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Incorporation of in vitro drug metabolism data into physiologically-based pharmacokinetic models
Pergamon
Toxicology
in Vitro
I 1 (1997) 473478
Incorporation of In Vitro Drug Metabolism Data into Physiologically-based Pharmacokinetic Models J. B. HOUSTON*
and
D. J. CARLILE
Department of Pharmacy, University of Manchester, Manchester Ml3 9PL, UK Summary-The liver poses particular problems in constructing physiologically-based pharmacokinetic models since this organ is not only a distribution site for drugs/chemicals but frequently the major site of metabolism. The impact of hepatic drug metabolism in modelling is substantial and it is crucial to the success of the model that in oitro data on biotransformation be incorporated in a judicious manner. The value of in uirro/in viva extrapolation is clearly demonstrated by considering kinetic data from incubations with freshly isolated hepatocytes. The determination of easily measurable in virro parameters, such as Vmr. and K.,, from initial rate studies and scaling to the in viuo situation by accounting for hepatocellularity provides intrinsic clearance estimates. A scaling factor of I200 x IO’ cells per 250 g rat has proved to be a robust parameter independent of laboratory technique and insensitive to animal pretreatment. Similar procedures can also be adopted for other in ~irro systems such as hepatic microsomes and liver slices. An appropriate scaling factor for microsomal studies is the microsomal recovery index which allows for the incomplete recovery of cytochrome P-450 with standard differential centrifugation of liver homogenates. The hepatocellularity of a liver slice has been unsatisfactory in scaling kinetic parameters from liver slices. The level of success varies from drug to drug and substrate diffusion is a competing process to metabolism within the slice incubation system; hence, low clearance drugs are better predicted than high clearance drugs. The use of three liver models (venous-equilibration, undistributed sinusoidal and dispersion models) have been compared to predict hepatic clearance from in oifro intrinsic clearance values. As no consistent advantage of one model over the others could be demonstrated, the simplest, the venous-equilibration model, is adequate for the currently available data in hepatocytes. While these successes are encouraging as they establish the fidelity of in vitro systems for in viuo prediction, the level of success varies from drug to drug. It is important to address the reasons for failure of prediction by each in virro system and it is noteworthy that the current approach simplifies several key issues. 0 1997 Published by Elseoier Science Ltd Abbreviations: CE = free substrate concentration; CL, = hepatic clearance; CLint = intrinsic clearance; CYPs = cytochrome P-450 isoenzymes; SRW = standard rat weight; V,., = maximum rate of metabolism.
Introduction In constructing a physiologically-based pharmacokinetic model, it is standard practice to use the physicochemical/partitioning properties of the drug/ chemical together with known physiological constants. This approach can predict distribution into tissues that do not carry out metabolism; however, in the case of the liver, particular problems exist, since this organ is not only a distribution site for drugs but invariably the major site of metabolism. The impact of hepatic drug metabolism in modelling is substantial and it is crucial to the success of the model that in vitro data on the kinetics of metabolism be incorporated in a judicious manner. There has been recent notable success in relating the rate of in vitro metabolism to the corresponding events in viuo for certain drugs. This represents a major advance since, traditionally, the value of in vitro drug metabolism systems was considered to be *Author for correspondence.
purely qualitative in nature. Here we summarize the current situation for extrapolation of in vitro kinetic data to the in vivo situation. The main emphasis is placed on work with freshly isolated hepatocytes in short-term suspension culture, since this has proved to be the most amenable in vitro system for this type of extrapolation. The utility of hepatic microsomes and liver slices is also discussed.
A basis for incorporation Intrinsic
clearance
The key parameter that enables the incorporation of in vitro drug metabolism data into a physiologically-based pharmacokinetic model is intrinsic clearance (CLint), which is a pure measure of enzyme activity towards a substrate and is independent of other physiological determinants of clearance such as blood flow or drug binding within the blood matrix (Rane et al., 1977). CLint has units of flow rate (ml/min) and acts as a proportionality constant to
0887-2333/97/$17.00 + 0.00 0 1997 Published by Elsevier Science Ltd. All rights reserved. Printed in Great Britain SSDI 0887-2333(97)00056-R
474
J. B. Houston and D. J. Carlile
describe the relationship between the rate of metabolism of a substrate and its concentration at the enzyme site. CLint can be considered in classical biochemical terms using K:,,.,,, K,,, and the Michaelis-Menten relationship shown below (Eq. I):
