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Record W4226210299 · doi:10.1093/clinchem/hvac064

Commentary on Perplexingly High Tacrolimus Concentrations in a Renal Transplant Patient with HIV

2022· letter· en· W4226210299 on OpenAlexaboutno aff
Nicholas E. Heger

Bibliographic record

VenueClinical Chemistry · 2022
Typeletter
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsTacrolimusMedicineTherapeutic drug monitoringPharmacologyImmunoassayCalcineurinAdverse effectTherapeutic indexImmunosuppressionImmunosuppressive drugWhole bloodPharmacokineticsTransplantationAntibodyInternal medicineDrugImmunology

Abstract

fetched live from OpenAlex

Cytochrome P450 3A5 (CYP3A5) genotype is a primary determinant of tacrolimus blood concentration (1). Extensive and intermediate metabolizers may require 1.5–2 times the recommended starting tacrolimus dose, whereas poor metabolizers can start with the standard recommended dose. CYP3A5 genotyping is generally not performed routinely, and while there is no direct evidence of improved clinical outcomes, knowing genotype prior to initiation of tacrolimus therapy achieves target concentrations more quickly than through therapeutic drug monitoring alone (1). Inducers and inhibitors of CYP3A4/5 can have profound effects on drug metabolism. The product label for extended-release tacrolimus, sold by Astellas Pharma Canada (Advagraf XL), specifically warns about the use of the CYP3A inhibitor ritonavir (prescribed to the patient as HIV-1 treatment), because it may result in tacrolimus-associated adverse reactions through increased tacrolimus blood concentrations (2). Accurate blood tacrolimus concentrations are important for maintaining immunosuppression, avoiding graft rejection, and minimizing adverse reactions. Falsely increased tacrolimus concentrations due to metabolite cross reactivity with immunoassays has been reported with concentrations up to 20% higher than reference methods (LC–MS/MS), although this varies by immunoassay manufacturer (3). Additional case reports describe falsely increased tacrolimus concentrations due to the presence of antitacrolimus antibodies, antibetagalactosidase antibodies, human antimouse antibodies, and rheumatoid factor (4). According to the College of American Pathologists (CAP), immunoassays represent the largest methodological group (5). Notably, the positive bias observed with patient samples using immunoassay methods is not evident with proficiency testing materials from CAP, since these simulated samples are spiked with tacrolimus only, and do not represent a physiologically-relevant mixture of parent drug and metabolites. Laboratories using tacrolimus immunoassays should be aware of these differences if referring patient samples to reference laboratories that use LC–MS/MS. Tacrolimus results from LC–MS/MS analysis should never trend in the medical record with results obtained by immunoassay methods, and potential bias in results should be discussed with the ordering provider.

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), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.315
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2022
Admission routes1
Has abstractyes

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