Tuberculosis (TB) is one of the oldest, contagious, fatal, and pervasive respiratory infections caused by the gram-positive bacteria Mycobacterium tuberculosis (MTB) [1, 2]. In recent years, during the COVID-19 pandemic, TB has re-emerged as a major world health problem that causes severe impairment in patients who need long-term treatment [3]. The report of the World Health Organization (WHO) on TB suggests that the rate of infection and death trolls increased tremendously during this pandemic due to a significant rise in the frequency of multiple drug-resistant TB (MDR-TB) and extremely drug-resistant TB (XDR-TB) cases because of non-adhere and non-compliance towards the available drugs regimen by the patients[4, 5]. However, the emergence and spread of resistance to the currently available chemotherapeutic agents is a growing risk for the population worldwide, with increasingly favorable conditions for the bacteria including the HIV epidemic and other co-morbidities such as type 2 diabetes and low-quality life conditions in underdeveloped and economically backward countries, which indicates an urgent need for the development of drugs with shorter treatment time, simpler regimen, more potency and multi-targeted anti-tubercular agents which can be used against the drug-resistant forms of this disease [6–8]. To achieve this objective, we used a computer-based drug-designing approach having aims to identify potential drug candidates and targets against drug-resistant strains of MTB [9]. In this present work, we used computational techniques like atom-based three-dimensional quantitative structure active relationship (3D-QSAR), pharmacophore modeling, molecular docking, pharmacokinetic, dynamic, toxicity study, and molecular dynamic simulation study to identify potential multi-targeted drug candidates used to treat drug-resistant tuberculosis [10–12].
In recent decades many promiscuous drug targets for anti-tubercular action were reported but two targets, Enoyl acyl carrier protein reductase (InhA) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) are considered as the most clinically reproducible, effective and highly vulnerability targets for treatment against MTB, MDR-TB and XDR-TB [13–15]. In the present work an attempt has been made to identify new and effective antagonist towards these two vital druggable targets for the treatment of TB by computational approach.
The NADH-dependent enoyl-ACP reductase (InhA) enzyme, is clinically validated as the target of the frontline anti-TB drug isoniazid (INH) and second line drug ethionamide (ETA), encoded by the gene InhA of MTB [14]. The enzyme InhA catalyse the biosynthesis of mycolic acid which is the central constituent of mycobacterial cell wall (Fig. 01). Mycolic acid biosynthesis follows fatty acid synthase (FAS) pathway which involves two enzymatic system, fatty acid synthase I (FAS I) and fatty acid synthase II (FAS II) [16, 17]. In FAS I short chain fatty acids are produces while elongation of these chains takes place by FAS II pathway [18]. X-ray structure of InhA reveals that each subunits has several α-helices and β-strands which contain NADH binding site. In the final step of FAS II, InhA enzyme is responsible for reduction of double bond in the fatty acyl-ACP (acyl carrier protein) into the saturated fatty acyl-ACP which helps to carried out the final step of the fatty acid elongation process [19, 20]. Therefore, compounds that can directly inhibit InhA without any activation disrupts the biosynthesis of mycolic acid in the mycobacterium and ultimately lead to death of the organism [21]. Hence InhA inhibitors have a very promising opportunity towards the treatment of MTB, MDR-TB and XDR-TB [22].
Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) has been reported as a potential drug target for the treatment of TB. The heteromeric protein DprE1 is an essential component for growth and survival of mycobacterium (Fig. 01). Mycobacterial cell wall is composed of polysaccharide arabinogalactan, which is synthesised through DprE1 enzyme mediated redox reaction. During the reaction the oxidase enzyme DprE1 carried out conversion of decaprenylphosphoryl-d-ribose (DPR) to decaprenylphosphoryl- d-arabinose (DPA) by epimerization via an intermediate decaprenylphosphoryl-2-keto-β-derythro-pentofuranose (DPX) [23–25]. Inhibition of DprE1 disrupts the synthesis of arabinogalactan, weakening the bacterial cell wall and making the bacteria more susceptible towards the chemotherapeutic agents used for the treatment of MTB, MDR-TB and XDR-TB [26].
The process of development of new molecules using the virtual screening workflow has a crucial significance due to the addition of artificial intelligence (AI) and machine learning (ML) [27]. Identifying hit molecules through computational drug discovery has proved to be a meaningful methodology in the recent years [28]. Among the various ways of drug design and discovery, structure based similarity search and screening is a key concept which now has been routinely used in the designing and discovery of new chemotherapy molecules [29]. Similarity search is based on the concept that the two molecules having structural similarity shares similar properties and biological action [30]. Thus finding molecules similar to a known active molecule is one of the key towards drug discovery. Drug discovery based on similarity search improve the odds of researchers of finding more active molecules at the lowest cost and with the highest probability of success [30, 31]. Now a days involvement of different in silico modules of computer aided drug designing (CADD) like 3D-QSAR, molecular docking, ADME-T prediction and simulation study gain enormous importance and helps in a great deal towards finding of most effective drug compounds for a particular drug target of any disease [32]. In this regard we have performed the study on all 58, 2-nitroimidazooxazines derivatives anti-tubercular agents through the use of CADD techniques to detect and identify highly effective multi targeted drug candidates that will produce more stable chemical bonding with the two most potential protein targets InhA and DprE1 of mycobacterium for the treatment of tuberculosis.
In the first phase of work, we performed atom-based 3D-QSAR and ligand-based pharmacophore hypotheses to identify the features responsible for the biological activity of the data set compounds for anti-tubercular function. Subsequently, molecular docking study was performed for the ligands to establish the intermolecular interaction of ligands towards the amino acid residues at the active site of the two target proteins InhA and DprE1.
In the second phase, virtual screening of pubchem database was carried out by taking the best docked compound from the series as reference compound for finding the structurally similar compounds. The selected compounds were then screened by their docking results with the two target proteins InhA and DprE1. Based on docking results, the screened compounds were finally subjected to the study of ADME-Tox and drug likeliness applying the Lipinski rule of five. The work has concluded with molecular dynamic simulation study and density functional theory analysis to investigate the stability and reactivity of the identified ligand within the protein- ligand complex against InhA and DprE1 proteins.