Prediction of drug distribution in subcutaneous xenografts of human tumor cell lines and healthy tissues in mouse: application of the tissue composition-based model to antineoplastic drugs.

Advanced tissue composition-based models can predict the tissue-plasma partition coefficient (Kp ) values of drugs under in vivo conditions on the basis of in vitro and physiological input data. These models, however, focus on healthy tissues and do not incorporate data from tumors. The objective of this study was to apply a tissue composition-based model to six marketed antineoplastic drugs (docetaxel, DOC; doxorubicin, DOX; gemcitabine, GEM; methotrexate, MTX; topotecan, TOP; and fluorouracil, 5-FU) to predict their Kp values in three human tumor xenografts (HCT-116, H2122, and PC3) as well as in healthy tissues (brain, muscle, lung, and liver) under steady-state in vivo conditions in female NCR nude mice. The mechanisms considered in the tissue/tumor composition-based model are the binding to lipids and to plasma proteins, but the transporter effect was also investigated. The method consisted of analyzing tissue composition, performing the pharmacokinetics studies in mice, and calculating the corresponding in vivo Kp values. Analyses of tumor composition indicated that the tumor xenografts contained no or low amounts of common transporters by contrast to lipids. The predicted Kp values were within twofold and threefold of the measured values in 77% and 93% of cases, respectively. However, predictions for brain for each drug, for liver for MTX, and for each tumor xenograft for GEM were disparate from the observed values, and, therefore, not well served by the model. Overall, this study is the first step toward the mechanism-based prediction of Kp values of small molecules in healthy and tumor tissues in mouse when no transporter and permeation limitation effect is evident. This approach will be useful in selecting compounds based on their abilities to penetrate human cancer xenografts with a physiologically based pharmacokinetic (PBPK) model, thereby increasing therapeutic index for chemotherapy in oncology study.

Correlation of tissue-plasma partition coefficients between normal tissues and subcutaneous xenografts of human tumor cell lines in mouse as a prediction tool of drug penetration in tumors.

Understanding drug distribution and accumulation in tumors would be informative in the assessment of efficacy in targeted therapy; however, existing methods for predicting tissue drug distribution focus on normal tissues and do not incorporate tumors. The main objective of this study was to describe the relationships between tissue-plasma concentration ratios (Kp ) of normal tissues and those of subcutaneous xenograft tumors under nonsteady-state conditions, and establish regression equations that could potentially be used for the prediction of drug levels in several human tumor xenografts in mouse, based solely on a Kp value determined in a normal tissue (e.g., muscle). A dataset of 17 compounds was collected from the literature and from Genentech. Tissue and plasma concentration data in mouse were obtained following oral gavage or intraperitoneal administration. Linear regression analyses were performed between Kp values in several normal tissues (muscle, lung, liver, or brain) and those in human tumor xenografts (CL6, EBC-1, HT-29, PC3, U-87, MCF-7-neo-Her2, or BT474M1.1). The tissue-plasma ratios in normal tissues reasonably correlated with the tumor-plasma ratios in CL6, EBC-1, HT-29, U-87, BT474M1.1, and MCF-7-neo-Her2 xenografts (r(2) in the range 0.62-1) but not with the PC3 xenograft. In general, muscle and lung exhibited the strongest correlation with tumor xenografts, followed by liver. Regression coefficients from brain were low, except between brain and the glioblastoma U-87 xenograft (r(2) in the range 0.62-0.94). Furthermore, reasonably strong correlations were observed between muscle and lung and between muscle and liver (r(2) in the range 0.67-0.96). The slopes of the regressions differed depending on the class of drug (strong vs. weak base) and type of tissue (brain vs. other tissues and tumors). Overall, this study will contribute to our understanding of tissue-plasma partition coefficients for tumors and facilitate the use of physiologically based pharmacokinetics (PBPK) modeling for chemotherapy in oncology studies. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:1355-1369, 2013.