The Prediction of Drug Metabolism, Tissue Distribution, and Bioavailability of 50 Structurally Diverse Compounds in Rat Using Mechanism-Based Absorption, Distribution, and Metabolism Prediction Tools

Abstract

ABSTRACT:The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n ‫؍‬ 50). Hepatic blood clearance (CL h ) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL h were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V dss ) and tissue-to-plasma partitioning (P tp ). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V dss (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V dss (60% within 2-fold). The first approach also yielded accurate predictions of P tp in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P tp prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V dss and CL h resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL h data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics.Obtaining rapid information regarding the pharmacokinetics (PK) of new drug candidates can be a bottleneck in early drug discovery. Considerable resources are required to assess the PK properties of potential drug candidates in vivo in animals. To optimize the use of such in vivo testing, there has been a growing interest in predicting the PK behavior of drug candidates as early as possible (Norris et al., 2000;van de Waterbeemd and Gifford, 2003). If sufficiently reliable, such simulations could also help to select the best candidates for development and to reject those with a low probability of success.The characterization of a drug's PK requires elucidation of each of the coincident processes of absorption, distribution, metabolism, and elimination (ADME). A large number of methodologies have been established for this purpose, including empirical and physiologically based methods (Boxenbaum and Ronfeld, 1983;Theil et al., 2003). Until recently, PK prediction has been predominantly descriptive, using empirical methods. Although in some cases these methods give good predictions, their physiological basis is low and inaccurate results can be obtained, in particular when there are large interspecies differences in metabolic clearance (Lave et al., 1995;Zuegge et al., ...

Modelling and PBPK Simulation in Drug Discovery

“…We note the broad agreement (67% within twofold error of the observed value) between predicted and observed distribution into muscle and heart tissue. In addition, while the overall prediction of distribution into lung tissue was quantitatively less accurate (33% within twofold), it is interesting to note that our findings do not mirror the tendency to under-predict distribution into the lung tissue that has been demonstrated previously (15,54). Perhaps the most difficult PK parameter to predict is CL; however, again, this is an essential input parameter for PBPK modeling.…”

“…Methodologies proposed by both Rodgers and Rowland and Poulin and Theil have been outlined in the previous section and can be employed to prioritize candidates for progression to pre-clinical in vivo studies. A representative selection of the literature in this area, in addition to the experience in our own laboratories, is summarized in Table I. A number of different authors (12,17,20,54) have reported the encouraging performance of these mechanistic equations, each having demonstrated accurate prediction of rat V ss (within twofold of observed) for greater than 60% of candidates. Poorer prediction accuracy has been reported by Germani et al (53); however, this work focused primarily on the equations proposed by Poulin and Theil.…”

Contribution of Rat Pulmonary Metabolism to the Elimination of Lidocaine, Midazolam, and Nifedipine

“…In addition to hepatic microsomes, the use of pulmonary microsomes allows more accurate prediction of drug-drug interactions. Hepatic metabolism is the most important factor for drug disposition, and the quantitative prediction of hepatic clearance of various compounds from in vitro data has been reported (Naritomi et al, 2001;Austin et al, 2002;Nestorov et al, 2002;De Buck et al, 2007). Our study showed the importance of the liver and lung as metabolic organs for the drugs examined.…”

Role of Biotransformation Studies in Minimizing Metabolism-Related Liabilities in Drug Discovery

“…Anari and co-workers described the metabolic profiling of indinavir with an approach combining data-dependent LC-MS/MS and knowledge-based metabolite predictions that were generated using a substructure similarity search of the MDL Metabolite Database (38). Several other programs have been developed for this purpose (39)(40)(41)(42)(43)(44), including an approach co-developed at Bristol-Myers Squibb for LC-MS/MS data mining of potential metabolites. As shown in Fig.…”