| Autor: |
Sun, Sha‐Sha, Shao, Kun, Lu, Jia‐Qian, An, Hui‐Min, Shi, Hao‐Qiang, Zhou, Pei‐Jun, Chen, Bing |
| Předmět: |
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| Zdroj: |
Journal of Clinical Pharmacology; Apr2023, Vol. 63 Issue 4, p410-420, 11p |
| Abstrakt: |
There is significant enterohepatic circulation (EHC) during the disposition of mycophenolic acid (MPA). The aim of this study was to elucidate factors influencing the EHC of MPA in Chinese adult renal allograft recipients. After 2 weeks of therapy with mycophenolate mofetil or enteric‐coated mycophenolate sodium, blood samples were collected from 125 patients at 0 to 12 hours post‐administration and MPA concentrations were determined. The influence of calcineurin inhibitors (CNIs) and genetic polymorphisms on MPA exposure and EHC was studied. The Shapley additive explanations method was used to estimate the impact of various factors on the area under the plasma drug concentration–time curve (AUC0–12h) for MPA. An extreme gradient boosting (XGboost) machine learning‐based model was established to predict AUC0–12h. Results showed that the dose‐normalized AUC6–12h (dn‐AUC6–12h) of MPA was significantly lower in patients co‐administered with cyclosporine (CsA) than in patients co‐administered with tacrolimus (TAC) (P <.05). For patients co‐administered with TAC, patients with ABCC2 C‐24T CC or SLCO1B1 T521C TT genotypes had significantly higher values of dn‐AUC6‐12h (P <.05). Patients with SLCO1B3 334T/699G alleles had significantly lower dn‐AUC6–12h values than homozygotes (P <.05). By introducing body weight, age, and EHC‐related factors, including co‐administered CNIs and genetic polymorphism of drug transporters, as covariates in the XGboost machine learning model, the prediction performance of AUC0–12h for MPA in Chinese adult renal allograft recipients can be improved. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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