Clinical and Genetic Determinants of Tacrolimus Dose During Induction and Stable Phase of Therapy.
Bibliographic record
Abstract
Background Tacrolimus is a calcineurin inhibitor widely used in patients undergoing solid organ transplantation. However, tacrolimus therapy is hindered by highly variable dose requirements in patients associated with toxicity and efficacy (prevention of rejection). Metabolism of tacrolimus via the cytochrome P450 (CYP) 3A family accounts for a significant portion of tacrolimus dosing requirements. Currently, there is paucity of data regarding predictors of tacrolimus dosing, particularly during the initiation (induction) phase of therapy. Thus, we assessed the differential impact of CYP3A genetic polymorphisms on tacrolimus dose and plasma exposure during induction and maintenance phases of therapy. Methods We enrolled 167 renal transplant patients treated with tacrolimus (as bid Prograf) who were at least 3 months post-transplant. Tacrolimus trough concentrations and dose adjustments during induction (0 - 3 months) and stable phase (after 3 months) were retrospectively analyzed. Results The mean tacrolimus dose required during both induction and stable phase was significantly greater in CYP3A5 expressors (CYP3A5*1/*1 and *1/*3) compared to non-expressors (CYP3A5*3/*3; P<0.0001). Importantly, dose-normalized trough concentrations were significantly lower for CYP3A5 expressors (P<0.0001). In addition, dose-normalized concentrations appeared to be consistent throughout the treatment periods, indicating that intrinsic metabolism of tacrolimus does not vary between induction and stable phase of therapy. Regression analysis indicated that CYP3A5*3 and CYP3A4*22 genotype, hemoglobin, and weight were significant predictors of induction dose, accounting for 30% of the variation, while CYP3A5*3 genotype, months post transplant, and age were significant predictors of stable dose, accounting for 42% of the variation. Other significant variables with less impact on tacrolimus dose include POR*28, ABCB1 3435C>T, PXR -25385C>T, and creatinine clearance. Conclusions We demonstrated that CYP3A5*3 genotype is the major determinant of tacrolimus dose variability during induction and stable therapy. Genetic variation in CYP3A4 (*22) was identified as yet unrecognized significant contributor to tacrolimus dose. These findings have the potential to individualize tacrolimus dosing, reduce time to target concentration, improve efficacy, and minimize toxicities. DISCLOSURES:Muirhead, N.: Grant/Research Support, Astella. Kim, R.: Grant/Research Support, Astella.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".