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Development of a Plasminogen Population PK model supporting prophylactic replacement therapy for Plasminogen deficient patients within the WAPPS-Hemo platform.
INTRODUCTION: Plasminogen deficiency is an ultra rare disease whose patients may develop ligneous lesions if untreated. Prophylactic replacement therapy with plasma derived plasminogen, Ryplazim, is efficient in treating lesions and could benefit from pharmacokinetic (PK) tailoring.
AIM: The objectives of this study are to develop, evaluate and integrate into the WAPPS-Hemo platform a Population PK model supporting prophylactic replacement therapy for Plasminogen deficient patients.
METHODS: Population PK modelling and evaluations followed the same protocol performed for factor VIII and IX concentrates. Limited sampling analysis used dosing and sampling scenarios in accordance with recommended treatment for Ryplazim.
RESULTS: The population PK model, derived from 16 participants included in previous clinical studies, was a 2-compartment model whose variability was best described by fat-free mass. Evaluations showed that the model described well the data and Bayesian forecasting in limited sampling environment led to acceptable precision for PK parameters relevant to plasminogen treatment.
CONCLUSION: The model was integrated into the WAPPS-Hemo webservice to help individualize prophylactic treatment in plasminogen deficient patients. Prospective PK data to be collected through the WAPPS-Hemo database will be used to better understand plasminogen PK and improve patient care.
AIM: The objectives of this study are to develop, evaluate and integrate into the WAPPS-Hemo platform a Population PK model supporting prophylactic replacement therapy for Plasminogen deficient patients.
METHODS: Population PK modelling and evaluations followed the same protocol performed for factor VIII and IX concentrates. Limited sampling analysis used dosing and sampling scenarios in accordance with recommended treatment for Ryplazim.
RESULTS: The population PK model, derived from 16 participants included in previous clinical studies, was a 2-compartment model whose variability was best described by fat-free mass. Evaluations showed that the model described well the data and Bayesian forecasting in limited sampling environment led to acceptable precision for PK parameters relevant to plasminogen treatment.
CONCLUSION: The model was integrated into the WAPPS-Hemo webservice to help individualize prophylactic treatment in plasminogen deficient patients. Prospective PK data to be collected through the WAPPS-Hemo database will be used to better understand plasminogen PK and improve patient care.
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