Molecular design, ADMET evaluation, and molecular dynamic simulation of some potent sulfonamide-based compounds as anti-tuberculosis agents

Tuberculosis (TB) is a chronic bacterial infection caused by Mycobacterium tuberculosis, affecting millions of people worldwide. Despite the availability of anti-TB drugs, the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB strains has become a significant public health concern. Therefore, there is an urgent need to discover and develop new anti-TB agents with improved efficacy and reduced toxicity. In this study, we use computational methods to identify and design new chemical entities that could be effective anti-tuberculosis agents. The developed model meets numerous organizations' recommendations for statistically valid QSAR, with R² values of 0.990 and 0.978 for internal and external validation, respectively. The designed molecules 29f and 29l exhibit higher binding affinities (∆G) of -37.15 kcal/mol and -37.31 kcal/mol, respectively, when compared to rifampin as the reference drug (RC) with ∆G of -24.13 kcal/mol, which indicates that it is more stable than RC. ADMET evaluation shows improved therapeutic qualities of these newly developed compounds, demonstrated by a reduced maximum acceptable dosage. Further validation was carried out via molecular dynamic simulation, and these analyses prove that changes in protein conformation and dynamics as a response to ligand binding provide an in-depth view of the molecular mechanisms that determine the ligand’s efficacy and protein roles.

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Publication Details

Journal
In Silico Research in Biomedicine
Published
2025-01-01
DOI
https://doi.org/10.1016/j.insi.2025.100035
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50
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article

Molecular design, ADMET evaluation, and molecular dynamic simulation of some potent sulfonamide-based compounds as anti-tuberculosis agents

Anne Jibrin, Vipin Kumar Mishra, Gideon Adamu Shallangwa, Stephen Eyije Abechi et al.
1 citations
In Silico Research in Biomedicine
Computational Drug Discovery Methods
1.50
article

Molecular design, ADMET evaluation, and molecular dynamic simulation of some potent sulfonamide-based compounds as anti-tuberculosis agents

Anne Jibrin, Vipin Kumar Mishra, Gideon Adamu Shallangwa, Stephen Eyije Abechi, Rakesh Kumar Srivastava, Adamu Uzairu, Abdullahi Bello Umar
article en
1 citations

Abstract

Tuberculosis (TB) is a chronic bacterial infection caused by Mycobacterium tuberculosis, affecting millions of people worldwide. Despite the availability of anti-TB drugs, the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB strains has become a significant public health concern. Therefore, there is an urgent need to discover and develop new anti-TB agents with improved efficacy and reduced toxicity. In this study, we use computational methods to identify and design new chemical entities that could be effective anti-tuberculosis agents. The developed model meets numerous organizations' recommendations for statistically valid QSAR, with R² values of 0.990 and 0.978 for internal and external validation, respectively. The designed molecules 29f and 29l exhibit higher binding affinities (∆G) of -37.15 kcal/mol and -37.31 kcal/mol, respectively, when compared to rifampin as the reference drug (RC) with ∆G of -24.13 kcal/mol, which indicates that it is more stable than RC. ADMET evaluation shows improved therapeutic qualities of these newly developed compounds, demonstrated by a reduced maximum acceptable dosage. Further validation was carried out via molecular dynamic simulation, and these analyses prove that changes in protein conformation and dynamics as a response to ligand binding provide an in-depth view of the molecular mechanisms that determine the ligand’s efficacy and protein roles.

In Silico Research in BiomedicineVol. 1
Advanced Materials and Processes Research Institute (IN), Ahmadu Bello University (NG), Madhya Pradesh Bhoj Open University (IN), Rajiv Gandhi Technical University (IN), Artificial Intelligence in Medicine (Canada) (CA), Barkatullah University (IN)
Good health and well-being
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
1.50
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