Mar 26, 2026
In Vitro Drug Sensitivity Testing Using Patient-Derived Organoids in Oncology: A Systematic Review
- Amir Seif Belahcene1,
- Hajar Haddouchi1,
- Amal Boutib1
- 1Euromed University of Fez, Morocco
Protocol Citation: Amir Seif Belahcene, Hajar Haddouchi, Amal Boutib 2026. In Vitro Drug Sensitivity Testing Using Patient-Derived Organoids in Oncology: A Systematic Review. protocols.io https://dx.doi.org/10.17504/protocols.io.dm6gp7mp5gzp/v1
Manuscript citation:
Amir Seif Belahcene, Hajar Haddouchi, Amal Boutib. In Vitro Drug Sensitivity Testing Using Patient-Derived Organoids in Oncology: A Systematic Review. (In Preparation).
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: In development
We are still developing and optimizing this protocol
Created: March 24, 2026
Last Modified: March 26, 2026
Protocol Integer ID: 313861
Keywords: Patient-derived organoids, 3D culture, Drug sensitivity testing, Precision oncology, Functional screening, IC50, Clinical correlation, Systematic review, Protocol standardization, Functional Precision Medicine, vitro drug sensitivity testing, organoids in oncology, functional precision oncology, drug testing, derived organoid, oncology, clinical acceptance, using human patient, 3d models transition from laboratory tool, clinical diagnostic, varying experimental parameter
Abstract
This systematic review focuses on functional precision oncology, investigating the methodological landscape of in vitro drug testing using human patient-derived organoids (PDOs). We aim to synthesize evidence on how varying experimental parameters influence the predictive power and reproducibility of these models. By analyzing the current literature, we seek to understand how 3D models transition from laboratory tools to clinical diagnostics, emphasizing the standardization of protocols for regulatory and clinical acceptance.
Guidelines
The extraction phase is conducted systematically using an independent workflow. Two reviewers extract data separately to minimize errors, comparing datasets afterward for consistency. Discrepancies are resolved through discussion or a third reviewer. Extracted categories include Study Identification, Organoid Model Characteristics, Culture Methodology, and Experimental Design. Outcome Data such as IC^^50 and clinical correlation metrics are prioritized. The primary output is a comprehensive Data Extraction Table managed in Microsoft Excel or Google Sheets. Quality evaluation is performed independently by two reviewers. Discrepancies are resolved through consensus or adjudication by a third reviewer. We focus on standardizing in vitro pre-clinical oncology evidence using the SYRCLE Risk of Bias tool and the ToxRTool. Domains include Selection Bias, Performance Bias, Detection Bias, and Reporting Bias. Outputs include a Risk of Bias Summary Figure (Traffic light plot) and a Risk of Bias Graph as per PRISMA guidelines. This review adheres to the PRISMA statement, the international standard for transparent reporting. Strategy begins with a narrative synthesis organized by cancer type and methodology. A PRISMA Flowchart will illustrate the flow of records from identification to inclusion. If sufficient homogeneity is observed, a formal meta-analysis will be performed using random-effects models. This provides pooled estimates of treatment efficacy and clinical utility. Statistical heterogeneity is assessed using the I^^2 statistic.
Troubleshooting
REVIEW TITLE AND BASIC DETAILS
Review Title: In Vitro Drug Sensitivity Testing Using Patient-Derived Organoids in Oncology: A
Systematic Review
Citation: Amir Seif Belahcene, Hajar Haddouchi, Amal Boutib. In Vitro Drug Sensitivity Testing
Using Patient-Derived Organoids in Oncology: A Systematic Review. (In Preparation).
Condition or domain being studied: Precision Oncology; Patient-Derived Organoids (PDOs);
Functional Precision Medicine; Drug Sensitivity Screening; Personalized Pharmacotherapy.
This systematic review focuses on functional precision oncology, investigating the method-
ological landscape of in vitro drug testing using human patient-derived organoids (PDOs). We
aim to synthesize evidence on how varying experimental parameters influence the predictive
power and reproducibility of these models. By analyzing the current literature, we seek to un-
derstand how 3D models transition from laboratory tools to clinical diagnostics, emphasizing
the standardization of protocols for regulatory and clinical acceptance.
RATIONALE FOR THE REVIEW
Conventional 2D cell cultures and in vivo animal models often fail to accurately predict patient-
specific drug responses. 2D cultures provide high-throughput environments but lack the struc-
tural complexity and microenvironmental cues of human tumor biology. Animal models are
expensive, time-consuming, and frequently fail to translate to human clinical outcomes.
