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1Department of Pharmacy, Ashokrao Mane College of Pharmacy, Peth Vadgaon
2Department of Pharmacy, Assistant Professor, Ashokrao Mane College of Pharmacy, Peth Vadgaon.
Drug-resistant tuberculosis (DR-TB), driven by the emergence of multidrug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) strains, continues to pose a serious global health threat, largely due to genetic mutations in Mycobacterium tuberculosis that reduce the efficacy of first- and second-line anti-tubercular drugs; the prolonged treatment duration, associated toxicity, and declining effectiveness of existing regimens highlight the urgent need for novel therapeutic strategies. In this context, computational drug repurposing has emerged as a promising approach to accelerate drug discovery by identifying new therapeutic uses for already approved drugs with established safety profiles, thereby significantly reducing the time, cost, and risk associated with traditional drug development pipelines. Among in silico techniques, molecular docking plays a crucial role in predicting the binding affinity and interaction patterns between ligands and target proteins, facilitating the identification of potential drug candidates, while molecular dynamics (MD) simulations further enhance the reliability of these predictions by providing insights into the stability and behavior of protein–ligand complexes under physiological conditions. Recent studies integrating molecular docking and MD simulations have successfully identified promising repurposed drug candidates targeting key M. tuberculosis enzymes such as DNA gyrase and enoyl-acyl carrier protein reductase (InhA), establishing a robust framework for screening and validating potential therapies against DR-TB; accordingly, this study highlights recent advancements in docking and dynamics-based drug repurposing approaches, while also addressing current challenges, limitations, and future prospects, including the integration of artificial intelligence and the necessity of experimental validation to translate computational findings into clinical applications.
Mycobacterium tuberculosis, which causes tuberculosis (TB), is still one of the main infectious disease-related causes of death globally. With millions of new cases and fatalities reported each year, especially in low- and middle-income countries, tuberculosis (TB) continues to be a major worldwide health concern despite being a preventable and curable illness [1]. Additional factors that contribute to the disease's persistence and transmission include poverty, malnutrition, co-infection with HIV, and restricted access to healthcare services [2]. The emergence of drug-resistant strains, particularly extensively drug-resistant TB (XDR-TB), which exhibits additional resistance to second-line medications like fluoroquinolones and injectable agents, and multidrug-resistant TB (MDR-TB), which is resistant to at least isoniazid and rifampicin, is a major challenge in TB control [3]. therapy becomes more complicated, time-consuming, and ineffective as a result of these resistance forms, which are mostly caused by inadequate or incorrect therapy, poor patient adherence, and genetic alterations in bacterial targets [4]. For tuberculosis, traditional drug discovery methods are frequently costly, time-consuming, and linked to high failure rates. Target identification, lead optimization, preclinical testing, and clinical trials are all part of the typical pipeline, which can take more than ten years and cost a significant amount of money [5]. Drug research efforts are further complicated by the distinct biology of M. tuberculosis, which includes its slow growth rate and capacity to remain in latent forms [6]. Faster, more economical, and effective methods are desperately needed to find novel treatment alternatives for drug-resistant tuberculosis. In this sense, computational methods have drawn a lot of attention due to their capacity to expedite the drug development process by facilitating quick screening and assessment of sizable chemical libraries [7]. Drug repurposing, sometimes referred to as drug repositioning, has become one of these methods' most promising tactics. It entails finding novel therapeutic applications for already-approved or safety-tested medications, thereby cutting down on development time and expense [8]. Drug repurposing offers a potent platform for finding new anti-tubercular medicines and quickening their transition to clinical use when paired with sophisticated computational methods like molecular docking and molecular dynamics simulations [9].
