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Record W2475358753 · doi:10.1039/c6dt01264g

Pyridylphosphinate metal complexes: synthesis, structural characterisation and biological activity

2016· article· en· W2475358753 on OpenAlexaff
Jasmine M. Cross, Natalie Gallagher, Jason H. Gill, Mohit Jain, Archibald W. McNeillis, Kimberly L. Rockley, Fiona H. Tscherny, Natasha J. Wirszycz, Dmitry S. Yufit, James W. Walton

Bibliographic record

VenueDalton Transactions · 2016
Typearticle
Languageen
FieldMedicine
TopicMetal complexes synthesis and properties
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsMetalChemistryCombinatorial chemistryNanotechnologyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

For the first time, a series of 25 pseudo-octahedral pyridylphosphinate metal complexes (Ru, Os, Rh, Ir) has been synthesised and assessed in biological systems. Each metal complex incorporates a pyridylphosphinate ligand, a monodentate halide and a capping η(6)-bound aromatic ligand. Solid- and solution-state analyses of two complexes reveal a structural preference for one of a possible two diastereomers. The metal chlorides hydrolyse rapidly in D2O to form a 1 : 1 equilibrium ratio between the aqua and chloride adducts. The pKa of the aqua adduct depends upon the pyridyl substituent and the metal but has little dependence upon the phosphinate R' group. Toxicity was measured in vitro against non-small cell lung carcinoma H460 cells, with the most potent complexes reporting IC50 values around 50 μM. Binding studies with selected amino acids and nucleobases provide a rationale for the variation in toxicity observed within the series. Finally, an investigation into the ability of the chelating amino acid l-His to displace the phosphinate O-metal bond shows the potential for phosphinate complexes to act as prodrugs that can be activated in the intracellular environment.

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 categoriesInsufficient payload (model declined to judge)
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 score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.058
GPT teacher head0.273
Teacher spread0.215 · 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 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

Citations19
Published2016
Admission routes1
Has abstractyes

Explore more

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