Utility of Resazurin, Horseradish Peroxidase, and NMR Assays to Identify Redox-Related False-Positive Behavior in High-Throughput Screens-Reference-Cited by-同舟云学术

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Utility of Resazurin, Horseradish Peroxidase, and NMR Assays to Identify Redox-Related False-Positive Behavior in High-Throughput Screens

Published:2018-04 Issue:3 Volume:16 Page:171-191
ISSN:1540-658X
Container-title:ASSAY and Drug Development Technologies
language:en
Short-container-title:ASSAY and Drug Development Technologies

Author:

Tarnowski Matthew¹²,Barozet Amélie²³,Johansson Carina²,Eriksson Per-Olof²,Engkvist Ola²,Walsh Jarrod⁴,Nissink J. Willem M.¹

Affiliation:

1. Oncology IMED, AstraZeneca, Cambridge, United Kingdom.

2. Discovery Sciences, AstraZeneca R&D Gothenburg, Gothenburg, Sweden.

3. Department of Biology, Lund University, Lund, Sweden.

4. Discovery Sciences, AstraZeneca, Alderley Park, United Kingdom.

Publisher

Mary Ann Liebert Inc

Subject

Drug Discovery,Molecular Medicine

Link

http://www.liebertpub.com/doi/pdf/10.1089/adt.2017.838

Reference23 articles.

1. Drug Discovery: A Historical Perspective

2. Increasing the delivery of next generation therapeutics from high throughput screening libraries

3. Apparent activity in high-throughput screening: origins of compound-dependent assay interference

4. A High-Throughput Screen for Aggregation-Based Inhibition in a Large Compound Library

5. Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries

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1. Utilising acoustic mist ionisation mass spectrometry to identify redox cycling compounds in high throughput screening outputs;SLAS Discovery;2022-09

2. Redox active or thiol reactive? Optimization of rapid screens to identify less evident nuisance compounds;DRUG DISCOV TODAY;2022

3. Redox active or thiol reactive? Optimization of rapid screens to identify less evident nuisance compounds;Drug Discovery Today;2022-06

4. Fragment screening at AstraZeneca: developing the next generation biophysics fragment set;RSC Medicinal Chemistry;2022

5. Nuisance small molecules under a machine-learning lens;Digital Discovery;2022

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