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

2020 ◽  
Vol 25 (7) ◽  
pp. 770-782 ◽  
Author(s):  
Rebecca E. Hughes ◽  
Richard J. R. Elliott ◽  
Alison F. Munro ◽  
Ashraff Makda ◽  
J. Robert O’Neill ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Esophageal Adenocarcinoma ◽  
Cell Lines ◽  
Cancer Biology ◽  
Large Scale ◽  
Chemical Library ◽  
Screening Platform ◽  
Poor Outcomes ◽  
Cell Phenotypes ◽  
Selective Activity

Esophageal adenocarcinoma (EAC) 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. We describe the application of a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six EAC cell lines and two tissue-matched control lines. We built an automated high-content image analysis pipeline to identify compounds that selectively modified the phenotype of EAC cell lines. We further trained a machine-learning model to predict the mechanism of action of EAC selective compounds using phenotypic fingerprints from a library of reference compounds. We identified a number of phenotypic clusters enriched with similar pharmacological classes, including methotrexate and three other antimetabolites that are highly selective for EAC cell lines. We further identify a small number of hits from our diverse chemical library that show potent and selective activity for EAC cell lines and that do not cluster with the reference library of compounds, indicating they may be selectively targeting novel esophageal cancer biology. Overall, our results demonstrate that our EAC phenotypic screening platform can identify existing pharmacologic classes and novel compounds with selective activity for EAC cell phenotypes.

scholarly journals Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

2019 ◽  
Vol 36 (5) ◽  
pp. 1607-1613 ◽  
Author(s):  
Joseph C Boyd ◽  
Alice Pinheiro ◽  
Elaine Del Nery ◽  
Fabien Reyal ◽  
Thomas Walter
Keyword(s):  
Drug Discovery ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Molecular Subtype ◽  
Supplementary Information ◽  
Breast Cancer Cell Lines ◽  
Molecular Heterogeneity ◽  
Drug Mechanism

Abstract Motivation High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. Results The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease. Availability and implementation https://github.com/jcboyd/multi-cell-line or https://zenodo.org/record/2677923. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals An in vivo large-scale chemical screening platform using Drosophila for anti-cancer drug discovery

2012 ◽  
Vol 6 (2) ◽  
pp. 521-529 ◽  
Author(s):  
L. F. Willoughby ◽  
T. Schlosser ◽  
S. A. Manning ◽  
J. P. Parisot ◽  
I. P. Street ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Large Scale ◽  
Cancer Drug ◽  
Chemical Screening ◽  
Cancer Drug Discovery ◽  
Anti Cancer ◽  
Screening Platform

scholarly journals Roadmap for the use of base editors to decipher drug mechanism of action

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257537
Author(s):  
Estel Aparicio-Prat ◽  
Dong Yan ◽  
Marco Mariotti ◽  
Michael Bassik ◽  
Gaelen Hess ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Mechanism Of Action ◽  
Large Scale ◽  
Reporter System ◽  
Experimental Conditions ◽  
Drug Mechanism ◽  
Β2 Adrenergic Receptor ◽  
Set Up ◽  
G Protein Coupled ◽  
Early Drug

CRISPR base editors are powerful tools for large-scale mutagenesis studies. This kind of approach can elucidate the mechanism of action of compounds, a key process in drug discovery. Here, we explore the utility of base editors in an early drug discovery context focusing on G-protein coupled receptors. A pooled mutagenesis screening framework was set up based on a modified version of the CRISPR-X base editor system. We determine optimized experimental conditions for mutagenesis where sgRNAs are delivered by cell transfection or viral infection over extended time periods (>14 days), resulting in high mutagenesis produced in a short region located at -4/+8 nucleotides with respect to the sgRNA match. The β2 Adrenergic Receptor (B2AR) was targeted in this way employing a 6xCRE-mCherry reporter system to monitor its response to isoproterenol. The results of our screening indicate that residue 184 of B2AR is crucial for its activation. Based on our experience, we outline the crucial points to consider when designing and performing CRISPR-based pooled mutagenesis screening, including the typical technical hurdles encountered when studying compound pharmacology.

