Feng Yang, Ph.D: No financial relationships to disclose
Objectives: PharmaInsight Explorer is a web app [1] tailored for clinical pharmacology and safety science (CPSS). It offers interactive exploratory data analysis (EDA) through customizable shiny modules. Users can upload diverse datasets and leverage powerful filtering and visualization tools. The platform facilitates the analysis of immunogenicity, PK/PD datasets, and dose-response relationships. It promotes reproducibility by generating scripts and fosters collaboration through shared modules.
Introduction: Exploratory analysis can be helpful for understanding the general behavior of data and extracting valuable insights promptly. However, the process can be hindered by the lack of programming resources to generate extensive tables, figures, or listings, as well as the time-consuming and error-prone nature of constantly visualizing data in ad hoc manner. To address these challenges, we propose the implementation of a systematic checklist comprising suggested exploratory plots integrated into an interactive framework. This framework not only streamlines the creation of essential plots but also mitigates the risk of pharmacometricians prematurely applying mixed effects modeling techniques [2].
Methods: Pre-Standardized Input Datasets: Before importing into the app, input datasets must undergo pre-standardization. Detailed data structure and specifications are provided within the app, following an ADAM-like structure (e.g., ADSL, ADPC, ADLB), where ADSL serves as the master dataset for all loaded data. Identified Essential EDA: A checklist of essential exploratory data analysis (EDA) plots is compiled, focusing on immunogenicity assessment, PK/PD datasets, and the Dose-Exposure-Response relationship. This includes plots such as spaghetti plots, dose proportionality assessments, hysteresis plots, Sigmoid-Emax model fits, ADA incidence tables, heatmaps, etc. [2]. Adoption of Teal Shiny Framework: Our platform utilizes the Teal framework [3], comprising customizable Shiny modules. It offers powerful filtering functions, standardized tables and plots, intuitive interactivity, and reproducibility through generated R/Rmarkdown scripts.
Results: • Developed and showcased a powerful and modularized app for exploratory analysis in the context of clinical pharmacology. • Modularized framework not only expedited the app development, also fostered collaboration through shared modules. • By “Show R code” and being able to download Rmarkdown-based report, it promotes reproducibility and further customization.
Conclusions: PharmaInsight Explorer is an interactive visualization platform designed specifically for exploratory pharmacokinetic (PK), pharmacodynamic (PD), and safety analyses. This platform facilitates efficient and reproducible exploration of data, empowering CPSS scientists to uncover insights and prompt for deep analyses, if needed.
