Journal of Tuberculosis and Therapeutics
Open Access

In the initial era of pre-genomics, it was estimated that about 8.7 million cases with tuberculosis were reported globally and that is an approximate of 125 cases reported per 100,000 individuals, and approximately about 1.4 million people died of the disease [1]. In order to The Stop the spread of TB, WHO recommends a standard 6-month regimen of four-drugs as the first-line therapy [2-4]. However, in some cases, the specified number cannot be administered within the targeted time period because of drug toxicity [2,5]. Furthermore, the drug induced injury in liver (DILI) can cause morbidity [6-8]. These adverse drug events can confer a substantial and additional costs associated with increased frequency of outpatient visits, laboratory tests and hospitalization in more serious cases. Second-line anti-TB medications could cause greater toxicity-related problems and are often less effective than the medications in first line and the treatment could be prolonged in spite of the attendant challenges to ensure compliance. As a result, there was a risk of treatment failure and relapse.

In the initial era of Post genomics [9], Pharmacogenetics (PGx)- based testing was anticipated to be vital across all specialties in the medical field, as a pillar towards the movement of the personalized medicine [10]. The PGx-based testing involves the assessment of risk with respect to the likelihood of patient response to a given drug to facilitating the selection of drug and its dosage [11]. The PGx-based testing is a relatively new field and will have an impact in the treatment of latent TB infection (LTBI).

Rifamycin was consistently utilized against Mycobacterium tuberculosis in both in vitro and in vivo models [12-15]. There variation of concentrations in rifamycin among patients on the standard therapy for tuberculosis and the basis for variation is not well understood. Advanced HIV infection has been associated with the low concentrations of rifampin, but marked as a difference in patients with tuberculosis and without HIV (Figures 1-3) [16-19].

Figure 1: Predicted Structure of HNF1A by Homology Modeling in Galaxy Server.

Figure 2: Validation of HNF1A in Rampage Server (97% of residues in allowed region of Ramachandran Plot).

Figure 3: Docked complex of Rifampin Analog (Zinc database Id 71049520) with the predicted structure of HNF1A in MTI AutoDock with a minimum energy of -5.96.

A novel approach to identify a therapeutic target was initiated in this manuscript. In this approach, two novel therapeutic targets (HNF1A and hsa-miR-511-5p) to diagnose and treat tuberculosis were identified by a rational approach of data mining with bioinformatics. Since we do not have a crystal structure of HNF1A, homology modeling was done and the best analog of rifampicin (Zinc database Id 71049520) was docked with HNF1A to find the compatibility of binding and the molecule have a maximum probability to be considered as a lead molecule in the process of ration drug design of tuberculosis.