Senior Scientist Sanofi Cambridge, Massachusetts, United States
Disclosure(s):
Saroj Dhakal, n/a: No relevant disclosure to display
Objective: The aim of this work is the development of a user-friendly, web-based application that facilitates static drug-drug interaction (DDI) risk assessment in accordance with the international council for harmonization (ICH) of technical requirements for pharmaceuticals for human use guidelines.
Method: DDIapp was developed using the Shiny for Python framework to create a web-based platform for static DDI risk assessment. The app implements a modular, equation-based engine in Python, using models consistent with the ICH M12 guidance (ICH-M12, 2024), including: 1. Reversible enzyme inhibition, 2. Time-dependent inhibition (TDI), 3. Enzyme induction, and 4. Transporter inhibition (intestinal, hepatic, renal).
A “net effect” model was incorporated to estimate the combined impact of inhibition and induction on the exposure of a victim drug, reported as the area under the curve ratio (AUCR). The app allows method-specific estimation of unbound fractions in microsomes and hepatocytes (fu,mic and fu,hep) using published approaches (Austin, 2002; Hallifax, 2006), ensuring alignment with regulatory practices.
Results: The DDIapp interface was successfully developed and validated, offering a user-friendly environment for assessing drug-drug interaction risks. The app supports flexible input of compound-specific parameters and allows users to select relevant interaction mechanisms from multiple categories, including enzyme and transporter pathways.
The dynamic UI enables real-time calculations across several DDI scenarios. Results are displayed in a structured table format, with automated risk categorization based on predefined regulatory thresholds. Visual cues (e.g., red, green, and bold text) help users quickly interpret the level of concern for each interaction.
The net effect panel allows integrated evaluation of inhibition and induction mechanisms, aligning with the AUCR-based approach described in the ICH M12 guideline. This provides a comprehensive view of interaction potential for each enzyme, including CYP3A substrates.
The application includes features to Flag potential DDIs based on risk thresholds (e.g., >1.25 or < 0.8), Display equations and some intermediate calculations to support transparency, Export results to structured PDF and Excel formats, and a glossary panel and links to regulatory resources (e.g., FDA and EMA M12 documents) enhance the interpretability of outputs, making the application suitable for both research and regulatory use.
Conclusion: A web-based, user-friendly interface was developed and validated using the open-source Shiny for Python framework. This application, enables application of a static approach to evaluate DDIs risks in line with regulatory guidance. By integrating established DDI models and providing intuitive parameter entry, interactive output, and built-in documentation, DDIapp offers an accessible tool for risk assessment of pharmacokinetic drug interactions mediated by metabolic enzymes and drug transporters.
Disclosure: All authors are Sanofi employees and may hold shares and/or stock options in the company.
Citations: 1. Austin, R. P. (2002). The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties. Drug Metabolism and Disposition, 1497-1503. 2. Hallifax, D. &. (2006). Drug metabolism and disposition, 724-726. 3. ICH-M12. (2024, August). M12-Drug Interaction. Retrieved from M12-Drug Interaction: https://www.fda.gov/media/161199/download
Keywords: Drug-Drug Interation, Shiny App, ICH 2024 Guideline
