Commentary on Perplexingly High Tacrolimus Concentrations in a Renal Transplant Patient with HIV
Bibliographic record
Abstract
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. All authors confirmed they have contributed to the intellectual content of this paper and have met the following 4 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. No authors declared any potential conflicts of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.030 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".