Purpose The aim of this study was to characterize the the effects of clonidine on sedation, heart rate (HR) and mean arterial blood pressure (MAP) in critically ill, mechanically ventilated adult patients, using population pharmacodynamic modelling. Methods Thirty-two patients received continuous intravenous clonidine, added to standard sedation, at 0, 600, 1200 or 1800 μg/day. Sedation, HR, and MAP were modelled as a function of predicted clonidine concentrations using NONMEM. Model performance was assessed by goodness-of-fit plots, bootstrapping, and visual predictive checks. Results The sedation model included 773 Richmond Agitation-Sedation scale (RASS) assessments with corresponding predicted clonidine concentrations. The probability of deep sedation (RASS ≤-4) was described by a logistic regression model with an intercept of 2.23 (relative standard error (RSE) 25%), slope of -0.383 (RSE 17%), and covariate effects of midazolam (-2.48, RSE 7%) and propofol (-1.54, RSE 13%). The hemodynamic models included 783 paired HR and MAP measurements. HR was best described by a linear model with a baseline of 90.6 bpm (RSE 2%) and slope of -3.30 bpm per µg/L (RSE 10%). MAP was also modelled linearly, with a baseline of 76.9 mmHg (RSE 2%), and slope of -0.887 mmHg per µg/L (RSE 42%). Including inter-individual variability on baseline values significantly improved both models. Conclusion In mechanically ventilated ICU patients, clonidine was associated with a dose-dependent increase in the probability of deep sedation, and with linear, dose-dependent reductions in HR and MAP.

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.