4. Drug-Resistant Tuberculosis: An Overview
Due in large part to Mycobacterium tuberculosis's capacity to become resistant to widely used anti-tubercular medications, drug-resistant tuberculosis (DR-TB) has become a significant obstacle to the global control of tuberculosis. Genetic modifications that allow the bacterium to endure in the presence of antimicrobial drugs are the main cause of resistance, which lowers treatment effectiveness and increases disease transmission [10]. This adaptive ability is especially worrisome since it permits the virus to endure long-term medication exposure and is impacted by:
Mutations in target genes encoding drug-binding proteins are one of the main mechanisms of resistance. For example, mutations in the katG and inhA genes are linked to isoniazid resistance, whereas changes in the rpoB gene confer resistance to rifampicin by changing the RNA polymerase β-subunit [11]. Drug-target interactions are directly impacted by these alterations via:
Consequently, therapeutic outcomes are degraded and traditional medicines become ineffective. Furthermore, the development of multidrug-resistant and extensively drug-resistant tuberculosis (MDR-TB and XDR-TB) might result from the accumulation of numerous mutations over time, which can lead to resistance against many medications simultaneously [12].
The upregulation of efflux pumps, which actively remove medications from the bacterial cell and reduce intracellular drug concentrations below therapeutic levels, is another significant resistance mechanism. Major facilitator superfamily (MFS) proteins and ATP-binding cassette (ABC) transporters are examples of efflux pump systems that are important for both innate and acquired drug resistance in M. tuberculosis [13]. These mechanisms support resistance by
Crucially, efflux-mediated resistance frequently works in concert with genetic alterations. Efflux pumps may provide low-level resistance at first, but if they remain active, they might help people survive at less-than-ideal drug doses, which can lead to the selection of long-lasting genetic changes [14]. The bacterium's resistance to therapy is greatly increased by this combined action, making DR-TB more challenging to cure and underscoring the need for innovative treatment approaches.
Fig.1 Mechanisms of Drug Resistance in Mycobacterium tuberculosis
DR-TB is divided into several groups according to the degree of resistance. Resistance to at least isoniazid and rifampicin, the two most effective first-line anti-TB medications, is known as multidrug-resistant tuberculosis (MDR-TB). Treatment options are severely restricted for extensively drug-resistant tuberculosis (XDR-TB), a more severe form of the disease marked by further resistance to fluoroquinolones and at least one second-line injectable medication [15]. Because of their complexity and high treatment failure rates, these types of tuberculosis provide serious problems to healthcare systems [16].
Table 1: Drug-Resistant TB Classification
Current treatment regimens present a number of issues that further complicate the management of DR-TB [17]. These include high toxicity of second-line medications, poor patient adherence, extended treatment duration (often 18–24 months), and an elevated risk of adverse drug reactions [18]. Effective disease control is further hampered by the high cost of therapy and restricted accessibility in environments with limited resources [19]. The effectiveness of recently discovered medications is also threatened by the introduction of additional resistant strains, underscoring the critical need for innovative therapeutic approaches [20].
