INTRODUCTION: infliximab is used in inflammatory bowel disease, which has a great inter-individual pharmacokinetic variability. Thus, it is necessary to individualize the therapy in many cases. The main objective of our study was to compare two methods of a dose adjustment strategy using therapeutic drug monitoring: a) based on an algorithm and b) based on Bayesian prediction, to achieve an optimal infliximab trough level in patients with inflammatory bowel diseases. The secondary objective was to evaluate the predictive performance of a population pharmacokinetic model of infliximab in patients with inflammatory bowel diseases and therefore, its clinical utility. Furthermore, the factors associated with a suboptimal adjustment of the model were analyzed. METHODS: a retrospective observational cohort analysis was performed of patients with inflammatory bowel disease and available serum levels of infliximab. The relationship between trough concentration and dosing strategy was compared in both groups. The external validation of a previously published population pharmacokinetic model was performed using the NONMEM software. The mean prediction error and mean absolute prediction error were calculated to evaluate the predictive performance of the model. RESULTS: a total of 94 infliximab serum samples were obtained from 47 patients. After the adjustment, a total of 30 patients (63.8 %) achieved optimal infliximab trough levels. A dosing strategy based on Bayesian was associated with optimal infliximab trough levels that were higher than the strategy based on an algorithm (OR: 8.94 [95 % CI: 2.24 - 35.6], p = 0.001). For the individual predictions, the mean prediction error was 0.118 µg/ml (95 % CI: -0.149-0.384) and the mean absolute prediction error was 0.935 µg/ml (95 % CI: 0.569-1.075). CONCLUSIONS: the application of a population pharmacokinetic model based on Bayesian prediction is an important advance in the optimization of infliximab dosage in the treatment of inflammatory bowel disease.
INTRODUCTION: infliximab is used in inflammatory bowel disease, which has a great inter-individual pharmacokinetic variability. Thus, it is necessary to individualize the therapy in many cases. The main objective of our study was to compare two methods of a dose adjustment strategy using therapeutic drug monitoring: a) based on an algorithm and b) based on Bayesian prediction, to achieve an optimal infliximab trough level in patients with inflammatory bowel diseases. The secondary objective was to evaluate the predictive performance of a population pharmacokinetic model of infliximab in patients with inflammatory bowel diseases and therefore, its clinical utility. Furthermore, the factors associated with a suboptimal adjustment of the model were analyzed. METHODS: a retrospective observational cohort analysis was performed of patients with inflammatory bowel disease and available serum levels of infliximab. The relationship between trough concentration and dosing strategy was compared in both groups. The external validation of a previously published population pharmacokinetic model was performed using the NONMEM software. The mean prediction error and mean absolute prediction error were calculated to evaluate the predictive performance of the model. RESULTS: a total of 94 infliximab serum samples were obtained from 47 patients. After the adjustment, a total of 30 patients (63.8 %) achieved optimal infliximab trough levels. A dosing strategy based on Bayesian was associated with optimal infliximab trough levels that were higher than the strategy based on an algorithm (OR: 8.94 [95 % CI: 2.24 - 35.6], p = 0.001). For the individual predictions, the mean prediction error was 0.118 µg/ml (95 % CI: -0.149-0.384) and the mean absolute prediction error was 0.935 µg/ml (95 % CI: 0.569-1.075). CONCLUSIONS: the application of a population pharmacokinetic model based on Bayesian prediction is an important advance in the optimization of infliximab dosage in the treatment of inflammatory bowel disease.