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Record W4385969075 · doi:10.1080/14756366.2023.2242714

Design and synthesis of new spirooxindole candidates and their selenium nanoparticles as potential dual Topo I/II inhibitors, DNA intercalators, and apoptotic inducers

2023· article· en· W4385969075 on OpenAlexaff
Samar El‐Kalyoubi, Mohamed M. Khalifa, Mahmoud T. Abo‐Elfadl, Ahmed A. El‐Sayed, Ahmed Elkamhawy, Kyeong Lee, Ahmed A. Al‐Karmalawy

Bibliographic record

VenueJournal of Enzyme Inhibition and Medicinal Chemistry · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsBiotechnology Research Institute
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaMansoura UniversityKorea Institute of Science and TechnologyNational Research Foundation
KeywordsApoptosisSeleniumChemistryDNAInducerIntercalation (chemistry)Dual (grammatical number)DNA damageCombinatorial chemistryCell biologyBiochemistryNanotechnologyCancer researchBiologyMaterials scienceGeneOrganic chemistry

Abstract

fetched live from OpenAlex

A new wave of dual Topo I/II inhibitors was designed and synthesised via the hybridisation of spirooxindoles and pyrimidines. In situ selenium nanoparticles (SeNPs) for some derivatives were synthesised. The targets and the SeNP derivatives were examined for their cytotoxicity towards five cancer cell lines. The inhibitory potencies of the best members against Topo I and Topo II were also assayed besides their DNA intercalation abilities. Compound 7d NPs exhibited the best inhibition against Topo I and Topo II enzymes with IC50 of 0.042 and 1.172 μM, respectively. The ability of compound 7d NPs to arrest the cell cycle and induce apoptosis was investigated. It arrested the cell cycle in the A549 cell at the S phase and prompted apoptosis by 41.02% vs. 23.81% in the control. In silico studies were then performed to study the possible binding interactions between the designed members and the target proteins.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.235
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2023
Admission routes1
Has abstractyes

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