Existing methods for the prediction of drug clearance in humans involve the use of in vitro human metabolic stability (intrinsic clearance, CL_int) data (Iwatsubo et al., 1997), consideration of preclinical animal data (Boxenbaum, 1982), or a combination of these approaches (Lave et al., 1997a; Naritomi et al., 2001). In vitro drug metabolism kinetic parameters can provide an estimate of in vivo CL_int via “scaling” with established biological scaling factors (SFs) e.g., hepatocellularity for isolated hepatocytes, or a SF for microsomes based on incomplete microsomal recovery from human liver tissue using the cytochrome P450 (P450) content in homogenate and microsomes (Houston, 1984). CL_int may subsequently be used to provide an estimate of hepatic clearance (CL_h) using several liver models (Houston, 1984; Ito and Houston, 2004).

To date, the more extensive analyses of human clearance predictions have concentrated on P450 substrates, and data have therefore been generated in human liver microsomes (Iwatsubo et al., 1997; Obach, 1999; Naritomi et al., 2001). In general, these studies have been less comprehensive in the range of approaches investigated, with only occasional attention given to chemical class (Obach et al., 1997). Interestingly, these reports have also assessed the ability to predict human CL_h rather than the more fundamental parameter, CL_int, as advocated initially (Houston, 1984; Ito and Houston, 2004). Some controversy also still exists over use of fu_inc, with some laboratories having suggested that fu_inc and fu_b may cancel, negating their inclusion in liver models (Obach et al., 1997). Recent reports have challenged this assumption (Obach, 1999; Austin et al., 2002) and perhaps suggest that consideration of CL_h rather than CL_{int, in vivo} may desensitize such analyses, particularly to errors associated with higher enzyme activities (Ito and Houston, 2004).

The aim of this study was to investigate direct in vitro-in vivo scaling of human in vitro data generated in hepatocytes and microsomes for predicting human clearance in vivo by applying recently described models for estimating fu_inc (Austin et al., 2002, 2005). To provide a more mechanistic insight, in vitro human CL_int data were compiled from recent in-house and published studies, and associated in vivo CL_int values were derived from published CL_h data. Combining datasets permitted extension of models described in previous studies. In addition, the relative drug binding within blood and in vitro incubation matrices is considered further, with respect to their incorporation into liver models.

Materials and Methods

Data Collection. CL_{int, in vitro} data derived from hepatocyte (Lave et al., 1997a,b; Lau et al., 2002; Shibata et al., 2002; Naritomi et al., 2003) or microsomal incubations (Carlile et al., 1999; Obach, 1999; Naritomi et al., 2001; Andersson et al., 2004) were generated in the authors' laboratory and collated from several published studies (Tables 1, 2, 3). Incubation conditions for data generated in the authors' laboratory have been detailed previously (Austin et al., 2002; McGinnity et al., 2004). Data from microsomal studies reflected a variety of methods including formal Michaelis-Menten kinetic analysis [CL_int = V_max/K_m for specific metabolite(s) formation (Carlile et al., 1999; Andersson et al., 2004)] and substrate depletion at low substrate concentrations (Obach, 1999; Naritomi et al., 2001), which was used for all hepatocyte data. Datasets were compiled with several key objectives in mind: to expand existing databases substantially, to provide some assessment of interlaboratory variability, and to complement external datasets in terms of representation from different chemical classes covering a range of physicochemical properties. Particular emphasis was put on hepatocyte data generated in this laboratory for acidic drugs, which were sparsely represented in external datasets. Data produced in the authors' laboratory represent the mean of at least three hepatocyte donors. Replicates and variability in other data can be found in the original reports.