involving secondary and tertiary metabolites formed from several precursors. Alternatively, it is possible to measure the rate of substrate depletion from the incubation media with time; however, it is important to establish that drug loss equates with metabolism and not with a process such as physical adsorption or incorporation into cellular components. Rate of metabolism = H The second stage involves a scaling step to quantify [II 111 t the full activity of the liver as measured in isolated whereV,,,,,is the maximal rate of metabolism and K,,, hepatocytes; thus the in vitro CLint units, ul/min/M is the Michaelis constant for the substrateenzyme cells, are converted to in uico units, ml/min/SRW interaction. Under linear conditions, when the free (where SRW is a standard rat weight of 250 g). An substrate concentration (C,) is 10% or less of the K,,,, allowance for parallel hepatic routes of metabolism that may not be assayed in the in vitro incubation then Eq I reduces (Eq. 2): should also be included at this stage if necessary. VmrrG Rate of metabolism = 7 Stage three of the strategy requires the use of a liver PI 111 model to incorporate the estimated CLint into the which is analogous to the in cico definition of CLint. predicted CL, and so a blood flow value and Therefore: information on the binding of drug in the blood is required. A liver model enables the expression of CLint = V,,,, = Rate of metabolism [31 clearance in terms of the circulating blood drug C, R,,, concentration, as opposed to the drug concentration Hence the Michaelis-Menten parameters. or a within the liver. measure of rate of metabolism at a substrate Finally, non-hepatic routes of clearance, for concentration well below K,,,, can be used to obtain example renal excretion of unchanged drug, need to an in aifro CLint. Hepatocyte CLint values will be be incorporated. Clearance terms are additive when expressed in units of $/min/M cells, where M = IO’. they describe parallel routes of elimination. In Go CLint is not the sole determinant of hepatic clearance (CL,). Although overall total body IN VITRO INCUBATION clearance can be partitioned to provide CL,, this latter term is also influenced by hepatic blood flow Initial Rate of Substrate depletion-time and the degree of drug binding to blood macromolecules. The quantitative interrelationships between metabolite formation prOtIe CL,,, blood flow, unbound fraction of drug in the blood and CLint are complex and cannot be adequately defined without knowledge of the relationship between circulating drug concentrations Km and C,. Thus, a liver model is necessary to relate these latter two concentrations and enables CLint to be estimated from in viuo data. In vitro CLint In vitro/in
for metabolite formation
uivo extrapolation
Figure I presents a four-stage strategy that enables the prediction of in oivo clearance from in vitro kinetic data. It is assumed that enzyme systems in vitro operate in a comparable manner to that in Go, that cofactors (NADPH], oxygen for cytochrome P-450 isoenzymes) are not rate limiting and that substrate accessibility to the enzyme active site is not an issue. Although the applicability of this strategy is discussed below with reference to hepatocyte suspension data, the principles apply to other in vitro systems. Stage one involves determination of the MichaelisMenten parameters under linear conditions with respect to time and cell density; Eq. 3 is then employed to obtain CLint. Usually, specific metabolite formation rates are employed to determine these parameters, although ‘total’ metabolite production is feasible and may be more appropriate-for example, when there is sequential metabolism
I
Scaling
Total CLint
Liver Model
1
Hcpatic clearance
Other parallel
routes
1
IN
WV0
BODY
CLJMRANCE
Fig. 1. Strategy for the prediction of in zriw body clearance
trom in aitro systems.
In uirro prediction of hepatic clearance
415
Liver model selection
I
IO
loo
loo0
Hepatocyte CLint @l/min/M cells)
Fig. 2. Relationship between in oivo and hepatoeyte intrinsic clearance for 21 cytochrome P-450 substrates in the rat. Data based on V,, and K., estimates (I) or on substrate depletion-time profiles (A). The line shown represents a scaling factor of 1.2 x IO9cells/SRW.
Validation of approach
Database A strategy requires validation before it can be used prospectively and, to achieve this we have employed an updated and extended database on hepatocyte and in uiuo CLint values (Houston, 1994; Houston and Carlile, 1997). The database comprises 21 drugs with a range of CLint values covering over three orders of magnitude. The drugs in the data base are all substrates for cytochrome P-450 in rat and this represents the most well documented group of kinetic data. Scaling factors Scaling factors should ideally operate as simple product functions and be biologically based, as shown below (Eq. 4): CLint(in vivo) = CLint(in oitro). Scaling factor
[4]
Scaling factors appertain to the relationship between the in vitro system and the intact liver; therefore, a hepatocyte scaling factor should reflect the total number of parenchyma per liver (hepatocellularity). A hepatocellularity of 1.2 x lo9 cells/SRW has been calculated on the basis of literature reports and our own experimental determination (Carlile et al., 1997). There is a relatively small degree (30%) of interlaboratory variability for this parameter, which is also independent of various pretreatments, demonstrating its universal value. Figure 2 presents the relationship between hepatocyte CLint values and the in uivo CLint values, with a line representing the scaling factor of 1.2 x lo9 cells/SRW. The wide range covered necessitates the use of logarithmic scales. The strength of the relationship is striking, considering the wide range of chemical structures and cytochrome P-450 isoenzymes (CYPs) involved and the sources of data, indicating that the scaling factor approach is applicable to hepatocytes.
