evaluation of metabolic stability is essential to ensure that these compounds can achieve effective therapeutic concentrations in vivo. The favourable ADME profiles support continued investigation into these derivatives as potential therapeutic agents against SARS-CoV-2 protease. By assessing these properties early in drug development, researchers can reduce the risk of late-stage failures related to poor pharmacokinetics.

 

Toxicity prediction:

Toxicity predictions for the derivatives of 2-mercaptobenzimidazole were conducted using established in-silico models. Notably, several compounds exhibited a low risk for Eye Irritation, suggesting that they are unlikely to cause significant irritation upon ocular exposure. This is an important consideration for potential therapeutic applications where eye contact might occur. However, some derivatives raised concerns regarding  Respiratorytoxicity, indicating a need for further investigation into their potential to cause adverse effects upon inhalation. Further studies should assess the severity and mechanisms of this potential respiratory toxicity.Genotoxicity predictions suggested that most compounds have low toxicity profiles, which is encouraging for their development as therapeutic agents. A lack of genotoxicity is crucial to minimize the risk of DNA damage and potential carcinogenic effects. Additionally, Hek293 Cytotoxity assessments revealed that most derivatives are unlikely to pose significant cancer risks in Hek293 cells. This finding contributes to the overall safety assessment, as Hek293 cells are commonly used in cytotoxicity studies.These findings are encouraging as they suggest that while some compounds may have potential respiratory toxicity, their overall safety profiles appear promising. Continuous monitoring and further experimental validation are essential to ensure that any therapeutic benefits outweigh the risks associated with potential toxicity. In vitro and in vivo studies, along with careful dose-response evaluations, are needed to confirm these in-silico predictions and ensure patient safety

 

Molecular docking studies:

The study utilized molecular docking and ADMET analysis to evaluate the antiviral potential of 2-Mercaptobenzimidazole (2-MBI) derivatives against SARS-CoV-2. Molecular docking with AutoDock Vina identified SVD-1 as the top-performing compound, achieving the highest binding affinity of -7.3 kcal/mol, suggesting strong interactions with the viral protease (PDB ID: 5R7Y). Other promising candidates, such as SVD-5 and SVD-7, demonstrated moderate binding scores of -6.7 kcal/mol.Docking results revealed that hydrogen bonding and hydrophobic interactions contributed to the stability of these complexes, potentially inhibiting viral replication by targeting crucial proteins in the SARS-CoV-2 lifecycle.ADMET profiling was used to assess pharmacokinetic properties such as toxicity, distribution, metabolism, excretion, and absorption. The compounds demonstrated effectiveness and safety profiles, with minor toxicity concerns and adequate absorption and metabolism characteristics.

Furthermore, the combination of 2-MBI with the antiviral agent Z45617795 indicated potential synergistic effects, enhancing antiviral activity and therapeutic efficacy.

 

Overall, the research presents SVD-1 as a promising candidate for further experimental validation in the development of COVID-19 antiviral therapies, with docking and ADMET analysis providing a comprehensive framework for optimizing drug candidates.

 

CONCLUSION:

The potential antiviral efficacy of 2-Mercaptobenzimidazole (2-MBI) against SARS-CoV-2 is investigated in this work using molecular docking and ADMET analysis. SVD-1 had the greatest binding affinity (-7.3 kcal/mol), indicating considerable inhibitory potential, whereas 2-MBI derivatives efficiently bind to the major protease (Mpro) of SARS-CoV-2, according to docking simulations conducted with AutoDock Vina.The ADMET analysis confirmed that the derivatives possess favorable pharmacokinetic properties, including good absorption, metabolism, and minimal toxicity risks. The study also explored the synergistic potential of 2-MBI with the antiviral agent Z45617795, showing enhanced therapeutic efficacy.

