In Silico Strategies in Tuberculosis Drug Discovery

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis Reveal Insights into the Molecular Mechanism of Cordia myxa in the Treatment of Liver Cancer

Traditional treatments of cancer have faced various challenges, including toxicity, medication resistance, and financial burdens. On the other hand, bioactive phytochemicals employed in complementary alternative medicine have recently gained interest due to their ability to control a wide range of molecular pathways while being less harmful. As a result, we used a network pharmacology approach to study the possible regulatory mechanisms of active constituents of Cordia myxa for the treatment of liver cancer (LC). Active constituents were retrieved from the IMPPAT database and the literature review, and their targets were retrieved from the STITCH and Swiss Target Prediction databases. LC-related targets were retrieved from expression datasets (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790) through gene expression omnibus (GEO). The DAVID Gene Ontology (GO) database was used to annotate target proteins, while the Kyoto Encyclopedia and Genome Database (KEGG) was used to analyze signaling pathway enrichment. STRING and Cytoscape were used to create protein-protein interaction networks (PPI), while the degree scoring algorithm of CytoHubba was used to identify hub genes. The GEPIA2 server was used for survival analysis, and PyRx was used for molecular docking analysis. Survival and network analysis revealed that five genes named heat shot protein 90 AA1 (HSP90AA1), estrogen receptor 1 (ESR1), cytochrome P450 3A4 (CYP3A4), cyclin-dependent kinase 1 (CDK1), and matrix metalloproteinase-9 (MMP9) are linked with the survival of LC patients. Finally, we conclude that four extremely active ingredients, namely cosmosiin, rosmarinic acid, quercetin, and rubinin influence the expression of HSP90AA1, which may serve as a potential therapeutic target for LC. These results were further validated by molecular dynamics simulation analysis, which predicted the complexes with highly stable dynamics. The residues of the targeted protein showed a highly stable nature except for the N-terminal domain without affecting the drug binding. An integrated network pharmacology and docking study demonstrated that C. myxa had a promising preventative effect on LC by working on cancer-related signaling pathways.

The Structural and Molecular Mechanisms of Mycobacterium tuberculosis Translational Elongation Factor Proteins

Targeting translation factor proteins holds promise for developing innovative anti-tuberculosis drugs. During protein translation, many factors cause ribosomes to stall at messenger RNA (mRNA). To maintain protein homeostasis, bacteria have evolved various ribosome rescue mechanisms, including the predominant trans-translation process, to release stalled ribosomes and remove aberrant mRNAs. The rescue systems require the participation of translation elongation factor proteins (EFs) and are essential for bacterial physiology and reproduction. However, they disappear during eukaryotic evolution, which makes the essential proteins and translation elongation factors promising antimicrobial drug targets. Here, we review the structural and molecular mechanisms of the translation elongation factors EF-Tu, EF-Ts, and EF-G, which play essential roles in the normal translation and ribosome rescue mechanisms of Mycobacterium tuberculosis (Mtb). We also briefly describe the structure-based, computer-assisted study of anti-tuberculosis drugs.

In silico Screening of Food and Drug Administration-approved Compounds against Trehalose 2-sulfotransferase (Rv0295c) in Mycobacterium tuberculosis: Insights from Molecular Docking and Dynamics Simulations

BACKGROUND

Tuberculosis (TB) remains a prominent global health challenge, distinguished by substantial occurrences of infection and death. The upsurge of drug-resistant TB strains underscores the urgency to identify novel therapeutic targets and repurpose existing compounds. Rv0295c is a potentially druggable enzyme involved in cell wall biosynthesis and virulence. We evaluated the inhibitory activity of Food and Drug Administration (FDA)-approved compounds against Rv0295c of Mycobacterium tuberculosis, employing molecular docking, ADME evaluation, and dynamics simulations.

METHODS

The study screened 1800 FDA-approved compounds and selected the top five compounds with the highest docking scores. Following this, we subjected the initially screened ligands to ADME analysis based on their dock scores. In addition, the compound exhibited the highest binding affinity chosen for molecular dynamics (MD) simulation to investigate the dynamic behavior of the ligand-receptor complex.

RESULTS

Dihydroergotamine (CHEMBL1732) exhibited the highest binding affinity (-12.8 kcal/mol) for Rv0295c within this set of compounds. We evaluated the stability and binding modes of the complex over extended simulation trajectories.

