The potential of using chatGPT in pharmacometrics was explored in this study, with a focus on developing a 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 identify and 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 interpretability of AI. Overall, our study demonstrates the potential of using chatGPT in pharmacometrics, but researchers must carefully evaluate its use for their specific needs.

Background Recombinant factor IX Fc fusion protein (rFIX-Fc) is an extended half-life (EHL) factor concentrate administered to haemophilia B patients. So far, a population pharmacokinetic (PK) model has only been published for patients ≥12 years of age. Aim Assess 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 (FIX activity level ≤5 IU/dL) treated with rFIX-Fc and included in the OPTI-CLOT TARGET study (NTR7523) or United Kingdom (UK)-EHL Outcome Registry (NCT02938156). Predictive performance was assessed by comparing predicted with observed FIX activity levels. A novel population PK model was constructed using nonlinear mixed-effects modelling. Results Real world data was obtained from 37 patients (median age: 16 years, range 2-71) of whom 14 were <12 years of age. Observed FIX activity levels were significantly higher than levels predicted using the published model, with a median prediction error (PE) of -48.8%. The novel model showed a lower median PE (3.4%) and better described rFIX-Fc PK, especially for children <12 years of age. In the novel model, an increase in age was correlated with a decrease in clearance (p<0.01). Conclusion The published population PK model significantly underpredicted FIX activity levels. The novel model better describes rFIX-Fc PK, especially for children <12 years of age. This study underlines the necessity to strive for representative population PK models, thereby avoiding extrapolation outside the studied population.

Aim Antidepressants are well-established fall-risk increasing drugs(FRIDs) and therefore falls should be considered an important adverse drug event(ADE) of antidepressants. However, not all antidepressant users experience fall incidents and factors associated with increased fall risk among antidepressant users are incompletely understood. Our objective was to explore whether antidepressant plasma concentrations are associated with falls in older antidepressant users. Methods For this study, we included antidepressant users of the multicenter B-PROOF study. Fall incidents were recorded prospectively using fall calendars. Antidepressant plasma concentrations were analyzed by Liquid chromatography-mass spectrometry(LC-MS) at baseline and at 2 years follow-up. The associations between the observed antidepressant concentration, or concentration change over time (delta) and fall risk were assessed using Cox proportional hazard and logistic regression models and adjusted for potential confounders. Results In total 93 selective serotonin reuptake inhibitor(SSRI) and 41 antidepressant(TCA) users were identified. There was a significant association between baseline TCA plasma concentration and fall risk within users (HR 2.50, 95% CI 1.07-5.87, crude model). Adjusted there were no significant associations between concentrations of SSRIs and fall risk. Also, for delta concentrations there was no association with fall risk in users. Conclusion There might be an association between plasma concentrations of TCAs and the risk of falling in older users. However, these results needs to be interpreted with caution considering the small sample size and accompanying limitation of confinement to crude analyses. Therefore, replication in a larger cohort, preferably including adjustment for potential confounders and more frequent measures of plasma concentrations is needed.

Aim: In our study we examined whether anthropometric and body composition parameters, i.e. body surface area (BSA), lean body mass (LBM) and total body weight (TBW), are correlated with docetaxel clearance and exposure. In addition, LBM, TBW and a fixed dose were compared to BSA as dosing parameters for dose individualisation of docetaxel. Methods: Thirty-six patients affected by breast or castration-resistant prostate carcinoma receiving docetaxel chemotherapy entered the study. LBM was measured by a Dual Energy Xray Absorptiometry (DEXA) scanner before treatment. Blood samples were collected up to 180 minutes after dosing to analyse docetaxel concentrations and to determine individual pharmacokinetic (PK) parameters. Results: No significant correlations were found between the docetaxel pharmacokinetic parameters clearance and volume of distribution and the anthropometric and body composition variables BSA, LBM and TBW. AUC was significantly but poorly correlated with BSA (r=0.452 [p=0.016]) and with TBW (r=0.476 (p=0.011]). The Mean Absolute Percentage Error and Mean Error of simulated dosing based on LBM and fixed dosing ME were not significant different compared to BSA. For TBW, only the MAPE of dosing was significant higher compared to BSA (24.1 vs. 17.1, P=0.001). Conclusion: There is no correlations between docetaxel pharmacokinetics and the anthropometric and body composition variables BSA, LBM and TBW. Dose individualisation of docetaxel based on LBM or TBW or fixed dosing cannot be recommended over BSA based dosing.