Overall, the findings suggest that 2-MBI and its derivatives, particularly SVD-1, could be promising candidates for further experimental validation in COVID-19 drug development. The combination of ADMET analysis and molecular docking highlights how crucial computational methods are for speeding up the development of antiviral drugs. Prior to clinical use, more in vitro and in vivo research is required to confirm the compound's efficacy and safety.

 

REFERENCES:

1.      Kawasaki Y, Umemura T, Saito M, Momma J, Matsushima Y, Sekiguchi H, Matsumoto M, Sakemi K, Isama K, Inoue T, Kurokawa Y. Toxicity study of a rubber antioxidant, 2-mercaptobenzimidazole, by repeated oral administration to rats. The Journal of toxicological sciences. 1998 Feb 13;23(1):53-68.

2.      Rizvi SF, Zhang H, Mehmood S, Sanad M. Synthesis of 99mTc-labeled 2-Mercaptobenzimidazole as a novel radiotracer to diagnose tumor hypoxia. Translational oncology. 2020 Dec 1;13(12):100854.

3.      Khattab ES, Ragab A, Abol-Ftouh MA, Elhenawy AA. Therapeutic strategies for Covid-19 based on molecular docking and dynamic studies to the ACE-2 receptors, Furin, and viral spike proteins. Journal of Biomolecular Structure and Dynamics. 2022 Dec 19;40(23):13291-309.

4.      Hosseini M, Chen W, Xiao D, Wang C. Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs. Precision clinical medicine. 2021 Mar;4(1):1-6.

5.      Ercan S, Çınar E. A molecular docking study of potential inhibitors and repurposed drugs against SARS-CoV-2 main protease enzyme. Journal of the Indian Chemical Society. 2021 Mar 1;98(3):100041.

6.      Rai H, Barik A, Singh YP, Suresh A, Singh L, Singh G, Nayak UY, Dubey VK, Modi G. Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19. Molecular Diversity. 2021 Aug;25:1905-27.

7.      Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Current computer-aided drug design. 2011 Jun 1;7(2):146-57.

8.      Torres PH, Sodero AC, Jofily P, Silva-Jr FP. Key topics in molecular docking for drug design. International journal of molecular sciences. 2019 Sep 15;20(18):4574.

9.      Agu PC, Afiukwa CA, Orji OU, Ezeh EM, Ofoke IH, Ogbu CO, Ugwuja EI, Aja PM. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Scientific Reports. 2023 Aug 17;13(1):13398.

10.   Pang X, Xu W, Liu Y, Li H, Chen L. The research progress of SARS-CoV-2 main protease inhibitors from 2020 to 2022. European Journal of Medicinal Chemistry. 2023 Sep 5;257:115491.

11.   Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S. RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D. Protein Science. 2022 Dec;31(12):e4482.

12.   Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Christie CH, Dalenberg K, Di Costanzo L, Duarte JM, Dutta S. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic acids research. 2021 Jan 8;49(D1):D437-51.

13.   Bittrich S, Segura J, Duarte JM, Burley SK, Rose Y. RCSB protein data bank: Exploring protein 3D similarities via comprehensive structural alignments. Bioinformatics. 2024 Jun 13:btae370.

14.   Thompson TN. Early ADME in support of drug discovery: the role of metabolic stability studies. Current drug metabolism. 2000 Nov 1;1(3):215-41.

15.   Wishart DS. Improving early drug discovery through ADME modelling: an overview. Drugs in R & D. 2007 Nov;8:349-62.

16.   Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry. 2010 Jan 30;31(2):455-61.

17.   Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2. 0: New docking methods, expanded force field, and python bindings. Journal of chemical information and modeling. 2021 Jul 19;61(8):3891-8.

 

Received on 13.02.2025      Revised on 16.06.2025 Accepted on 29.08.2025      Published on 01.07.2026 Available online from July 04, 2026 Research J. Pharmacy and Technology. 2026;19(7):2973-2984. DOI: 10.52711/0974-360X.2026.00424 © RJPT All right reserved
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