CONCLUSION

Our in silico analysis demonstrates that FDA-approved drugs can serve as potential Rv0295c inhibitors through repurposing. The combination of molecular docking and MD simulation offers a comprehensive understanding of the interactions between ligands and the protein target, providing valuable guidance for further experimental validation. Identifying Rv0295c inhibitors may contribute to new anti-TB drugs.

Combined Structure- and Ligand-Based Approach for the Identification of Inhibitors of AcrAB-TolC in Escherichia coli

The inhibition of efflux pumps is a promising approach to combating multidrug-resistant bacteria. We have developed a combined structure- and ligand-based model, using OpenEye software, for the identification of inhibitors of AcrB, the inner membrane protein component of the AcrAB-TolC efflux pump in Escherichia coli. From a database of 1391 FDA-approved drugs, 23 compounds were selected to test for efflux inhibition in E. coli. Seven compounds, including ivacaftor (25), butenafine (19), naftifine (27), pimozide (30), thioridazine (35), trifluoperazine (37), and meloxicam (26), enhanced the activity of at least one antimicrobial substrate and inhibited the efflux pump-mediated removal of the substrate Nile Red from cells. Ivacaftor (25) inhibited efflux dose dependently, had no effect on an E. coli strain with genomic deletion of the gene encoding AcrB, and did not damage the bacterial outer membrane. In the presence of a sub-minimum inhibitory concentration (MIC) of the outer membrane permeabilizer colistin, ivacaftor at 1 μg/mL reduced the MICs of erythromycin and minocycline by 4- to 8-fold. The identification of seven potential AcrB inhibitors shows the merits of a combined structure- and ligand-based approach to virtual screening.

Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis

The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents, antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

Identification of natural potent inhibitors against Mycobacterium tuberculosis isocitrate lyase: an in silico study

Tuberculosis (TB) is a global burden to humanity due to its adverse effects on health and society since time is not clearly defined. The existence of drug-resistant strains and the potential threat posed by latent tuberculosis act as strong impetuses for developing novel anti-tuberculosis drugs. In this study, various flavonoids were tested against the Mycobacterium tuberculosis (Mtb) Isocitrate Lyase (ICL), which has been identified as an authorised therapeutic target for treating Mtb infection. Using in silico drug discovery approach, a library of 241 flavonoid compounds was virtually screened against the binding pocket of the crystalline ligand, the VGX inhibitor, in the Mtb ICL protein. As a result, the top four flavonoids were selected based on binding score and were further considered for redocking and intermolecular contact profiling analysis. The global and local fluctuations in the protein and ligand structure were analysed using their root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values obtained from the GROMACS generated 100 ns molecular dynamics (MD) simulation trajectories. The end-state binding free energy was also calculated using the MMPBSA approach for all the respective docked complexes. All four selected compounds exhibited considerable stability and affinity compared to control ligands, i.e. VGX inhibitor; however, Vaccarin showed the highest stability and affinity against the Mtb ICL protein active site, followed by the Genistin, Glabridin, and Corylin. Therefore, this study recommends selected flavonoids for in vitro and in vivo experimental studies to check their potency and efficacy against Mtb.

An Update on Toll-like Receptor 2, Its Function and Dimerization in Pro- and Anti-Inflammatory Processes

While a certain level of inflammation is critical for humans to survive infection and injury, a prolonged inflammatory response can have fatal consequences. Pattern recognition Toll-like receptors (TLRs) are key players in the initiation of an inflammatory process. TLR2 is one of the most studied pattern recognition receptors (PRRs) and is known to form heterodimers with either TLR1, TLR4, TLR6, and TLR10, allowing it to recognize a wide range of pathogens. Although a large number of studies have been conducted over the past decades, there are still many unanswered questions regarding TLR2 mechanisms in health and disease. In this review, we provide an up-to-date overview of TLR2, including its homo- and heterodimers. Furthermore, we will discuss the pro- and anti-inflammatory properties of TLR2 and recent findings in prominent TLR2-associated infectious and neurodegenerative diseases.

