External qualification of the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) models for octocog alfa using real patient data

Autor: Alfonso Iorio, Dagmar M. Hajducek, Mohammed Mahdi, Josh Silvertown, Andrea N. Edginton, Stuart Young, Pierre Chelle
Rok vydání: 2021
Předmět:
Zdroj: Research and Practice in Thrombosis and Haemostasis
Research and Practice in Thrombosis and Haemostasis, Vol 5, Iss 7, Pp n/a-n/a (2021)
ISSN: 2475-0379
Popis: Background Existing adult patient pharmacokinetic (PK) data from the published Advate vs Kovaltry PK crossover study were used for this validation study. This data set is appropriate for qualification, given that it has not been previously submitted to Web‐Accessible Population Pharmacokinetic Service–Hemophilia (WAPPS‐Hemo) and will not have impacted the WAPPS‐Hemo models for Kovaltry. Objective To compare the population PK parameters for Kovaltry (BAY 81‐8973) derived from the WAPPS‐Hemo models with PK parameters derived from noncompartmental analysis (NCA), using a validation PK dataset. Methods The qualification data set included Kovaltry factor activity (10 samples per infusion) and anthropometric data for 18 patients. Two analyses were performed comparison of Bayesian forecasting from the WAPPS‐Hemo models versus NCA using the full 10‐sample data set; and comparison of Bayesian forecasting using the full versus reduced 4‐ and 3‐sample data sets. Agreement between outcomes was assessed by quantifying the variability and bias of the error. Results Comparison of WAPPS‐Hemo models versus NCA led to well‐correlated outcomes despite a systematic overprediction of clearance. Population PK models demonstrated greater consistency with NCA on one‐stage data, compared with chromogenic data. WAPPS‐Hemo model results were consistent in reduced sampling compared to full sampling. Inclusion of a 48‐hour time point in the reduced sampling greatly improved the consistency with full sampling. Discussion Qualification of population PK models and their use for Bayesian forecasting in full and reduced sampling is an essential step toward their validation. The evaluations performed in this study support the confidence of PK parameter estimates provided by the models.
Databáze: OpenAIRE
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