A liver model is required in order to integrate CLint with the other physiological parameters that influence CL”, namely hepatic blood flow and drug binding. There are several liver models in use to interrelate these variables: these are the venous-equilibration (well-stirred) model, the undistributed sinusoidal (parallel-tube) model and the dispersion model. These models have been reviewed in detail (Wilkinson, 1987) and they all share certain assumptions. First, the distribution of drug into the liver is assumed to be limited by perfusion rate and not subject to any diffusional barriers. Secondly, it is assumed that only the unbound drug in blood crosses the cell membrane and occupies the enzyme site. Finally, a homogeneous distribution of drug-metabolizing enzymes within the liver acinus is adopted. The latter assumption, at least, is incorrect as it is known that CYP isoforms are distributed heterogeneously across the liver acinus (Murray and Burke, 1995). It is important to remember that liver models are a gross approximation of the liver rather than a true reflection of the complex physiology of the organ. The difference between these three liver models is their description of the drug-concentration profile within the liver and consequently their mathematical complexity. The venous-equilibration model assumes that there is no concentration difference between the drug in the liver and in the blood leaving the liver. In contrast, the distributed sinusoidal model assumes an exponential decline in the drug concentration as blood passes through the liver. The dispersion model has been proposed as a unifying model that consists of two key parameters-a dispersion number and an efficiency number: the former represents the degree of dispersion in residence times of drug passing through the liver; the latter describes the efficiency of removal of xenobiotic from circulating blood and is dependent on CLint, blood flow and drug binding. Using the hepatocyte database, we have systematically compared the use of these three liver models to determine which liver model is the most appropriate for predicting CL” from in vitro data. Predicted CL, values have been estimated using these models from scaled hepatocyte Clint values. Hepatic blood flow was taken as 20 ml/min/SRW and the unbound fraction of drug in the blood was obtained from the original literature reports. In addition, for the dispersion model, a dispersion number of 0.6 was used in the calculations (R. Oliver, A. Jones and M. Rowland, personal communication, 1996). The CL,, values predicted from hepatocytes with the observed in uivo CL, are shown in Fig. 3. The models exhibit no differences in their predictions at low clearance values ( c 2.5 ml/min/SRW) but, as CL, increases, and there is an appreciable gradient in the drug concentration across the liver, the differences between the predictions from these models become more apparent. The venous-equilibration
476
J. B. Houston
and D. J. Carlile
model predictions are the lowest, the undistributed sinusoidal model estimates are the highest and the dispersion model predictions are intermediate, owing to the inherent descriptions of xenobiotic concentration across the liver in each model. Since there is no consistent difference between these three liver models in the overall accuracy of the CL, predictions from hepatocyte data, the simplest model-the venous-equilibration model-is recommended for use. Acceptable precision limits
The database has also been examined in a rigorous manner to assess the accuracy and precision of the predicted in vivo Clint values. To define acceptable predictions we have calculated the ratio of the predicted CLint to the observed CLint and adopted a precision limit of i twofold of the in viva prediction (i.e. between a 50% underprediction and a 100% overprediction). In order to achieve a symmetrical distribution, the logarithm of the ratio is used; a twofold precision limit for this ratio corresponds to k 0.3 log units. Perfect predictions would appear as zero values, underpredictions and overpredictions as negative and positive values, respectively. The hepatocyte CLint data have been transformed to this ratio (Fig. 4). The sources of data are illustrated (i.e. from the authors’ laboratory, from other laboratories where both the in Ctro and in tVro studies were implemented and from pooling different laboratories’ studies), and it is evident that the sets are interspersed. Since some 70% of the xenobiotics fall within the & 0.3 precision limits, this clearly demonstrates the utility of hepatocytes. Moreover, most of the data that lie outside these limits fall within k 0.5 limits, which encompasses a 30% underprediction to a 200% overprediction (i.e. _+ threefold precision limit). The use of these precision limits highlights the fidelity of isolated hepatocytes as a tool for in viva predictions.
Observed in viva CL (ml/min/SRW) Fig. 3. Hepatic clearance predictions from scaled hepatocyte CLint values using the venous equilibration (a). undistributed sinusoidal (A) and dispersion (*)models. The line shown represents unity (n = 21).
Predicted in viva CLint (ml/min/SRW) Fig. 4. Precision of hepatocyte predictions of intrinsic clearance assessed by the ratio of predicted intrinsic clearance/observed intrinsic clearance. Data taken from authors’ laboratory (0) from other laboratories that carried out both in ciro and in vitro studies (A), and pooled from different laboratories (*) (n = 21). The dotted lines at + 0.3 log units represent 50% underprediction to 100% overprediction precision limits.
Choice of in vitro system Isolated hepatocytes
and hepatic microsomes
Hepatic microsomal preparations represent the in vitro drug metabolism system of longest standing and most frequent use. Of the 21 drugs in the hepatocyte database, there are corresponding data for I4 of these compounds in rat hepatic microsomes. Using a scaling factor for microsomes based on microsomal protein recovery, which is a measure of the efficiency of the preparation technique, these data were used to obtain predicted CLint values. In both in vitro systems individual metabolite formation rates were available; thus, from these 14 drugs, 26 sets of data each specific for particular pathways were generated. The scaled microsomal formation CLint and scaled hepatocyte formation CLint values are illustrated in Fig. 5. In general, the data are evenly distributed around the line of identity, with 70% of the data within the twofold range indicated by the dotted lines. Data that fall within the l&l00 ml/min/SRW range on the ordinate, a region where microsomal CLint differs by only threefold in some cases, tends to be underpredictive of the hepatocyte CLint values. Two examples from this microsomal underpredieted region are phenytoin and diazepam. Both drugs are more slowly turned over in microsomes than would be expected from hepatocyte studies or in viuo. In the former system, where the conjugating reactions are not functional due to their absence or to a lack of cofactors, end-product inhibition has been proposed to explain these findings (Ashforth et al., 1995; Zomorodi et al., 1995); this phenomenon may be linked to specific isoform involvement. Figure 5 also clearly demonstrates that microsomes can accurately estimate high as well as low in vivo CLint values, indicating that there is no upper limit for microsomal turnover. The reason for some
In rirro prediction of hepatic clearance underpredictions is not a consequence of an upper limit for metabolism rates in microsomes. Possible reasons for poor prediction may arise from interlaboratory variation in microsomal preparation (Carlile et al., 1997) and/or the involvement of specific isoforms. Isolated hepatocytes and liver slices
Tissue slicing is an inexpensive and technically simple procedure compared with hepatocyte isolation and with the advent of new technology there has been a renewal of interest in this in vitro system. Studies with ethoxycoumarin and tolbutamide have demonstrated that xenobiotics can be differentiated into low and high clearance groups (Worboys et al., 1995). Moreover, the same general metabolic and kinetic behaviour of a number of other drugs seen in other in vitro systems and in uivo is also apparent in liver slices (Worboys et al., 1996). For drugs metabolized to numerous products, qualitatively and quantitatively the same products are formed. By determining slice hepatocellularity, the slice data can be expressed per million cells and compared quantitatively with the corresponding kinetic parameters from isolated hepatocytes. Using biochemical markers an estimate of 2.05 M cells per slice was obtained by the authors for 260 wrn slices. Slice CLints are compared with hepatocyte CLint values in Fig. 6 for six drugs and in two cases more than one pathway was characterized; thus nine sets of data are shown. Slices consistently metabolize at a slower rate than the isolated cells, with the discrepancy between the two systems being least with low turnover compounds and greatest with highturnover compounds. Generally, K,,,values are higher in slices than in cells. The lower rates and higher K,,, values observed in slices relative to cells, together
IO
loo
loo0
I 0,coo
Hepatocyte formation CLint (ml/min/SRW) Fig. 5. Comparison of scaled formation intrinsic clearance values from hepatic microsomes and isolated hepatocytes using data obtained from the literature (m), and rats in the authors’ laboratory (0) (n = 26). The dotted lines shown represent a slope of unity, a 50% underprediction and a 100% overprediction. Hepatocyte data were scaled using the hepatocellularity of 1.2 x IO’ cells/SRW; microsomal data from our laboratory were scaled using 660 mg/SRW, whereas literature data were scaled using the mean scaling factor of 500 mg/SRW (Carlile er al., 1997).
