DOI: 10.1002/psp4.13029 ISSN:

A dynamic model of the intestinal epithelium integrates multiple sources of preclinical data and enables clinical translation of drug‐induced toxicity.

Louis Gall, Ferran Jardi, Lieve Lammens, Janet Piñero, Terezinha M. Souza, Daniela Rodrigues, Danyel G. J. Jennen, Theo M. de Kok, Luke Coyle, Seung‐Wook Chung, Sofia Ferreira, Heeseung Jo, Kylie. A. Beattie, Colette Kelly, Carrie A. Duckworth, D. Mark Pritchard, Carmen Pin
  • Pharmacology (medical)
  • Modeling and Simulation


We have built a QST modelling framework focussed on the early prediction of oncotherapeutics‐induced clinical intestinal adverse effects. The model describes stem and progenitor cell dynamics in the small intestinal epithelium and integrates heterogeneous epithelial‐related processes, such as transcriptional profiles, citrulline kinetics and probability of diarrhea.

We fitted a mouse‐specific version of the model to quantify doxorubicin and 5‐fluorouracil (5‐FU) induced toxicity, which included pharmacokinetics and 5‐FU metabolism and assumed that both drugs led to cell cycle arrest and apoptosis in stem cells and proliferative progenitors. The model successfully recapitulated observations in mice regarding dose‐dependent disruption of proliferation which could lead to villus shortening, decrease of circulating citrulline, increased diarrhea risk and transcriptional induction of the p53 pathway.

Using a human‐specific epithelial model, we translated the cytotoxic activity of doxorubicin and 5‐FU quantified in mouse into human intestinal injury and predicted with accuracy clinical diarrhea incidence. However, for gefitinib, a specific‐molecularly targeted therapy, the mouse failed to reproduce epithelial toxicity at exposures much higher than those associated with clinical diarrhea. This indicates that, regardless of the translational modelling approach, preclinical experimental settings have to be suitable to quantify drug‐induced clinical toxicity with precision at the structural scale of the model.

Our work demonstrates the usefulness of translational models at early stages of the drug development pipeline to predict clinical toxicity and highlights the importance of understanding cross‐settings differences in toxicity when building these approaches.

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