ABSTRACT:Glucuronidation via UDP-glucuronosyltransferase (UGT) is an increasingly important clearance pathway. In this study intrinsic clearance (CL int ) values for buprenorphine, carvedilol, codeine, diclofenac, gemfibrozil, ketoprofen, midazolam, naloxone, raloxifene, and zidovudine were determined in pooled human liver microsomes using the substrate depletion approach. The in vitro clearance data indicated a varying contribution of glucuronidation to the clearance of the compounds studied, ranging from 6 to 79% for midazolam and gemfibrozil, respectively. The CL int was obtained using either individual or combined cofactors for cytochrome P450 (P450) and UGT enzymes with alamethicin activation and in the presence and absence of 2% bovine serum albumin (BSA). In the presence of combined P450 and UGT cofactors, CL int ranged from 2.8 to 688 l/min/mg for zidovudine and buprenorphine, respectively; the clearance was approximately equal to the sum of the CL int values obtained in the presence of individual cofactors. The unbound intrinsic clearance (CL int, u ) was scaled to provide an in vivo predicted CL int ; the data obtained in the presence of combined cofactors resulted in 5-fold underprediction on average. Addition of 2% BSA to the incubation with both P450 and UGT cofactors reduced the bias in the clearance prediction, with 8 of 10 compounds predicted within 2-fold of in vivo values with the exception of raloxifene and gemfibrozil. The current study indicates the applicability of combined cofactor conditions in the assessment of clearance for compounds with a differential contribution of P450 and UGT enzymes to their elimination. In addition, improved predictability of microsomal data is observed in the presence of BSA, in particular for UGT2B7 substrates.

The need to incorporate the fraction unbound in microsomes (fu mic ) to obtain meaningful drug concentrations for the prediction of intrinsic clearance and cytochrome P450 inhibition potential is widely accepted (Obach, 1996;Ito and Houston, 2005;Rostami-Hodjegan and Tucker, 2007). Recently, two equations based on drug lipophilicity have been developed for prediction of fu mic (Austin et al., 2002;Hallifax and Houston, 2006) that avoid experimental determinations. The limitations of these empirical predictive tools and their applicability for fu mic predictions over a range of lipophilicity and microsomal protein concentrations have been addressed (Gertz et al., 2008).Analogous to applying a correction for microsomal drug binding to in vitro clearance and inhibition parameters, it is important that the fraction unbound in hepatocyte incubations (fu hep ) is also considered for in vitro-in vivo extrapolation (McGinnity et al., 2006;Brown et al., 2007b). However, this need has yet to be broadly applied, perhaps due to the lack of comprehensive demonstration of its value. In a recent study by Austin et al. (2005), the extent of binding between the microsomal and hepatocyte incubations was compared using hepatocyte data (n ϭ 14) and the corresponding fu mic from a previous study (Austin et al., 2002). The authors proposed a linear 1:1 relationship between microsomal and hepatocyte binding for incubations of 1 mg/ml and 10 6 cells/ml, respectively, indicating that the fu hep can be extrapolated from microsomal studies. However, the applicability of this correlation has been questioned because of the small number of compounds investigated (Hallifax and Houston, 2007).To further explore the relationship between binding in microsomal and hepatocyte systems, a detailed analysis was carried out involving 39 drugs. A nonlinear empirical equation is proposed as an alternative to the linear relationship to relate binding between the two systems. In addition, prediction of fu hep directly from the logP/D metric is assessed over a wide range of lipophilicity (Ϫ0.13 to 5.93). The implications of these findings on the estimation of hepatocellular drug concentration for intrinsic clearance and inhibition parameter predictions are discussed. Materials and MethodsA database of 39 drugs and their corresponding fu mic and fu hep values was collated either from in house data or from the literature (Austin et al., 2002(Austin et al., , 2005Brown et al., 2007a;Hallifax and Houston, 2007). In the aforementioned studies, different methods were used to determine the drug binding in hepatocyte incubations, namely oil centrifugation (using live cells), dialysis (using dead cells), and ultrafiltration (using dead cells). Microsomal and hepatocyte binding are defined by eqs. 1 and 2, respectively: ABBREVIATIONS: fu mic , fraction unbound in microsomes; fu hep , fraction unbound in hepatocyte incubations; K a , microsomal protein binding affinity; K p , hepatocyte/medium concentration ratio; V R , ratio of the cell and incubation volume; logP/D...

Variability in individual capacity for hepatic elimination of therapeutic drugs is well recognized and is associated with variable expression and activity of liver enzymes and transporters. Although genotyping offers some degree of stratification, there is often large variability within the same genotype. Direct measurement of protein expression is impractical due to limited access to tissue biopsies. Hence, determination of variability in hepatic drug metabolism and disposition using liquid biopsy (blood samples) is an attractive proposition during drug development and in clinical practice. This study used a multi-"omic" strategy to establish a liquid biopsy technology intended to assess hepatic capacity for metabolism and disposition in individual patients. Plasma exosomal analysis (n = 29) revealed expression of 533 pharmacologically relevant genes at the RNA level, with 147 genes showing evidence of expression at the protein level in matching liver tissue. Correction of exosomal RNA expression using a novel shedding factor improved correlation against liver protein expression for 97 liver-enriched genes. Strong correlation was demonstrated for 12 key drug-metabolizing enzymes and 4 drug transporters. The developed test allowed reliable patient stratification, and in silico trials demonstrated utility in adjusting drug dose to achieve similar drug exposure between patients with variable hepatic elimination. Accordingly, this approach can be applied in characterization of volunteers prior to enrollment in clinical trials and for patient stratification in clinical practice to achieve more precise individual dosing.

