INTEGRATION OF ARTIFICIAL INTELLIGENCE AND UNCERTAINTY ANALYSIS IN THE IN SILICO BIOEQUIVALENCE ASSESSMENT OF IBUPROFEN

• Grant number: 25/02944-3
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: December 01, 2025
• End date: March 31, 2028
• Field of knowledge: Health Sciences - Pharmacy - Medicines Analysis and Control
• Principal Investigator: Felipe Rebello Lourenço
• Grantee: Jheniffer Rabelo Cunha
• Host Institution: Faculdade de Ciências Farmacêuticas (FCF). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

This study investigates the use of Artificial Intelligence (AI) to enhance Physiologically Based Pharmacokinetic (PBPK) modeling in the context of Ibuprofen dissolution. Given the need for greater efficiency in predicting drug behavior in the human body, this research proposes the integration of Machine Learning (ML) and Deep Learning (DL) algorithms to systematize data from literature and computational simulations, aiming to optimize bioequivalence predictions. The methodology is structured into three main stages: 1) automated data collection using AI, 2) development and calibration of the PBPK model, and 3) validation based on measurement uncertainty analysis. This approach is expected to contribute to accelerating the development of Class II drugs within the Biopharmaceutics Classification System (BCS), reducing costs and time for in vitro testing while improving the reliability of pharmacokinetic estimates. (AU)