scholarly journals A phenotypic screening platform utilising human spermatozoa identifies new compounds with contraceptive activity

2019 ◽  
Author(s):  
Franz Gruber ◽  
Christopher L.R. Barratt ◽  
Paul D. Andrews
Keyword(s):  
Drug Discovery ◽  
Target Identification ◽  
Human Sperm ◽  
Phenotypic Screening ◽  
Major Barrier ◽  
Drug Candidates ◽  
Critical Gap ◽  
Comprehensive Collection ◽  
Screening Platform ◽  
Novel Drug

AbstractThere is an urgent need to develop new methods for male contraception, however a major barrier to drug discovery has been the lack of validated targets and the absence of an effective high-throughput phenotypic screening system. To address this deficit, we developed a fully-automated robotic screening platform that provided quantitative evaluation of compound activity against two key attributes of human sperm function: motility and acrosome reaction. In order to accelerate contraceptive development, we screened the comprehensive collection of 12,000 molecules that make up the ReFRAME repurposing library, comprising nearly all the small molecules that have been approved or have undergone clinical development, or have significant preclinical profiling. We identified several compounds that potently inhibit motility representing either novel drug candidates or routes to target identification. This platform will now allow for major drug discovery programmes that address the critical gap in the contraceptive portfolio as well as uncover novel human sperm biology.

scholarly journals A phenotypic screening platform utilising human spermatozoa identifies compounds with contraceptive activity

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Franz S Gruber ◽  
Zoe C Johnston ◽  
Christopher LR Barratt ◽  
Paul D Andrews
Keyword(s):  
Drug Discovery ◽  
Target Identification ◽  
Human Sperm ◽  
Phenotypic Screening ◽  
Major Barrier ◽  
Drug Candidates ◽  
Critical Gap ◽  
Comprehensive Collection ◽  
Screening Platform ◽  
Novel Drug

There is an urgent need to develop new methods for male contraception, however a major barrier to drug discovery has been the lack of validated targets and the absence of an effective high-throughput phenotypic screening system. To address this deficit, we developed a fully-automated robotic screening platform that provided quantitative evaluation of compound activity against two key attributes of human sperm function: motility and acrosome reaction. In order to accelerate contraceptive development, we screened the comprehensive collection of 12,000 molecules that make up the ReFRAME repurposing library, comprising nearly all the small molecules that have been approved or have undergone clinical development, or have significant preclinical profiling. We identified several compounds that potently inhibit motility representing either novel drug candidates or routes to target identification. This platform will now allow for major drug discovery programmes that address the critical gap in the contraceptive portfolio as well as uncover novel human sperm biology.

scholarly journals Machine Learning Methods in Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5277
Author(s):  
Lauv Patel ◽  
Tripti Shukla ◽  
Xiuzhen Huang ◽  
David W. Ussery ◽  
Shanzhi Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Drug Candidates ◽  
Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

scholarly journals Compounds enhancing human sperm motility identified using a high-throughput phenotypic screening platform

2021 ◽  
Author(s):  
Franz S. Gruber ◽  
Zoe C. Johnston ◽  
Neil R. Norcross ◽  
Irene Georgiou ◽  
Caroline Wilson ◽  
...  
Keyword(s):  
Sperm Motility ◽  
High Throughput ◽  
Dose Response ◽  
High Throughput Screening ◽  
Human Sperm ◽  
Sperm Function ◽  
Phenotypic Screening ◽  
Enhancing Effect ◽  
Short Period ◽  
Screening Platform

