A framework for the optimal design of a minimum set of clinical trials to characterize von Willebrand disease

Abstract

Background and ObjectiveVon Willebrand disease (VWD) is one of the most severe inherited bleeding disorder in humans, and it is associated with a qualitative and/or quantitative deficiency of von Willebrand factor, a multimeric glycoprotein fundamental in the coagulation process. At present, the diagnosis of VWD is extremely challenging and mostly based on clinical experience. Kinetic models have been recently proposed and applied to help in the diagnosis and characterization of VWD, but the complexity of these models is such that they requires long and stressful clinical tests, such as the desmopressin response test (DDAVP), to achieve a satisfactory estimation of the individual haemostatic parameters. The goal of this paper is to design a minimal set of clinical tests for the identification of akinetic model to decrease the required time and effort for the characterization and diagnosis of VWD. MethodsA model proposed in the literature is used as a building block to develop a new model, where response surface methodologies have been applied to determine a set of explicit correlations linkingkinetic model parameters to basal clinical trials data. Model-based design of experiments techniques are then used to devise optimally informative tests for model validation which are shorter and easier to implement. ResultsResults show an excellent agreement between the original model for VWD and the new proposed model on representing healthy and VWD subjects. The application of experimental design techniques for model validation shows the possibility to drastically reduce the duration of DDAVP tests from 24 h–3 h by exploiting complementary information from basal clinical tests. ConclusionsBasal clinical tests can be used alongside a time-reduced DDAVP test to validate pharmacokinetic models for a quantitative characterisation of subjects affected by VWD and for a quicker and easier diagnosis of the disease.