Predicting Antitumor Effect of Deoxypodophyllotoxin in NCI-H460 Tumor-Bearing Mice on the Basis of In Vitro Pharmacodynamics and a Physiologically Based Pharmacokinetic-Pharmacodynamic Model.

Antitumor evaluation in tumor-bearing mouse is time- and energy-consuming. We aimed to investigate whether in vivo antitumor efficacy could be predicted on the basis of in vitro pharmacodynamics using deoxypodophyllotoxin (DPT), an antitumor candidate in development, as a model compound. Proliferation kinetics of monolayer-cultivated NCI-H460 cells under various DPT concentrations were quantitatively investigated and expressed as calibration curves. Koch two-phase natural growth model combined with sigmoid Emax model, i.e., dM/dt = 2 λ 0 λ 1 M/( λ 1 + 2 λ 0 M) - Emax C γ /(EC50 γ + C γ )·M, was introduced to describe cell proliferation (M) against time under DPT treatment (C). Estimated in vitro pharmacodynamic parameters were: EC50 , 8.97 nM; Emax , 0.820 day-1 , and γ , 7.13. A physiologically based pharmacokinetic model including tumor compartment was introduced to predict DPT disposition in plasma, tumor tissue, and main normal tissues of NCI-H460 tumor-bearing mice following a single dose. The in vivo pharmacodynamic model and parameters were assumed the same as the in vitro ones, and linked with simulated tumor pharmacokinetic profiles by a physiologically based pharmacokinetic (PBPK) model to build a PBPK-pharmacodynamic (PBPK-PD) model. After natural growth parameters ( λ 0 and λ 1 ) were estimated, the objective in this study was to predict with the PBPK-PD model the tumor growth in NCI-H460 tumor-bearing mice during multidose DPT treatment, a use of the model similar to what others have reported. In our work, the model was successfully applied to predict tumor growth in SGC-7901 tumor-bearing mice. The resulting data indicated that in vivo antitumor efficacy might be predicted on the basis of in vitro cytotoxic assays via a PBPK-PD model approach. We demonstrated that the approach is reasonable and applicable and may facilitate and accelerate anticancer candidate screening and dose regimen design in the drug discovery process.