lo
Cell formation CLint (/tl/min/M cells) Fig. 6. Comparison of formation intrinsic clearance values in liver slices and hepatocytes. Both clearances are expressed per million (M) hepatocytes (n = 9). The solid line represents the line of unity.
with trends in the slice/cell CLint ratios, indicate that with slices there is delayed accessibility of substrate. However, it is unlikely that only the outer layers of cells in a slice are taking part in metabolism. Indeed, this hypothesis has been disproved by the demonstration of increases of metabolite formation with slice thickness for both low and high clearance drugs (Worboys et al., 1996). The process of drug transport within the slice appears to influence the metabolism rate for all the drugs shown in Fig. 6. The impact of transport is least for low-clearance drugs, for which the substrate concentration gradient across the slice is shallowest. Hence slice CLint and K,,, values are closest to those in isolated hepatocytes. In contrast, for high clearance drugs, the rapid turnover of substrate results in steep substrate concentration gradients within the slice. Consequently, the slice CLint values are low and K,,,values are high relative to the isolated hepatocyte incubation. It would appear that the use of liver slices to obtain kinetic parameters may be limited to identifying lowand high-clearance drugs. Currently, extrapolation of the CLint of a slice to in viuo whole liver cannot be made. Scaling CLint on the basis of hepatocellularity of a slice is inadequate and it will be necessary to study the process of drug uptake into, and transport within, the slice to assess the possible value of such extrapolations in the future. Future use The use of in vitro systems affords the opportunity to produce quantitative data on drug metabolism for incorporation into physiologicatty-based pharmacokinetic models. The evidence presented here demonstrates that data from isolated hepatocytes can be used to determine Clint accurately. Thus CL, can be determined from these data through the use of a liver model and this can subsequently be incorporated into full whole body models. In the few cases of
J. B. Houston and D. J. Carlile
478 poor
correlations
between
hepatocytes
and in vivo
CLint, this may reflect inaccuracies in the determination of the actual in civo values rather than problems associated with this in vitro system. In contrast, other in vitro systems appear to require more elaborate strategies for routine use. Currently, liver slices are poorly predictive of the in vivo state although, with appropriate modelling to account for delayed accessibility of substrate to cells within the core of the slice, this position may change. Hepatic microsomes are less consistent than hepatocytes as predictors of CLint. Some underpredictions may be due to inappropriate scaling factors while others arise from microsomal artifacts, such as end-product inhibition phenomena. However, this limitation may well be circumscribed by appropriate experimental design. More extensive experience in using in vitro kinetic data for quantitative purposes, such as physiologically-based pharmacokinetic models, will allow the development of more advanced strategies to progress beyond the current approach, which clearly simplifies several key issues and restricts the utility of certain in vitro drug-metabolizing systems. REFERENCES
Ashforth E. 1. L., Carlile D. J., Chenery R. and Houston J. 9. (1995) Prediction of in vivo disposition from in vitro systems: clearance of phenytoin and tolbutamide using rat hepatic microsomal and hepatocyte data. Journal of
Pharmacology 766.
and Experimental
Therapeutics
274, 76l-
Carlile D. J., Zomorodi K. and Houston J. B. (1997) Scaling factors to relate drug metabolic clearance in hepatic microsomes, isolated hepatocytes and the intact liver. Studies with induced livers involving diazepam. Drug Metabolism and Disposition. In press. Houston J. B. (1994) Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochemical Pharmacology
47, 1469-1479.
Houston J. B. and Carlile D. J. (1997) Prediction of hepatic clearance from microsomes, hepatocytes and liver slices. Drug
Merabolism
Reviews 29, 891-922.
Murray G. I. and Burke M. D. (1995) Immunohistochemistry of drug-metabolizing enzymes. Biochemical Pharmacorogy 50, 895-903.
-
-
Rane A.. Wilkinson G. R. and Shand D. (1977) Prediction of hepatic extraction ratio from in vitrdmeasurement of intrinsic clearance. Journal of Pharmacology and Experimental
Therapeutics
200, 420-424.
Wilkinson G. R. (1987) Clearance approaches in pharmacology. Pharmacological Reviews 39, 147. Worboys P. D., Bradbury A. and Houston J. B. (1995) Kinetics of drug metabolism in rat liver slices. Rates of oxidation of ethoxycoumarin and tolbutamide, examples of high- and low-clearance compounds. Drug Metabolism and Disposition
23, 393-397.
Worboys P. D., Bradbury A. and Houston J. B. (1996) Kinetics of drug metabolism in rat liver slices. II. Comparison of clearance by liver slices and freshly isolated hepatocytes. Drug Metabolism and Disposition 24, 676-681.
