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Record W2586430873 · doi:10.1111/jcpt.12493

The influence of combination use of CYP450 inducers on the pharmacokinetics of voriconazole: a systematic review

2017· review· en· W2586430873 on OpenAlexaboutno aff
T.-y. Li, Wei Liu, K. Chen, Shuang Liang, F. Liu

Bibliographic record

VenueJournal of Clinical Pharmacy and Therapeutics · 2017
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsnot available
FundersCollege of Pharmacy, University of KentuckyUniversity of Kentucky
KeywordsVoriconazolePharmacokineticsPharmacologyMedicineAntifungalDermatology

Abstract

fetched live from OpenAlex

What is known and objectives Voriconazole is a triazole antifungal agent and is extensively metabolized via cytochrome P450 (CYP450); therefore, special precautions need to be taken when co-administered with a known CYP450 inducer, which may lead to treatment failure. The influence of some CYP450 inducers on the pharmacokinetics of voriconazole has been described in previous studies, but a systematic review was lacking. In this study, we carried out a systematic review to assess the influence of CYP450 inducers on the pharmacokinetic (PK) parameters of voriconazole. Methods Pubmed, Embase, Cochrane Library, Clinicaltrials.gov and three Chinese databases (CNKI, CBM and WanFang) were searched through January 2016. Interventional and observational studies comparing the PK parameters of voriconazole used alone or with CYP450 inducers in healthy volunteers and patients were included. The outcomes included were the area under the plasma concentration–time curve (AUC), peak plasma concentrations (Cmax) and trough plasma concentrations (Cmin). The quality of the included studies was assessed using Cochrane's risk of bias tool, Newcastle–Ottawa Scale (NOS) and a modified risk of bias tool for pharmacokinetic before-and-after studies. Results and discussion Sixteen studies were included in this review: three randomized controlled trials (RCTs), five single-arm before–after studies (SBAs), six cohort studies and two case reports. All studies except case reports had moderate to high quality. Of the 11 inducers reviewed, efavirenz, ritonavir (chronic use), phenytoin, rifampin and rifabutin significantly decreased mean AUC and Cmax of voriconazole; St John's wort significantly decreased only mean AUC; rifampin, rifabutin, phenobarbital and carbamazepine significantly decreased mean Cmin. Etravirine and Ginkgo biloba did not reveal any such influence. The influence of glucocorticoids may depend on its type and dose. What is new and conclusions To conclude, the combination use of high-dose efavirenz, high-dose ritonavir, St John's wort, rifampin, phenobarbital, or carbamazepine with voriconazole is contraindicated as instructed in the drug label. Low-dose efavirenz, low-dose ritonavir, rifabutin and phenytoin may be used together with voriconazole provided TDM and dose adjustment of voriconazole. Moreover, this study shows there is low risk of drug–drug interactions when voriconazole is co-administered with etravirine or G. biloba; however, whether the use of glucocorticoids has a clinically significant effect on voriconazole still requires more evidence. This study also highlights the lack of clinical studies and future high-quality studies assessing the influence of CYP450 inducers on voriconazole. PK parameters and dosing optimization should be designed to provide a more definitive answer regarding the necessity of TDM and the recommendations for dose adjustment of voriconazole.

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.356
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.438
GPT teacher head0.551
Teacher spread0.113 · 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 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

Citations40
Published2017
Admission routes1
Has abstractyes

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