OBJECTIVE: Heart transplantation studies have shown a relationship between the mycophenolic acid area under the curve (AUC) 0-12 h (MPA AUC(0-12h)) values and risk of acute rejection episodes and fewer side-effects in patient receiving cyclosporine during the first year post-transplant. However, measurement of full AUC is costly and time consuming and in this case it is an impractical approach to drug monitoring. Therefore, the authors describe a limited sampling strategy to estimate the MPA AUC(0-12h) value in adult heart transplant recipients. METHODS: Ninety MPA pharmacokinetic (PK) profiles were studied. The samples were collected immediately before and 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 9, 12 h after the morning dose of mycophenolate mofetil (MMF) following an overnight fast. PK profiles were determined at 6-8 weeks, 6, 12 months and more than 1 year after transplantation. Using stepwise multiple linear regression analysis a sampling strategy from 60 of PK profiles was obtained and next the bias and precision of the model were evaluated in another 30 PK profiles. RESULTS: The three-point model using C(0.5h), C(1h), C(2h) was found to be superior to all other models tested (r(2) = 0.841). The regression equation for AUC estimation which gave the best fit to this model is: 9.69 + 0.63C(0.5) + 0.61C(1) + 2.20C(2). Using that model 63 of the 90 (70%) full AUC values were estimated within 15% of their actual value. For the best-fit model, the mean prediction error was 3.2%, with 95% confidence intervals for prediction error to range from -42.2% to 40.3%. All other models which use one, two or three time-points over the first 2 h are poorer predictors of the full AUC than the model above. CONCLUSION: The proposed three time-point equation to estimate AUC will be helpful in optimizing immunosuppressive therapy in heart transplantation.
OBJECTIVE: Heart transplantation studies have shown a relationship between the mycophenolic acid area under the curve (AUC) 0-12 h (MPA AUC(0-12h)) values and risk of acute rejection episodes and fewer side-effects in patient receiving cyclosporine during the first year post-transplant. However, measurement of full AUC is costly and time consuming and in this case it is an impractical approach to drug monitoring. Therefore, the authors describe a limited sampling strategy to estimate the MPA AUC(0-12h) value in adult heart transplant recipients. METHODS: Ninety MPA pharmacokinetic (PK) profiles were studied. The samples were collected immediately before and 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 9, 12 h after the morning dose of mycophenolate mofetil (MMF) following an overnight fast. PK profiles were determined at 6-8 weeks, 6, 12 months and more than 1 year after transplantation. Using stepwise multiple linear regression analysis a sampling strategy from 60 of PK profiles was obtained and next the bias and precision of the model were evaluated in another 30 PK profiles. RESULTS: The three-point model using C(0.5h), C(1h), C(2h) was found to be superior to all other models tested (r(2) = 0.841). The regression equation for AUC estimation which gave the best fit to this model is: 9.69 + 0.63C(0.5) + 0.61C(1) + 2.20C(2). Using that model 63 of the 90 (70%) full AUC values were estimated within 15% of their actual value. For the best-fit model, the mean prediction error was 3.2%, with 95% confidence intervals for prediction error to range from -42.2% to 40.3%. All other models which use one, two or three time-points over the first 2 h are poorer predictors of the full AUC than the model above. CONCLUSION: The proposed three time-point equation to estimate AUC will be helpful in optimizing immunosuppressive therapy in heart transplantation.