5. Drug Repurposing: Concept and Significance
Drug repurposing, sometimes referred to as drug repositioning, is the process of finding novel therapeutic applications for already-approved medications that have undergone extensive clinical testing [21]. This approach reduces the uncertainty usually associated with early-stage drug development by utilizing previously published pharmacological, toxicological, and clinical data [13]. Drug repurposing has drawn a lot of attention lately as a successful substitute for traditional drug development methods, especially when it comes to treating complicated and neglected illnesses like drug-resistant tuberculosis (DR-TB). The necessity for such creative approaches has been further highlighted by the rising incidence of extensively drug-resistant (XDR-TB) and multidrug-resistant (MDR-TB) tuberculosis, as well as the sluggish pace of new medication development [22]. The enormous time and cost savings in medication development is one of the biggest benefits of repurposing drugs. Conventional drug development processes are time-consuming, resource-intensive, and frequently take over 10 years to successfully approve a medicine. They also require significant financial outlays and have a high failure rate [23]. On the other hand, since these parameters have already been determined in earlier research, repurposed medications can avoid a number of early stages of development, such as preliminary toxicity and safety assessments. Additionally, these medications have well-defined pharmacokinetic and pharmacodynamic profiles, which increases their chances of success in clinical trials and speeds up the transfer to therapeutic use [24]. Drug repurposing is therefore especially beneficial for illnesses like tuberculosis (TB), which disproportionately impact low- and middle-income nations with inadequate healthcare resources. Through repurposing techniques, a number of currently available medications have shown encouraging anti-tubercular activity. Due to their capacity to block DNA gyrase, a crucial enzyme involved in bacterial DNA replication, fluoroquinolones, such as moxifloxacin and levofloxacin, are among the most extensively researched examples and are presently included in treatment regimens for drug-resistant tuberculosis [25]. In a similar vein, linezolid, which was first created to treat Gram-positive bacterial infections, has demonstrated notable effectiveness against MDR-TB and XDR-TB strains and is currently utilized in combination treatments [26]. More recently, medications like bedaquiline and delamanid, which were created especially for tuberculosis, show that it is possible to successfully target new biological pathways like ATP synthase and mycolic acid biosynthesis.This has prompted additional research into the repurposing of both antimicrobial and non-antimicrobial agents [27]. Further demonstrating the growing range of repurposing strategies are new candidates like gepotidacin, a novel topoisomerase inhibitor, which has demonstrated promise in computational simulations. Drug repurposing has many benefits, but it also has drawbacks. One of the main issues is that because current medications were not created for those particular interactions, they could not be as effective against novel biological targets. Higher doses may occasionally be necessary to achieve therapeutic success, which may result in greater toxicity and other side effects [3].The approval and commercialization of repurposed medications may also be hampered by intellectual property and regulatory concerns, especially when ownership rights are ambiguous or patent protections have lapsed [28]. Another drawback is the lack of knowledge regarding drug-target interactions in novel illness situations, which might make it more difficult to accurately forecast treatment outcomes and lower the overall success rate of repurposing techniques [29]. Clinical efficacy may also be limited by pharmacokinetic issues, such as inadequate medication penetration into granulomatous lesions typical of tuberculosis. The use of sophisticated computational techniques has become more crucial in order to overcome these obstacles and improve the success rate of drug repurposing. Molecular dynamics (MD) simulations shed light on the stability and behavior of these interactions under physiological conditions, while methods like molecular docking allow the prediction of binding affinities and interaction patterns between medicines and target proteins. When combined, these techniques enable quick screening, validation, and optimization of possible therapeutic candidates, greatly increasing the effectiveness and precision of repurposing initiatives. Drug repurposing is therefore a potent and promising approach to speeding up the development of effective treatments for drug-resistant tuberculosis when paired with computational techniques.
Fig.2 Concept of Drug Repurposing in Tuberculosis Treatment
6. Molecular Docking in TB Drug Discovery
The preferred orientation of a ligand when bound to a target protein is predicted by molecular docking, a popular computational method in structure-based drug development that estimates the type and strength of the interaction. By assessing how tiny compounds fit into the active site of biological targets important to Mycobacterium TB, it plays a critical role in discovering possible treatment candidates [30]. This method greatly speeds up the initial phases of anti-tubercular drug discovery by enabling quick screening of sizable chemical libraries. Its significance in TB research is further demonstrated by its capacity to:
Docking techniques can be broadly divided into two categories: ligand-based and structure-based. In order to anticipate ligand binding modes, structure-based docking uses the target protein's three-dimensional structure, which is acquired by experimental techniques like NMR spectroscopy or X-ray crystallography [31]. When high-resolution protein structures are available, this method is very helpful for accurately predicting ligand–protein interactions. However, in situations when the target structure is not accessible, ligand-based docking makes use of data from existing active drugs to forecast the activity of structurally similar molecules [32]. Although these methods are applied differently, they all aid in the search for new drugs by
Both methods are valuable in TB research, depending on data availability and the nature of the biological target.