Target Identification in Anti-Tuberculosis Drug Discovery

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that, although preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance, and uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limiting factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bacterial proteins are targeted during the biosynthesis of the cell wall and the energetic metabolism of two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and explains how chemical probes for protein profiling enabled the identification of new targets and biomarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents

This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative's Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R2 of 0.9092, R2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Study of the Rv1417 and Rv2617c Membrane Proteins and Their Interactions with Nicotine Derivatives as Potential Inhibitors of Erp Virulence-Associated Factor in Mycobacterium tuberculosis: An In Silico Approach

The increasing emergence of Mycobacterium tuberculosis (Mtb) strains resistant to traditional anti-tuberculosis drugs has alarmed health services worldwide. The search for new therapeutic targets and effective drugs that counteract the virulence and multiplication of Mtb represents a challenge for the scientific community. Several studies have considered the erp gene a possible therapeutic target in the last two decades, since its disruption negatively impacts Mtb multiplication. This gene encodes the exported repetitive protein (Erp), which is located in the cell wall of Mtb. In vitro studies have shown that the Erp protein interacts with two putative membrane proteins, Rv1417 and Rv2617c, and the impairment of their interactions can decrease Mtb replication. In this study, we present five nicotine analogs that can inhibit the formation of heterodimers and trimers between these proteins. Through DFT calculations, molecular dynamics, docking, and other advanced in silico techniques, we have analyzed the molecular complexes, and show the effect these compounds have on protein interactions. The results show that four of these analogs can be possible candidates to counteract the pathogenicity of Mtb. This study aims to combine research on the Erp protein as a therapeutic target in the search for new drugs that serve to create new therapies against tuberculosis disease.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds

The cell wall of Mycobacterium tuberculosis and related organisms has a very complex and unusual organization that makes it much less permeable to nutrients and antibiotics, leading to the low activity of many potential antimycobacterial drugs against whole-cell mycobacteria compared to their isolated molecular biotargets. The ability to predict and optimize the cell wall permeability could greatly enhance the development of novel antitubercular agents. Using an extensive structure-permeability dataset for organic compounds derived from published experimental big data (5371 compounds including 2671 penetrating and 2700 non-penetrating compounds), we have created a predictive classification model based on fragmental descriptors and an artificial neural network of a novel architecture that provides better accuracy (cross-validated balanced accuracy 0.768, sensitivity 0.768, specificity 0.769, area under ROC curve 0.911) and applicability domain compared with the previously published results.

Assay development and inhibition of the Mt-DprE2 essential reductase from Mycobacterium tuberculosis

DprE2 is an essential enzyme in the synthesis of decaprenylphosphoryl-β-d-arabinofuranose (DPA) and subsequently arabinogalactan, and is a significant new drug target for M. tuberculosis. Two compounds from the GSK-177 box set, GSK301A and GSK032A, were identified through Mt-DprE2-target overexpression studies. The Mt-DprE1-DprE2 complex was co-purified and a new in vitro DprE2 assay developed, based on the oxidation of the reduced nicotinamide adenine dinucleotide cofactor of DprE2 (NADH/NADPH). The Mt-DprE1-DprE2 complex showed interesting kinetics in both the DprE1 resazurin-based assay, where Mt-DprE2 was found to enhance Mt-DprE1 activity and reduce substrate inhibition; and also in the DprE2 assay, which similarly exhibited substrate inhibition and a difference in kinetics of the two potential cofactors, NADH and NADPH. Although, no inhibition was observed in the DprE2 assay by the two GSK set compounds, spontaneous mutant generation indicated a possible explanation in the form of a pro-drug activation pathway, involving fgd1 and fbiC.

Advances in the design of combination therapies for the treatment of tuberculosis

INTRODUCTION

Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects.

AREAS COVERED

This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken.

EXPERT OPINION

We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

Glycosylated Flavonoid Compounds as Potent CYP121 Inhibitors of Mycobacterium tuberculosis

