Prediction of biliary clearance and excretion in humans
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Excretion via bile is an important elimination pathway for many drugs and/or metabolites. Biliary excretion can impact drug exposure and, thus, contributes to the inter-individual differences in response to drugs that are observed in healthy subjects and patients. However, it is difficult to measure biliary excretion of compounds in humans. The objective of this dissertation is to develop novel methods, especially in silico approaches, to predict biliary excretion in humans. The information will be valuable for estimating the importance of biliary excretion in the total elimination of drugs, assessing the impact of certain hepatobiliary diseases on drug disposition, predicting potential drug-drug interactions, and evaluating the extent of enterohepatic cycling. Biliary excretion data were collected from the literature for a large number of structurally unrelated compounds. Datasets were complied for biliary clearance (CL b ) data in rats and humans, and the percentage of administered dose excreted into bile as parent drug (PD b ) data in rats, dogs, and humans. The receiver operating characteristic (ROC) curve analysis was utilized to examine the relationship between molecular weight (MW) and PD b . MW thresholds of 375, 375-400, and 475 Da were demonstrated for anions in rats, dogs, and humans, respectively, while there was no statistically significant MW threshold found for cations or cation/neutral compounds. The MW thresholds observed may partially reflect molecular size. The species differences in MW threshold for anions may be related to the species differences in the hepatic canalicular membrane transport protein--Multidrug Resistance Protein 2 (MRP-2). Overall, the MW threshold for biliary excretion in humans may provide a qualitative estimation about PD b for anion compounds in humans. To obtain more accurate predictions, quantitative structure pharmacokinetic relationship (QSPKR) models were explored. Successful models were developed to predict both CL b and PD b in rats and humans. The prediction performance was not only supported by internal validation using the leave-one-out method, but was also confirmed by external validation using test sets. QSPKR models were also successfully developed for compounds transported at the hepatic canalicular membrane by MRP-2 and P-glycoprotein (P-gp). These submodels offered better predictions for the substrates of MRP-2 and P-gp in the test sets, and reduced the prediction error from the general models. To further verify the applicability of the QSPKR models, CL b and PD b values of mitoxantrone were measured in rats. The experimental values were very close to those predicted from the models that were developed for cation/neutral compounds in rats. Overall, the QSPKR models derived can provide satisfactory quantitative predictions for CL b and PD b values of compounds in humans. Lastly, allometric scaling was evaluated for the prediction of CL b values. We found that predictions were greatly improved by the incorporation of protein binding data. Multiple species scaling can offer reasonable predictions, while single species scaling from rats alone did not. We suggested that rabbits should be added as a third animal species, along with rats and dogs, in order to improve predictions of biliary clearance in humans. In summary, in this dissertation we successfully developed in silico approaches to predict biliary excretion of compounds in humans. These in silico methods can be readily applied at the early stages of drug discovery and development. It is noteworthy that our methods were based on non-congeneric compounds with diversified structures and, thus, may have broader applicability. Additionally, we first reported that allometric scaling could be used to directly predict CL b in humans. Allometric scaling is usually employed at the late stage of drug discovery for compounds with favorable features. In combination with the in silico methods, these approaches will provide valuable information to greatly facilitate the drug discovery and development processes.