Several computational tools and web servers have been developed to facilitate molecular docking studies. Widely used software includes AutoDock and AutoDock Vina, which provide efficient algorithms for predicting ligand–protein interactions and binding energies [31]. These tools are popular due to:
CB-Dock2 is a recent blind docking server that automatically identifies potential binding cavities and performs docking without requiring prior knowledge of the active site, making it particularly useful in exploratory studies [33]. This tool offers additional advantages such as:
Together, these computational tools have become essential in screening and prioritizing potential anti-TB compounds, providing a strong foundation for subsequent analyses such as molecular dynamics simulations and experimental validation.
Fig. 3 Principle of Molecular Docking
Binding affinity and interaction patterns are important factors examined in molecular docking research. The degree of interaction between the ligand and the target protein is indicated by binding affinity, which is commonly given as binding energy (kcal/mol); lower values imply greater binding [4]. In order to understand the molecular underpinnings of pharmacological action, interaction analysis looks at hydrogen bonds, hydrophobic interactions, and other non-covalent forces that stabilize the ligand–protein complex [34]. By focusing on vital proteins including DNA gyrase, RNA polymerase, and enoyl-ACP reductase (InhA), molecular docking has been widely used to find new anti-tubercular options. Docking has been effectively employed in numerous investigations to screen natural chemicals and repurposed medications, identifying promising candidates for additional experimental validation [46]. Molecular docking, thus, is a fundamental tool in contemporary TB drug discovery, directing the identification and optimization of possible treatments.
7. Molecular Dynamics (MD) Simulations
The physical movements of atoms and molecules over time are studied using computational methods called molecular dynamics (MD) simulations, which offer in-depth understanding of the dynamic behavior of biomolecular systems. In contrast to molecular docking, which provides a static snapshot of protein–ligand interactions, MD simulations provide a more accurate depiction of biological systems by taking environmental factors and conformational flexibility into consideration [35]. Because of this, MD is a crucial technique for contemporary drug discovery, including studies on Mycobacterium tuberculosis.
By evaluating the stability of protein–ligand complexes under near-physiological conditions, MD simulations are essential for confirming molecular docking data. In order to reduce false positives and improve candidate selection, MD simulations assess whether the complex is stable over time, whereas docking predicts the most advantageous binding position [36]. In the search for anti-tubercular drugs, this integrated strategy improves the accuracy of computational predictions. During MD simulations, a number of important parameters are examined to assess the stability and behavior of biomolecular systems. The protein–ligand complex's overall structural stability over time is measured using Root Mean Square Deviation (RMSD), which indicates whether the system has reached equilibrium [36]. The flexibility of individual amino acid residues is revealed by Root Mean Square Fluctuation (RMSF), which aids in locating areas with notable mobility [37]. The protein structure's compactness is reflected in the radius of gyration (Rg), which shows folding stability throughout the simulation [38]. Furthermore, understanding the intensity and durability of interactions between the ligand and target protein—which support binding stability—requires hydrogen bond (H-bond) studies.
Table 2: Key Parameters in MD Simulations
MD simulations are commonly carried out using a number of software programs. Because of its excellent computational efficiency and applicability for large biomolecular systems, GROMACS is one of the most widely used tools [39]. Another widely used program, AMBER, is renowned for its accurate biomolecular interaction simulation and strong force fields [40]. With the use of these tools, scientists may conduct intricate simulations and examine intricate molecular interactions that are crucial to the development of TB drugs. MD simulations have been effectively used in tuberculosis research to examine the stability of treatment candidates that target vital proteins like RNA polymerase, DNA gyrase, and InhA. Numerous studies have shown that the identification of promising anti-TB medicines, such as repurposed medications and new inhibitors, is improved when docking and MD simulations are combined [46]. These case studies demonstrate how crucial MD simulations are to improving computational forecasts and developing successful treatments for drug-resistant tuberculosis.