AbstractStudy questionCan a high-throughput screening platform facilitate male fertility drug discovery?Summary answerA high-throughput screening platform identified a large number of compounds that enhanced sperm motility.What is known alreadySeveral efforts to find small molecules modulating sperm function have been performed but not using high-throughput technology.Study design, size, durationHealthy donor semen samples were used and samples were pooled (3-5 donors per pool). Primary screening was performed in singlicate; dose-response screening was performed in duplicate (independent donor pools).Participants/materials, setting, methodsSpermatozoa isolated from healthy donors were prepared by density gradient centrifugation and incubated in 384-well plates with compounds (6.25 uM) to identify those compounds with enhancing effects on motility. A total of ∼17,000 compounds from the following libraries: ReFRAME, Prestwick, Tocris, LOPAC, CLOUD and MMV Pathogen Box were screened. Dose response experiments of screening hits were performed to confirm the enhancing effect on sperm motility. Experiments were performed in a University setting.Main results and the role of chanceFrom our primary single concentration screening, 105 compounds elicited an enhancing effect on sperm motility compared to DMSO treated wells. Confirmed enhancing compounds were grouped based on their annotated targets/target classes. A major target class, phosphodiesterase inhibitors, were identified in particular PDE10A inhibitors as well as number of compounds not previously identified/known to enhance human sperm motility such as those related to GABA signaling.Limitations, reasons for cautionCompounds have been tested with prepared donor spermatozoa and only incubated for a short period of time. Therefore, the effect of compounds on whole semen or with longer incubation time may be different. All experiments were performed in vitro.Wider implications of the findingsThis phenotypic screening assay identified a large number of compounds that increased sperm motility. In addition to furthering our understanding of human sperm function, for example identifying new avenues for discovery, we highlight potential inhibitors as promising start-point for a medicinal chemistry programme for potential enhancement of male infertility. Moreover, with disclosure of the results of screening we present a substantial resource to inform further work in the fieldStudy funding/competing interest(s)This study was supported by the Bill and Melinda Gates Foundation and Scottish Funding Council and Scottish Universities Life Science Alliance.

scholarly journals Functional patient-derived cellular models for neuropsychiatric drug discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Santiago G. Lago ◽  
Jakub Tomasik ◽  
Sabine Bahn
Keyword(s):  
Drug Discovery ◽  
Mononuclear Cells ◽  
Induced Pluripotent Stem Cell ◽  
Functional Responses ◽  
Peripheral Blood Mononuclear ◽  
Disease Heterogeneity ◽  
Drug Candidates ◽  
Cellular Models ◽  
Novel Strategy ◽  
Novel Drug

AbstractMental health disorders are a leading cause of disability worldwide. Challenges such as disease heterogeneity, incomplete characterization of the targets of existing drugs and a limited understanding of functional interactions of complex genetic risk loci and environmental factors have compromised the identification of novel drug candidates. There is a pressing clinical need for drugs with new mechanisms of action which address the lack of efficacy and debilitating side effects of current medications. Here we discuss a novel strategy for neuropsychiatric drug discovery which aims to address these limitations by identifying disease-related functional responses (‘functional cellular endophenotypes’) in a variety of patient-derived cells, such as induced pluripotent stem cell (iPSC)-derived neurons and organoids or peripheral blood mononuclear cells (PBMCs). Disease-specific alterations in cellular responses can subsequently yield novel drug screening targets and drug candidates. We discuss the potential of this approach in the context of recent advances in patient-derived cellular models, high-content single-cell screening of cellular networks and changes in the diagnostic framework of neuropsychiatric disorders.

scholarly journals High Content Phenotypic Profiling in Oesophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery

2020 ◽  
Author(s):  
Rebecca E Hughes ◽  
Richard J R Elliott ◽  
Alison F Munro ◽  
Ashraff Makda ◽  
J Robert O’Neill ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Cell Lines ◽  
Cancer Biology ◽  
Large Scale ◽  
Oesophageal Adenocarcinoma ◽  
Phenotypic Screening ◽  
Chemical Library ◽  
Drug Mechanism ◽  
Screening Platform ◽  
Selective Activity

