Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Mycophenolic acid in diabetic renal transplant recipients

Limited sampling strategies may be useful in optimizing therapeutic drug monitoring of mycophenolic acid (MPA). Their use, however, may be limited by several patient factors, including comorbidity. In this study the pharmacokinetics of MPA in diabetic and nondiabetic renal transplant recipients were compared, and it was evaluated whether a limited sampling strategy developed and validated for nondiabetic patients can also be used in diabetic patients. The pharmacokinetics of MPA were analyzed on days 7 and 11 after transplantation in 136 renal transplant patients, among whom 7 patients had diabetes. All patients received cyclosporine and corticosteroids as maintenance immunosuppressive therapy. A limited sampling strategy [AUC (mg x h/L) = 7.182 + 4.607 C0 + 0.998 C0.67 + 2.149 C2] was developed and validated for nondiabetic patients and was subsequently tested for its usefulness in diabetic patients. Diabetic renal transplant patients did not have significantly different dose-normalized MPA area under concentration-time curve (AUC), MPA clearance, or MPA maximum concentration (Cmax). However, in diabetic patients Tmax (time of Cmax, 1.59 hours) was higher than for nondiabetic patients (0.67 hours) on day 11 (P = 0.04). The developed and validated limited sampling strategy performed acceptably, estimating MPA AUC in nondiabetic patients with a mean bias of 0.2 mg x h/L (95% confidence interval from -1.3 to 1.6 mg x h/L). Applying the limited sampling strategy in diabetic patients revealed a mean bias of -1.5 (-5.7, 2.7 mg x h/L). In conclusion, although diabetic renal transplant patients exhibit increased Tmax, this does not affect the accuracy of the limited sampling strateg

Population Pharmacokinetics of Clotting Factor Concentrates and Desmopressin in Hemophilia

Hemophilia A and B are bleeding disorders caused by a deficiency of clotting factor VIII and IX, respectively. Patients with severe hemophilia (< 0.01 IU mL−1) and some patients with moderate hemophilia (0.01–0.05 IU mL−1) administer clotting factor concentrates prophylactically. Desmopressin (d-amino d-arginine vasopressin) can be applied in patients with non-severe hemophilia A. The aim of administration of factor concentrates or desmopressin is the prevention or cessation of bleeding. Despite weight-based dosing, it has been demonstrated that factor concentrates still exhibit considerable pharmacokinetic variability. Population pharmacokinetic analyses, in which this variability is quantified and explained, are increasingly performed in hemophilia research. These analyses can assist in the identification of important patient characteristics and can be applied to perform patient-tailored dosing. This review aims to present and discuss the population pharmacokinetic analyses that have been conducted to develop population pharmacokinetic models describing factor levels after administration of factor VIII or factor IX concentrates or d-amino d-arginine vasopressin. In total, 33 publications were retrieved from the literature. Two approaches were applied to perform population pharmacokinetic analyses, the standard two-stage approach and non-linear mixed-effect modeling. Using the standard two-stage approach, four population pharmacokinetic models were established describing factor VIII levels. In the remaining 29 analyses, the non-linear mixed-effect modeling approach was applied. NONMEM was the preferred software to establish population pharmacokinetic models. In total, 18 population pharmacokinetic analyses were conducted on the basis of data from a single product. From all available population pharmacokinetic analyses, 27 studies also included data from pediatric patients. In the majority of the population pharmacokinetic models, the population pharmacokinetic parameters were allometrically scaled using actual body weight. In this review, the available methods used for constructing the models, key features of these models, patient population characteristics, and established covariate relationships are described in detail