Systems Pharmacology Model of Gastrointestinal Damage Predicts Species Differences and Optimizes Clinical Dosing...

Area of Science:

• Pharmacology
• Mathematical Biology
• Oncology

Background:

• Gastrointestinal (GI) adverse events (AEs) are common dose-limiting toxicities for oncology agents.
• Optimizing chemotherapy dosing schedules is crucial for managing AEs and improving patient outcomes.
• Predictive models can reduce the need for extensive clinical testing of dosing regimens.

Purpose of the Study:

• To develop a translational mathematical model for predicting GI AEs from preclinical toxicity data.
• To incorporate known GI tract biology into a predictive model.
• To validate the model using preclinical and clinical data for irinotecan.

Main Methods:

• Developed a mathematical model of GI tract cell dynamics (stem cells, daughter cells, enterocytes).
• Parameterized the model using published data for humans and rats, and preclinical histopathology data.
• Validated the model against preclinical irinotecan-induced pathology and clinical AE profiles.

Main Results:

• The model accurately fitted preclinical irinotecan-induced pathology in rodents.
• Predicted enterocyte loss in patients receiving weekly chemotherapy matched observed AE profiles.
• The model predicted lower AEs for a less frequent (Q3W) dosing schedule compared to weekly administration.

Conclusions:

• A translational mathematical model can predict clinical GI AEs from preclinical toxicity data.
• The developed model aids in identifying optimal dosing schedules for oncology agents.
• This approach can improve the efficiency of clinical testing for chemotherapy-induced AEs.