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      Phenotypic screening of 1,953 FDA-approved drugs reveals 26 hits with potential for repurposing for Peyronie’s disease

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          Abstract

          Drug repurposing has been shown to bring safe medications to new patient populations, as recently evidenced by the COVID-19 pandemic. We investigated whether we could use phenotypic screening to repurpose drugs for the treatment of Peyronie’s disease (PD). PD is a fibrotic disease characterised by continued myofibroblast presence and activity leading to formation of a plaque in the penile tunica albuginea (TA) that can cause pain during erection, erectile dysfunction, and penile deformity. PD affects 3–9% of men with treatment options limited to surgery or injection of collagenase which can only be utilised at late stages after the plaque is formed. Currently there are no approved medications that can be offered to patients presenting with early disease before the formation of the plaque. Drug repurposing may therefore be the ideal strategy to identify medical treatments to address this unmet medical need in early PD. We used primary human fibroblasts from PD patients in a phenotypic screening assay that measures TGF-β1-induced myofibroblast transformation which is the main cellular phenotype that drives the pathology in early PD. A library of FDA-approved 1,953 drugs was screened in duplicate wells at a single concentration (10 μM) in presence of TGF-β1. The myofibroblast marker α-SMA was quantified after 72h incubation. A positive control of SB-505124 (TGF-β1 receptor antagonist) was included on each plate. Hits were defined as showing >80% inhibition, whilst retaining >80% cell viability. 26 hits (1.3%) were identified which were divided into the following main groups: anti-cancer drugs, anti-inflammation, neurology, endocrinology, and imaging agents. Five of the top-ten drugs that increase myofibroblast-transformation appear to act on VEGFR. This is the first phenotypic screening of FDA-approved drugs for PD and our results suggest that it is a viable method to predict drugs with potential for repurposing to treat early PD.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report

            Abstract Background Coronavirus disease 2019 (Covid-19) is associated with diffuse lung damage. Glucocorticoids may modulate inflammation-mediated lung injury and thereby reduce progression to respiratory failure and death. Methods In this controlled, open-label trial comparing a range of possible treatments in patients who were hospitalized with Covid-19, we randomly assigned patients to receive oral or intravenous dexamethasone (at a dose of 6 mg once daily) for up to 10 days or to receive usual care alone. The primary outcome was 28-day mortality. Here, we report the preliminary results of this comparison. Results A total of 2104 patients were assigned to receive dexamethasone and 4321 to receive usual care. Overall, 482 patients (22.9%) in the dexamethasone group and 1110 patients (25.7%) in the usual care group died within 28 days after randomization (age-adjusted rate ratio, 0.83; 95% confidence interval [CI], 0.75 to 0.93; P<0.001). The proportional and absolute between-group differences in mortality varied considerably according to the level of respiratory support that the patients were receiving at the time of randomization. In the dexamethasone group, the incidence of death was lower than that in the usual care group among patients receiving invasive mechanical ventilation (29.3% vs. 41.4%; rate ratio, 0.64; 95% CI, 0.51 to 0.81) and among those receiving oxygen without invasive mechanical ventilation (23.3% vs. 26.2%; rate ratio, 0.82; 95% CI, 0.72 to 0.94) but not among those who were receiving no respiratory support at randomization (17.8% vs. 14.0%; rate ratio, 1.19; 95% CI, 0.91 to 1.55). Conclusions In patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. (Funded by the Medical Research Council and National Institute for Health Research and others; RECOVERY ClinicalTrials.gov number, NCT04381936; ISRCTN number, 50189673.)
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              A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays.

              The ability to identify active compounds (³hits²) from large chemical libraries accurately and rapidly has been the ultimate goal in developing high-throughput screening (HTS) assays. The ability to identify hits from a particular HTS assay depends largely on the suitability or quality of the assay used in the screening. The criteria or parameters for evaluating the ³suitability² of an HTS assay for hit identification are not well defined and hence it still remains difficult to compare the quality of assays directly. In this report, a screening window coefficient, called ³Z-factor,² is defined. This coefficient is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment. The Z-factor is a dimensionless, simple statistical characteristic for each HTS assay. The Z-factor provides a useful tool for comparison and evaluation of the quality of assays, and can be utilized in assay optimization and validation.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                12 December 2022
                2022
                : 17
                : 12
                : e0277646
                Affiliations
                [1 ] Medical Technology Research Centre, School of Allied Health, Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
                [2 ] Department of Urology, University College London Hospital, London, United Kingdom
                University of Bergen, NORWAY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-4856-285X
                Article
                PONE-D-22-05095
                10.1371/journal.pone.0277646
                9744312
                36508413
                fbcdd48e-4319-4d31-9a48-62ec3fcea152
                © 2022 Ilg et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 February 2022
                : 1 November 2022
                Page count
                Figures: 7, Tables: 2, Pages: 23
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Connective Tissue Cells
                Fibroblasts
                Biology and Life Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Medicine and Health Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Medicine and Health Sciences
                Pharmacology
                Drug Screening
                Biology and Life Sciences
                Developmental Biology
                Fibrosis
                Medicine and Health Sciences
                Pharmaceutics
                Drug Therapy
                Medicine and Health Sciences
                Pharmaceutics
                Drug Therapy
                Receptor Antagonist Therapy
                Biology and Life Sciences
                Biochemistry
                Neurochemistry
                Neurotransmitters
                Biogenic Amines
                Histamine
                Biology and Life Sciences
                Neuroscience
                Neurochemistry
                Neurotransmitters
                Biogenic Amines
                Histamine
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Histamine
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Histamine
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Biology and Life Sciences
                Neuroscience
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                Physical Sciences
                Materials Science
                Material Properties
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                Custom metadata
                The data underlying the results presented in the study are available from figshare at https://doi.org/10.25411/aru.21501531.v1.

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