Dynamic Model of Protease State and Inhibitor Trafficking to Predict Protease Activity in Breast Cancer Cells.

Introduction: Cysteine cathepsins are implicated in breast cancer progression, produced by both transformed epithelial cells and infiltrated stromal cells in tumors, but to date, no cathepsin inhibitor has been approved for clinical use due to unexpected side effects. This study explores cellular feedback to cathepsin inhibitors that might yield non-intuitive responses, and uses computational models to determine underlying cathepsin-inhibitor dynamics.

Methods: MDA-MB-231 cells treated with E64 were tested by multiplex cathepsin zymography and immunoblotting to quantify total, active, and inactive cathepsins S and L. This data was used to parameterize mathematical models of intracellular free and inhibited cathepsins, and then applied to a dynamic model predicting cathepsin responses to other classes of cathepsin inhibitors that have also failed clinical trials.

Results: E64 treated cells exhibited increased amounts of active cathepsin S and reduced amount of active cathepsin L, although E64 binds tightly to both. This inhibitor response was not unique to cancer cells or any one cell type, suggesting an underlying fundamental mechanism of E64 preserving activity of cathepsin S, but not cathepsin L. Computational models were able to predict and differentiate between inhibitor-bound, active, and inactive cathepsin species and demonstrate how different classes of cathepsin inhibitors can have drastically divergent effects on active cathepsins located in different intracellular compartments.

Conclusions: Together, this work has important implications for the development of mathematical model systems for protease inhibition in tissue destructive diseases, and consideration of preservation mechanisms by inhibitors that could alter perceived benefits of these treatment modalities.