UGT1A8 and UGT1A9 as Molecular Determinants of Mycophenolate Mofetil (MMF) Pharmacokinetics.
Bibliographic record
Abstract
Abstract Background: Mycophenolic acid (MPA), the active metabolite of the prodrug mycophenolate mofetil (MMF), is a standard immunosuppressive drug used after haematopoietic stem cell and solid organ transplantation. The pharmacokinetic profile of the drug and its phenolic (MPAG) and acyl (AcMPAG) glucuronides is characterized by unexplained interindividual variation. Despite the remarkable variability, a unique dosage is still currently used in clinics. A better knowledge of the factors influencing MMF disposition in patients is essential in order to minimize risk for the development of acute rejection and prevent toxicity. In this work, we were interested in the variability in MMF pharmacokinetics, particularly as a function of genetic background in the main metabolic pathway of MPA involving UDP-glucuronosyltransferase (UGT) enzymes. Our previous work demonstrated that the formation of the main metabolite (MPAG) is catalyzed by UGT1A9, present in both hepatic and extrahepatic tissues, and UGT1A8, expressed in the gastrointestinal tract and kidney. UGT2B7 forms the minor metabolite AcMPAG, in hepatic and extrahepatic tissues, while UGT1A8 is also involved to a minor extent in its formation. Methods: To evaluate the contribution of genetic variation in UGT1A8 (MPAG>AcMPAG) and UGT1A9 (MPAG) to the variability of MPA pharmacokinetics, 52 healthy volunteers were given a single 1.5g oral dose of MMF. These individuals were selected among 307 for the absence (n=17; controls) or the presence of UGT1A8*2 (A173G) (n=9), UGT1A8*3 (C277Y) (n=4), UGT1A9*3 (M33T) (n=5) and UGT1A9 −275/−2152 (n=17). Pharmacokinetics was measured in plasma and urine by high performance liquid chromatography coupled with tandem mass spectrometry over 12 h after drug intake. The mean age of pharmacokinetics participants was 29.2 ± 9.6 (range 20–54) and this cohort was composed of 31 and 21 healthy males and females, respectively. Results: Compared to controls, MPA exposure is lower in subjects with the low activity UGT1A8*3 but elevated in those with the high activity UGT1A9*3 (p<0.05). In contrast, AcMPAG is almost twofold higher in subjects with low activity UGT1A9*3 (p=0.021). As a result, the metabolic ratio of AcMPAG/MPAG is higher in UGT1A9*3 carriers but lower in carriers of the high activity UGT1A9 −275/−2152 and those carrying UGT1A8*3. MPAG is similar in all groups except in subjects with UGT1A9 −275/−2152. In this group, a trend toward higher Cmax and AUC of MPAG were observed, consistent with the expected higher glucuronidation capacity for the formation of MPAG associated with this genotype in vitro. Conclusions: Overall, findings indicate that the UGT1A8 and UGT1A9 genotypes significantly alter the pharmacokinetic profile of MPA and its primary glucuronide metabolites, and this influence is detectable after a single dose of MMF. The UGT1A9 −275/−2152 genotype is likely associated with an increase in MPAG formation. In contrast, it is speculated that lower UGT1A9 activity due to UGT1A9*3, may lead to a reduced amount of MPAG by this enzyme in the liver. This metabolic state would provide an increased availability of MPA for the UGT1A8- and UGT2B7-mediated pathways, therefore increasing the overall AcMPAG formation. In support of this hypothesis, the reverse situation was observed with carriers of a deficient UGT1A8*3 allele.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".