Influence of phenothiazines, phenazines and phenoxazine on cation transport in Candida albicans

Efficacy of Toluidine Blue-Mediated Antimicrobial Photodynamic Therapy on Candida spp. A Systematic Review

The effectiveness of antimicrobial photodynamic therapy (aPDT) in the treatment of oral yeast infections was examined many times in recent years. The authors of this review tried to address the question: "Should TBO (toluidine blue ortho)-mediated aPDT be considered a possible alternative treatment for oral candidiasis?". PubMed/Medline and the Cochrane Central Register of Controlled Trials (CEN-TRAL) databases were searched from 1997 up to the 27th of October 2020 using a combination of the following keywords: (Candida OR Candidiasis oral OR Candidosis oral OR denture stomatitis) AND (toluidine blue OR photodynamic therapy OR aPDT OR photodynamic antimicrobial chemotherapy OR PACT OR photodynamic inactivation OR PDI). Animal studies or in vitro studies involving Candida albicans (C. albicans) and/or nonalbicans stain, randomized clinical trials (RCT) involving patients with oral candidiasis or denture stomatitis published solely in English language were included. Candida elimination method in animal, in vitro studies and RCT used was TBO-mediated aPDT. Exactly 393 studies were taken into consideration. Then, after analyzing titles and abstracts of said studies, 361 were excluded. Only 32 studies ended up being selected for in-depth screening, after which 21 of them were included in this study. All studies reported the antifungal effectiveness of aPDT with TBO against C. albicans and non-albicans Candida. In studies conducted with planktonic cells, only one study showed eradication of C. albicans. All others showed partial elimination and only one of them was not statistically significant. Experiments on yeast biofilms, in all cases, showed partial, statistically significant cell growth inhibition and weight reduction (a reduction in the number of cells-mainly hyphae) and the mass of extracellular polymeric substance (EPS). In vivo aPDT mediated by TBO exhibits antifungal effects against oral Candida spp.; however, its clinical effectiveness as a potent therapeutic strategy for oral yeast infections requires further investigation.

Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs

The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds.