# Towards functional precision medicine? Evidence standards of organoids as patient-specific models

## TL;DR

This paper explores how patient-derived organoids can help bridge the gap between genomic data and personalized treatment outcomes in precision medicine.

## Contribution

The paper introduces functional precision medicine as a novel strategy using organoids to predict treatment efficacy while addressing philosophical and validation challenges.

## Key findings

- Functional precision medicine uses organoids to test treatments and predict individual responses.
- Organoids must be validated with population-based evidence despite their personalized aim.
- The approach simplifies biological complexity by focusing on phenotypic responses rather than detailed mechanisms.

## 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.