Evidence map›Paper›PMID 38951694›Full record

ArticleCommunications chemistry2024

Development of Nurr1 agonists from amodiaquine by scaffold hopping and fragment growing.

Minh Sai, Emily C Hank, Hin-Man Tai, Till Kasch, Max Lewandowski, Michelle Vincendeau, Julian A Marschner, Daniel Merk

Abstract read
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Article in Communications chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

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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

8 authors.

Minh SaiLudwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany.ORCID http://orcid.org/0009-0004-6053-9566
Emily C HankLudwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany.ORCID http://orcid.org/0000-0002-3777-4697
Hin-Man TaiHelmholtz Munich, Institute of Virology, 85764, Munich, Germany.
Till KaschLudwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany.
Max LewandowskiLudwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany.ORCID http://orcid.org/0009-0007-0497-6464
Michelle VincendeauHelmholtz Munich, Institute of Virology, 85764, Munich, Germany.
Julian A MarschnerLudwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany.
Daniel MerkLudwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany. daniel.merk@cup.lmu.de.ORCID http://orcid.org/0000-0002-5359-8128

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 101040355
6 · The paper itself

Abstract

The neuroprotective transcription factor nuclear receptor-related 1 (Nurr1) has shown great promise as a therapeutic target in Parkinson's and Alzheimer's disease as well as multiple sclerosis but high-quality chemical tools for pharmacological target validation of Nurr1 are rare. We have employed the weak Nurr1 modulator amodiaquine (AQ) and AQ-derived fragments as templates to design a new Nurr1 agonist chemotype by scaffold hopping and fragment growing strategies. Systematic structural optimization of this scaffold yielded Nurr1 agonists with nanomolar potency and binding affinity. Comprehensive in vitro profiling revealed efficient cellular target engagement and compliance with the highest probe criteria. In human midbrain organoids bearing a Parkinson-driving LRRK2 mutation, a novel Nurr1 agonist rescued tyrosine hydroxylase expression highlighting the potential of the new Nurr1 modulator chemotype as lead and as a chemical tool for biological studies.

Identifiers

PMID38951694
PMCPMC11217349

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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.