The use of in vitro data to predict in vivo clearance or assess drug-drug interaction potential is well established (Bjornsson et al., 2003;Ito and Houston, 2005;Huang et al., 2007;Rostami-Hodjegan and Tucker, 2007). Binding to microsomal protein and phospholipids has been recognized as an important parameter in the in vitro-in vivo extrapolation strategies (Obach, 1999;McLure et al., 2000;Tucker et al., 2001;Margolis and Obach, 2003;Ito and Houston, 2005;Brown et al., 2006). Nonspecific binding to microsomes may lead to underestimation of in vivo clearance (Obach, 1996(Obach, , 1999Ito and Houston, 2005;Grime and Riley, 2006) or can result in significantly higher IC 50 or K i values in the assessment of inhibition interaction potential (Margolis and Obach, 2003;Brown et al., 2006). Although generally accepted to improve the accuracy of in vitro-in vivo predictions (in conjunction with other in vitro parameters), the assessment of microsomal binding in the form of fu inc is still challenging.One way to avoid the complications of nonspecific binding is to use very low microsomal concentrations, a common practice in highthroughput screening, in particular with recombinant enzymes showing high enzyme activity (Obach et al., 2006). This is also consistent with recommendations that depletion incubations should be carried out at microsomal protein concentrations below 0.5 mg/ml (Jones and Houston, 2004). However, higher microsomal protein concentrations are required under certain conditions, such as when studying phase II metabolic reactions (Soars et al., 2002;Mohutsky et al., 2006) or intestinal metabolism . In addition, most of the in vitro assessment of the time-dependent inhibition potential is based on the use of high protein concentrations (1-2 mg/ml) in order to allow adequate dilution in the two-step experimental procedure (Ghanbari et al., 2006). It should also be noted that highly lipophilic drugs might show significant nonspecific binding even at low microsomal protein concentration.Recently, two algorithms have been introduced by Austin et al. (2002) and Hallifax and Houston (2006) for the prediction of fu inc . Both predictive tools are based on the lipophilicity of the compounds investigated, as defined by either logD 7.4 (for acidic and neutral compounds) or logP for bases and the microsomal protein concentra- ABBREVIATIONS: fu inc , fraction unbound in the incubation; pK a , acid ionization constant; logD 7.4 , distribution coefficient of all drug species between octanol and water at pH ϭ 7.4; C, microsomal protein concentration; afe, average fold error; rmse, root mean squared error; logP, partition coefficient of unionized drug between octanol and water at pH that favors the unionized drug species; logP/D, descriptor for lipophilicity (logP for drugs where pK a Ͼ 7.4; logD for drugs where pK a Ͻ 7.4); LC-MS/MS, liquid chromatography-tandem mass spectrometry.

The penetration of drugs into the central nervous system is a composite of both the rate of drug uptake across the blood-brain barrier and the extent of distribution into brain tissue compartments. Clinically, positron emission tomography (PET) is the primary technique for deriving information on drug biodistribution as well as target receptor occupancy. In contrast, rodent models have formed the basis for much of the current understanding of brain penetration within pharmaceutical Drug Discovery. Linking these two areas more effectively would greatly improve the translation of candidate compounds into therapeutic agents. This paper examines two of the major influences on the extent of brain penetration across species, namely plasma protein binding and brain tissue binding. An excellent correlation was noted between unbound brain fractions across species (R(2) > 0.9 rat, pig, and human, n = 21), which is indicative of the high degree of conservation of the central nervous system environment. In vitro estimates of human brain-blood or brain-plasma ratios of marketed central nervous system drugs and PET tracers agree well with in vivo values derived from clinical PET and post-mortem studies. These results suggest that passive diffusion across the blood-brain barrier is an important process for many drugs in humans and highlights the possibility for improved prediction of brain penetration across species.

Physiologically‐based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug–drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism‐based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (C max ) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both C max and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP‐mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

Ritonavir is a well-known CYP3A4 and CYP2D6 enzyme inhibitor, frequently used to assess the drug–drug interaction (DDI) liability of susceptible drugs. It is also used as a pharmacokinetic booster to increase exposure to CYP3A4 substrates. This study aimed to develop a mechanistic absorption and disposition model to describe exposure to ritonavir following oral dosing of the commercial amorphous solid dispersion tablet, Norvir, under fasted and fed conditions. A mechanistic description of ritonavir absorption from Norvir tablets may help to improve the design of DDI studies. Key parameters of amorphous ritonavir including free base solubility (solubility of the unbound, un-ionized species), bile micelle partition coefficients, formulation wetting/disintegration, and in vivo precipitation parameters were either obtained from the literature or estimated by modeling in vitro biopharmaceutic experiments. Based on variety of in vitro evidence, a main assumption of the model is that ritonavir does not form a crystalline precipitate while resident in the gastrointestinal tract. In the model, if simulated luminal concentration exceeds the amorphous solubility limit, then precipitation to an amorphous form is immediate. Simulated and observed C max and AUC0‑t parameters were well captured (within 1.5-fold) for both fasted and fed states in healthy volunteers. By accounting for luminal fluid viscosity differences in the different prandial states (affecting drug diffusivity) as well as the effect of drug free fraction on gut wall permeation rates, it was possible to explain the negative food effect observed for Norvir tablets in humans. In summary, a biopharmaceutic in vitro in vivo extrapolation approach provides confidence in (verification of) key input parameters of the physiologically-based pharmacokinetic ritonavir model which resulted in successful simulation of observed plasma profiles.

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