Zomorodi K., Carlile D. J. and Houston J. B. (1995) Kinetics of diazepam metabolism in rat hepatic microsomes and hepatocytes and their use in predicting in vivo hepatic clearance. Xenobiotica 25, 907-916.
Toxicology
in Vitro
I 1 (1997) 473478
Incorporation of In Vitro Drug Metabolism Data into Physiologically-based Pharmacokinetic Models J. B. HOUSTON*
and
D. J. CARLILE
Department of Pharmacy, University of Manchester, Manchester Ml3 9PL, UK Summary-The liver poses particular problems in constructing physiologically-based pharmacokinetic models since this organ is not only a distribution site for drugs/chemicals but frequently the major site of metabolism. The impact of hepatic drug metabolism in modelling is substantial and it is crucial to the success of the model that in oitro data on biotransformation be incorporated in a judicious manner. The value of in uirro/in viva extrapolation is clearly demonstrated by considering kinetic data from incubations with freshly isolated hepatocytes. The determination of easily measurable in virro parameters, such as Vmr. and K.,, from initial rate studies and scaling to the in viuo situation by accounting for hepatocellularity provides intrinsic clearance estimates. A scaling factor of I200 x IO’ cells per 250 g rat has proved to be a robust parameter independent of laboratory technique and insensitive to animal pretreatment. Similar procedures can also be adopted for other in ~irro systems such as hepatic microsomes and liver slices. An appropriate scaling factor for microsomal studies is the microsomal recovery index which allows for the incomplete recovery of cytochrome P-450 with standard differential centrifugation of liver homogenates. The hepatocellularity of a liver slice has been unsatisfactory in scaling kinetic parameters from liver slices. The level of success varies from drug to drug and substrate diffusion is a competing process to metabolism within the slice incubation system; hence, low clearance drugs are better predicted than high clearance drugs. The use of three liver models (venous-equilibration, undistributed sinusoidal and dispersion models) have been compared to predict hepatic clearance from in oifro intrinsic clearance values. As no consistent advantage of one model over the others could be demonstrated, the simplest, the venous-equilibration model, is adequate for the currently available data in hepatocytes. While these successes are encouraging as they establish the fidelity of in vitro systems for in viuo prediction, the level of success varies from drug to drug. It is important to address the reasons for failure of prediction by each in virro system and it is noteworthy that the current approach simplifies several key issues. 0 1997 Published by Elseoier Science Ltd Abbreviations: CE = free substrate concentration; CL, = hepatic clearance; CLint = intrinsic clearance; CYPs = cytochrome P-450 isoenzymes; SRW = standard rat weight; V,., = maximum rate of metabolism.
Introduction In constructing a physiologically-based pharmacokinetic model, it is standard practice to use the physicochemical/partitioning properties of the drug/ chemical together with known physiological constants. This approach can predict distribution into tissues that do not carry out metabolism; however, in the case of the liver, particular problems exist, since this organ is not only a distribution site for drugs but invariably the major site of metabolism. The impact of hepatic drug metabolism in modelling is substantial and it is crucial to the success of the model that in vitro data on the kinetics of metabolism be incorporated in a judicious manner. There has been recent notable success in relating the rate of in vitro metabolism to the corresponding events in viuo for certain drugs. This represents a major advance since, traditionally, the value of in vitro drug metabolism systems was considered to be *Author for correspondence.
purely qualitative in nature. Here we summarize the current situation for extrapolation of in vitro kinetic data to the in vivo situation. The main emphasis is placed on work with freshly isolated hepatocytes in short-term suspension culture, since this has proved to be the most amenable in vitro system for this type of extrapolation. The utility of hepatic microsomes and liver slices is also discussed.
A basis for incorporation Intrinsic
clearance
The key parameter that enables the incorporation of in vitro drug metabolism data into a physiologically-based pharmacokinetic model is intrinsic clearance (CLint), which is a pure measure of enzyme activity towards a substrate and is independent of other physiological determinants of clearance such as blood flow or drug binding within the blood matrix (Rane et al., 1977). CLint has units of flow rate (ml/min) and acts as a proportionality constant to
0887-2333/97/$17.00 + 0.00 0 1997 Published by Elsevier Science Ltd. All rights reserved. Printed in Great Britain SSDI 0887-2333(97)00056-R
474
J. B. Houston and D. J. Carlile
describe the relationship between the rate of metabolism of a substrate and its concentration at the enzyme site. CLint can be considered in classical biochemical terms using K:,,.,,, K,,, and the Michaelis-Menten relationship shown below (Eq. I):
