Feasibility of a Web-Based Visualization Tool for Estimating Ibuprofen Pharmacokinetic and Pharmacodynamic Profiles to Support Self-Medication

Background: Ibuprofen is an over-the-counter (OTC) medication widely used as an antipyretic and analgesic. However, frequent use without medical supervision can lead to improper use and unintended side effects. Nonetheless, obtaining advice from medical professionals regarding OTC drugs can be difficult for ordinary users. Hence, this study aimed to develop a user-friendly visualization-based web application for ibuprofen and to verify the feasibility of enabling general users to simulate and visualize blood drug concentrations and predict drug efficacy, thereby informing proper usage. Methods: Previously reported population pharmacokinetic and pharmacodynamic (PK/PD) models were reconstructed using Phoenix® NLME, and the estimates were validated via visual predictive check against reported population observations and values derived from physiologically based pharmacokinetic (PBPK) analysis. Additionally, to facilitate visualization of the effects of ibuprofen on the body, a web application for PK/PD simulation was developed based on the reconstructed model and implemented in Python using the Runge–Kutta method. Results: The developed web application (VisiblePharm™) visualized the relationship between the administered dose and ibuprofen concentration in plasma over time and simultaneously assess its efficacy. However, individual differences are limited to variations resulting from differences in physique. The simulation results also showed good agreement with estimates derived from PBPK analysis. Conclusion: VisiblePharm demonstrated that, despite the use of a limited set of covariates within the reconstruction model, it is possible to readily estimate the pharmacokinetics and antipyretic effects of ibuprofen following administration. We believe that with further clinical validation, this application will contribute to society as a health planning support tool, facilitating successful self-medication.