8. Integration of Docking and MD in Drug Repurposing
In computational drug repurposing, the combination of molecular docking and molecular dynamics (MD) simulations has emerged as a potent and popular approach, especially for complicated diseases like drug-resistant tuberculosis (DR-TB). The total dependability of in silico predictions is increased by this integrated method, which makes it possible to identify possible therapeutic candidates and validate their stability and efficacy at the molecular level [30]. Target selection is the first step in a typical workflow for combining docking and MD simulations. Biologically significant Mycobacterium tuberculosis proteins, such as DNA gyrase, enoyl-ACP reductase (InhA), and RNA polymerase, are selected based on their crucial roles in bacterial survival and pathogenicity [30]. Docking screening, which uses computational methods to assess the binding affinity and interaction patterns of a library of compounds, including repurposed pharmaceuticals, is carried out once the target has been identified [36]. Candidates with strong anticipated interactions are given priority in this step. Top-ranking docked complexes are next subjected to MD simulation validation to evaluate their structural stability, conformational alterations, and interaction persistence under physiologically simulated settings [41]. When docking and MD simulations are used together, there are a number of benefits compared to when they are used separately. Large chemical libraries may be quickly and effectively screened via docking, and MD simulations offer a dynamic viewpoint by taking solvent effects and protein flexibility into consideration [36]. This integration improves the selection of viable drug candidates by lowering false-positive docking findings and offering a more precise understanding of ligand–protein interactions [41]. Additionally, by providing comprehensive insights into the binding stability, interaction strength, and conformational adaptability of the ligand within the active site, this method improves the accuracy of medication efficacy prediction. Research has shown that chemicals that exhibit persistent interactions in MD simulations are more likely to show biological activity in validation experiments [41]. Because of this, the combination of docking and MD simulations has emerged as a key component of contemporary drug repurposing techniques, making it easier to find efficient treatments for drug-resistant tuberculosis.
Fig. 4: Computational Drug Repurposing Workflow
Target Selection
↓
Ligand Library Preparation
↓
Molecular Docking
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Top Hit Selection
↓
Molecular Dynamics Simulation
↓
Stable Complex Identification
↓
Experimental Validation
9. Key Targets in Mycobacterium tuberculosis
Finding crucial molecular targets in Mycobacterium tuberculosis is key for developing successful treatments, especially when it comes to drug-resistant tuberculosis (DR-TB). Using computational and experimental methods, a number of well-characterized enzymes and protein complexes involved in essential physiological functions have been thoroughly investigated as potential therapeutic targets [15]. Since DNA gyrase is essential for DNA replication, transcription, and supercoiling, it is one of the most significant targets in M. tuberculosis. Inhibition of this type II topoisomerase, which is made up of the GyrA and GyrB subunits, causes DNA topological disruption and bacterial cell death. Structure-based drug design and docking studies targeting this enzyme have been made easier by structural investigations, particularly those based on crystallographic data like PDB ID: 1ZXM [42]. This aim is further validated by the widespread use of fluoroquinolones, which block DNA gyrase, in the treatment of drug-resistant tuberculosis [42]. The enoyl-acyl carrier protein reductase (InhA), a crucial enzyme in the fatty acid synthesis (FAS-II) pathway necessary for the manufacture of mycolic acid, is another important target. The mycobacterial cell wall's structural integrity and resistance to host defenses are largely dependent on mycolic acids. One of the first-line anti-TB medications, isoniazid, primarily targets InhA, and drug resistance is linked to mutations in the inhA gene [43]. InhA continues to be a popular target for the creation of novel inhibitors because of its crucial function and well-understood mechanism. Another confirmed target is RNA polymerase, which is in charge of M. tuberculosis transcription and gene expression. Rifampicin, a key medication in TB treatment, targets the β-subunit of RNA polymerase. RNA polymerase is an important target in drug-resistant tuberculosis research because mutations in the rpoB gene producing this subunit are a primary cause of rifampicin resistance [44]. The development of new inhibitors to combat resistance has been made possible by structural understanding of RNA polymerase. To fight DR-TB, a number of new targets are being investigated in addition to these well-established ones. These include ATP synthase, targeted by bedaquiline, which disrupts energy metabolism; DprE1 (decaprenylphosphoryl-β-D-ribose 2′-epimerase), involved in cell wall biosynthesis; and protein kinases that regulate cellular signaling pathways [45]. The exploration of such novel targets expands the scope of drug discovery and provides new opportunities for developing effective anti-tubercular therapies.