The hepatocyte data were compiled from several sources: incubations conducted with more simple cell suspensions (Lau et al., 2002; Naritomi et al., 2003; authors' laboratory), and cells cultured in the presence of exogenous protein. Additional protein was either autologous (human) serum (Shibata et al., 2002) or 10% fetal calf serum (FCS) for incubations conducted with cultured cells for up to 72 h (Lave et al., 1997a,b; Schneider et al., 1999). CL_{int, in vitro} was estimated using only an unbound fraction in plasma (fu_p) correction for incubations that included serum, since binding to plasma proteins was deemed to be greater than any hepatocyte binding (Shibata et al., 2002; Austin et al., 2005). Since interspecies differences in plasma protein binding precluded such a simple correction for incubations conducted in the presence of FCS, a correction was made only for binding to cells, and this dataset was treated separately. For the remaining datasets, the unbound fraction in the incubation (fu_inc) was applied either as reported by the authors (Naritomi et al., 2003) or predicted from a consideration of chemical class and either log D_7.4 or log P: for microsomes, log(1 – fu/fu) = 0.53 log P/D –1.42 (Austin et al., 2002); and for hepatocytes, log(1 – fu/fu) = 0.40 log P/D –1.38 (Austin et al., 2005).

CL_{int, ub, in vivo} was calculated from values of CL_h, the fu_p, and blood-to-plasma concentration ratio (R_B; fraction unbound in blood, fu_b = fu_p/R_B) reported for each drug according to the well stirred or dispersion liver model (Shibata et al., 2002). Where values of R_B were not provided or readily available, this parameter was assumed to be unity for neutral and basic compounds and 0.55 for acids. The assumptions of the various liver models have been detailed previously (Ito and Houston, 2004).

Where the same drug had been studied by several laboratories (Tables 4 and 5), individual values for CL_h and fu_b were used to estimate CL_{int, ub, in vivo}, and CL_{int, in vitro} and fu_inc were used to project (scaled) CL_{int, ub, in vivo}. The mean values for key parameters were then used for further modeling and statistical analyses. The sources used to derive key parameters are too extensive to be listed here but can be found in the original references cited.

Prediction of the in Vivo Intrinsic Clearance. CL_{int, in vivo} was predicted using SFs for hepatocytes and microsomes to convert the unit of the CL_int from μl/min/10⁶ cells or μl/min/mg protein to ml/min/kg using an estimate of human liver hepatocyte content. Hepatocyte content (or hepatocellularity) was routinely 120 × 10⁶ cells/g liver. Some variability was evident in the microsomal protein concentration per gram of liver (45–50 mg protein/g liver) and the human liver weight values (1500–1800 g liver/70 kg) cited, but this was not considered to impact significantly on the conclusions of this study.

Impact of Plasma and in Vitro Binding. In in vitro studies, some drugs bind nonspecifically within the matrix; hence, the kinetic parameters estimated need to be corrected to reflect the unbound drug. Obviously, if fu_b and fu_inc were equivalent, their terms in the equations for liver models would cancel out where CL_int* = (CL_{int, in vitro} × SF) and fu_inc is the unbound fraction in hepatocytes or microsomes; Q_h = liver blood flow (20 ml/min/kg).

The role of fu_inc and fu_b was investigated using several approaches. First, the human CL_{int, in vivo} values were predicted assuming fu_inc to be unity and compared with CL_{int, in vivo} calculated from “deconvolution” of the well stirred model, acknowledging the potential limitations of this model for highly extracted compounds (Ito and Houston, 2004): CL_{int, in vivo} data were provided in several reports (Shibata et al., 2002; Naritomi et al., 2003). Relationships obtained using the parallel tube model to compute CL_{int, in vivo} were very similar to those reported (data not shown).

CL_{int, in vivo} values were also compared without consideration of both fu_inc and fu_b. Finally, CL_{int, in vivo} predictions using fu_inc were compared with estimates from clinical pharmacokinetics invoking fu_b (CL_{int, ub, in vivo}).

