MétaCan
Menu
Back to cohort
Record W4380730007 · doi:10.1080/14756366.2023.2220084

Organoboronic acids/esters as effective drug and prodrug candidates in cancer treatments: challenge and hope

2023· review· en· W4380730007 on OpenAlexaffabout
Mothana K. Al-Omari, Mai Elaarag, Raed M. Al‐Zoubi, Ahmad R. Al‐Qudimat, Ayman A. Zarour, Enas A. Al-Hurani, Zainab E. Fares, Leena M. Alkharraz, Mohanad Shkoor, Abdulilah Dawoud Bani‐Yaseen, Omar M. Aboumarzouk, Aksam Yassin, Abdulla Al‐Ansari

Bibliographic record

VenueJournal of Enzyme Inhibition and Medicinal Chemistry · 2023
Typereview
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsUniversité de Montréal
FundersQatar National Library
KeywordsProdrugDrugCancerProstate cancerPharmacologyMultiple myelomaBoronic acidMedicineChemistryCancer drugsCombinatorial chemistryInternal medicine

Abstract

fetched live from OpenAlex

Boronic acids/esters have recently emerged in the field of medicinal and pharmaceutical research due to their exceptional oxophilicity, low toxicity, and unique structure. They are known as potent enzyme inhibitors, cancer therapy capture agents, and can mimic certain types of antibodies to fight infections. They have been designed and developed into drugs, and this approach has emerged in the last 20 years. Five boronic acid drugs have been approved by the FDA and Health Canada, two of which are used to treat cancer, specifically multiple myeloma. The purpose of this review is to investigate boronic acid/ester derivatives as potential pharmaceutical agents as well as the mechanism of action. It will concentrate on six types of cancer: multiple myeloma, prostate cancer, breast cancer, lung cancer, cervical cancer, and colon cancer. Some newly developed boron-containing compounds have already demonstrated highly promising activities, but further investigation is required before final conclusions can be drawn.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.344
Teacher spread0.322 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations31
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Enzyme Inhibition and Medicinal ChemistrySame topicPeptidase Inhibition and AnalysisFrench-language works237,207