UGT1A8 and UGT1A9 as Molecular Determinants of Mycophenolate Mofetil (MMF) Pharmacokinetics.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».