Prediction of Hepatic (Blood) Clearance. Using the in vivo intrinsic clearance estimates outlined above, CL_h, was calculated according to the well stirred liver model as follows:

Accuracy of Predictions.Quantitative linear regression analysis was not considered appropriate for the predicted CL_h data since it is clearly not homoscedastic; i.e., the error in the y data are not even approximately constant across the full range of the data: the spread in the y data decreases with increasing log(predicted clearance). This behavior is a consequence of the format of the well stirred model, which gradually forces compounds with increasingly high intrinsic clearance toward the same value of predicted clearance, that of hepatic blood flow, Q_h.

Quantitative regression analyses were performed, however, for the log (predicted) and log (observed) values for CL_{int, in vivo} to obtain the regression equation, correlation coefficient (r²), and a summary of its statistics (standard deviation, S.D., the F statistic, and the p value). The average fold error (afe) of each prediction method was also calculated to provide a measure of bias with equal value to under- and over-predictions:

Results

Prediction of CL_{int, ub} and CL_h from Hepatocyte Incubations.Figure 1 (A–C) shows plots of log(observed CL_h) against log(predicted CL_h) where hepatic clearance is predicted from the well stirred model, including or excluding the various fu terms. For incubations conducted without FCS, the model with both fu_inc and fu_b corrections shows the best qualitative relationship between log(observed CL_h) and log(predicted CL_h). The model with an fu_b correction only (i.e., ignoring fu_inc) appears to show the next best qualitative trend, with the uncorrected model showing a very scattered relationship with the acidic compounds becoming separated from the neutral and basic compounds.

Interlaboratory variability in CL_{int, in vitro} was acceptable (≤3-fold) for some compounds studied in hepatocytes under similar conditions (diclofenac, imipramine, naloxone, propranolol, and tolbutamide) and microsomes (diazepam, ibuprofen, and tolbutamide). Variability was more significant for other compounds, possibly due to differences in quality of liver samples, established interdonor differences, and incubation conditions, e.g., potential effects of coincubating hepatocytes with serum. This highlights the challenges in compiling datasets from several sources.

A more appropriate transformation of the data for linear regression analysis is to consider CL_{int, ub}, i.e., log(CL_{int, in vivo, ub}), plotted against log(predicted CL_{int, ub}) as shown in Figs. 1 and 2 (D to F), which also provides a substantial dynamic range with which to study in vitro-in vivo comparisons at a detailed, mechanistic level (Ito and Houston, 2004). These plots show a fairly constant spread in the y data across the full range. Linear regression has been applied to the 3 different scaling models in Fig. 1, D to F, and the resulting statistical parameters are given in Table 6, along with the average fold error of each method. In terms of the statistics of linear regression, the model, which includes both fu_inc and fu_b corrections, provides the best fit to the data as shown by the significantly higher r² value and lower regression standard deviation (S.D.) compared with the other two models (Table 6). The F statistic and p value of this model are also clearly superior to those of the other two models. The slope of the model including both correction terms (1.08) is close to unity, but the intercept (0.38) reveals a constant bias in the model leading to an approximately 5-fold under-prediction of CL_{int, ub} across the range of compounds. Despite evidence of interlaboratory variability for some data, this model appears largely independent of laboratory and chemical class. In terms of regression statistics, the model utilizing only an fu_b correction (i.e., omitting fu_inc) has the next best performance, with the uncorrected model performing poorly.

Interestingly, subdividing this dataset into chemical class indicated that the predictions using the model without f_ub and fu_inc corrections were very poor for acidic drugs, in particular: predictions appeared better for basic and neutral compounds. Closer inspection of the ratio between fu_inc and fu_b for each chemical class provided further mechanistic insight (Fig. 3). For the majority of the basic and neutral drugs studied, this ratio was between 1 and 10. However, for some neutral (including the benzodiazepines, diazepam and oxazepam, and the lipophilic calcium channel blocker, felodipine) and lipophilic, basic compounds (for example, mibefradil), the ratio was somewhat larger. By contrast, for acidic drugs, this ratio was much higher on average, reflecting the extensive binding to albumin for many of these compounds in vivo.