AbstractOesophageal adenocarcinoma (OAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies contributing to poor outcomes for patients diagnosed with OAC. We describe the development and application of a phenotypic-led OAC drug discovery platform incorporating image-based, high-content cell profiling and associated image-informatics tools to classify drug mechanism-of-action (MoA). We applied a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six OAC cell lines representing the genetic heterogeneity of disease, a pre-neoplastic Barrett’s oesophagus line and a non-transformed squamous oesophageal line. We built an automated phenotypic screening and high-content image analysis pipeline to identify compounds that selectively modified the phenotype of OAC cell lines. We further trained a machine-learning model to predict the MoA of OAC selective compounds using phenotypic fingerprints from a library of reference compounds.We identified a number of phenotypic clusters enriched with similar pharmacological classes e.g. Methotrexate and three other antimetabolites which are highly selective for OAC cell lines. We further identify a small number of hits from our diverse chemical library which show potent and selective activity for OAC cell lines and which do not cluster with the reference library of known MoA, indicating they may be selectively targeting novel oesophageal cancer biology. Our results demonstrate that our OAC phenotypic screening platform can identify existing pharmacological classes and novel compounds with selective activity for OAC cell phenotypes.

scholarly journals Deep Learning in Drug Discovery and Medicine; Scratching the Surface

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2384 ◽  
Author(s):  
Dibyendu Dana ◽  
Satishkumar Gadhiya ◽  
Luce St. Surin ◽  
David Li ◽  
Farha Naaz ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Precision Medicine ◽  
A Priori ◽  
Treatment Strategies ◽  
Molecular Therapy ◽  
Molecular Profile ◽  
Drug Candidates ◽  
Practice Of Medicine ◽  
New Chemical Entities ◽  
Novel Drug

The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy. Indeed, precision medicine-based therapies that first found their place in oncology are rapidly finding uses in autoimmune, renal and other diseases. More recently a new renaissance that is shaping everyday life is making its way into healthcare. Drug discovery and medicine that started with Ayurveda in India are now benefiting from an altogether different artificial intelligence (AI)—one which is automating the invention of new chemical entities and the mining of large databases in health-privacy-protected vaults. Indeed, disciplines as diverse as language, neurophysiology, chemistry, toxicology, biostatistics, medicine and computing have come together to harness algorithms based on transfer learning and recurrent neural networks to design novel drug candidates, a priori inform on their safety, metabolism and clearance, and engineer their delivery but only on demand, all the while cataloging and comparing omics signatures across traditionally classified diseases to enable basket treatment strategies. This review highlights inroads made and being made in directed-drug design and molecular therapy.

Combined siRNA and Small-Molecule Phenotypic Screening Identifies Targets Regulating Rhinovirus Replication in Primary Human Bronchial Epithelial Cells

2020 ◽  
Vol 25 (6) ◽  
pp. 634-645 ◽  
Author(s):  
Mei Ding ◽  
Christian Tyrchan ◽  
Elisabeth Bäck ◽  
Jörgen Östling ◽  
Steffen Schubert ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Drug Targets ◽  
Phenotypic Screening ◽  
Bronchial Epithelial ◽  
Phenotypic Screen ◽  
Tract Infections ◽  
Novel Drug ◽  
Novel Drug Targets

Human rhinovirus (RV) is the most common cause of acute upper respiratory tract infections and has recently been shown to play a significant role in exacerbations of asthma and chronic obstructive pulmonary disease (COPD). There is a significant unmet medical need for agents for the prevention and/or treatment of exacerbations triggered by human RV infection. Phenotypic drug discovery programs using different perturbation modalities, for example, siRNA, small-molecule compounds, and CRISPR, hold significant value for identifying novel drug targets. We have previously reported the identification of lanosterol synthase as a novel regulator of RV2 replication through a phenotypic screen of a library of siRNAs against druggable genes in normal human bronchial epithelial (NHBE) cells. Here, we describe a follow-up phenotypic screen of small-molecule compounds that are annotated to be pharmacological regulators of target genes that were identified to significantly affect RV2 replication in the siRNA primary screen of 10,500 druggable genes. Two hundred seventy small-molecule compounds selected for interacting with 122 target gene hits were screened in the primary RV2 assay in NHBE cells by quantifying viral replication via in situ hybridization followed by secondary quantitative PCR-based assays for RV2, RV14, and RV16. The described follow-up phenotypic screening allowed us to identify Fms-related tyrosine kinase 4 (FLT4) as a novel target regulating RV replication. We demonstrate that a combination of siRNA and small-molecule compound screening models is a useful phenotypic drug discovery approach for the identification of novel drug targets.

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