10. Recent Advances and Case Studies
The use of computational techniques, especially molecular docking and molecular dynamics (MD) simulations, to find repurposed medications against drug-resistant tuberculosis (DR-TB) has advanced significantly in recent years. These in silico techniques make it possible to quickly analyze current drug libraries and offer insightful information about ligand–protein interactions, which makes it easier to find potentially more effective anti-tubercular medicines [46]. Repurposing authorized and experimental medications that target important Mycobacterium tuberculosis proteins has been the subject of several computational studies. For example, in docking tests, gepotidacin, a new triazaacenaphthylene antibiotic that was first created for bacterial infections, shown a promising binding affinity toward DNA gyrase. It is a possible candidate for TB medication repurposing due to its distinct mechanism of blocking bacterial topoisomerases [42]. Similarly, because of their potent inhibitory effect against DNA gyrase, fluoroquinolones like levofloxacin and moxifloxacin have been thoroughly investigated using computational techniques and are already included in treatment plans for MDR-TB [25]. The selection of potential candidates has been reinforced by comparative analysis utilizing MD simulations and molecular docking. While MD simulations confirm the stability of these connections over time, docking experiments usually offer preliminary insights into binding affinities and interaction patterns. According to studies, molecules with consistent hydrogen bonding, stable RMSD values, and little structural fluctuation during MD simulations are more likely to display biological activity [36]. These integrated analytics lower the possibility of false-positive predictions and enable improved medication candidate prioritizing. The efficacy of computational drug repurposing in tuberculosis research is demonstrated by a number of success stories. For instance, although not being created specifically for tuberculosis, linezolid and bedaquiline have demonstrated notable effectiveness against drug-resistant bacteria and are currently included in treatment plans [47]. These achievements highlight the potential of repurposing tactics backed by computational techniques. Nevertheless, there are also several drawbacks, such as inconsistencies between experimental results and in silico predictions, a poor comprehension of intricate biological systems, and difficulties in converting computational discoveries into practical uses [48].
Although further experimental validation is still necessary, recent developments show that combining medication repurposing techniques with molecular docking and MD simulations has considerable promise for expediting the discovery of viable therapeutics against DR-TB.
11. Challenges and Limitations
The use of molecular docking and molecular dynamics (MD) simulations to find viable treatments for drug-resistant tuberculosis (DR-TB) still faces a number of obstacles and restrictions, despite the tremendous progress made in computational drug development. These restrictions may have an impact on the precision, dependability, and possibility for translation of in silico results [48]. The accuracy of docking forecasts is one of the main issues. In order to evaluate binding affinity, molecular docking uses simplified scoring functions, which could not accurately reflect the intricacy of protein–ligand interactions in a real setting. Protein flexibility, solvent effects, and entropic contributions are examples of factors that are frequently estimated or ignored, potentially producing false-positive or false-negative results [48]. Docking data should therefore be carefully evaluated and verified by other computational or experimental techniques. Another issue is computational constraints, especially in MD simulations and large-scale virtual screening. Large computer resources and time are needed for high-quality simulations, particularly when studying complex biomolecular systems or doing lengthy simulation runs to reach equilibrium [36]. Furthermore, the quality of force fields and input structures affects how accurate MD simulations are, which could lead to uncertainty in the outcomes [36]. Lack of experimental confirmation for many computationally predicted therapeutic candidates is another significant drawback. Although in silico approaches are useful for screening and ranking drugs, biological activity, safety, and efficacy must be established through in vitro and in vivo research to validate their predictions [11]. One major obstacle in drug discovery is still the discrepancy between computer predictions and experimental validation. Additionally, while repurposing medications, concerns about drug toxicity and bioavailability must be taken into account. Due to inadequate absorption, distribution, metabolism, and excretion (ADME) characteristics or undesirable toxicity profiles, a chemical that has great binding affinity in docking experiments may fail in biological systems [29]. To increase the success rate of possible medication candidates, pharmacokinetic and toxicity evaluations must be integrated with computational screening.
