Evidence map›Paper›PMID 38130684›Full record

Article3 Biotech2024

In silico screening of chalcone derivatives as promising EGFR-TK inhibitors for the clinical treatment of cancer.

Shalini Mathpal, Tushar Joshi, Priyanka Sharma, Priyanka Maiti, Mahesha Nand, Veena Pande, Subhash Chandra

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Article in 3 Biotech, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.8field-weighted citation impact, top 16% 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

9 citing papers in PubMed, 12 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

7 authors at 3 institutions in 1 country.

Shalini MathpalDepartment of Biotechnology, Bhimtal Campus, Kumaun University, Bhimtal, Uttarakhand 263136 India.
Tushar JoshiDepartment of Biotechnology, Bhimtal Campus, Kumaun University, Bhimtal, Uttarakhand 263136 India.
Priyanka SharmaDepartment of Botany, D.S.B Campus, Kumaun University, Nainital, Uttarakhand India.
Priyanka MaitiCentre for Environmental Assessment and Climate Change, G.B. Pant, National Institute of Himalayan Environment (GBP-NIHE), Kosi-Katarmal, Almora, Uttarakhand 263643 India.
Mahesha NandENVIS Centre on Himalayan Ecology, G.B. Pant National Institute of Himalayan Environment (GBP-NIHE), Kosi-Katarmal, Almora, Uttarakhand 263643 India.
Veena PandeDepartment of Biotechnology, Bhimtal Campus, Kumaun University, Bhimtal, Uttarakhand 263136 India.
Subhash ChandraComputational Biology and Biotechnology Laboratory, Department of Botany, Soban Singh Jeena University, Almora, Uttarakhand 263601 India.ORCID 0000-0002-8978-5427
Kumaun University · ING.B. Pant Institute of Himalayan Environment and Development · INUttarakhand Seva Nidhi Paryavaran Shiksha Sansthan · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidermal growth factor receptor (EGFR) promotes tumorigenic characteristics and activates cancer-associated signaling pathways such as Wnt/-catenin, transforming growth factor (TGF-β), and phosphoinositide-3-kinase (PI3K). Several inhibitors have been reported to suppress the activity of EGFR and are being used in cancer treatment. However, patients in the malignant stage of cancer show resistance to those inhibitors, opening a wide space for research to discover novel inhibitors. Therefore, we carried out machine learning and virtual screening to discover novel inhibitors with high affinity against EGFR-TK. Initially, a library of 2640 chalcones were screened out using a machine-learning model developed based on the random forest algorithm, exhibiting high sensitivity and a Receiver Operating Characteristic curve (ROC area) of 0.99. Furthermore, out of the initial 2640 screened compounds, 412 compounds exhibiting potential activity are subjected to evaluation for drug-likeness properties through different filters: Blood-brain barrier penetration, Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter, alongside Cell Line Cytotoxicity Prediction. A total of 30 compounds that successfully pass through all these filters are selected for molecular docking. Of these, 6 compounds display substantial binding affinity and closer interaction with the conserved catalytic residues of the target EGFR-TK compared to the reference molecule (erlotinib). Furthermore, molecular dynamics simulation studies were conducted on four compounds (CID-375861, CID-375862, CID-23636403, and CID-259166) to confirm the stability of the docked complexes over a 100 ns simulation trajectory. Additionally, the binding free energy calculations by MMPBSA reveal that these four chalcone compounds exhibit strong affinity towards the EGFR-TK enzyme, with binding free energies of - 65.421 kJ/mol, - 94.266 kJ/mol, - 80.044 kJ/mol, and - 79.734 kJ/mol, respectively. The findings from this investigation highlight a set of promising chalcone compounds that have the potential to be developed into effective drugs for the treatment of various cancers. Further research and development on these compounds could pave the way for novel therapeutic interventions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03858-8.

Indexed as

Chalcone compoundsEGFRMachine learningMD simulationMolecular docking

Identifiers

PMID38130684
PMCPMC10730483
OpenAlexW4389934346

What OpenQuestion holds

Textmetadata
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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.