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is one of the most important human pathogens that is responsible for a variety of diseases ranging from skin and soft tissue infections to endocarditis and sepsis. In recent decades, the treatment of staphylococcal infections has become increasingly difficult as the prevalence of multi-drug resistant strains continues to rise. With increasing mortality rates and medical costs associated with drug resistant strains, there is an urgent need for alternative therapeutic options. Many innovative strategies for alternative drug development are being pursued, including disruption of biofilms, inhibition of virulence factor production, bacteriophage-derived antimicrobials, anti-staphylococcal vaccines, and light-based therapies. While many compounds and methods still need further study to determine their feasibility, some are quickly approaching clinical application and may be available in the near future.
Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.
Secondary metabolite discovery requires an unbiased, comprehensive workflow to detect unknown unknowns for which little to no molecular knowledge exists. Untargeted mass spectrometry-based metabolomics is a powerful platform, particularly when coupled with ion mobility for high-throughput gas-phase separations to increase peak capacity and obtain gas-phase structural information. Ion mobility data are described by the amount of time an ion spends in the drift cell, which is directly related to an ion's collision cross section (CCS). The CCS parameter describes the size, shape, and charge of a molecule and can be used to characterize unknown metabolomic species. Here, we describe current and emerging applications of ion mobility-mass spectrometry for prioritization, discovery and structure elucidation, and spatial/temporal characterization.
Copyright © 2017 Elsevier Ltd. All rights reserved.
Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.
The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure-activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model's performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.
The cellular responses that occur following acute kidney injury (AKI) are complex and dynamic, involving multiple cells types and molecular pathways. For this reason, early selection of defined molecular targets for therapeutic intervention is unlikely to be effective in complex in vivo models of AKI, let alone Phase 3 clinical trials in patients with even more complex AKI pathobiology. Phenotypic screening using zebrafish provides an attractive alternative that does not require prior knowledge of molecular targets and may identify compounds that modify multiple targets that might be missed in more traditional target-based screens. In this review, we discuss results of an academic drug discovery campaign that used zebrafish as a primary screening tool to discover compounds with favorable absorption, metabolism, and toxicity that enhance repair when given late after injury in multiple models of AKI. We discuss how this screening campaign is being integrated into a more comprehensive, phenotypic, and target-based screen for lead compound optimization.
© 2017 S. Karger AG, Basel.
Both historical clinical and recent preclinical data suggest that the M muscarinic acetylcholine receptor is an exciting target for the treatment of Alzheimer's disease and the cognitive and negative symptom clusters in schizophrenia; however, early drug discovery efforts targeting the orthosteric binding site have failed to afford selective M activation. Efforts then shifted to focus on selective activation of M via either allosteric agonists or positive allosteric modulators (PAMs). While M PAMs have robust efficacy in rodent models, some chemotypes can induce cholinergic adverse effects (AEs) that could limit their clinical utility. Here, we report studies aimed at understanding the subtle structural and pharmacological nuances that differentiate efficacy from adverse effect liability within an indole-based series of M ago-PAMs. Our data demonstrate that closely related M PAMs can display striking differences in their in vivo activities, especially their propensities to induce adverse effects. We report the discovery of a novel PAM in this series that is devoid of observable adverse effect liability. Interestingly, the molecular pharmacology profile of this novel PAM is similar to that of a representative M PAM that induces severe AEs. For instance, both compounds are potent ago-PAMs that demonstrate significant interaction with the orthosteric site (either bitopic or negative cooperativity). However, there are subtle differences in efficacies of the compounds at potentiating M responses, agonist potencies, and abilities to induce receptor internalization. While these differences may contribute to the differential in vivo profiles of these compounds, the in vitro differences are relatively subtle and highlight the complexities of allosteric modulators and the need to focus on in vivo phenotypic screening to identify safe and effective M PAMs.
Human disease-causing mutations and genetically modified mouse models have established the importance of KCC2 and KCC3 in nervous system physiology. These two proteins mediate the electroneutral cotransport of K and Cl ions across the neuronal membrane. Disruption of KCC2 function affects inhibitory synaptic transmission with consequences for epilepsy, pain perception, and potentially some neuropsychiatric disorders, whereas disruption of KCC3 affects both central and peripheral nervous systems, resulting in psychosis and peripheral neuropathy. Until recently, the KCC field has suffered from an almost complete lack of pharmacological tools with which to probe cotransporter function. The only available tools being the very poorly potent loop diuretics (e.g., furosemide EC = 6 × 10 M). To address this deficiency, efforts that focused on the discovery of KCC modulators have been undertaken. This work has resulted in the discovery of novel inhibitory compounds that are up to four orders of magnitude more potent (EC = 6 × 10 M) and with increased specificity. While useful for ex vivo studies, these tools possess poor pharmacokinetic properties, severely limiting their utility in vivo. In addition, only a few agents acting on regulatory molecules have been identified as putative KCC activators. Thus, further research is required to develop tools suitable to advance our understanding of how KCC modulation may be useful for the treatment of disease.
The rising problem of antimicrobial resistance in Staphylococcus aureus necessitates the discovery of novel therapeutic targets for small-molecule intervention. A major obstacle of drug discovery is identifying the target of molecules selected from high-throughput phenotypic assays. Here, we show that the toxicity of a small molecule termed '882 is dependent on the constitutive activity of the S. aureus virulence regulator SaeRS, uncovering a link between virulence factor production and energy generation. A series of genetic, physiological, and biochemical analyses reveal that '882 inhibits iron-sulfur (Fe-S) cluster assembly most likely through inhibition of the Suf complex, which synthesizes Fe-S clusters. In support of this, '882 supplementation results in decreased activity of the Fe-S cluster-dependent enzyme aconitase. Further information regarding the effects of '882 has deepened our understanding of virulence regulation and demonstrates the potential for small-molecule modulation of Fe-S cluster assembly in S. aureus and other pathogens.
Copyright © 2016 Elsevier Ltd. All rights reserved.