12. Future Perspectives
The future of drug development for drug-resistant tuberculosis (DR-TB) is becoming more and more influenced by the combination of multidisciplinary techniques and cutting-edge computer technology. Among these, machine learning (ML) and artificial intelligence (AI) are showing promise as game-changing instruments that can greatly improve the accuracy and efficiency of drug discovery pipelines. Compared to conventional techniques, AI-driven models are more accurate at analyzing big datasets, predicting drug–target interactions, and optimizing lead compounds [6]. ML algorithms have been effectively used in TB research to find new inhibitors, forecast resistance patterns, and rank repurposed medication options. Integrating computational techniques with high-throughput screening (HTS) is another interesting avenue. Combining HTS with in silico methods like molecular docking and MD simulations can enhance hit identification and lower false positives, even if HTS enables quick experimental evaluation of thousands of molecules [30]. By reducing candidate libraries prior to experimental validation, this hybrid approach saves time and money while improving overall drug development efficiency. In the context of treating tuberculosis, personalized medicine approaches are also becoming more popular. Treatment results can be impacted by variations in host genetics, immunological response, and pathogen strain diversity. Patient-specific variables and resistance profiles can be identified thanks to developments in genetics and bioinformatics, enabling customized treatment approaches [7]. Patients with DR-TB may benefit from more focused and successful treatments if computational drug development and customized medicine are combined. Even with these developments, strong experimental validation is still essential. Despite their strength, computational predictions need to be validated by in vitro and in vivo research to ensure biological activity, safety, and effectiveness. Computational scientists, microbiologists, and doctors must work together to bridge the gap between in silico results and practical application [11]. For promising computational discoveries to be translated into successful anti-tubercular treatments, this integration must be strengthened.
CONCLUSION
Drug-resistant tuberculosis (DR-TB) is still a major worldwide health concern that calls for the creation of novel and efficient treatment approaches. This review emphasizes how important computational methods are to the advancement of drug development against Mycobacterium tuberculosis, especially molecular docking and molecular dynamics (MD) simulations. These methods greatly increase the effectiveness of finding possible anti-tubercular medicines by facilitating quick screening, interaction analysis, and stability evaluation of drug–target complexes. A possible option to get around the drawbacks of conventional drug development is to combine in silico techniques with drug repurposing tactics. While computational methods make it easier to find novel targets and optimize candidate compounds, repurposed medications benefit from known pharmacokinetic and safety profiles. While MD simulations confirm the dynamic stability and dependability of these connections under physiological settings, molecular docking offers a preliminary knowledge of binding affinity and interaction patterns. When combined, these techniques provide a strong framework for hastening the development of potent treatments. All things considered, the use of docking and MD simulations together has a great deal of promise for accelerating drug repurposing initiatives for DR-TB. However, thorough experimental validation and interdisciplinary cooperation are necessary for the effective translation of computational insights into therapeutic applications. It is anticipated that ongoing developments in computational technology and the incorporation of cutting-edge strategies like artificial intelligence would bolster the medication discovery process and aid in the worldwide battle against tuberculosis.
REFERENCES
Ananya Sangar*1, Sanika Sawant1, R. Patil, Advances in Computational Drug Repurposing for Drug-Resistant Tuberculosis: A Combined Molecular Docking and Molecular Dynamics Approach, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 5, 3761-3775. https://doi.org/10.5281/zenodo.20214751
10.5281/zenodo.20214751