Chemopreventive and anti-angiogenic effects of dietary phenethyl isothiocyanate in an N-methyl nitrosourea-induced breast cancer animal model.

The effect of phenethyl isothiocyanate (PEITC), a component of cruciferous vegetables, on the initiation and progression of cancer was investigated in a chemically induced estrogen-dependent breast cancer model. Breast cancer was induced in female Sprague Dawley rats (8 weeks old) by the administration of N-methyl nitrosourea (NMU). Animals were administered 50 or 150 µmol/kg oral PEITC and monitored for tumor appearance for 18 weeks. The PEITC treatment prolonged the tumor-free survival time and decreased the tumor incidence and multiplicity. The time to the first palpable tumor was prolonged from 69 days in the control, to 84 and 88 days in the 50 and 150 µmol/kg PEITC-treated groups. The tumor incidence in the control, 50 µmol/kg, and 150 µmol/kg PEITC-treated groups was 56.6%, 25.0% and 17.2%, while the tumor multiplicity was 1.03, 0.25 and 0.21, respectively. Differences were statistically significant (p < 0.05) from the control, but there were no significant differences between the two dose levels. The intratumoral capillary density decreased from 4.21 ± 0.30 vessels per field in the controls to 2.46 ± 0.25 in the 50 µmol/kg and 2.36 ± 0.23 in the 150 µmol/kg PEITC-treated animals. These studies indicate that supplementation with PEITC prolongs the tumor-free survival, reduces tumor incidence and burden, and is chemoprotective in NMU-induced estrogen-dependent breast cancer in rats. For the first time, it is reported that PEITC has anti-angiogenic effects in a chemically induced breast cancer animal model, representing a potentially significant mechanism contributing to its chemopreventive activity.

Reevaluation of a quantitative structure pharmacokinetic model for biliary excretion in rats.

Quantitative structure pharmacokinetic relationship (QSPKR) modeling can be used to predict the biliary clearance and percentage of dose eliminated in bile (PD(b)) in humans before clinical studies. Recently, a QSPKR model based on in-house compounds was derived using simple physicochemical descriptors to predict the PD(b) in rats (Drug Metab Dispos 38:422-430, 2010). Our objective was to evaluate the QSPKR model derived for the prediction of PD(b) for our larger dataset of 164 compounds in the rat and for the 97 compounds in our human dataset (AAPS J 11:511-525, 2009). Re-analysis of the published QSPKR model revealed the model to be statistically insignificant (Drug Metab Dispos 38:422-430, 2010). Thus, a new statistically significant QSPKR model, consisting of one less descriptor than the published model, was derived from the published data. The newly derived model performed as well as the published model in predicting the PD(b) for the training and test sets (Drug Metab Dispos 38:422-430, 2010). In contrast, the new model performed poorly in predicting the PD(b) for our larger rat (r(2) = 0.253) and human dataset (r(2) = 0.013). The poor predictions for our datasets may be due, in part, to the quality and diversity of the data used to derive and test the model. Our reevaluation suggests that hepatobiliary excretion is a process that cannot truly be captured by simple physicochemical descriptors when examining chemically dissimilar compounds. A simple approach involving a limited number of physicochemical predictors may be useful when examining a structurally similar series of compounds.

Interspecies scaling: prediction of human biliary clearance and comparison with QSPKR.

The aim of this study was to evaluate the prediction performance of various allometric scaling methods in predicting human biliary clearance (CL(b)) from data in rats or multiple animal species and to compare the prediction performance with that of quantitative structure pharmacokinetic relationship (QSPKR) models. CL(b) data of parent drugs in rats and humans were collected from the literature for 18 compounds. A simple allometric approach was applied to CL(b) or unbound CL(b) using 0.75 or 0.66 as the allometric exponent. For scaling from rat studies alone, the prediction using 0.66 as the exponent was better than that using 0.75, and a better prediction was obtained for unbound CL(b) than CL(b). For a subset of compounds, six multiple-species scaling methods were compared, with the best prediction achieved with the simple unbound CL(b) approach. However, in the absence of protein binding data, the correction with maximum life-span potential (MLP) or 'Rule of exponent' (ROE) method offered the best prediction. Overall, multiple species had better predictability than scaling with the rat alone. Comparison of predicted human CL(b) values using multiple animal species and QSPKR offered similar prediction performance. In conclusion, the results of the present study, although based on limited data, suggested that the prediction for human CL(b) by allometry was greatly improved by the incorporation of protein binding. Human CL(b) prediction using rat data alone was not satisfactory. Additionally, QSPKR provides an alternative approach to allometry for the prediction of human biliary clearance.

