Evidence map›Paper›PMID 38602846›Full record

ArticleJournal of medicinal chemistry2024

Discovery of the First-in-Class G9a/GLP PROTAC Degrader.

Julia Velez, Yulin Han, Hyerin Yim, Peiyi Yang, Zhijie Deng, Kwang-Su Park, Md Kabir, H Ümit Kaniskan, Yan Xiong, Jian Jin

Open access · greenAbstract read
In one paragraph

Article in Journal of medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
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  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Julia VelezMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Yulin HanMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Hyerin YimMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Peiyi YangMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Zhijie DengMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Kwang-Su ParkMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.ORCID 0000-0002-1910-3937
Md KabirMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
H Ümit KaniskanMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Yan XiongMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Jian JinMount Sinai Center for Therapeutics Discovery, Departments of Pharmacological Science, Oncological Science, and Neuroscience, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.ORCID 0000-0002-2387-3862
Icahn School of Medicine at Mount Sinai · US

Funding

THE TISCH CANCER INSTITUTE - CANCER CENTER SUPPORT GRANTP30CA196521 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Deanna L Benson · 2015 to 2026
$35.4M
TRAINING PROGRAM IN CANCER THERAPYT32CA078207 · NCI · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI James J Manfredi · 1999 to 2026
$11.6M
An AVANCE NEO 400 MHz NMR Spectrometer for Chemical Biology and Drug DiscoveryS10OD028504 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ZHOU, MING-MING · 2020 to 2020
$599k
An AVANCE NEO 600 MHz NMR Spectrometer System for Structural and Chemical BiologyS10OD025132 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ZHOU, MING-MING · 2019 to 2019
$535k
NCI NIH HHS P30 CA196521NCI NIH HHS T32 CA078207NIH HHS S10 OD025132NIH HHS S10 OD028504
6 · The paper itself

Abstract

Aberrantly expressed lysine methyltransferases G9a and GLP, which catalyze mono- and dimethylation of histone H3 lysine 9 (H3K9), have been implicated in numerous cancers. Recent studies have uncovered both catalytic and noncatalytic oncogenic functions of G9a/GLP. As such, G9a/GLP catalytic inhibitors have displayed limited anticancer activity. Here, we report the discovery of the first-in-class G9a/GLP proteolysis targeting chimera (PROTAC) degrader

Indexed as

Antineoplastic AgentsHistone-Lysine N-MethyltransferaseAnimalsCell Line, TumorCell ProliferationDrug DiscoveryHistocompatibility AntigensHumansMaleMiceProteolysisStructure-Activity RelationshipAntineoplastic AgentsEHMT2 protein, humanHistocompatibility AntigensHistone-Lysine N-Methyltransferase

Identifiers

PMID38602846
PMCPMC11069390
OpenAlexW4394728036

What OpenQuestion holds

Textmetadata
LicenceTDM
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.