Evidence map›Paper›PMID 35793809›Full record

ArticleACS chemical biology2022

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature.

Maria-Anna Trapotsi, Elizabeth Mouchet, Guy Williams, Tiziana Monteverde, Karolina Juhani, Riku Turkki, Filip Miljković, Anton Martinsson, Lewis Mervin, Kenneth R Pryde and 5 more

Open access · greenAbstract read
In one paragraph

Article in ACS chemical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
13.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 46 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 5 institutions in 6 countries.

Maria-Anna TrapotsiDepartment of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Elizabeth MouchetHigh Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K.
Guy WilliamsHigh Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K.
Tiziana MonteverdeHigh Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K.
Karolina JuhaniHigh Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K.
Riku TurkkiData Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden.
Filip MiljkovićImaging and Data Analytics, Clinical Pharmacology & Safety Sciences R&D, AstraZeneca, Gothenburg SE-43183, Sweden.ORCID 0000-0001-5365-505X
Anton MartinssonImaging and Data Analytics, Clinical Pharmacology & Safety Sciences R&D, AstraZeneca, Gothenburg SE-43183, Sweden.
Lewis MervinMolecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K.ORCID 0000-0002-7271-0824
Kenneth R PrydeOncology Safety, Clinical Pharmacology and Safety Sciences R&D, AstraZeneca, Cambridge CB2 0AA, U.K.
Erik MüllersBioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden.
Ian BarrettData Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K.
Ola EngkvistMolecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden.ORCID 0000-0003-4970-6461
Andreas BenderDepartment of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.ORCID 0000-0002-6683-7546
Kevin MoreauSafety Innovation, Clinical Pharmacology and Safety Sciences R&D, AstraZeneca, Cambridge CB2 0AA, U.K.ORCID 0000-0002-3688-3998
AstraZeneca (Sweden) · SEAstraZeneca (Poland) · PLAstraZeneca (Australia) · AUAstraZeneca (South Korea) · KRUniversity of Cambridge · GB

Funding

Biotechnology and Biological Sciences Research Council BB/M011194/1
6 · The paper itself

Abstract

PROteolysis TArgeting Chimeras (PROTACs) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances made in targeted protein degradation technology have been remarkable, with several molecules having moved into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step toward delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high-content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.

Indexed as

High-Throughput Screening AssaysProteolysisUbiquitin-Protein LigasesHumansProteasome Endopeptidase ComplexProteasome Endopeptidase ComplexUbiquitin-Protein Ligases

Identifiers

PMID35793809
PMCPMC9295119
OpenAlexW4284671611

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.