Biliary excretion in dogs: evidence for a molecular weight threshold.

Molecular weight (MW) is known as an important factor of biliary excretion in rats, guinea pigs, rabbits and humans. The objective of this study was to evaluate the relationship between the biliary excretion and MW of drugs in dogs. Data on the percentage of dose excreted into bile as parent drug (PD(b)) in dogs were collected from the literature for 134 compounds. Receiver operating characteristic (ROC) curve analysis was utilized to determine whether a MW threshold exists for PD(b). A MW threshold of 375-400 Da was established for anions in dogs, which is similar with the cutoff value observed in rats (400 Da) but lower than the one in humans (475 Da). No MW threshold was found for cations or cations/neutral compounds. A molecular volume threshold of 300A(3) was also determined for anions in dogs, which corresponds to a MW of 394 Da. In conclusion, our analysis suggested the presence of a MW cutoff for anions in dogs, which may be related with the molecular size of a compound. This represents the first report of the influence of MW or molecular volume as a determinant of biliary excretion for a structurally diverse set of compounds in dogs.

Prediction of biliary excretion in rats and humans using molecular weight and quantitative structure-pharmacokinetic relationships.

The aims were (1) to evaluate the molecular weight (MW) dependence of biliary excretion and (2) to develop quantitative structure-pharmacokinetic relationships (QSPKR) to predict biliary clearance (CL(b)) and percentage of administered dose excreted in bile as parent drug (PD(b)) in rats and humans. CL(b) and PD(b) data were collected from the literature for rats and humans. Receiver operating characteristic curve analysis was utilized to determine whether a MW threshold exists for PD(b). Stepwise multiple linear regression (MLR) was used to derive QSPKR models. The predictive performance of the models was evaluated by internal validation using the leave-one-out method and external test groups. A MW threshold of 400 Da was determined for PD(b) for anions in rats, while 475 Da was the cutoff for anions in humans. MW thresholds were not present for cations or cations/neutral compounds in either rats or humans. The QSPKR model for human CL(b) showed a significant correlation (R (2) = 0.819) with good prediction performance (Q (2) = 0.722). The model was further assessed using a test group, yielding a geometric mean fold-error of 2.68. QSPKR models with significant correlation and good predictability were also developed for CL(b) in rats and PD(b) data for anions or cation/neutral compounds in rats and humans. Both CL(b) and PD(b) data were further evaluated for subsets of MRP2 or P-glycoprotein substrates, and significant relationships were derived. QSPKR models were successfully developed for biliary excretion of non-congeneric compounds in rats and humans, providing a quantitative prediction of biliary clearance of compounds.

Structure-activity relationships and quantitative structure-activity relationships for breast cancer resistance protein (ABCG2).

Breast cancer resistance protein (ABCG2), the newest ABC transporter, was discovered independently by three groups in the late 1990s. ABCG2 is widely distributed in the body with expression in the brain, intestine, and liver, among others. ABCG2 plays an important role by effluxing drugs at the blood-brain, blood-testis, and maternal-fetal barriers and in the efflux of xenobiotics at the small intestine and kidney proximal tubule brush border and liver canalicular membranes. ABCG2 transports a wide variety of substrates including HMG-CoA reductase inhibitors, antibiotics, and many anticancer agents and is one contributor to multidrug resistance in cancer cells. Quantitative structure-activity relationship (QSAR) models and structure-activity relationships (SARs) are often employed to predict ABCG2 substrates and inhibitors prior to in vitro and in vivo studies. QSAR models correlate in vivo biological activity to physicochemical properties of compounds while SARs attempt to explain chemical moieties or structural features that contribute to or are detrimental to the biological activity. Most ABCG2 datasets available for in silico modeling are comprised of congeneric series of compounds; the results from one series usually cannot be applied to another series of compounds. This review will focus on in silico models in the literature used for the prediction of ABCG2 substrates and inhibitors.