ChatGPT in pharmacometrics? Potential opportunities and limitations.

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.

A new population pharmacokinetic model for recombinant factor IX-Fc fusion concentrate including young children with haemophilia B.

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.