Prediction of CL_{int, ub} and of CL_b from Hepatocyte Incubations Containing FCS. By contrast, for hepatocyte incubations conducted (up to 72 h) in the presence of FCS, CL_b was described well under all conditions, and CL_{int, ub, in vivo} was predicted well from CL_{int, in vitro} (Fig. 3; Table 6). However, models incorporating only fu_b or both fu_inc and fu_b, although statistically significant, yielded a large bias as evidenced by deviations in the gradient, the intercept, and the associated afe (42- to 86-fold; Table 6). The most robust model (in terms of bias in gradient, intercept, and afe) for CL_{int, ub, in vivo} was derived from ignoring both fu_inc and fu_b.

Prediction of CL_{int, ub} from Microsomal Data. Applying this knowledge to the microsomal data again yielded a robust, highly significant relationship between the scaled CL_{int, ub, in vivo} estimate (using both fu_b and fu_inc) and that derived from clinical pharmacokinetic data (r² = 0.77, p = 1.22 × 10^–12; Fig. 4). The afe (6.1) for this dataset was very similar to the larger human hepatocyte dataset.

Further Model Development. For the most extensive hepatocyte dataset, since the fu_inc and fu_b corrected model was highly correlated but with a constant offset, the regression equation was applied to data to evaluate the most precise clearance prediction for test compounds: and.... Transformation of these predictions of CL_{int, in vivo} to projections of CL_b finally give afe values of 1.9 for the fu_inc and fu_b corrected method, 2.0 for the fu_b corrected method and 15.1 for the uncorrected method. Therefore the model with both correction terms has the potential for making the most precise predictions of CL_h for external test compounds.

Discussion

Prediction of human pharmacokinetics remains an intense area of research. It is widely accepted that simple allometric methods are generally not suitable for predicting human clearance for compounds, which exhibit low to intermediate extraction via metabolism (Lave et al., 1997a; Nagilla and Ward, 2004). In vitro-in vivo scaling of metabolic clearance has received much attention (Houston, 1984; Houston and Carlile, 1997; Iwatsubo et al., 1997; Naritomi et al., 2001) and also provides input for physiologically based pharmacokinetic models (Theil et al., 2003). Early, extensive investigations of in vitro-in vivo scaling of hepatic metabolic clearance focused largely on the rat as a model species (Houston, 1984; Houston and Carlile, 1997). More recently, several laboratories have extended these analyses of human clearance predictions. These studies have tended to concentrate on a relatively small number of drugs metabolized by P450s, and data have therefore been generated in human liver microsomes (Iwatsubo et al., 1997; Obach et al., 1997). In general, these studies have been less comprehensive in the range of approaches investigated, with only occasional attention given to chemical class (Obach, 1999). Interestingly, these reports have also assessed the ability to predict human CL_h rather than the more fundamental parameter, CL_int, as advocated in the seminal work by Houston and colleagues (Houston, 1984; Houston and Carlile, 1997; Ito and Houston, 2004).

To assess various approaches to predicting human in vivo hepatic metabolic clearance, a database has been collated from in-house and literature sources. Hepatocytes appear to be the most appropriate in vitro system to assess hepatic metabolic stability. Studies in the rat have advocated hepatocytes for more accurate prediction of rapid clearance (Houston, 1984; Houston and Carlile, 1997), and hepatocytes not only contain P450s and the major phase 2 enzymes but also hepatobiliary transporters, which may modulate concentrations of substrate accessible to drug-metabolizing enzymes (Shitara et al., 2003; Liu and Pang, 2005). Advances in cryopreservation technology have recently enabled studies with human hepatocytes to become more routine (Li et al., 1999; McGinnity et al., 2004). The present analysis considered primarily in vitro and in vivo CL_int estimates, the latter obtained from deconvoluting CL_h to generate a wide range of values to allow detailed, mechanistic comparisons. Predictions of the kinetic parameter CL_h were also evaluated.

