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Record W3111685685 · doi:10.1111/bcp.14692

Population pharmacokinetics and exposure‐response analysis of tigecycline in patients with hospital‐acquired pneumonia

2020· article· en· W3111685685 on OpenAlexaff
Yangang Zhou, Ping Xu, LI Huan-de, Feng Wang, Han Yan, Wu Liang, Daxiong Xiang, Bikui Zhang, Hoan Linh Banh

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTigecyclineMedicinePharmacokineticsPopulationInternal medicinePharmacodynamicsVolume of distributionHospital-acquired pneumoniaPharmacologyPneumoniaAntibioticsMicrobiologyBiology

Abstract

fetched live from OpenAlex

Background Tigecycline has been widely used to treat hospital‐acquired pneumonia (HAP) off‐label since it is effective against a wide range of multidrug‐resistant bacteria. However, no recommended dosage for this indication has been evaluated, resulting in possible inadequate treatment. Aims The aims of this study are to establish the population pharmacokinetic (PPK) model of tigecycline in Chinese patients with HAP, as well as to evaluate the exposure‐response relationship for the treatment of HAP with multidrug‐resistant gram‐negative bacteria. Methods A PPK analysis of tigecycline was conducted on pooled data from 328 blood samples obtained from 89 patients with HAP. Tigecycline plasma concentrations were measured by a two‐dimensional liquid chromatographic system and the data were analysed using Phoenix NLMETM software. Exposure‐response analyses for efficacy were performed based on the data from 79 HAP patients with multidrug‐resistant gram‐negative infections. Classification and regression tree and logistic regression analyses were employed to identify which pharmacokinetic‐pharmacodynamic (PK‐PD) indices and magnitudes were the significant predictors of tigecycline efficacy. Results A two‐compartment model with zero‐order absorption and first‐order elimination adequately described the data. A larger body weight was associated with increased central volume of distribution and clearance ( P < .005), and increased age, baseline creatinine concentration and aspertate aminotransferase were associated with decreased clearance ( P < .005). The AUC 0‐12h × V/MIC ratio, APACHEII score and combined Pseudomonas aeruginosa infection are the strong predictors for tigecycline clinical response. Classification and regression tree analyses indicated that the combination of APACHEII score < 24 and AUC 0‐12h × V/MIC ratio ≥ 100 was associated with clinical success. Conclusions The proposed PPK model may serve as the basis for estimating tigecycline exposure for PK‐PD analyses, and the PK‐PD index and magnitude found in this study could be used for designing proper dosage regimens of tigecycline.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.321
Teacher spread0.308 · 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 designObservational
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

Citations30
Published2020
Admission routes1
Has abstractyes

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