Methods for Antifungal Discovery and Development

A special issue of Methods and Protocols (ISSN 2409-9279).

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 3016

Special Issue Editor


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Guest Editor
Department of Ophthalmology, Department of Microbiology & Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
Interests: fungal pathogenesis; fungal genetics; signal transduction; antifungal therapy

Special Issue Information

Dear Colleagues,

Fungi can cause devastating infections, ranging from site-threating keratitis to life-threatening invasive disease. As the immunosuppressed population continues to rise, e.g., in the setting of organ transplantation or HIV/AIDs, so too will the clinical and economic burden of these infections. The current repertoire of antifungals is unfortunately limited in both scope and effectiveness, as highlighted by the unacceptably high mortalities rates of systemic mycoses overall (25%–90%) and compounded by the emergence of drug resistant Aspergillus and Candida infections. The need for novel and better antifungals is therefore obvious, but their development faces numerous challenges. For example, fungi are eukaryotes, so drugs that inhibit their growth are often toxic to the host. Moreover, the presence of efflux mechanisms in the fungal cell renders many compounds, though theoretically promising, practically ineffective. Thus, improved techniques for identifying and optimizing novel drugs, ensuring their safety, and testing their feasibility in infection models are critical to combat fungal disease.

The aim of this Special Issue is to centralize ideas and methods that span the entire spectrum of antifungal research. This includes primary or review articles covering the following topics: (1) compound discovery (natural product isolation, library screening); (2) drug optimization (modeling, synthetic chemistry); (3) target deconvolution (i.e., elucidating drug mechanisms); (4) pharmacokinetics and toxicity testing; and (5) treatment studies (cell culture or animal infection models). We are also interested in novel forward or reverse genetic approaches (e.g., CRISPR libraries) that allow researchers to identify putative drug targets. Through this issue, we hope researchers specializing in one facet of antifungal development will become aware of techniques used in another and, as a consequence, expand their own research program or seek collaborations towards that end. By sharing ideas and resources, we hope to expedite antifungal development and improve outcomes in these underserved patients.

We hope you will contribute!

Dr. Kevin K. Fuller
Guest Editor

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Published Papers (1 paper)

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Research

11 pages, 1365 KiB  
Article
Predictive Quantitative Structure–Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds
by Michael Appell, David L. Compton and Kervin O. Evans
Methods Protoc. 2021, 4(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/mps4010002 - 27 Dec 2020
Cited by 2 | Viewed by 2587
Abstract
Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus [...] Read more.
Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R2 values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds. Full article
(This article belongs to the Special Issue Methods for Antifungal Discovery and Development)
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