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Drug-resistant species of tuberculosis (TB), which spread faster than traditiona TB, is a severely infectious disease. The conventional drug therapy used in the management of tuberculosis has several challenges linked with adverse effects. Hence, nanotherapeutics served as an emerging technique to overcome problems associated with current treatment. Nanotherapeutics helps to overcome toxicity and poor solubility issues of several drugs used in the management of tuberculosis. Due to their diameter and surface chemistry, nanocarriers encapsulated with antimicrobial drugs are readily taken up by macrophages. Macrophages play a crucial role as they serve as target sites for active and passive targeting for nanocarriers. The surface of the nanocarriers is coated with ligand-specific receptors, which further enhances drug concentration locally and indicates the therapeutic potential of nanocarriers. This review highlights tuberculosis’s current facts, figures, challenges associated with conventional treatment, different nanocarrier-based systems, and its application in vaccine development.

The present study describes the design and development of buoyant matrices of dipyridamole. The matrices were prepared by direct compression method using simplex lattice design as an optimization technique. Amount of HPMC K4M (X1), sodium bicarbonate (X2) and ethyl cellulose (X3) were used as the independent variables where floating lag time (Y1) and percentage drug release at 6h (Y2) were considered as the response variables. As per the simplex lattice design total 7 formulations were formulated. Matrices were evaluated for physical parameters, in-vitro buoyancy, in-vitro drug release, water uptake studies. Drug release data was fitted into different kinetic models. The results of response variables were statistically evaluated using design expert 8.0 software. Polynomial models were generated for all the response variables using multiple linear regression analysis (MLRA) approach. A statistical model incorporating 7 interactive terms was used to evaluate the responses. The results of response variables are expressed for model analysis by Scheffe’s special cubic model. Graphical representation was done by response surface plots and contour plots. The resulted model equation showed that factor X1 responsible for prolongation of drug release. On the basis of acceptance criteria the formulation coded by DP3 was selected as a promising formulation from the simplex lattice batches which fitted best to zero order release kinetic model.