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Updated: May 5, 2026
Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing
Published on: September 15, 2023
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.
Abstract:
Ovarian cancer (OC) remains one of the leading causes of gynecologic cancer mortality, largely due to late diagnosis, frequent relapse, and the emergence of chemoresistance. An important but often-overlooked contributor to treatment failure is the heterogeneous penetration of anticancer drugs within tumors. Structural and biochemical barriers-including abnormal vasculature, elevated interstitial pressure, dense extracellular matrix, drug efflux transporters, and malignant ascites-generate steep intratumoral concentration gradients that conventional preclinical models fail to capture. As a result, systemic pharmacokinetic measurements frequently provide limited insight into tumor-level drug exposure. Patient-derived organoids (PDOs) have emerged as physiologically relevant 3D models that preserve the genetic, architectural, and functional characteristics of the original tumor. These systems enable controlled investigation of pharmacokinetic and pharmacodynamic processes, including drug penetration, metabolism, retention, and exposure-response relationships. Adding cell-free malignant ascites supernatant enhances PDOs' ability to mimic the metastatic peritoneal microenvironment of OC. This review discusses recent advances in PDO technologies and examines how PDO-derived data can inform intratumoral pharmacokinetics and dosing strategies using physiologically based pharmacokinetic modeling and in vitro-in vivo extrapolation. Emerging hybrid platforms, including organoid-on-chip systems, vascularized co-cultures, and multi-omics integration, are crucial to improve translational prediction and support precision oncology.
Insights
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.