involving secondary and tertiary metabolites formed from several precursors. Alternatively, it is possible to measure the rate of substrate depletion from the incubation media with time; however, it is important to establish that drug loss equates with metabolism and not with a process such as physical adsorption or incorporation into cellular components. Rate of metabolism = H The second stage involves a scaling step to quantify [II 111 t the full activity of the liver as measured in isolated whereV,,,,,is the maximal rate of metabolism and K,,, hepatocytes; thus the in vitro CLint units, ul/min/M is the Michaelis constant for the substrateenzyme cells, are converted to in uico units, ml/min/SRW interaction. Under linear conditions, when the free (where SRW is a standard rat weight of 250 g). An substrate concentration (C,) is 10% or less of the K,,,, allowance for parallel hepatic routes of metabolism that may not be assayed in the in vitro incubation then Eq I reduces (Eq. 2): should also be included at this stage if necessary. VmrrG Rate of metabolism = 7 Stage three of the strategy requires the use of a liver PI 111 model to incorporate the estimated CLint into the which is analogous to the in cico definition of CLint. predicted CL, and so a blood flow value and Therefore: information on the binding of drug in the blood is required. A liver model enables the expression of CLint = V,,,, = Rate of metabolism [31 clearance in terms of the circulating blood drug C, R,,, concentration, as opposed to the drug concentration Hence the Michaelis-Menten parameters. or a within the liver. measure of rate of metabolism at a substrate Finally, non-hepatic routes of clearance, for concentration well below K,,,, can be used to obtain example renal excretion of unchanged drug, need to an in aifro CLint. Hepatocyte CLint values will be be incorporated. Clearance terms are additive when expressed in units of $/min/M cells, where M = IO’. they describe parallel routes of elimination. In Go CLint is not the sole determinant of hepatic clearance (CL,). Although overall total body IN VITRO INCUBATION clearance can be partitioned to provide CL,, this latter term is also influenced by hepatic blood flow Initial Rate of Substrate depletion-time and the degree of drug binding to blood macromolecules. The quantitative interrelationships between metabolite formation prOtIe CL,,, blood flow, unbound fraction of drug in the blood and CLint are complex and cannot be adequately defined without knowledge of the relationship between circulating drug concentrations Km and C,. Thus, a liver model is necessary to relate these latter two concentrations and enables CLint to be estimated from in viuo data. In vitro CLint In vitro/in
for metabolite formation
uivo extrapolation
Figure I presents a four-stage strategy that enables the prediction of in oivo clearance from in vitro kinetic data. It is assumed that enzyme systems in vitro operate in a comparable manner to that in Go, that cofactors (NADPH], oxygen for cytochrome P-450 isoenzymes) are not rate limiting and that substrate accessibility to the enzyme active site is not an issue. Although the applicability of this strategy is discussed below with reference to hepatocyte suspension data, the principles apply to other in vitro systems. Stage one involves determination of the MichaelisMenten parameters under linear conditions with respect to time and cell density; Eq. 3 is then employed to obtain CLint. Usually, specific metabolite formation rates are employed to determine these parameters, although ‘total’ metabolite production is feasible and may be more appropriate-for example, when there is sequential metabolism
I
Scaling
Total CLint
Liver Model
1
Hcpatic clearance
Other parallel
routes
1
IN
WV0
BODY
CLJMRANCE
Fig. 1. Strategy for the prediction of in zriw body clearance
trom in aitro systems.
In uirro prediction of hepatic clearance
415
Liver model selection
I
IO
loo
loo0
Hepatocyte CLint @l/min/M cells)
Fig. 2. Relationship between in oivo and hepatoeyte intrinsic clearance for 21 cytochrome P-450 substrates in the rat. Data based on V,, and K., estimates (I) or on substrate depletion-time profiles (A). The line shown represents a scaling factor of 1.2 x IO9cells/SRW.
Validation of approach
Database A strategy requires validation before it can be used prospectively and, to achieve this we have employed an updated and extended database on hepatocyte and in uiuo CLint values (Houston, 1994; Houston and Carlile, 1997). The database comprises 21 drugs with a range of CLint values covering over three orders of magnitude. The drugs in the data base are all substrates for cytochrome P-450 in rat and this represents the most well documented group of kinetic data. Scaling factors Scaling factors should ideally operate as simple product functions and be biologically based, as shown below (Eq. 4): CLint(in vivo) = CLint(in oitro). Scaling factor
[4]
Scaling factors appertain to the relationship between the in vitro system and the intact liver; therefore, a hepatocyte scaling factor should reflect the total number of parenchyma per liver (hepatocellularity). A hepatocellularity of 1.2 x lo9 cells/SRW has been calculated on the basis of literature reports and our own experimental determination (Carlile et al., 1997). There is a relatively small degree (30%) of interlaboratory variability for this parameter, which is also independent of various pretreatments, demonstrating its universal value. Figure 2 presents the relationship between hepatocyte CLint values and the in uivo CLint values, with a line representing the scaling factor of 1.2 x lo9 cells/SRW. The wide range covered necessitates the use of logarithmic scales. The strength of the relationship is striking, considering the wide range of chemical structures and cytochrome P-450 isoenzymes (CYPs) involved and the sources of data, indicating that the scaling factor approach is applicable to hepatocytes.
