Literature DB >> 29503208

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

Jaak Simm1, Günter Klambauer2, Adam Arany1, Marvin Steijaert3, Jörg Kurt Wegner4, Emmanuel Gustin4, Vladimir Chupakhin4, Yolanda T Chong4, Jorge Vialard4, Peter Buijnsters4, Ingrid Velter4, Alexander Vapirev5, Shantanu Singh6, Anne E Carpenter6, Roel Wuyts7, Sepp Hochreiter2, Yves Moreau1, Hugo Ceulemans8.   

Abstract

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Bayesian matrix factorization; computational chemistry; deep learning; drug discovery; high-content imaging; high-throughput screening; machine learning; matrix factorization

Mesh:

Substances:

Year:  2018        PMID: 29503208      PMCID: PMC6031326          DOI: 10.1016/j.chembiol.2018.01.015

Source DB:  PubMed          Journal:  Cell Chem Biol        ISSN: 2451-9448            Impact factor:   8.116


  20 in total

1.  Phenotypic screening of small molecule libraries by high throughput cell imaging.

Authors:  J C Yarrow; Y Feng; Z E Perlman; T Kirchhausen; T J Mitchison
Journal:  Comb Chem High Throughput Screen       Date:  2003-06       Impact factor: 1.339

Review 2.  Imaged-based high-throughput screening for anti-angiogenic drug discovery.

Authors:  Lasse Evensen; Wolfgang Link; James B Lorens
Journal:  Curr Pharm Des       Date:  2010       Impact factor: 3.116

Review 3.  High-throughput fluorescence microscopy for systems biology.

Authors:  Rainer Pepperkok; Jan Ellenberg
Journal:  Nat Rev Mol Cell Biol       Date:  2006-07-19       Impact factor: 94.444

4.  Application of random forest approach to QSAR prediction of aquatic toxicity.

Authors:  Pavel G Polishchuk; Eugene N Muratov; Anatoly G Artemenko; Oleg G Kolumbin; Nail N Muratov; Victor E Kuz'min
Journal:  J Chem Inf Model       Date:  2009-11       Impact factor: 4.956

5.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.

Authors:  Jonathan B Baell; Georgina A Holloway
Journal:  J Med Chem       Date:  2010-04-08       Impact factor: 7.446

Review 6.  Applications in image-based profiling of perturbations.

Authors:  Juan C Caicedo; Shantanu Singh; Anne E Carpenter
Journal:  Curr Opin Biotechnol       Date:  2016-04-17       Impact factor: 9.740

7.  Multiplex cytological profiling assay to measure diverse cellular states.

Authors:  Sigrun M Gustafsdottir; Vebjorn Ljosa; Katherine L Sokolnicki; J Anthony Wilson; Deepika Walpita; Melissa M Kemp; Kathleen Petri Seiler; Hyman A Carrel; Todd R Golub; Stuart L Schreiber; Paul A Clemons; Anne E Carpenter; Alykhan F Shamji
Journal:  PLoS One       Date:  2013-12-02       Impact factor: 3.240

Review 8.  Increasing the Content of High-Content Screening: An Overview.

Authors:  Shantanu Singh; Anne E Carpenter; Auguste Genovesio
Journal:  J Biomol Screen       Date:  2014-04-07

9.  Screening compounds with a novel high-throughput ABCB1-mediated efflux assay identifies drugs with known therapeutic targets at risk for multidrug resistance interference.

Authors:  Megan R Ansbro; Suneet Shukla; Suresh V Ambudkar; Stuart H Yuspa; Luowei Li
Journal:  PLoS One       Date:  2013-04-10       Impact factor: 3.240

10.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes.

Authors:  Anne E Carpenter; Thouis R Jones; Michael R Lamprecht; Colin Clarke; In Han Kang; Ola Friman; David A Guertin; Joo Han Chang; Robert A Lindquist; Jason Moffat; Polina Golland; David M Sabatini
Journal:  Genome Biol       Date:  2006-10-31       Impact factor: 13.583

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  39 in total

Review 1.  Looking beyond the hype: Applied AI and machine learning in translational medicine.

Authors:  Tzen S Toh; Frank Dondelinger; Dennis Wang
Journal:  EBioMedicine       Date:  2019-08-26       Impact factor: 8.143

2.  Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity.

Authors:  Chuipu Cai; Pengfei Guo; Yadi Zhou; Jingwei Zhou; Qi Wang; Fengxue Zhang; Jiansong Fang; Feixiong Cheng
Journal:  J Chem Inf Model       Date:  2019-02-15       Impact factor: 4.956

3.  Development of a Kidney Calcification Inhibitor Employing Image-Based Profiling: A Proof-of-Concept Study.

Authors:  Anna Kletzmayr; Melina Bigler; Elita Montanari; Makoto Kuro-O; Hirosaka Hayashi; Mattias E Ivarsson; Jean-Christophe Leroux
Journal:  ACS Pharmacol Transl Sci       Date:  2020-11-23

4.  Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability.

Authors:  Oliver Laufkötter; Noé Sturm; Jürgen Bajorath; Hongming Chen; Ola Engkvist
Journal:  J Cheminform       Date:  2019-08-08       Impact factor: 5.514

5.  Harnessing the power of microscopy images to accelerate drug discovery: what are the possibilities?

Authors:  Justin Boyd; Myles Fennell; Anne Carpenter
Journal:  Expert Opin Drug Discov       Date:  2020-03-21       Impact factor: 6.098

6.  Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Authors:  Nicholas J Schaub; Nathan A Hotaling; Petre Manescu; Sarala Padi; Qin Wan; Ruchi Sharma; Aman George; Joe Chalfoun; Mylene Simon; Mohamed Ouladi; Carl G Simon; Peter Bajcsy; Kapil Bharti
Journal:  J Clin Invest       Date:  2020-02-03       Impact factor: 14.808

Review 7.  Deep learning for cellular image analysis.

Authors:  Erick Moen; Dylan Bannon; Takamasa Kudo; William Graf; Markus Covert; David Van Valen
Journal:  Nat Methods       Date:  2019-05-27       Impact factor: 28.547

Review 8.  Machine and deep learning approaches for cancer drug repurposing.

Authors:  Naiem T Issa; Vasileios Stathias; Stephan Schürer; Sivanesan Dakshanamurthy
Journal:  Semin Cancer Biol       Date:  2020-01-03       Impact factor: 15.707

Review 9.  Recent advances in drug repurposing using machine learning.

Authors:  Fabio Urbina; Ana C Puhl; Sean Ekins
Journal:  Curr Opin Chem Biol       Date:  2021-07-16       Impact factor: 8.822

10.  Target identification among known drugs by deep learning from heterogeneous networks.

Authors:  Xiangxiang Zeng; Siyi Zhu; Weiqiang Lu; Zehui Liu; Jin Huang; Yadi Zhou; Jiansong Fang; Yin Huang; Huimin Guo; Lang Li; Bruce D Trapp; Ruth Nussinov; Charis Eng; Joseph Loscalzo; Feixiong Cheng
Journal:  Chem Sci       Date:  2020-01-13       Impact factor: 9.969

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