Development of 3D Bioprinted Tumor Models with Artificial Intelligence to Enhance Breast Cancer Treatment Testing

• Grant number: 24/19744-4
• Support Opportunities: Generation Project Research Grant
• Start date: February 01, 2026
• End date: January 31, 2031
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Amanda Blanque Becceneri
• Grantee: Amanda Blanque Becceneri
• Host Institution: Faculdade de Ciências Farmacêuticas de Ribeirão Preto (FCFRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• City of the host institution: Ribeirão Preto
• Associated researchers: Andre Luis Dias ; João Vítor Silva Robazzi ; Roberto Santana da Silva

Abstract

Breast cancer, one of the most prevalent types of cancer among women, is characterized by high mortality and significant biological heterogeneity, which limits the effectiveness of conventional treatments. However, traditional methods for testing new drugs, based on 2D cultures and animal models, do not faithfully replicate the human tumor microenvironment, resulting in a high failure rate during clinical phases. To address this challenge, this project proposes the development of high-precision 3D bioprinted models integrated with artificial intelligence (AI) to test breast cancer therapies in a more realistic and personalized manner.The project combines three innovative technologies: 3D bioprinting, patient-derived organoids (PDOs), and AI. 3D bioprinting will enable the creation of tumor models that replicate the human tumor microenvironment. AI will be applied to optimize the bioprinting process, ensuring precision and reproducibility in cell deposition and control over model parameters. PDOs will provide the necessary structural complexity for more accurate and personalized studies. Initially, clinically used compounds, such as cisplatin and doxorubicin, will be tested to validate the effectiveness of the 3D bioprinted models. Later, new compounds, including metallic complexes, will be evaluated.The validation of results will include comparisons with data from 2D models and real clinical information, aiming to reduce the use of animal models and increase the accuracy of preclinical studies. The multidisciplinary team includes specialists in cell culture, chemistry, and AI, as well as undergraduate, master's, and PhD students. This project also directly contributes to the United Nations Sustainable Development Goals (SDGs), especially in the areas of health and innovation, promoting advances in research and the development of models that can more effectively evaluate treatments in preclinical stages. (AU)