A liver model is required in order to integrate CLint with the other physiological parameters that influence CL”, namely hepatic blood flow and drug binding. There are several liver models in use to interrelate these variables: these are the venous-equilibration (well-stirred) model, the undistributed sinusoidal (parallel-tube) model and the dispersion model. These models have been reviewed in detail (Wilkinson, 1987) and they all share certain assumptions. First, the distribution of drug into the liver is assumed to be limited by perfusion rate and not subject to any diffusional barriers. Secondly, it is assumed that only the unbound drug in blood crosses the cell membrane and occupies the enzyme site. Finally, a homogeneous distribution of drug-metabolizing enzymes within the liver acinus is adopted. The latter assumption, at least, is incorrect as it is known that CYP isoforms are distributed heterogeneously across the liver acinus (Murray and Burke, 1995). It is important to remember that liver models are a gross approximation of the liver rather than a true reflection of the complex physiology of the organ. The difference between these three liver models is their description of the drug-concentration profile within the liver and consequently their mathematical complexity. The venous-equilibration model assumes that there is no concentration difference between the drug in the liver and in the blood leaving the liver. In contrast, the distributed sinusoidal model assumes an exponential decline in the drug concentration as blood passes through the liver. The dispersion model has been proposed as a unifying model that consists of two key parameters-a dispersion number and an efficiency number: the former represents the degree of dispersion in residence times of drug passing through the liver; the latter describes the efficiency of removal of xenobiotic from circulating blood and is dependent on CLint, blood flow and drug binding. Using the hepatocyte database, we have systematically compared the use of these three liver models to determine which liver model is the most appropriate for predicting CL” from in vitro data. Predicted CL, values have been estimated using these models from scaled hepatocyte Clint values. Hepatic blood flow was taken as 20 ml/min/SRW and the unbound fraction of drug in the blood was obtained from the original literature reports. In addition, for the dispersion model, a dispersion number of 0.6 was used in the calculations (R. Oliver, A. Jones and M. Rowland, personal communication, 1996). The CL,, values predicted from hepatocytes with the observed in uivo CL, are shown in Fig. 3. The models exhibit no differences in their predictions at low clearance values ( c 2.5 ml/min/SRW) but, as CL, increases, and there is an appreciable gradient in the drug concentration across the liver, the differences between the predictions from these models become more apparent. The venous-equilibration
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model predictions are the lowest, the undistributed sinusoidal model estimates are the highest and the dispersion model predictions are intermediate, owing to the inherent descriptions of xenobiotic concentration across the liver in each model. Since there is no consistent difference between these three liver models in the overall accuracy of the CL, predictions from hepatocyte data, the simplest model-the venous-equilibration model-is recommended for use. Acceptable precision limits
The database has also been examined in a rigorous manner to assess the accuracy and precision of the predicted in vivo Clint values. To define acceptable predictions we have calculated the ratio of the predicted CLint to the observed CLint and adopted a precision limit of i twofold of the in viva prediction (i.e. between a 50% underprediction and a 100% overprediction). In order to achieve a symmetrical distribution, the logarithm of the ratio is used; a twofold precision limit for this ratio corresponds to k 0.3 log units. Perfect predictions would appear as zero values, underpredictions and overpredictions as negative and positive values, respectively. The hepatocyte CLint data have been transformed to this ratio (Fig. 4). The sources of data are illustrated (i.e. from the authors’ laboratory, from other laboratories where both the in Ctro and in tVro studies were implemented and from pooling different laboratories’ studies), and it is evident that the sets are interspersed. Since some 70% of the xenobiotics fall within the & 0.3 precision limits, this clearly demonstrates the utility of hepatocytes. Moreover, most of the data that lie outside these limits fall within k 0.5 limits, which encompasses a 30% underprediction to a 200% overprediction (i.e. _+ threefold precision limit). The use of these precision limits highlights the fidelity of isolated hepatocytes as a tool for in viva predictions.
Observed in viva CL (ml/min/SRW) Fig. 3. Hepatic clearance predictions from scaled hepatocyte CLint values using the venous equilibration (a). undistributed sinusoidal (A) and dispersion (*)models. The line shown represents unity (n = 21).
Predicted in viva CLint (ml/min/SRW) Fig. 4. Precision of hepatocyte predictions of intrinsic clearance assessed by the ratio of predicted intrinsic clearance/observed intrinsic clearance. Data taken from authors’ laboratory (0) from other laboratories that carried out both in ciro and in vitro studies (A), and pooled from different laboratories (*) (n = 21). The dotted lines at + 0.3 log units represent 50% underprediction to 100% overprediction precision limits.
Choice of in vitro system Isolated hepatocytes
and hepatic microsomes
Hepatic microsomal preparations represent the in vitro drug metabolism system of longest standing and most frequent use. Of the 21 drugs in the hepatocyte database, there are corresponding data for I4 of these compounds in rat hepatic microsomes. Using a scaling factor for microsomes based on microsomal protein recovery, which is a measure of the efficiency of the preparation technique, these data were used to obtain predicted CLint values. In both in vitro systems individual metabolite formation rates were available; thus, from these 14 drugs, 26 sets of data each specific for particular pathways were generated. The scaled microsomal formation CLint and scaled hepatocyte formation CLint values are illustrated in Fig. 5. In general, the data are evenly distributed around the line of identity, with 70% of the data within the twofold range indicated by the dotted lines. Data that fall within the l&l00 ml/min/SRW range on the ordinate, a region where microsomal CLint differs by only threefold in some cases, tends to be underpredictive of the hepatocyte CLint values. Two examples from this microsomal underpredieted region are phenytoin and diazepam. Both drugs are more slowly turned over in microsomes than would be expected from hepatocyte studies or in viuo. In the former system, where the conjugating reactions are not functional due to their absence or to a lack of cofactors, end-product inhibition has been proposed to explain these findings (Ashforth et al., 1995; Zomorodi et al., 1995); this phenomenon may be linked to specific isoform involvement. Figure 5 also clearly demonstrates that microsomes can accurately estimate high as well as low in vivo CLint values, indicating that there is no upper limit for microsomal turnover. The reason for some
In rirro prediction of hepatic clearance underpredictions is not a consequence of an upper limit for metabolism rates in microsomes. Possible reasons for poor prediction may arise from interlaboratory variation in microsomal preparation (Carlile et al., 1997) and/or the involvement of specific isoforms. Isolated hepatocytes and liver slices
Tissue slicing is an inexpensive and technically simple procedure compared with hepatocyte isolation and with the advent of new technology there has been a renewal of interest in this in vitro system. Studies with ethoxycoumarin and tolbutamide have demonstrated that xenobiotics can be differentiated into low and high clearance groups (Worboys et al., 1995). Moreover, the same general metabolic and kinetic behaviour of a number of other drugs seen in other in vitro systems and in uivo is also apparent in liver slices (Worboys et al., 1996). For drugs metabolized to numerous products, qualitatively and quantitatively the same products are formed. By determining slice hepatocellularity, the slice data can be expressed per million cells and compared quantitatively with the corresponding kinetic parameters from isolated hepatocytes. Using biochemical markers an estimate of 2.05 M cells per slice was obtained by the authors for 260 wrn slices. Slice CLints are compared with hepatocyte CLint values in Fig. 6 for six drugs and in two cases more than one pathway was characterized; thus nine sets of data are shown. Slices consistently metabolize at a slower rate than the isolated cells, with the discrepancy between the two systems being least with low turnover compounds and greatest with highturnover compounds. Generally, K,,,values are higher in slices than in cells. The lower rates and higher K,,, values observed in slices relative to cells, together
IO
loo
loo0
I 0,coo
Hepatocyte formation CLint (ml/min/SRW) Fig. 5. Comparison of scaled formation intrinsic clearance values from hepatic microsomes and isolated hepatocytes using data obtained from the literature (m), and rats in the authors’ laboratory (0) (n = 26). The dotted lines shown represent a slope of unity, a 50% underprediction and a 100% overprediction. Hepatocyte data were scaled using the hepatocellularity of 1.2 x IO’ cells/SRW; microsomal data from our laboratory were scaled using 660 mg/SRW, whereas literature data were scaled using the mean scaling factor of 500 mg/SRW (Carlile er al., 1997).
