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Updated: May 5, 2026
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Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing
Published on: September 15, 2023
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Re-Thinking Pharmacokinetics in Ovarian Cancer: What Do Organoids Add?
Ana Emanuela Cisne de Lima1,2, Mariana Nunes1,2, Cristina P R Xavier1,2,3
1Applied Molecular Biosciences Unit (UCIBIO), Toxicologic Pathology Research Laboratory, University Institute of Health Sciences, Cooperative for Polytechnic and University Higher Education (1H-TOXRUN, IUCS-CESPU), 4585-116 Gandra, Portugal.
International Journal of Molecular Sciences
|May 4, 2026
Summary
Patient-derived organoids (PDOs) offer a 3D model to study ovarian cancer drug penetration. This approach helps understand drug exposure within tumors, improving dosing strategies for better treatment outcomes.
Area of Science:
- Gynecologic Oncology
- Cancer Pharmacology
- Translational Medicine
Background:
- Ovarian cancer (OC) is a leading cause of cancer mortality due to late diagnosis, relapse, and chemoresistance.
- Heterogeneous drug penetration within tumors, caused by physical and biochemical barriers, is a key factor in treatment failure.
- Conventional preclinical models fail to accurately represent intratumoral drug concentration gradients and tumor microenvironments.
Purpose of the Study:
- To review advances in patient-derived organoid (PDO) technologies for modeling ovarian cancer.
- To examine how PDO-derived data can inform intratumoral pharmacokinetics and optimize dosing strategies.
- To explore the role of PDOs in improving the prediction of drug efficacy in precision oncology.
Main Methods:
- Utilizing patient-derived organoids (PDOs) as 3D models that retain tumor characteristics.
- Incorporating cell-free malignant ascites supernatant into PDOs to mimic the peritoneal metastatic microenvironment.
- Applying physiologically based pharmacokinetic (PBPK) modeling and in vitro-in vivo extrapolation (IVIVE) to PDO data.
- Investigating emerging hybrid platforms like organoid-on-chip systems and vascularized co-cultures.
Main Results:
- PDOs provide a physiologically relevant platform to study drug penetration, metabolism, and retention within tumors.
- Enhanced PDO models with ascites supernatant better recapitulate the metastatic peritoneal environment of OC.
- PDO-derived pharmacokinetic and pharmacodynamic data can inform more accurate dosing strategies.
- Hybrid platforms and multi-omics integration show promise for enhanced translational prediction.
Conclusions:
- PDOs represent a significant advancement in preclinical cancer modeling, particularly for ovarian cancer.
- Integrating PDO data with PBPK modeling and IVIVE can improve understanding of intratumoral drug exposure.
- Advanced PDO-based platforms are crucial for advancing precision oncology and improving patient outcomes.
