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

Abstract

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.

Homeostasis, injury, and recovery dynamics at multiple scales in a self-organizing mouse intestinal crypt

“…We did not implement the inhibition of thymidylate synthase (TS) by FdUMP because the impact of this mechanism on intestinal toxicity is not completely understood ( Pritchard et al, 1997 ). A previously published 5-FU PK model ( Gall et al, 2023 ) was integrated into the ABM to describe the dynamic profile of the concentration of 5-FU and its metabolites in plasma and GI epithelium after dosing ( Figure 5B ).…”

“…Lower levels of DNA damage induced P21 activation, which, together with RNA damage, slow down and could eventually arrest the cycle. ( B ) Predicted concentration (ng/ml) of 5-FU, FUTP, and FdUTP in plasma in mouse (pharmacokinetics model of 5-FU described in Gall et al, 2023 ). ( C, D ) Cell cycle protein dynamics and fate decision when 5-FU challenge starts ( C ) prior to or at the beginning of S-phase, leading to DNA damage and cell death at the G2-M-phase checkpoint, and ( D ) at the end of S-phase, resulting in not enough DNA damage, the cell finishes the cycle.…”

“…We considered the two main downstream metabolites of 5-FU, FdUTP and FUTP, causing DNA and RNA damage, respectively ( Longley et al, 2003 ). To do this, we implemented in the ABM a previously published model that describes 5-FU distribution post-dosing in mouse and a reduced version of the 5-FU metabolic pathway ( Gall et al, 2023 ). Furthermore, we implemented the effect of FdUTP and FUTP on DNA and RNA synthesis, respectively, on each cell of our ABM using a Hill function as follows: Parameter values can be found in Appendix 1—table 1 .…”

“…We considered the two main downstream metabolites of 5-FU, FdUTP and FUTP, causing DNA and RNA damage, respectively ( Longley et al, 2003 ). To do this, we implemented in the ABM a previously published model that describes 5-FU distribution post-dosing in mouse and a reduced version of the 5-FU metabolic pathway ( Gall et al, 2023 ).…”