lo
Cell formation CLint (/tl/min/M cells) Fig. 6. Comparison of formation intrinsic clearance values in liver slices and hepatocytes. Both clearances are expressed per million (M) hepatocytes (n = 9). The solid line represents the line of unity.
with trends in the slice/cell CLint ratios, indicate that with slices there is delayed accessibility of substrate. However, it is unlikely that only the outer layers of cells in a slice are taking part in metabolism. Indeed, this hypothesis has been disproved by the demonstration of increases of metabolite formation with slice thickness for both low and high clearance drugs (Worboys et al., 1996). The process of drug transport within the slice appears to influence the metabolism rate for all the drugs shown in Fig. 6. The impact of transport is least for low-clearance drugs, for which the substrate concentration gradient across the slice is shallowest. Hence slice CLint and K,,, values are closest to those in isolated hepatocytes. In contrast, for high clearance drugs, the rapid turnover of substrate results in steep substrate concentration gradients within the slice. Consequently, the slice CLint values are low and K,,,values are high relative to the isolated hepatocyte incubation. It would appear that the use of liver slices to obtain kinetic parameters may be limited to identifying lowand high-clearance drugs. Currently, extrapolation of the CLint of a slice to in viuo whole liver cannot be made. Scaling CLint on the basis of hepatocellularity of a slice is inadequate and it will be necessary to study the process of drug uptake into, and transport within, the slice to assess the possible value of such extrapolations in the future. Future use The use of in vitro systems affords the opportunity to produce quantitative data on drug metabolism for incorporation into physiologicatty-based pharmacokinetic models. The evidence presented here demonstrates that data from isolated hepatocytes can be used to determine Clint accurately. Thus CL, can be determined from these data through the use of a liver model and this can subsequently be incorporated into full whole body models. In the few cases of
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correlations
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hepatocytes
and in vivo
CLint, this may reflect inaccuracies in the determination of the actual in civo values rather than problems associated with this in vitro system. In contrast, other in vitro systems appear to require more elaborate strategies for routine use. Currently, liver slices are poorly predictive of the in vivo state although, with appropriate modelling to account for delayed accessibility of substrate to cells within the core of the slice, this position may change. Hepatic microsomes are less consistent than hepatocytes as predictors of CLint. Some underpredictions may be due to inappropriate scaling factors while others arise from microsomal artifacts, such as end-product inhibition phenomena. However, this limitation may well be circumscribed by appropriate experimental design. More extensive experience in using in vitro kinetic data for quantitative purposes, such as physiologically-based pharmacokinetic models, will allow the development of more advanced strategies to progress beyond the current approach, which clearly simplifies several key issues and restricts the utility of certain in vitro drug-metabolizing systems. REFERENCES
Ashforth E. 1. L., Carlile D. J., Chenery R. and Houston J. 9. (1995) Prediction of in vivo disposition from in vitro systems: clearance of phenytoin and tolbutamide using rat hepatic microsomal and hepatocyte data. Journal of
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Carlile D. J., Zomorodi K. and Houston J. B. (1997) Scaling factors to relate drug metabolic clearance in hepatic microsomes, isolated hepatocytes and the intact liver. Studies with induced livers involving diazepam. Drug Metabolism and Disposition. In press. Houston J. B. (1994) Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochemical Pharmacology
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Houston J. B. and Carlile D. J. (1997) Prediction of hepatic clearance from microsomes, hepatocytes and liver slices. Drug
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Murray G. I. and Burke M. D. (1995) Immunohistochemistry of drug-metabolizing enzymes. Biochemical Pharmacorogy 50, 895-903.
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Rane A.. Wilkinson G. R. and Shand D. (1977) Prediction of hepatic extraction ratio from in vitrdmeasurement of intrinsic clearance. Journal of Pharmacology and Experimental
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Wilkinson G. R. (1987) Clearance approaches in pharmacology. Pharmacological Reviews 39, 147. Worboys P. D., Bradbury A. and Houston J. B. (1995) Kinetics of drug metabolism in rat liver slices. Rates of oxidation of ethoxycoumarin and tolbutamide, examples of high- and low-clearance compounds. Drug Metabolism and Disposition
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Worboys P. D., Bradbury A. and Houston J. B. (1996) Kinetics of drug metabolism in rat liver slices. II. Comparison of clearance by liver slices and freshly isolated hepatocytes. Drug Metabolism and Disposition 24, 676-681.
Zomorodi K., Carlile D. J. and Houston J. B. (1995) Kinetics of diazepam metabolism in rat hepatic microsomes and hepatocytes and their use in predicting in vivo hepatic clearance. Xenobiotica 25, 907-916.