Patient-derived organoids (PDOs) have emerged as a transformative 3D technology that pre-
serves the genomic and histological features of parent tumors. They offer a representative
”patient-in-a-dish” model for functional drug testing. However, the field is currently limited by
significant methodological heterogeneity.
Discrepancies in matrix types, growth factor compositions, drug exposure durations, and
readout assays create challenges for cross-study comparisons. This variability hinders the large-
scale implementation of PDO-based diagnostics in clinical settings. A systematic synthesis is
required to identify evidence-based benchmarks for protocol standardization.
REVIEW OBJECTIVES
The main objective is to evaluate the methodological dimensions and reported efficacy of in
vitro drug exposure in PDO models compared to standard controls. We aim to highlight suc-
cessful strategies for establishing drug-responsive lines and identify common pitfalls in current
designs.
We will also analyze how variables like matrix type, passage number, and seeding density
affect outcomes like IC50 and AUC. Furthermore, we intend to compare viability assay platforms
and assess the correlation between in vitro results and matched clinical outcomes. Finally, we
aim to propose minimum reporting standards to guide future research toward transparency
and clinical integration.
KEYWORDS
Patient-derived organoids; 3D culture; Drug sensitivity testing; Precision oncology; Functional
screening; IC50; Clinical correlation; Systematic review; Protocol standardization; Functional
Precision Medicine.
INFORMATION SOURCES
The search encompasses primary electronic databases: PubMed / MEDLINE, Embase, Web of
Science Core Collection, Scopus, and the Cochrane Central Register of Controlled Trials (CEN-
TRAL). These are the most reliable repositories for peer-reviewed oncological research.
We will also screen grey literature via preprint servers like bioRxiv and medRxiv. Supplemen-
tary hand-searching of reference lists will be performed. Primary authors may be contacted to
clarify missing methodological details. There are no language or date restrictions
SEARCH STRATEGY
PubMed / MEDLINE:
("Organoids"[MeSH] OR organoid* OR "patient-derived organoid*" OR "tumor organoid*" OR PDOs) AND
("Antineoplastic Agents"[MeSH] OR "drug testing" OR "drug sensitivity" OR "drug screening") AND
("Cell Survival"[MeSH] OR "cell viability" OR IC50 OR "dose-response" OR AUC)
Scopus:
TITLE-ABS-KEY ( ( organoid* OR "patient-derived organoid*" OR "tumor organoid*" OR pdo* ) AND
( "drug testing" OR "drug screening" OR "drug sensitivity" OR "chemosensitivity" ) AND ( "cell
viability" OR ic50 OR auc OR "dose-response" ) ) AND NOT TITLE-ABS-KEY ( review OR editorial OR
letter )
STUDY SCREENING AND SELECTION
The selection follows a rigorous two-step methodology to avoid bias. First, records are imported
into Rayyan.ai for duplicate removal. Reviewer A and Reviewer B will independently screen titles
and abstracts to remove clearly irrelevant records.
In the second stage, full texts of potentially relevant articles are retrieved and evaluated
independently. Every study must meet all predefined PICO criteria. Disagreements are resolved
by discussion or by a third senior reviewer. The output includes a PRISMA Flow Diagram detailing
every stage.
DATA COLLECTION AND EXTRACTION
The extraction phase is conducted systematically using an independent workflow. Two review-
ers extract data separately to minimize errors, comparing datasets afterward for consistency.
Discrepancies are resolved through discussion or a third reviewer.
Extracted categories include Study Identification, Organoid Model Characteristics, Culture
Methodology, and Experimental Design. Outcome Data such as IC50 and clinical correlation
metrics are prioritized. The primary output is a comprehensive Data Extraction Table managed
in Microsoft Excel or Google Sheets.
QUALITY ASSESSMENT AND RISK OF BIAS
Quality evaluation is performed independently by two reviewers. Discrepancies are resolved
through consensus or adjudication by a third reviewer. We focus on standardizing in vitro pre-
clinical oncology evidence using the SYRCLE Risk of Bias tool and the ToxRTool.
Domains include Selection Bias, Performance Bias, Detection Bias, and Reporting Bias. Out-
puts include a Risk of Bias Summary Figure (Traffic light plot) and a Risk of Bias Graph as per
PRISMA guidelines.
DATA SYNTHESIS AND REPORTING
This review adheres to the PRISMA statement, the international standard for transparent re-
porting. Strategy begins with a narrative synthesis organized by cancer type and methodology.
A PRISMA Flowchart will illustrate the flow of records from identification to inclusion.
If sufficient homogeneity is observed, a formal meta-analysis will be performed using random-
effects models. This provides pooled estimates of treatment efficacy and clinical utility. Statis-
tical heterogeneity is assessed using the I2 statistic.
REVIEW TIMELINE
Expected timeline