MPA Cpredose and MPA AUC are significantly related to the incidence of biopsy-proven rejection after kidney transplantation, whereas MMF dose is significantly related to the occurrence of adverse events.

Background: Significant relationships between the mycophenolic acid (MPA) area under the concentration–time curve (AUC0–12h) and the risks for acute rejection and side effects have been reported. We developed a practical method for estimation of MPA AUCs. Regression equations were developed using repeated cross-validation for randomly chosen subsets, characterized statistically, and verified for acceptable performance. Methods: Twenty-one renal transplant patients receiving 0.5 or 1.0 g of mycophenolate mofetil twice daily and concomitant tacrolimus provided a total of 50 pharmacokinetic profiles. MPA concentrations were measured by a validated HPLC method in 12 plasma samples collected at predose and at 30 and 60 min; 2, 3, 4, 6, 8, 9, 10, 11, and 12 h; 1 and 2 weeks; and 3 months after transplantation. Twenty-six 1-, 2-, or 3-sample estimation models were fit (r2 = 0.341–0.862) to a randomly selected subset of the profiles using linear regression and were used to estimate AUC0–12h for the profiles not included in the regression fit, comparing those estimates with the corresponding AUC0–12h values, calculated with the linear trapezoidal rule, including all 12 timed MPA concentrations. The 3-sample models were constrained to include no samples past 2 h. Results: The model using c0h, c0.5h, and c2h was superior to all other models tested (r2 = 0.862), minimizing prediction error for the AUC0–12h values not included in the fit (i.e., the cross-validation error). The regression equation for AUC estimation that gave the best performance for this model was: 7.75 + 6.49c0h + 0.76c0.5h + 2.43c2h. When we applied this model to the full data set, 41 of the 50 (82%) estimated AUC values were within 15% of the value of AUC0–12h calculated using all 12 concentrations. Conclusions: This limited sampling strategy provides an effective approach for estimation of the full MPA AUC0–12h in renal transplant patients receiving concomitant tacrolimus therapy.

Biomarkers that predict efficacy and safety for a given drug therapy become increasingly important for treatment strategy and drug evaluation in personalized medicine. Methodology for appropriately identifying and validating such biomarkers is critically needed, although it is very challenging to develop, especially in trials of terminal diseases with survival endpoints. The marker-by-treatment predictiveness curve serves this need by visualizing the treatment effect on survival as a function of biomarker for each treatment. In this article, we propose the weighted predictiveness curve (WPC). Based on the nature of the data, it generates predictiveness curves by utilizing either parametric or nonparametric approaches. Especially for nonparametric predictiveness curves, by incorporating local assessment techniques, it requires minimum model assumptions and provides great flexibility to visualize the marker-by-treatment relationship. WPC can be used to compare biomarkers and identify the one with the highest potential impact. Equally important, by simultaneously viewing several treatment-specific predictiveness curves across the biomarker range, WPC can also guide the biomarker-based treatment regimens. Simulations representing various scenarios are employed to evaluate the performance of WPC. Application on a well-known liver cirrhosis trial sheds new light on the data and leads to discovery of novel patterns of treatment biomarker interactions.