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Introduction: Some of the fungi have become pathogenic to humans, and these opportunistic pathogens are able to cause severe systemic mycoses., Areas Covered: The number of antifungals for systemic administration is limited and resistance to these drugs is common. This review summarizes various approaches to the discovery and development of new antifungals, provides an overview of important molecules in basic research and compounds in clinical trials, and focuses on drug repurposing strategy, while providing an overview of drugs of other indications that have been tested in vitro for their antifungal activity for possible expansion of antifungal drugs and/or support of existing antimycotics., Expert Opinion: Despite the limitations of the research of new antifungals by manufacturers, in addition to innovated molecules based on clinically used drugs, several completely new small entities with unique mechanisms of actions have been identified. The identification of new molecular targets that offer alternatives for the development of new unique selective antifungal highly effective agents has been an important outcome of repurposing of non-antifungal drugs to antifungal drug. Also, given the advances in monoclonal antibodies and their application to immunosuppressed patients, it may seem possible to predict a more optimistic future for antifungal therapy than has been the case in recent decades.

The current study examines in silico characterization of the structure-inhibitory potency for a set of phenylcarbamic acid derivatives containing an N-arylpiperazine scaffold, considering the electronic, steric and lipophilic properties. The main objective of the ligand-based modelling was the systematic study of classical comparative molecular field analysis (CoMFA)/comparative molecular surface analysis (CoMSA) performance for the modelling of in vitro efficiency observed for these phenylcarbamates, revealing their inhibitory activities against a virulent Mycobacterium tuberculosis H 37 R v strain. We compared the findings of efficiency modelling produced by a standard 3D methodology (CoMFA) and its neural counterparts (CoMSA) regarding multiple training/test subsets and variables used. Moreover, systematic space inspection, splitting values into the analysed training/test subsets, was performed to monitor statistical estimator performance while mapping the probability-driven pharmacophore pattern. Consequently, a 'pseudo-consensus' 3D-quantitative structure-activity relationship (3D-QSAR) approach was applied to retrieve an 'average' pharmacophore hypothesis by the investigation of the most densely populated training/test subpopulations to specify the potentially important factors contributing to the inhibitory activity of phenylcarbamic acid analogues. In addition, examination of descriptor-based similarity with a principal component analysis (PCA) procedure was employed to visualize noticeable variations in the performance of these molecules with respect to their structure and activity profiles.

Finding a balance between a desired drug's potency and its physicochemical properties that are important for its molecule pharmacokinetic or pharmacodynamics profile is still a challenging issue in rational drug discovery. Quantitative assessment of the lipophilic characteristics of potential drug molecules is indispensable for efficient development of Absorption, Distribution, Metabolism, Excretion, Toxicity-tailored structure-activity models; therefore reliable procedures for deriving log P from molecular structure are desirable. In the current work a range of various software log P predictors for estimation of the numerical lipophilic values for a set of cholic acid derivatives were employed and subsequently cross-compared with the experimental parameters. Thus, the empirical lipophilicity (R M ) was compared with the corresponding log P characteristics calculated using alternative methods for deducing the lipophilic features. The mean values of the selected molecular descriptors that were averaged over the chosen calculation methods (consensus clog P) were subsequently correlated with the R M parameter. As an additional experiment, the iterative variable elimination partial least squares (IVE-PLS) methodology for an ensemble of descriptors retrieved from Dragon 6.0 software was applied for a set of drug transporters. To investigate the variations within the ensemble of cholic acid derivatives principal component analysis (PCA) and self-organizing neural network (SOM) procedures were used to visualize the major differences in the performance of drug promoters with respect to their lipophilic profile.