TY - JOUR
T1 - Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models
AU - Nigade, Prashant B.
AU - Gundu, Jayasagar
AU - Pai, K. Sreedhara
AU - Nemmani, Kumar V.S.
PY - 2018/6/1
Y1 - 2018/6/1
N2 - Background: 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. Methods: 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. Result: Both slow- and fast-growing tumor-Kps showed good correlation (r2 ≥ 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. Conclusion: 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.
AB - Background: 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. Methods: 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. Result: Both slow- and fast-growing tumor-Kps showed good correlation (r2 ≥ 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. Conclusion: 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.
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U2 - 10.1007/s13318-017-0454-6
DO - 10.1007/s13318-017-0454-6
M3 - Article
AN - SCOPUS:85038229675
SN - 0378-7966
SP - 1
EP - 16
JO - European Journal of Drug Metabolism and Pharmacokinetics
JF - European Journal of Drug Metabolism and Pharmacokinetics
ER -