Population Pharmacokinetics Of Mycophenolate Mofetil In Patients Undergoing Kidney Transplantation And Its Clinical Application

Objective: The clinical therapeutic effects of mycophenolate mofetil and its active metabolite mycophenolic acid exposure is closely related. MPA AUC can predict the risk of rejection very well. The low MPA AUC could increase the incidence of acute rejection confirmed by biopsy, and the high MPAAUC could increase the infection and side-effects. When renal transplant patients are given a fixed dose, there are large individual differences between individuals and within individuals. Current clinical dates support the therapeutic drug monitoring of MPA and its population pharmacokinetics model, especially in the early postoperative monitoring, they are very important to improve the treatment of MMF. The study collected a total of renal transplantation patients with MPA blood drug concentration and other relevant clinical dates, in view the population pharmacokinetics of mycophenolic acid among relatives living kidney transplantation group after oral mycophenolate mofetil. To establish an appropriate model of population pharmacokinetics in order to be used for clinical individual drug delivery.Methods: 1) High performance liquid chromatography HPLC fluorescence method,and than establish the method of determination of the concentration of plasma in the renal transplantation patients. Chromatographic conditions are as follows, the mobile phase: acetonitrile-32mmol/L glycine buffer solution(PH=9.2)(17:83); Column temperature:30?; flow rate: 1.0m L·min-1; fluorescence excitation wavelength: 342nm;emission wavelength: 425 nm. 2) Patients who are collected for renal transplantation and measured the effective blood drug concentration of the MPA were studied, we collected the relevant information factors of the subject' pathology, and we using nonlinear mixed effect method(NONMEM) to judge the model is suitable for the study and the correlation is good or bad. The effects of gender, body weight, age, height,liver function and other physiological factors on population pharmacokinetics parameters were verified. And last we use Bayesian method to compare the results of the fitting date with the results of NONMEM calculation, and the stability of the model was investigated.Results: 1) The linear regression equation of MPA: Y=9347.870X-127.645( r=0.999);extraction recovery rate:(94.63±2.18)%; accuracy: 100.69%~110.57%. 2) To analysis the collected dates of the patients, the log of the selector is not able to show a very good normal distribution. In this study, 261 patients with renal transplantation were collected from 102 patients with MPA. The results of blood drug concentration and related clinical data were collected, and the final data model with satisfactory accuracy,precision and stability was obtained.3)The collection of renal transplant patients 13cases(male 10 female 3 cases), 3 cases in which the concentration of less than1.0mg/L, 10 blood concentration cases are successfully be predicted, the average blood concentrations were determined by 1.41mg/L, and the prediction error of blood drug concentration is in the range of 6.1%.The final model equation: CL=55.9*EXP(ETA(1))V=10600**(POD/15)**1.25*(TBIL/12)**0.230*EXP(ETA(2))Conclusion:The high performance liquid chromatography with fluorescence method was established by this experiment, and it is simple and practicable. The combination of the use of cyclosporine, tacrolimus and other immune agents on the chromatogram peak type without interference, it can be more accurate to measure the concentration of the plasma in the plasma of the patients after renal transplantation. It is suitable for the pharmacokinetic study and routine blood drug concentration monitoring. NONMEM established the final pharmacokinetic model, many kinds of factors are selected, and finally, it is concluded that the postoperative time and total bilirubin are important parameters of pharmacokinetics in patients after renal transplantation, and it can be used as a guide for individual drug delivery based on the clinical drug treatment. The model has a good stability and good prediction of the blood drug concentration in small range of renal transplant patients can be obtained more accurate results, which can be used for clinical individualized drug delivery.