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Updated: Jul 21, 2026

Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research
Published on: May 5, 2021
Towards functional precision medicine? Evidence standards of organoids as patient-specific models
Sara Green1,2, Henrik Vogt3, Maxence Gaillard4
1Section for History and Philosophy of Science, Department of Science Education, University of Copenhagen, Copenhagen, Denmark. sara.green@ind.ku.dk.
Abstract:
Evidence-based medicine (EBM), with meta-analysis of randomized clinical trials as its gold standard, has been criticized for failing to represent the individuality and variability of disease. Precision medicine (PM) has been proposed as an alternative to EBM's "averaging approach", leveraging genomic and other biological information at the individual level. However, PM is still an emerging and changing concept. It is unclear what constitutes acceptable evidence, when the number of patients with a specific condition approaches one. Despite large investments, PM´s overall capacity to predict and improve treatment responses remains limited. This raises the question of whether PM has failed, or whether another strategy can improve the situation. Here, we examine the implications of functional precision medicine (FPM), a strategy aiming to bridge the gap between genomic information and phenotypic complexity through functional testing of treatments on patient-derived organoid (PDO), an advanced form of cell culture. We unpack how observed treatment effects in such personalized models are emerging as a means to predict treatment efficacy in individual patients. Drawing on exploratory interviews with scientists at the forefront of clinical implementation, we examine the philosophical implications of FPM in the contexts of cystic fibrosis and cancer. We unpack how the "functional approach" addresses biological complexity by black boxing many mechanistic details and focusing on phenotypic responses in PDOs. Moreover, we show that, to work as personalized models, they paradoxically must be validated by developing the same type of population-based evidence they aim to reduce reliance on.
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