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Patients with severe and sometimes moderate hemophilia B are prophylactically treated with factor IX concentrates to prevent bleeding. For some time now, various extended terminal half‐life (EHL) recombinant factor IX concentrates are available allowing less frequent administration during prophylaxis in comparison to standard half‐life recombinant FIX (rFIX). Especially, recombinant FIX‐Fc fusion protein (rFIXFc; Alprolix®) exhibits a rapid distribution phase, potentially due to binding to type IV collagen (Col4) in the extravascular space. Studies suggest that the presence of extravascular rFIXFc is protective against bleeding as without measurable FIX activity in plasma, and no extra bleeding seems to occur. The physiologically based pharmacokinetic (PBPK) model for rFIXFc which we describe in this study, is able to accurately predict the observed concentration‐time profiles of rFIXFc in plasma and is able to quantify the binding of rFIXFc to Col4 in the extravascular space after an intravenous dose of 50 IU/kg rFIXFc in a male population. Our model predicts that the total AUC of rFIXFc bound to Col4 in the extravascular space is approximately 19 times higher compared to the AUC of rFIXFc in plasma. This suggests that rFIXFc present in the extravascular compartment may play an important role in achieving hemostasis after rFIXFc administration. Further studies on extravascular distribution of rFIXFc and the distribution profile of other EHL‐FIX concentrates are needed to evaluate the predictions of our PBPK model and to investigate its clinical relevance. [ABSTRACT FROM AUTHOR]

Aims: Recombinant factor IX Fc fusion protein (rFIX-Fc) is an extended half-life factor concentrate administered to haemophilia B patients. So far, a population pharmacokinetic (PK) model has only been published for patients aged ≥12 years. The aim was to externally evaluate the predictive performance of the published rFIX-Fc population PK model for patients of all ages and develop a model that describes rFIX-Fc PK using real-world data., Methods: We collected prospective and retrospective data from patients with haemophilia B treated with rFIX-Fc and included in the OPTI-CLOT TARGET study (NTR7523) or United Kindom (UK)-EHL Outcomes Registry (NCT02938156). Predictive performance was assessed by comparing predicted with observed FIX activity levels. A new population PK model was constructed using nonlinear mixed-effects modelling., Results: Real-world data were obtained from 37 patients (median age: 16 years, range 2-71) of whom 14 were aged <12 years. Observed FIX activity levels were significantly higher than levels predicted using the published model, with a median prediction error of -48.8%. The new model showed a lower median prediction error (3.4%) and better described rFIX-Fc PK, especially for children aged <12 years. In the new model, an increase in age was correlated with a decrease in clearance (P < .01)., Conclusions: The published population PK model significantly underpredicted FIX activity levels. The new model better describes rFIX-Fc PK, especially for children aged <12 years. This study underlines the necessity to strive for representative population PK models, thereby avoiding extrapolation outside the studied population., (© 2023 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

The potential of using ChatGPT in pharmacometrics was explored in this study, with a focus on developing a population pharmacokinetic (PK) model for standard half-life factor VIII. Our results demonstrated that ChatGPT can be utilized to accurately obtain typical PK parameters from literature, generate a population PK model in R and develop an interactive Shiny application to visualize the results. ChatGPT's language generation capabilities enabled the development of R codes with minimal programming knowledge and helped to identify as well fix errors in the code. While ChatGPT presents several advantages, such as its ability to streamline the development process, its use in pharmacometrics also has limitations and challenges, including the accuracy and reliability of AI-generated data, the lack of transparency and reproducibility regarding codes generated by ChatGPT. Overall, our study demonstrates the potential of using ChatGPT in pharmacometrics, but researchers must carefully evaluate its use for their specific needs., (© 2023 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)