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 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 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 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>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Novel Conjugated Quinazolinone-Based Hydroxamic Acids: Design, Synthesis and Biological Evaluation

2020 ◽  
Vol 16 ◽  
Author(s):  
Tran Khac Vu ◽  
Nguyen Thi Thanh ◽  
Nguyen Van Minh ◽  
Nguyen Huong Linh ◽  
Nguyen Thi Phương Thao ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Cell Lines ◽  
Cancer Cell ◽  
Hdac Inhibitors ◽  
Cancer Cell Lines ◽  
Hydroxamic Acids ◽  
Biological Evaluation ◽  
Hdac Inhibition ◽  
Ic50 Value ◽  
Mcf 7

Background: Target-based approach to drug discovery currently attracts a great deal of interest from medicinal chemists in anticancer drug discovery and development. Histone deacetylase (HDAC) inhibitors represent an extensive class of targeted anti-cancer agents. Among the most explored structure moieties, hydroxybenzamides and hydroxypropenamides have been demonstrated to have potential HDAC inhibitory effects. Several compounds of these structural classes have been approved for clinical uses to treat different types of cancer, such as vorinostat and belinostat. Aims: This study aims at developing novel HDAC inhibitors bearing conjugated quinazolinone scaffolds with potential cytotoxicity against different cancer cell lines. Method: A series of novel N-hydroxyheptanamides incorporating conjugated 6-hydroxy-2 methylquinazolin-4(3H)- ones (15a-l) was designed, synthesized and evaluated for HDAC inhibitory potency as well as cytotoxicity against three human cancer cell lines, including HepG-2, MCF-7 and SKLu-1. Molecular simulations were finally performed to gain more insight into the structure-activity. relationships. Results: It was found that among novel conjugated quinazolinone-based hydroxamic acids synthesized, compounds 15a, 15c and 15f were the most potent, both in terms of HDAC inhibition and cytotoxicity. Especially, compound 15f displayed up to nearly 4-fold more potent than SAHA (vorinostat) in terms of cytotoxicity against MCF-7 cell line with IC50 value of 1.86 µM, and HDAC inhibition with IC50 value of 6.36 µM. Docking experiments on HDAC2 isozyme showed that these compounds bound to HDAC2 with binding affinities ranging from -10.08 to -14.93 kcal/mol compared to SAHA (-15.84 kcal/mol). It was also found in this research that most of the target compounds seemed to be more cytotoxic toward SKLu-1than MCF-7 and HepG-2. Conclusion: The resesrch results suggest that some hydroxamic acids could emerge for further evaluation and the results are well served as basics for further design of more potent HDAC inhibitors and antitumor agents.

Design, Synthesis and Antitubercular Evaluation of New Benzimidazole Scaffolds

2021 ◽  
Vol 18 (4) ◽  
pp. 375-383
Author(s):  
Smriti Yadav ◽  
Bharath Kumar Inturi ◽  
Shrinidhi B.R ◽  
Pooja H.J ◽  
Neenu Ganesh ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Cell Lines ◽  
Normal Cell ◽  
Acyl Carrier Protein ◽  
Antitubercular Activity ◽  
Resistance Mechanisms ◽  
Docking Studies ◽  
Chemical Library ◽  
Activity Data ◽  
Mycobacterium Tuberculosis H37rv

Background: To overcome one of the resistance mechanisms of Isoniazid (INH), there is a need for an antitubercular agent that can inhibit InhA enzyme by circumventing the formation of INH-NAD+ adduct. Objective: The objective of the study is the development of novel antitubercular agents that target Mycobacterium tuberculosis InhA (Enoyl Acyl Carrier Protein Reductase). Methods: A small-molecule chemical library was used for the identification of the novel InhA inhibitors using primary screening and molecular docking studies followed by the scaffold hopping approach. The designed molecules, 2-(2-(hydroxymethyl)-1H- benzo[d] imidazole-1-yl)- N- substituted acetamides were synthesized by reacting (1H- benzo[d]imidazole -2-yl)methanol with appropriate 2-chloro-N-substituted acetamides / dialkylamino carbonyl chlorides respectively in good yields (42-65%). The antitubercular activity of synthesized compounds was determined by Microplate Alamar Blue Assay (MABA) against Mycobacterium tuberculosis H37Rv strain. The selected compounds were screened for cytotoxicity on normal cell lines. Results: The antitubercular activity data revealed that the 4-chlorophenyl substituted derivative (3b) showed good MIC value at 6.25 μg/mL and, dimethylacetamide substituted derivative (3i) showed MIC at 25 μg/mL among the tested compounds. The substitution of dimethylacetamide (3i) group on the 1st position of benzimidazole has good antitubercular activity (25μg/mL) in comparison to the diethyl acetamide group (3j, 100μg/mL). Conclusion: The antitubercular activity data indicated that the tested compounds exhibited well to moderate inhibition of the H37Rv strains. The compounds (3b) with electronegative substitution on the phenyl moiety exhibited better antitubercular activity than that of the other substitutions. The active compounds have displayed a good safety profile on normal cell lines.

scholarly journals Identification of a juvenile-hormone signaling inhibitor via high-throughput screening of a chemical library

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takumi Kayukawa ◽  
Kenjiro Furuta ◽  
Keisuke Nagamine ◽  
Tetsuro Shinoda ◽  
Kiyoaki Yonesu ◽  
...  
Keyword(s):  
Juvenile Hormone ◽  
Cell Line ◽  
Signaling Pathway ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Ex Vivo ◽  
Agricultural Field ◽  
Chemical Library ◽  
Field Development

Abstract Insecticide resistance has recently become a serious problem in the agricultural field. Development of insecticides with new mechanisms of action is essential to overcome this limitation. Juvenile hormone (JH) is an insect-specific hormone that plays key roles in maintaining the larval stage of insects. Hence, JH signaling pathway is considered a suitable target in the development of novel insecticides; however, only a few JH signaling inhibitors (JHSIs) have been reported, and no practical JHSIs have been developed. Here, we established a high-throughput screening (HTS) system for exploration of novel JHSIs using a Bombyx mori cell line (BmN_JF&AR cells) and carried out a large-scale screening in this cell line using a chemical library. The four-step HTS yielded 69 compounds as candidate JHSIs. Topical application of JHSI48 to B. mori larvae caused precocious metamorphosis. In ex vivo culture of the epidermis, JHSI48 suppressed the expression of the Krüppel homolog 1 gene, which is directly activated by JH-liganded receptor. Moreover, JHSI48 caused a parallel rightward shift in the JH response curve, suggesting that JHSI48 possesses a competitive antagonist-like activity. Thus, large-scale HTS using chemical libraries may have applications in development of future insecticides targeting the JH signaling pathway.

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