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 Roadmap for the use of Base Editors to Decipher Drug Mechanism of Action

2020 ◽  
Author(s):  
Estel Aparicio Prat ◽  
Dong Yan ◽  
Marco Mariotti ◽  
Michael Bassik ◽  
Gaelen Hess ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Mechanism Of Action ◽  
Large Scale ◽  
Reporter System ◽  
Experimental Conditions ◽  
Base Editing ◽  
Drug Mechanism ◽  
Set Up ◽  
G Protein Coupled ◽  
Early Drug

Abstract Background: CRISPR base editors are powerful tools for large-scale mutagenesis studies. This kind of approach can elucidate the mechanism of action of compounds, a key process in drug discovery. Here, we explore the utility of base editors in an early drug discovery context, and we focus on G-protein coupled receptors.Results: We set up a pooled mutagenesis screening framework based on a modified version of the CRISPR-X base editor system. We determine optimized experimental conditions for mutagenesis where sgRNAs are delivered by cell transfection or viral infection over extended time periods (>14 days), resulting in high mutagenesis produced in a short region located at -4/+8 nucleotides with respect to the sgRNA match. We thus target the Beta 2 Adrenergic Receptor (B2AR) and employ a 6xCRE-mCherry reporter system to monitor its activity. The results of our screening indicate that residue 184 of B2AR is crucial for its activation. Based on our experience, we then outline the crucial points to consider when designing and performing CRISPR-based pooled mutagenesis screening, including the typical technical hurdles encountered when studying compound pharmacology. Conclusions: The base editing technology has a great potential to help deciphering the mechanism of action of drugs, and it is a very powerful tool in drug discovery. Here we show an application of pooled mutagenesis screening to study B2AR, and we provide a roadmap for successfully applying this approach to other target proteins.

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 Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

2019 ◽  
Author(s):  
Joseph C. Boyd ◽  
Alice Pinheiro ◽  
Elaine Del Nery ◽  
Fabien Reyal ◽  
Thomas Walter
Keyword(s):  
Drug Discovery ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Molecular Subtype ◽  
Disease Model ◽  
Breast Cancer Cell Lines ◽  
Molecular Heterogeneity ◽  
Drug Mechanism

AbstractHigh Content Screening is an important tool in drug discovery and characterisation. Often, high content drug screens are performed on one single cell line. Yet, a single cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterise drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. The contribution of this paper is twofold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimisation of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multitask autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease.

scholarly journals Searching for Pharmacogenomic Markers: The Synergy between Omic and Hypothesis-Driven Research

2001 ◽  
Vol 17 (2) ◽  
pp. 77-88 ◽  
Author(s):  
John N. Weinstein
Keyword(s):  
Drug Discovery ◽  
Cell Lines ◽  
Large Scale ◽  
Human Cancer ◽  
Biomedical Literature ◽  
Ovarian Cancer Cells ◽  
Human Cancer Cell Lines ◽  
Expression Studies ◽  
Drug Discovery Program ◽  
Pharmacogenomic Studies

With 35,000 genes and hundreds of thousands of protein states to identify, correlate, and understand, it no longer suffices to rely on studies of one gene, gene product, or process at a time. We have entered the “omic” era in biology. But large-scale omic studies of cellular molecules in aggregate rarely can answer interesting questions without the assistance of information from traditional hypothesis-driven research. The two types of science are synergistic. A case in point is the set of pharmacogenomic studies that we and our collaborators have done with the 60 human cancer cell lines of the National Cancer Institute’s drug discovery program. Those cells (the NCI-60) have been characterized pharmacologically with respect to their sensitivity to > 70,000 chemical compounds. We are further characterizing them at the DNA, RNA, protein, and functional levels. Our major aim is to identify pharmacogenomic markers that can aid in drug discovery and design, as well as in individualization of cancer therapy. The bioinformatic and chemoinformatic challenges of this study have demanded novel methods for analysis and visualization of high-dimensional data. Included are the color-coded “clustered image map” and also the MedMiner program package, which captures and organizes the biomedical literature on gene-gene and gene-drug relationships. Microarray transcript expression studies of the 60 cell lines reveal, for example, a gene-drug correlation with potential clinical implications – that between the asparagine synthetase gene and the enzyme-drug L-asparaginase in ovarian cancer cells.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Sign in / Sign up

Export Citation Format

Share Document