Drug resistance-reversal strategies: comparison of experimental data with model predictions

We previously developed a mathematical model to describe the emergence and dynamic growth of a drug-resistant subpopulation in a tumor. In the present study, our objective was to test the model's ability to mimic two strategies for reversal of drug resistance. We present data from one in vitro cell proliferation assay with drug-resistant LS174T human colon carcinoma variants and one in vivo assay of survival after treatment of female (C57BL/6 x DBA/2)F1 mice inoculated with doxorubicin-resistant P388/ADR leukemia cells. The in vitro assay examined the effects of inhibiting the biosynthesis of glutathione in cells resistant to alkylating agents or cisplatin. The in vivo assay compared the effects on cell survival of low-level continuous infusion versus high-intensity bolus dosing, with or without coadministration of the drug efflux pump blocker verapamil. Results in vitro and in vivo were comparable for qualitative accuracy and predictability to results with the model. Both the in vitro study and the model showed that, for resistant cells with high levels of glutathione, short-term cell survival was dose dependent and that even high doses of drug did not eliminate all of these cells. Addition of an inhibitor of glutathione biosynthesis did, however, augment elimination of the resistant cells. Resistant cells with low levels of glutathione could be eliminated with high drug doses or coadministration of drug and a glutathione synthesis inhibitor. In vivo, coadministration of doxorubicin with verapamil increased animal survival when either continuous infusion or bolus dosing regimens were used. The effectiveness of the blocker is crucial; when a partially (50%) effective blocker is used, continuous infusion achieves better elimination of resistant cells, but a completely (100%) effective blocker is efficacious in both dosing scenarios. Careful interpretation of these findings is necessary because the pharmacokinetics of drug in the small populations of cells in the model are not easily extrapolated to those in large tumors. This model may be useful in determining resistance mechanisms, their levels of effectiveness, and concentrations of compounds required at target sites to overcome them.