Pharmacokinetic model-based assessment of factor IX prophylaxis treatment regimens in severe hemophilia B

An important aspect of improving care for people with hemophilia B (HB) is developing optimal treatment strategies. Here we aimed to provide in-silico evidence, comparing the estimated optimal posology of factor IX (FIX) products to support the patient-physician decision-making process. A population...

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Veröffentlicht in:Scientific reports 2024-09, Vol.14 (1), p.20534-10, Article 20534
Hauptverfasser: Vandewalle, Björn, Castaman, Giancarlo, Álvarez-Román, Maria Teresa, Ettingshausen, Carmen Escuriola, Nemes, László, Tomic, Radovan, Martins, Paulo, Rodrigues, Joana F., Pinachyan, Karen
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Sprache:eng
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Zusammenfassung:An important aspect of improving care for people with hemophilia B (HB) is developing optimal treatment strategies. Here we aimed to provide in-silico evidence, comparing the estimated optimal posology of factor IX (FIX) products to support the patient-physician decision-making process. A population pharmacokinetic (popPK) model-based assessment comparing the performance of FIX products (rFIX, rIX-FP, rFIXFc, N9-GP) was developed. PopPK analyses were used to determine a product’s optimal posology to target predefined steady-state FIX activity trough levels in a hypothetical population of 10,000 people with severe HB. Model-derived optimal posologies were compared across several parameters including trough levels, proportion of patients per regimen and consumption, considering 64 hypothetical patient scenarios of different FIX trough level targets and ages. Results indicated a marked difference between FIX products estimated to achieve target trough levels, consumption and dosing frequencies. rIX-FP was associated with higher trough levels than rFIX and rFIXFc, at a lower weekly dose and administration frequency, across all age groups. N9-GP use in adolescents and adults was associated with lower consumption compared with rIX-FP. Insights from this study may be utilized by clinicians to inform decision-making, by considering the model-generated estimated optimal posologies alongside multiple clinical factors and patient preferences.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-70784-x