Bayesian Estimation of Pharmacokinetic Parameters in Children and Adolescents with Severe Hemophilia: A Single Center Experience

Abstract Background: Prophylaxis is considered as the optimal therapy for patients with severe hemophilia, but its high cost compels its individualization. Factor (F) VIII half-life (t1/2) is one of the most important variables for individualization of prophylaxis. It requires at least 5 time-points in children to be determined which constitutes a handicap due to several reason such as difficulties to obtain blood samples or those derived from the lost of hours of school or work in case of parents. The use of bayesian models to predict pharmacokinetic (PK) parameters is a promising tool for facilitating the tailoring of prophylaxis regimen in patients with hemophilia. However, software for the hemophilic population is lacking. MyPKFiT® (Baxalta®) is a novel website software based on bayesian analysis recently developed to predict PK parameters of FVIII in patients with hemophilia. This software needs only two well-selected blood samples to estimate the individual PK parameters. We hypothesized that the use of MyPKFiT® will facilitate individualization of prophylaxis and will help to detect anomalous FVIII PK profile in children and adolescents with haemophilia. Objectives: To evaluate the usefulness of the web application MyPKFiT® for the determination of individual pharmacokinetic parameters in patients under 18 years old (y) and detection of anomalous PK profiles in this patient population. Material and methods: Patients < 18 y under prophylaxis with FVIII concentrate (ADVATE®) and without inhibitors at inclusion were recruited. Washout period was not used due to it is not required for the use of the software. Joint health was determined by the Haemophilia Joint Health Score (HJHS) and evaluation of physical activity was based on the scale of Broderick. Both parameters were used together with PK parameters obtained by MyPKFiT for tailoring prophylaxis. Results: Eighteen patients were included. Mean age was 15 y (range 2-14). All patients were treated on primary prophylaxis. HJHS was zero in all the patients. PK profile was obtained with 2 samples in 14 patients and with 3 samples in 4 patients suspected to have atypical PK values. Physical activity had a mean relative risk of 1 in 15 patients and 2.7 in the rest. Based on physical activity, joint score and PK parameters provided by MyPKFit, through level was chosen to calculate the most appropriate dosage for each patient. The t1/2for FVIII was within the population curve in all patients except two in which the existence of an inhibitor were suspected. Conclusions: MyPKFit allowed us to obtain PK curve with fewer samples than required. The software helped to detect patients with anomalous PK profile. These patients were those suspected to have shorted t1/2. It is require further studies to detect the causes that might explain these altered profiles. Disclosures No relevant conflicts of interest to declare.