Effects of the encapsulation of usnic acid into liposomes and interactions with antituberculous agents against multidrug-resistant tuberculosis clinical isolates

Mycobacterium tuberculosis (Mtb) has acquired resistance and consequently the antibiotic therapeutic options available against this microorganism are limited. In this scenario, the use of usnic acid (UA), a natural compound, encapsulated into liposomes is proposed as a new approach in multidrug-resistant tuberculosis (MDR-TB) therapy. Thus the aim of this study was to evaluate the effect of the encapsulation of UA into liposomes, as well as its combination with antituberculous agents such as rifampicin (RIF) and isoniazid (INH) against MDR-TB clinical isolates. The in vitro antimycobacterial activity of UA-loaded liposomes (UA-Lipo) against MDR-TB was assessed by the microdilution method. The in vitro interaction of UA with antituberculous agents was carried out using checkerboard method. Minimal inhibitory concentration values were 31.25 and 0.98 μg/mL for UA and UA-Lipo, respectively. The results exhibited a synergistic interaction between RIF and UA [fractional inhibitory concentration index (FICI) = 0.31] or UA-Lipo (FICI = 0.28). Regarding INH, the combination of UA or UA-Lipo revealed no marked effect (FICI = 1.30-2.50). The UA-Lipo may be used as a dosage form to improve the antimycobacterial activity of RIF, a first-line drug for the treatment of infections caused by Mtb.

Pathway analysis for identification of potential biomarkers in severe cutaneous drug hypersensitivity reactions

Purpose: To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using pathways analysis in order to identify some potential biomarkers that may be used for early detection of SJS and epidermal necrolysis (TEN) manifestations. Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which are online software and databases. Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for topological analysis. It was found by geneMANIA database search that majority of these genes were coexpressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction with regards to hypersensitivity. Conclusion: Analysis of differential gene expressions by topological and database approaches in the current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed.