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Record W4409346883 · doi:10.31083/ph.2025.5510

Pharmacokinetic study of two sulfamethoxazole-phytochemical antimicrobial conjugates in mice

2025· article· en· W4409346883 on OpenAlexaff
Jian Yang, Binbing Ling, Saravana Babu Chidambaram, Meena Kishore Sakharkar

Bibliographic record

Venue˜Die œPharmazie · 2025
Typearticle
Languageen
FieldChemistry
TopicSynthesis and Biological Evaluation
Canadian institutionsNational Research Council CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsPhytochemicalAntimicrobialPharmacokineticsSulfamethoxazoleConjugateTraditional medicinePharmacologyMedicineMicrobiologyBiologyAntibioticsMathematics

Abstract

fetched live from OpenAlex

Antimicrobial resistance (AMR) has emerged to be a big threat to both human and animal health. Along the tighter control on antibiotic use, developing novel therapeutic agents and approaches is crucial for combating AMR. We recently designed and synthesized several antibiotic-phytochemical conjugates which exhibited potent antimicrobial activities. To understand their pharmacological behavior and obtain a guideline for further drug development, we undertook a pharmacokinetic study on sulfamethoxazole-gallate and sulfamethoxazole-caffeate. Cmax was determined to be 842 ± 544 ng/mL at dose of 1500 mg/kg and 733 ± 477 ng/mL at dose of 2000 mg/kg for sulfamethoxazole-gallate and 211 ± 100 at a dose of 150 mg/kg and 755 ± 705 ng/mL at dose of 300 mg/kg for sulfamethoxazole-caffeate. Tmax was 1 h for sulfamethoxazole-gallate under both doses and 0.5 h for sulfamethoxazole-caffeate under both doses. Since Cmax was significantly lower than the in vitro MIC for both conjugates, more formulations and administration routes such as IV injection will be investigated in our future studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.353
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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