Vanoxerine kills mycobacteria through membrane depolarization and efflux inhibition.

Mycobacterium tuberculosis is a deadly pathogen, currently the leading cause of death worldwide from a single infectious agent through tuberculosis infections. If the End TB 2030 strategy is to be achieved, additional drugs need to be identified and made available to supplement the current treatment regimen. In addition, drug resistance is a growing issue, leading to significantly lower treatment success rates, necessitating further drug development. Vanoxerine (GBR12909), a dopamine re-uptake inhibitor, was recently identified as having anti-mycobacterial activity during a drug repurposing screening effort. However, its effects on mycobacteria were not well characterized. Herein, we report vanoxerine as a disruptor of the membrane electric potential, inhibiting mycobacterial efflux and growth. Vanoxerine had an undetectable level of resistance, highlighting the lack of a protein target. This study suggests a mechanism of action for vanoxerine, which will allow for its continued development or use as a tool compound.

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis.

Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.

Anticancer Ruthenium Complexes with HDAC Isoform Selectivity.

The histone deacetylase (HDAC) enzymes have emerged as an important class of molecular targets in cancer therapy, with five inhibitors in clinical use. Recently, it has been shown that a lack of selectivity between the 11 Zn-dependent HDAC isoforms may lead to unwanted side-effects. In this paper, we show that piano stool Ru complexes can act as HDAC inhibitors, and variation in the capping arene leads to differences in HDAC isoform selectivity.

A Simple Polymicrobial Biofilm Keratinocyte Colonization Model for Exploring Interactions Between Commensals, Pathogens and Antimicrobials.

Skin offers protection against external insults, with the skin microbiota playing a crucial defensive role against pathogens that gain access when the skin barrier is breached. Linkages between skin microbes, biofilms and disease have not been well established although single-species biofilm formation by skin microbiota in vitro has been extensively studied. Consequently, the purpose of this work was to optimize and validate a simple polymicrobial biofilm keratinocyte model for investigating commensal, pathogen and keratinocyte interactions and for evaluating therapeutic agents or health promoting interventions. The model incorporates the commensals (Staphylococcus epidermidis and Micrococcus luteus) and pathogens (Staphylococcus aureus and Pseudomonas aeruginosa) which form robust polymicrobial biofilms on immortalized keratinocytes (HaCat cells). We observed that the commensals reduce the damage caused to the keratinocyte monolayer by either pathogen. When the commensals were combined with P. aeruginosa and S. aureus, much thinner biofilms were observed than those formed by the pathogens alone. When P. aeruginosa was inoculated with S. epidermidis in the presence or absence of M. luteus, the commensals formed a layer between the keratinocytes and pathogen. Although S. aureus completely inhibited the growth of M. luteus in dual-species biofilms, inclusion of S. epidermidis in triple or quadruple species biofilms, enabled M. luteus to retain viability. Using this polymicrobial biofilm keratinocyte model, we demonstrate that a quorum sensing (QS) deficient S. aureus agr mutant, in contrast to the parent, failed to damage the keratinocyte monolayer unless supplied with the exogenous cognate autoinducing peptide. In addition, we show that treatment of the polymicrobial keratinocyte model with nanoparticles containing an inhibitor of the PQS QS system reduced biofilm thickness and P. aeruginosa localization in mono- and polymicrobial biofilms.