Due to the concerning rise in the number of multiple- and prolonged-drug-resistant (MDR and XDR) Mycobacterium tuberculosis (Mtb) strains, unprecedented demand has been created to design and develop novel therapeutic drugs with higher efficacy and safety. In this study, with a focused view on implementing an in silico drug design pipeline, a diverse set of glycosylated flavonoids were screened against the Mtb cytochrome-P450 enzyme 121 (CYP121), which is established as an approved drug target for the treatment of Mtb infection. A total of 148 glycosylated flavonoids were screened using structure-based virtual screening against the crystallized ligand, i.e., the L44 inhibitor, binding pocket in the Mtb CYP121 protein. Following this, only the top six compounds with the highest binding scores (kcal/mol) were considered for further intermolecular interaction and dynamic stability using 100 ns classical molecular dynamics simulation. These results suggested a considerable number of hydrogen and hydrophobic interactions and thermodynamic stability in comparison to the reference complex, i.e., the CYP121-L44 inhibitor. Furthermore, binding free energy via the MMGBSA method conducted on the last 10 ns interval of MD simulation trajectories revealed the substantial affinity of glycosylated compounds with Mtb CYP121 protein against reference complex. Notably, both the docked poses and residual energy decomposition via the MMGBSA method demonstrated the essential role of active residues in the interactions with glycosylated compounds by comparison with the reference complex. Collectively, this study demonstrates the viability of these screened glycosylated flavonoids as potential inhibitors of Mtb CYP121 for further experimental validation to develop a therapy for the treatment of drug-resistant Mtb strains.

Synthesis, Antimicrobial Activity, and Molecular Modeling Studies of Some Benzoxazole Derivatives

Background:

The need to develop novel antimicrobial agents is apparent as infectious diseases are increasing and resistance is rapidly developing against the drugs used in the treatment.

Objective:

This study aimed at the synthesis, antimicrobial susceptibility testing, and computational elucidation of the mechanism of action of benzoxazole derivatives. It also aimed to compare the results obtained in this study with the previous studies by our group. This would pave the way for designing novel molecules with better antimicrobial activity. The other goal was pharmacophore analysis and in silico ADMET analysis of them.

Methods:

In this study, synthesis, antimicrobial susceptibility testing, molecular docking, pharmacophore analysis, and ADMET prediction were carried out.

Results:

The antimicrobial activity studies demonstrated that the synthesized compounds were active against standard strains and clinical isolates at high concentrations. Then, the antimicrobial testing results were compared to similar benzoxazoles tested by our group previously. Benzoxazole derivatives without a methylene bridge between oxazole and phenyl ring were found to be more active than those with the methylene bridge. This was also confirmed by molecular modeling undertaken in this study. The computational results indicated that the antibacterial activity could be achieved by DNA gyrase inhibition. Pharmacophore analysis showed that hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and hydrophobicity features would contribute to the inhibition. In addition, in silico ADMET property investigation of the compounds exhibited that they had the desired pharmacokinetics.

Conclusion:

Although antibacterial activity by inhibiting DNA gyrase is selective, the synthesized compounds were active at much higher concentrations than the standards. Therefore, in prospective antimicrobial studies, it is better to focus on benzoxazole derivatives without the methylene bridge. Since the compounds had suitable in silico ADMET properties, screening them against the other pharmacologic activities should be carried out. It is recommended to support the molecular modeling results with in vitro or in vivo studies.

Discovery of new riminophenazine analogues as antimycobacterial agents against drug-resistant Mycobacterium tuberculosis

Twenty-three new riminophenazine and pyrido[3,2-b]quinoxaline derivatives were prepared and examined for their antimycobacterial activities against Mycobacterium marinum and Mycobacterium tuberculosis H37Rv, taking clofazimine (1) as the lead. Structure-activity relationship (SAR) analysis revealed that the introduction of a heterocycle or diethylamine substituted benzene moiety on the N-5 atom might be beneficial for activity. The most potent compound 7m also displayed enhanced activity against wild-type as well as multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB clinical isolates, with the MICs ranging from 0.08 to 1.25 μg/mL, especially effective toward strain M20A507, resistant to 1. Further mechanism study indicated that its anti-TB activity was independent of cell membrane disruption, but related to NDH-2 reduction and the resulting high ROS production. Our study provides instructive guidance for the further development of clofazimine derivatives into promising antimicrobial agents against MDR and XDR TB.

Antibiotic resistant bacteria: current situation and treatment options to accelerate the development of a new antimicrobial arsenal

INTRODUCTION

Antibiotic resistance is one of the biggest public health threats worldwide. Currently, antibiotic-resistant bacteria kill 700,000 people every year. These data represent the near future in which we find ourselves, a "post-antibiotic era" where the identification and development of new treatments are key. This review is focused on the current and emerging antimicrobial therapies which can solve this global threat.

AREAS COVERED

Through a literature search using databases such as Medline and Web of Science, and search engines such as Google Scholar, different antimicrobial therapies were analyzed, including pathogen-oriented therapy, phagotherapy, microbiota and antivirulent therapy. Additionally, the development pathways of new antibiotics were described, emphasizing on the potential advantages that the combination of a drug repurposing strategy with the application of mathematical prediction models could bring to solve the problem of AMRs.

EXPERT OPINION

This review offers several starting points to solve a single problem: reducing the number of AMR. The data suggest that the strategies described could provide many benefits to improve antimicrobial treatments. However, the development of new antimicrobials remains necessary. Drug repurposing, with the application of mathematical prediction models, is considered to be of interest due to its rapid and effective potential to increase the current therapeutic arsenal.

Structural and functional characterization of a hypothetical protein in the RD7 region in clinical isolates of Mycobacterium tuberculosis - an in silico approach to candidate vaccines

Background

Mycobacterium tuberculosis has been ravaging humans by inflicting respiratory tuberculosis since centuries. Bacillus Calmette Guerine (BCG) is the only vaccine available for tuberculosis, and it is known to be poorly effective against adult tuberculosis. Proteins belonging to the ESAT-6 family and PE/PPE family show immune responses and are included in different vaccine trials. Herein, we study the functional and structural characterization of a 248 amino acid long putative protein novel hypothetical protein 1 (NHP1) present in the RD7 region of Mycobacterium tuberculosis (identified first by subtractive hybridization in the clinical isolate RGTB123) using bioinformatics tools.

Results

Physicochemical properties were studied using Expasy ProtParam and SMS software. We predicted different B-cell and T-cell epitopes by using the immune epitope database (IEDB) and also tested antigenicity, immunogenicity, and allergenicity. Secondary structure of the protein predicted 30% alpha helices, 20% beta strands, and 48% random coils. Tertiary structure of the protein was predicted using the Robetta server using the Mycobacterium smegmatis protein as the putative protein with homology. Structural evaluations were done with Ramachandran plot analysis, ProSA-web, and VERIFY3D, and with GalaxyWEB server, a more stable structure was validated with good stereo chemical properties.

Conclusion

The present study of a subtracted genomic locus using various bioinformatics tools indicated good immunological properties of the putative mycobacterial protein, NHP1. Evidence obtained from the analyses of NHP1 using structure prediction tools strongly point to the fact that NHP1 is an ancient protein having flavodoxin folding structure with ATP binding sites. Positive scores were obtained for antigenicity, immunogenicity, and virulence too, implying the possibility of NHP1 to be a potential vaccine candidate. Such computational studies might give clues for developing newer vaccines for tuberculosis, which is the need of the hour.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00340-5.

In Silico Drug Discovery Strategies Identified ADMET Properties of Decoquinate RMB041 and Its Potential Drug Targets against Mycobacterium tuberculosis

The highly adaptive cellular response of Mycobacterium tuberculosis to various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently, in silico drug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions between M. tuberculosis and decoquinate RMB041, a propitious new antituberculosis compound. Two of the 13 identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormant M. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated with the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 against M. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormant M. tuberculosis. This compound has previously showed promising in vitro safety and good oral bioavailability, which were both supported by this in silico study. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication.

IMPORTANCE This article elaborates on the mechanism of action of a novel antibiotic compound against both, active and dormant Mycobacterium tuberculosis and describes its pharmacokinetics (including oral bioavailability and toxicity). Information provided in this article serves useful during the search for drugs that shorten the treatment regimen for Tuberculosis and cause minimal adverse effects.

Identification of new putative inhibitors of Mycobacterium tuberculosis 3-dehydroshikimate dehydratase from a combination of ligand- and structure-based and deep learning in silico approaches

The development of new drugs against Mycobacterium tuberculosis is an essential strategy for fighting drug resistance. Although 3-dehydroquinate dehydratase (MtDHQ) is known to be a highly relevant target for M. tuberculosis, current research shows new putative inhibitors of MtDHQ selected by a large-scale ensemble-docking strategy combining ligand- and target-based chemoinformatic methods to deep learning. Initial chemical library was reduced from 216 million to approximately 460 thousand after pharmacophore, toxicity and molecular weight filters. Final library was subjected to an ensemble-docking protocol in GOLD which selected the top 300 molecules (GHITS). GHITS displayed different structures and characteristics when compared to known inhibitors (KINH). GHITS were further screened by post-docking analysis in AMMOS2 and deep learning virtual screening in DeepPurpose. DeepPurpose predicted that a number of GHITS had comparable or better affinity for the target than KINH. The best molecule was selected by consensus ranking using GOLD, AMMOS2 and DeepPurpose scores. Molecular dynamics revealed that the top hit displayed consistent and stable binding to MtDHQ, making strong interactions with active-site loop residues. Results forward new putative inhibitors of MtDHQ and reinforce the potential application of artificial intelligence methods for drug design. This work represents the first step in the validation of these molecules as inhibitors of MtDHQ.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Antimicrobial resistance: progress and challenges in antibiotic discovery and anti-infective therapy