The role of plasma protein binding in clearance prediction has been the subject of some controversy. Whereas the basic tenet of pharmacokinetics states that the unbound drug concentration in the plasma dictates tissue distribution, some reports using microsomes have suggested that in vitro CL_int may provide a better estimate of in vivo clearance of total rather than unbound drug (Obach et al., 1997; Lin et al., 1999). Presumably, the assumption was that fu_b and fu_inc effectively nullified in the liver model calculation, negating the measurement of either process. However, measurements of in vitro binding have shown that drug binding within these two matrices is not equivalent and hence fu_inc should not be ignored, in principle (Obach, 1999; Austin et al., 2002, 2005).

Most models were reasonable for neutral and basic drugs, particularly for CL_h, as reported previously (Davis and Riley, 2004; McGinnity et al., 2004; Riley and Kenna, 2004). However, examination of the hepatocyte data indicated the need to consider both fu_inc and fu_b for incubations not conducted in the presence of exogenous protein to include all chemistries. Previous studies have shown that, as for microsomes, fu_inc for hepatocytes may be substantial for lipophilic neutral and basic compounds and can be predicted from consideration of charge at physiological pH and lipophilicity (Austin et al., 2005). Some neutral compounds (e.g., the benzodiazepines diazepam and oxazepam) and lipophilic bases (e.g., mibefradil; Fig. 2) also showed a similar effect, as suggested previously (Riley and Kenna, 2004). By contrast, hepatocyte binding for most acidic drugs is low compared with their binding to distinct sites on albumin. Inclusion of both fu_inc and fu_b terms resulted in a unified model, the robustness of which was indicated by its ability to translate across both laboratories and chemical classes.

Interestingly, several reports on relatively small numbers of compounds have indicated that data generated from hepatocyte incubations containing exogenous protein (either FCS or serum) may yield more direct estimates of CL_{int, in vivo} and CL_h (Lave et al., 1997a,b; Shibata et al., 2003). However, the nonphysiological composition of such assays and effects other than those attributable to protein binding (Blanchard et al., 2005) remain the subject of much controversy and may contribute to some of the variability depicted in Table 4. Furthermore, such methodology poses challenges in terms of determining low CL_int values accurately and may necessitate long incubation periods (up to 72 h) with cultured cells, which may incur a (differential) loss of P450 activity.

Previous reports have debated the pros and cons of inclusion of fu_inc for data from human liver microsomes; hence, this topic was not analyzed in depth here. Inclusion of both fu_inc and fu_b yielded a highly significant correlation between predicted and observed CL_{int, ub} with a bias or offset similar to that shown in Fig. 1F, as suggested previously for both CL_h and CL_int (Iwatsubo et al., 1997; Obach, 1999; Naritomi et al., 2001).

A model incorporating in vivo and in vitro data from preclinical and clinical studies has the potential advantage of providing a drug-specific factor that would theoretically correct for any systematic difference between in vitro and in vivo parameters (Naritomi et al., 2001, 2003). However, further work using larger databases is required to validate this approach and evaluate whether various “factors” (which may be passive or active in origin) translate routinely across a range of species.

In summary, human in vitro CL_int provided accurate predictions of CL_{int, ub} and CL_h with more robust models resulting from incorporation of fu_inc for both hepatoyctes and microsomes. Using the standard biological SFs alone to scale in vitro CL_int to provide in vivo CL_int estimates resulted in a systematic under-prediction for data from both hepatocytes and microsomes, an observation consistent with other studies using human tissues. Interestingly, previous analyses of rat predictions from both in vitro systems did not show such a systematic bias (Ito and Houston, 2004). Although a scientific rationale for this observation is lacking currently for these specific datasets, likely contributors include active transport processes in vivo not reflected adequately in vitro (Liu and Pang, 2005; Shitara et al., 2005); incorrect assumptions within the liver models; interindividual variability atypical kinetics, e.g., for CYP3A4 substrates (Houston and Galetin, 2004); quality of tissue used for hepatocyte preparations; or some extrahepatic metabolism in vivo. Future studies will aim to provide a systematic analysis and mechanistic interpretation, which could then be applied to modify existing scaling strategies further.