High VWF:Act and accumulation of FVIII were observed after perioperative FVIII-based replacement therapy in patients with VWD, both underlining the necessity of personalization of dosing regimens to optimize perioperative treatment.

According to GlaxoSmithKline's Clinical Trial Register, data from the GlaxoSmithKline studies LAM100034 and LEP103944, corresponding to ClinicalTrials.gov identifiers NCT00113165 and NCT00264615, used in this work, have been used in previous publications (doi: https://doi.org/10.1212/01.wnl.0000277698.33743.8b , https://doi.org/10.1111/j.1528-1167.2007.01274.x ).

No abstract

Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time‐consuming to develop. There is great interest in the adoption of machine‐learning methods, but most implementations cannot be reliably extrapolated to treatment strategies outside of the training data. In order to solve this problem, we propose the deep compartment model (DCM), a combination of neural networks and ordinary differential equations. Using simulated datasets of different sizes, we show that our model remains accurate when training on small data sets. Furthermore, using a real‐world data set of patients with hemophilia A receiving factor VIII concentrate while undergoing surgery, we show that our model more accurately predicts a priori drug concentrations compared to a previous NLME model. In addition, we show that our model correctly describes the changing drug concentration over time. By adopting pharmacokinetic principles, the DCM allows for simulation of different treatment strategies and enables therapeutic drug monitoring.

Objective Most von Willebrand disease (VWD) patients can be treated with desmopressin during bleeding or surgery. Large interpatient variability is observed in von Willebrand factor (VWF) activity levels after desmopressin administration. The aim of this study was to develop a pharmacokinetic (PK) model to describe, quantify, and explain this variability. Methods Patients with either VWD or low VWF, receiving an intravenous desmopressin test dose of 0.3 µg kg−1, were included. A PK model was derived on the basis of the individual time profiles of VWF activity. Since no VWF was administered, the VWF dose was arbitrarily set to unity. Interpatient variability in bioavailability (F), volume of distribution (V), and clearance (Cl) was estimated. Results The PK model was developed using 951 VWF activity level measurements from 207 patients diagnosed with a VWD type. Median age was 28 years (range: 5–76), median predose VWF activity was 0.37 IU/mL (range: 0.06–1.13), and median VWF activity response at peak level was 0.64 IU/mL (range: 0.04–4.04). The observed PK profiles were best described using a one-compartment model with allometric scaling. While F increased with age, Cl was dependent on VWD type and sex. Inclusion resulted in a drop in interpatient variability in F and Cl of 81.7 to 60.5% and 92.8 to 76.5%, respectively. Conclusion A PK model was developed, describing VWF activity versus time profile after desmopressin administration in patients with VWD or low VWF. Interpatient variability in response was quantified and partially explained. This model is a starting point toward more accurate prediction of desmopressin dosing effects in VWD.