An Analysis of Failure of Category II DOTS Therapy

Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) impedes the End TB Strategy by the World Health Organization aiming for zero deaths, disease, and suffering at the hands of tuberculosis (TB). Mutations within anti-TB drug targets play a major role in conferring drug resistance within Mtb; hence, computational methods and tools are being used to understand the mechanisms by which they facilitate drug resistance. In this article, computational techniques such as molecular docking and molecular dynamics are applied to explore point mutations and their roles in affecting binding affinities for anti-TB drugs, often times lowering the protein’s affinity for the drug. Advances and adoption of computational techniques, chemoinformatics, and bioinformatics in molecular biosciences and resources supporting machine learning techniques are in abundance, and this has seen a spike in its use to predict mutations in Mtb. This article highlights the importance of molecular modeling in deducing how point mutations in proteins confer resistance through destabilizing binding sites of drugs and effectively inhibiting the drug action.

Noncompliance to DOTS: How it can be Decreased

BACKGROUND

Tuberculosis is a communicable disease requiring prolonged treatment.The therapeutic regimens as recommended by WHO have been shown to be highly effective for both preventing and treating tuberculosis but poor adherence to medication is a major barrier to its global control.

AIM

The aim was to elicit reasons of treatment default from a cohort of tuberculosis patients treated under Directly Observed Treatment Short course chemotherapy.

SETTINGS AND DESIGN

Thiscross-sectional study was conducted in Agra city using the multistage simple random sampling.

MATERIALS AND METHODS

A total of 900 patients attending DOTS centres of the selected designated microscopy centers (DMCs) were included in the study from January 2007 onward. The information was obtained from treatment cards of patients and those who defaulted were further interviewed in community.

STATISTICAL ANALYSIS

Chi-square test was applied to observe the significance of association using the Epi Info software (version 6).

RESULTS

More default was observed among the age group of >45 years (22.8%), male (18.7%), business men (30.6%), and retired and unemployed patients. Other factors associated with higher default were pulmonary disease (18.2%), retreatment cases (30.6%) and category II patients (26.4%). Important reasons of default were side effects following medication (43.2%), improvement in symptoms (14.4%), and lack of time (13.5%). No relief in symptoms and lack of awareness were other important reasons.

CONCLUSIONS

Noncompliance was found to be mainly due to side effects of medicines, lack of time, and unawareness. So educating the patient about various aspects of tuberculosis and some measures to decrease side effects are of utmost importance.