A personalized limited sampling approach to better estimate terminal half‐life of FVIII concentrates Journal Articles uri icon

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abstract

  • INTRODUCTION: Hemophilia A is a bleeding disorder characterized by a deficiency of a coagulation factor VIII and optimally treated using pharmacokinetics (PK)-guided prophylactic replacement therapy. To decrease patient burden, PK can be estimated from sparse sampling leveraging population PK modeling. However, recommendations for sampling times meant for patients with hemophilia A as a group may not be optimal at the individual level. OBJECTIVE: To evaluate a personalized limited sampling approach (Personalized LSA) that suggests a next sampling time point that would provide a more accurate estimation of terminal half-life of FVIII concentrates when using a population PK approach. METHODS: 331 PK studies with rich sampling were extracted from the WAPPS-Hemo database. Two sampling approaches were evaluated and compared: 974 PK studies consisting of two samples were built from the rich sampling data including one sample selected using the personalized LSA prediction; 974 PK studies consisting of two samples were built from the rich sampling data including one sample selected randomly. Half-life values were estimated on the sparse data and compared within patients to the estimates obtained on the rich data for assessing the error on half-life values. RESULTS: Relative errors between estimates from sparse sampling data using personalized LSA and from rich sampling data were always lower than 20% and significantly lower than the comparative approach that used random sampling (median-95th percentile were 3.8%-13.1% vs. 7.0%-23.5%, respectively, p-value < 10-10 ). Moreover, less than 4% of the samples suggested by the personalized LSA were below the limit of quantification. CONCLUSIONS: Identifying the most informative sampling points for PK assessment using a Personalized LSA approach that accounts for individual differences in PK improves the precision of FVIII terminal half-life estimates in sparse sampling.

publication date

  • September 2022