Leprosy is a Neglected Tropical Disease (NTDs) caused by Mycobacterium leprae (M. leprae). The treatment is considered effective, however, the high dose Multidrug Therapy (MDT) for a long period and its adverse effects result in the abandonment of the treatment by patients. Indeed, antimicrobial resistance is still an obstacle that must be overcome in the treatment of leprosy. In the present article, we reviewed the WHO guidelines for the chemotherapy of leprosy and the methods of synthesis of these drugs.

BACKGROUND: Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly.
RESULTS: We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 186 mutations were gathered from the literature for developing the classifier, using 80% of the data as the training set and the rest as the test set. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modeling. Classifiers based on five ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes, probabilistic neural network and support vector machine, were first built, and a majority consensus (MC) approach was then used to obtain a new classifier based on the classifications of the five individual ML algorithms. The MC classifier comprehensively improved the predictive performance, with accuracy, F-measure and AUC of 0.78, 0.83 and 0.81for training set whilst 0.84, 0.87 and 0.83 for test set, respectively.
CONCLUSIONS: The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.

Aim: The macrolide antibiotic rifampicin is prescribed against several infections, like tuberculosis disease. This drug decays to rifampicin quinone. Results/methodology: The biological fluids were diluted in a micellar solution and directly injected. Using a C18 column and a mobile phase of 0.15 M SDS-6% 1-pentanol phosphate-buffered at pH 7, running at 1 ml/min, the analytes were resolved in less than 15 min. The detection was by absorbance at 337 nm. Method was validated by the guidelines of the European Medicines Agency. Decomposition of rifampicin to rifampicin quinone was also studied. Discussion/conclusion: Procedure is rapid, easy-to-handle, economic, eco-friendly and with a high sample throughput. It was successfully used to monitor rifampicin in the plasma and urine of tubercular patients.

暂无摘要。

The rpoB gene codes for the RNA polymerase β subunit, which is the target of rifampicin, an essential drug in the treatment of tuberculosis and other mycobacterial infections. This gene is present in all bacteria, but its length and nucleotide sequence vary between bacterial species, including mycobacteria. Mutations in the rpoB gene alter the structure of this protein and cause drug resistance. To describe the resistance-associated mutations, the scientific and medical communities have been using, since 1993, a numbering system based on the Escherichia coli sequence annotation. Using E. coli reference for describing mutations in mycobacteria leads to misunderstandings, particularly with the increasing use of whole genome sequencing, which brought an alternative numbering system based on the Mycobacterium tuberculosis rpoB sequence. We propose using a consensus numbering system for the reporting of resistance mutations based on the reference genomes from the species interrogated (such as strain H37Rv for M. tuberculosis). This manuscript provides the necessary figures and tables allowing researchers, microbiologists and clinicians to easily convert other annotation systems into one common language.