Prediction of Human Pharmacokinetics in 2013 and Beyond

Abstract

The utility of in vitro generated kinetic data to provide quantitative prediction of in vivo pharmacokinetic behavior is well established and forms a cornerstone of many research projects in drug metabolism and disposition, particularly within the pharmaceutical industry. This issue provides several excellent examples of the use of in vitro techniques for prediction of human pharmacokinetics (PK). The general area of in vitro-in vivo extrapolation (IVIVE) is broad and hence the spectrum of topics covers various aspects drug clearance and distribution and drug-drug interactions. Some articles were commissioned whereas others were identified during the reviewing process. Overall they provide a snapshot of activity at the end of 2013. They document that whereas the translation of some in vitro approaches is now established, other areas are in their infancy and need more development.Prediction of metabolic clearance is an example of a mature in vitro-in vivo extrapolation (IVIVE) area. Di et al. (2013a) provide here an overview of best practices and illustrate a wide application. The area is dominated by examples of hepatic cytochrome P450 (P450), but application beyond P450 enzymes to UDP-glucuronosyltransferases (UGTs) and aldehyde oxidase are now evident. Clearance prediction, and IVIVE in general, is increasingly coupled with physiologically based pharmacokinetic (PBPK) models to allow prediction not limited to specific parameters but to generate a time course that can further inform quantitative aspects of drug absorption, distribution, metabolism, and excretion processes (as outlined here by Shardlow et al., 2013). The dynamic rather than static approaches with a time dimension rather than a time-averaged estimate results in far more comprehensive usage of the in vitro data. An excellent example is contained in this issue (Xu et al., 2013), where the importance of CYP2B6 polymorphisms is explored in defining efavirenz clearance.Prediction of transporter hepatic clearance is an area of great interest and contrasts markedly with the state of play for metabolic hepatic clearance. The complexity of this is becoming evident when delineating the interrelationships between not only enzymes and transporters but also between uptake and efflux transporters and between transporters and passive processes acknowledged (Parker and Houston, 2008;Yabe et al., 2011;Jones et al., 2012; Menochet et al., 2012). It would appear that multiple in vitro models are required, and to integrate these different sources of data requires PBPK modeling procedures. This and other aspects of best practices for transporter-related parameter estimations and predictions have been collated by the International Transporter Consortium (Zamek-Gliszczynski et al., 2013).The balance of active and passive processes in membrane transport of drugs is often helped by consideration of the Biopharmaceutical Drug Disposition Classification System (BDDCS) (Wu and Benet, 2005). Here Kikuchi et al. (2013) review the evidence for the utility of the BDDCS ...

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

“…The utility of in vitro data to provide quantitative prediction of real-life pharmacokinetic behavior is well established. PBPK models have been used in environmental toxicology and are becoming a vital component of modern drug development (Rowland et al, 2011; Rostami-Hodjegan, 2012; Houston, 2013). …”

Enantioselective capillary electrophoresis for the assessment of CYP3A4‐mediated ketamine demethylation and inhibition in vitro

“…3). Intrinsic clearance (CL int ), which defines the rate of metabolism for a given drug concentration, and maximal clearance due to autoactivation (CL max ), which provides an estimate of the highest clearance attained as substrate concentration increases before any saturation of the enzyme sites, were calculated according to CL int 5 V max /K m and CL max 5 (V max /K 0 ) [|(n-1)|/n(|n-1|) 1/n ], respectively [32]. Clearance values are listed in Table 1.…”

“…where v is the product formation rate (velocity) of the metabolic reaction, [S] is the substrate concentration, K m is DETECTION TIME [min] the Michaelis-Menten constant, which is the concentration at which the formation rate is 50% of V max , V max is the maximum formation rate, K 0 is a constant of the autoactivation model which is equivalent to K m when n 5 1, and n is the Hill coefficient [32,33]. Standard parameters such as coefficient of determination (R 2 ) and F-test were used to determine the quality of a fit to a specific model.…”

“…Symbols denote the mean of duplicates. Lines are predicted values based on nonlinear regression analysis using the Michaelis-Menten model (for incubation of racemic ketamine) and the Hill equation (for single ketamine enantiomers).Electrophoresis 2011,32,[2738][2739][2740][2741][2742][2743][2744][2745]…”

Utility of in vitro clearance in primary hepatocyte model for prediction of in vivo hepatic clearance of psychopharmacons

“…The hepatic metabolic clearance is considered to be the major component of the total clearance [3]; thus, in the early phase of drug development, hepatic stability screening is a widely used method to assess the metabolic stability and to predict in vivo hepatic clearance of a drug candidate [1,4].…”