The alarming rise in the emergence of antimicrobial resistance in human, animal and plant pathogens is challenging global health and food production. Traditional strategies used for antibiotic discovery persistently result in the re‐isolation of known compounds, calling for the need to develop more rational strategies to identify new antibiotics. Additionally, anti‐infective therapy approaches targeting bacterial signalling pathways related to virulence is emerging as an alternative to the use of antibiotics. In this perspective article, we critically analyse approaches aimed at revitalizing the identification of new antibiotics and to advance antivirulence therapies. The development of high‐throughput in vivo, in vitro and in silico platforms, together with the progress in chemical synthesis, analytical chemistry and structural biology, are reviving a research area that is of tremendous relevance for global health.

In silico drug discovery for a complex immunotherapeutic target - human c-Rel protein

Target evaluation and rational drug design rely on identifying and characterising small-molecule binding sites on therapeutically relevant target proteins. Immunotherapeutics development is especially challenging because of complex disease etiology and heterogenous nature of targets. c-Rel protein, a promising target in many human inflammatory and cancer pathologies, was selected as a case study for an effective in silico screening platform development since this transcription factor currently has no successful therapeutic inhibitors or modulators. This study introduces a novel in silico screening approach to probe binding sites using structural validation sets, molecular modelling and describes a method of a computer-aided drug design when a crystal structure is not available for the target of interest. In addition, we showed that binding sites can be analysed with the machine learning as well as molecular simulation approaches to help assess and systematically analyse how drug candidates can exert their mode of action. Finally, this cutting-edge approach was subjected to a high through-put virtual screen of selected 34 M drug-like compounds filtered from a library of 659 M compounds by identifying the most promising structures and proposing potential action mechanisms for the future development of highly selective human c-Rel inhibitors and/or modulators.

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.

Drug Resistance in Nontuberculous Mycobacteria: Mechanisms and Models

The genus Mycobacteria comprises a multitude of species known to cause serious disease in humans, including Mycobacterium tuberculosis and M. leprae, the responsible agents for tuberculosis and leprosy, respectively. In addition, there is a worldwide spike in the number of infections caused by a mixed group of species such as the M. avium, M. abscessus and M. ulcerans complexes, collectively called nontuberculous mycobacteria (NTMs). The situation is forecasted to worsen because, like tuberculosis, NTMs either naturally possess or are developing high resistance against conventional antibiotics. It is, therefore, important to implement and develop models that allow us to effectively examine the fundamental questions of NTM virulence, as well as to apply them for the discovery of new and improved therapies. This literature review will focus on the known molecular mechanisms behind drug resistance in NTM and the current models that may be used to test new effective antimicrobial therapies.

Mycobacterial drug discovery

Mycobacterium tuberculosis is the causative pathogen of the pulmonary disease tuberculosis. Despite the availability of effective treatment programs, there is a global pursuit of new anti-tubercular agents to respond to the developing threat of drug resistance, in addition to reducing the extensive duration of chemotherapy and any associated toxicity. The route to mycobacterial drug discovery can be considered from two directions: target-to-drug and drug-to-target. The former approach uses conventional methods including biochemical assays along with innovative computational screens, but is yet to yield any drug candidates to the clinic, with a high attrition rate owing to lack of whole cell activity. In the latter approach, compound libraries are screened for efficacy against the bacilli or model organisms, ensuring whole cell activity, but here subsequent target identification is the rate-limiting step. Advances in a variety of scientific fields have enabled the amalgamation of aspects of both approaches in the development of novel drug discovery tools, which are now primed to accelerate the discovery of novel hits and leads with known targets and whole cell activity. This review discusses these traditional and innovative techniques, which are widely used in the quest for new anti-tubercular compounds.

Innovations in mycobacterial drug discovery to accelerate the identification of new drug candidates with confirmed targets and whole cell activity.