Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models

Predicting target site drug concentrations is of key importance for rank ordering compounds before proceeding to chronic pharmacodynamic models. We propose generic tumor-specific correlation-based regression equations to predict tumor-to-plasma ratios (tumor-Kps) in slow- and fast-growing xenograft mouse models.

Disposition of 14 basic small molecules was investigated extensively in mouse plasma, tissues and tumors after a single oral dose administration. Linear correlation was assessed and compared between tumor-Kp and normal tissue-to-plasma ratio (tissue-Kps) separately for each tumor xenograft. The developed regression equations were validated by leave-one-out cross-validation (LOOCV) method.

Both slow- and fast-growing tumor-Kps showed good correlation (r ² ≥ 0.7) with majority of the normal tissue-Kps. Substantial difference was observed in the slopes of developed equations between two xenografts, which was in line with observed difference in tumor distribution. The linear correlations between tumor-Kp and skin- or spleen-Kp were within the acceptable statistical criteria (LOOCV) across xenografts and the class of compounds evaluated. Since > 70% of tumor-Kps from the test data sets were predicted within a factor of twofold for both slow- and fast-growing xenograft mouse models, the results validate the applicability of the developed equations across xenografts.

Tumor-specific correlation-based regression equations were developed and their applicability was adequately validated across xenografts. These equations could be successfully translated to predict tumor concentrations in order to preclude experimental tumor-Kp determination.