Abstract
Purpose
Pharmacokinetic/pharmacodynamic (PK/PD) models have been shown to be useful in predicting tumor growth rates in mouse xenografts. We applied novel PK/PD models to the published anticancer combination therapies of tumor growth inhibition to simulate synergistic changes in tumor growth rates. The parameters from the PK/PD model were further used to estimate clinical doses of the combination.
Methods
A PK/PD model was built that linked the dosing regimen of a compound to the inhibition of tumor growth in mouse xenograft models. Two subsequent PK/PD models were developed to simulate the published tumor growth profiles of combination treatments. Model I predicts the tumor growth curve assuming that the effect of two anticancer drugs, AZD7762 and irinotecan, is synergistic when given in combination. Model II predicts the tumor growth curve assuming that the effect of co-administering flavopiridol and irinotecan is maximally synergistic when dosed at an optimal interval.
Results
Model I was able to account for the synergistic effects of AZD7762 following the administration of irinotecan. When Model II was applied to the antitumor activity of irinotecan and flavopiridol combination therapy, the modeling was able to reproduce the optimal dosing interval between administrations of the compounds. Furthermore, Model II was able to estimate the biologically active dose of flavopiridol recommended for phase II studies.
Conclusions
The timing of clinical combination therapy doses is often selected empirically. PK/PD models provide a theoretical structure useful in the design of the optimal clinical dose, frequency of administration and the optimal timing of administration between anticancer agents to maximize tumor suppression.
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Acknowledgments
The irinotecan and flavopiridol study was supported by a Grant (R01 CA67819) from National Cancer Institute.
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Appendix
Appendix
Both Model I and Model II were able to quantitatively describe several experimental data sets. Each parameter in these models was incremently decreased and increased from the optimized fit and the effect on RMSE was evaluated. The sensitivity analysis of each of these parameters for Model I is given in Fig. 5. The sensitivity analysis of each of the parameters for Model II is given in Fig. 6. The sensitivity analysis shows which parameters are more sensitive to the RMSE. Based on this analysis a 5% shift in RMSE was considered to be the cut off to calculate the low and high value of the model parameter (Figs. 5, 6). These values low and high values for each model parameter estimates is given in Table 3. Parameters k1 was least sensitive in model I, whereas parameters k1, k3 and λ1 were less sensitive in model II. Although the low and high values were large for some parameters, the model does captures the tumor growth profiles when the drugs are given in combination. With limited data generated in drug discovery it is hard to get certainity around a parameter estimate, but the message of the utility of the models for such combinations remains.
Sensitivity analysis of various parameters for Model I
Sensitivity analysis of various parameters for Model II
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Goteti, K., Edwin Garner, C., Utley, L. et al. Preclinical pharmacokinetic/pharmacodynamic models to predict synergistic effects of co-administered anti-cancer agents. Cancer Chemother Pharmacol 66, 245–254 (2010). https://doi.org/10.1007/s00280-009-1153-z
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DOI: https://doi.org/10.1007/s00280-009-1153-z