Evidence map›Paper›PMID 36777618›Full record

ArticleACS omega2023

Decoding the Role of MDM2 as a Potential Ubiquitin E3 Ligase and Identifying the Therapeutic Efficiency of Alkaloids against MDM2 in Combating Glioblastoma.

Sudhanshu Sharma, Pravir Kumar

Abstract read
In one paragraph

Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The cross talk of ubiquitination and chemotherapy tolerance in colorectal cancer.Journal of cancer research and clinical oncology · 2024
    Review
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

2 authors.

Sudhanshu SharmaMolecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University, Shahabad Daulatpur, Bawana Road, Delhi 110042, India.
Pravir KumarMolecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University, Shahabad Daulatpur, Bawana Road, Delhi 110042, India.ORCID https://orcid.org/0000-0001-7444-2344

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioblastomas (GBMs) represent the most aggressive form of brain tumor arising from the malignant transformation of astrocytes. Despite various advancements, treatment options remain limited to chemotherapy and radiotherapy followed by surgery giving an overall survival of 14-15 months. These therapies are somewhere restricted in giving a better survival and cure. There is a need for new therapeutics that could potentially target GBM based on molecular pathways and pathology. Here, ubiquitin E3 ligases can be used as targets as they bind a wide array of substrates and therefore can be attractive targets for new inhibitors. Through this study, we have tried to sort various ubiquitin E3 ligases based on their expression, pathways to which these ligases are associated, and mutational frequencies, and then we tried to screen potent inhibitors against the most favorable E3 ligase as very few studies are available concerning inhibition of E3 ligase in GBM. Our study found MDM2 to be the most ideal E3 ligase and further we tried to target MDM2 against various compounds under the alkaloid class. Molecular Docking and MD simulations combined with ADMET properties and BBB scores revealed that only evodiamine and sanguinarine were effective in inhibiting MDM2. We also tried to give a proposed mechanism of how these inhibitors mediate the p53 signaling in GBM. Therefore, the new scaffolds predicted by the computational approach could help in designing promising therapeutic agents targeting MDM2 in glioblastoma.

Identifiers

PMID36777618
PMCPMC9910072

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Registered trials

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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.