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Vol. 27, Issue 11, 1350-1359, November 1999
Drug Metabolism Department, Candidate Synthesis, Enhancement, and Evaluation, Central Research Division, Pfizer, Inc., Groton, Connecticut
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Abstract |
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 Top
 Abstract
 Introduction
Experimental Procedures
Results
Discussion
References
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Twenty-nine drugs of disparate structures and physicochemical properties were used in an examination of the capability of human liver microsomal lability data ("in vitro T1/2" approach) to be useful in the prediction of human clearance. Additionally, the potential importance of nonspecific binding to microsomes in the in vitro incubation milieu for the accurate prediction of human clearance was investigated. The compounds examined demonstrated a wide range of microsomal metabolic labilities with scaled intrinsic clearance values ranging from less than 0.5 ml/min/kg to 189 ml/min/kg. Microsomal binding was determined at microsomal protein concentrations used in the lability incubations. For the 29 compounds studied, unbound fractions in microsomes ranged from 0.11 to 1.0. Generally, basic compounds demonstrated the greatest extent of binding and neutral and acidic compounds the least extent of binding. In the projection of human clearance values, basic and neutral compounds were well predicted when all binding considerations (blood and microsome) were disregarded, however, including both binding considerations also yielded reasonable predictions. Including only blood binding yielded very poor projections of human clearance for these two types of compounds. However, for acidic compounds, disregarding all binding considerations yielded poor predictions of human clearance. It was generally most difficult to accurately predict clearance for this class of compounds; however the accuracy was best when all binding considerations were included. Overall, inclusion of both blood and microsome binding values gave the best agreement between in vivo clearance values and clearance values projected from in vitro intrinsic clearance data.
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Introduction |
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Top
Abstract
Introduction
Experimental Procedures
Results
Discussion
References
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The use of in vitro drug
metabolism data in the understanding of in vivo pharmacokinetic data
has recently become an area of scientific interest (Houston, 1994
;
Houston and Carlile, 1997; Iwatsubo et al., 1997). This has partially
stemmed from a trend in the pharmaceutical industry to use in vitro
drug metabolism data, using human-derived reagents, as a criterion to
select compounds for further development (Rodrigues, 1997). Thus, in
vitro metabolism data is used in a prospective manner to choose those
compounds for further development that are expected to possess
commercially acceptable pharmacokinetic properties (e.g., half-life
permitting once-per-day administration regimens, low oral clearance to
reduce dose, etc.). Several investigators have recently described
methods whereby preclinical drug metabolism and pharmacokinetic data
can be used to predict human pharmacokinetic parameters (Obach et al.,
1997; Lave et al., 1997a,b; Mahmood, 1998a,b).
The first demonstration of the correlation between in vivo clearance
values and clearance values calculated from liver microsomal metabolism
intrinsic clearance data was made by Rane et al. (1977) for the rat.
Intrinsic clearance data were obtained by determination of the enzyme
kinetic parameters (Vmax and
KM). In our work, we described two related
methods whereby human clearance could be predicted from in vitro
metabolism data (Obach et al., 1997). In one method, the enzyme kinetic
parameters Vmax and
KM were determined and converted to
intrinsic clearance
(CL'int)1,
which is similar to that described by Rane et al. (1977). In the other
method, referred to as the "in vitro T1/2
method", CL'int was determined by measuring the
first-order rate constant for consumption of the substrate at a low
concentration. Interestingly, for both of these methods, a better
correlation was observed between the actual and predicted clearance
values if the free fraction in blood was disregarded in the
well-stirred or parallel-tube equations describing hepatic extraction.
One possible reason for the observation that a better prediction of
human clearance was made when disregarding plasma protein binding was
that the substrates were bound in the microsomal incubations, and that
the extent of this binding could be great enough so as to almost cancel
out the plasma protein binding term in the well-stirred and
parallel-tube equations (Obach, 1996). This possibility was further
substantiated in an examination of probe substrates propranolol, imipramine, and warfarin (Obach, 1997). In this report, it was demonstrated that the lipophilic amines propranolol and imipramine were
bound to microsomes, and that incorporation of this binding term aided
in the accurate prediction of human clearance from in vitro intrinsic
clearance data. The acidic drug, warfarin, exhibited this phenomenon to
a much lesser extent. However, for all three drugs overall,
incorporation of both plasma protein and microsome binding terms
generally yielded more accurate predictions of human clearance.
The objective of the experiments described herein is to more exhaustively test the hypothesis that microsomal binding is an important phenomenon in the prediction of in vivo pharmacokinetics from in vitro drug metabolism data. To this end, human hepatic microsomal metabolism data were gathered for 29 drugs, using the in vitro T1/2 approach. Additionally, the extent of nonspecific binding to microsomes in the in vitro matrix was measured for each drug. The drugs used in these experiments span a broad range of structural types (Fig. 1) and include basic compounds (positively charged at pH 7.5), acidic compounds (negatively charged at pH 7.5), and neutral compounds (no charge at pH 7.5). The data set was used to project human clearance from the in vitro intrinsic clearance data to determine whether the most accurate projections are made by disregarding all binding data, including only blood binding values, or including both blood and microsomal binding values.
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Experimental Procedures |
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Top
Abstract
Introduction
Experimental Procedures
Results
Discussion
References
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Materials.
The 29 drugs examined in these experiments were obtained from Sigma
Chemical Co. (St. Louis, MO) with the exception of lorcainide (obtained
from ICN, Aurora, OH), methoxsalen (obtained from Aldrich Chemical,
Milwaukee, WI), zolpidem (obtained from Research Biochemicals International, Natick, MA), and methohexital (obtained from Radian Inc., Dallas, TX). NADPH was obtained from Sigma. Solvents and other
reagents were from common sources and were of HPLC grade or better.
Human liver microsomes were from an in-house bank of liver microsomes
maintained at Pfizer Central Research (Groton, CT). A pool was prepared
from six liver microsomal preparations from six individual donors that
were selected on the basis of having average activities for five of the
major drug metabolizing cytochrome P-450 (CYP) enzymes (CYP1A2,
CYP2C9, CYP2C19, CYP2D6, and CYP3A) normalized per microsomal protein
content. Microsomes from putative CYP2D6 and CYP2C19 poor metabolizers
were excluded. The P-450 content of this pool, as determined by
spectral means (Omura and Sato, 1964) was 0.26 nmol/mg
microsomal protein. CYP isoform specific marker substrate activities
were as follows: CYP1A2, phenacetin O-deethylase of 0.147 nmol/min/mg protein (at 50 µM phenacetin); CYP2C9, tolbutamide
4-hydroxylase of 0.23 nmol/min/mg protein (at 1.0 mM tolbutamide);
CYP2C19, S-mephenytoin 4'-hydroxylase of 0.093 nmol/min/mg
protein (at 1.0 mM S-mephenytoin); CYP2D6, bufuralol
1'-hydroxylase of 0.075 nmol/min/mg protein (at 10 µM bufuralol); and
CYP3A4, testosterone 6
-hydroxylase of 2.7 nmol/min/mg protein (at
250 µM testosterone). All glassware was subjected to gas phase
silylation before use.
Metabolic Incubations. Human liver microsomal incubations were conducted in triplicate. General conditions are described as follows with details specific to each drug listed in Table 1. Incubation mixtures consisted of liver microsomes (0.3-10 mg microsomal protein/ml), substrates (1.0 µM), MgCl2 (3.3 mM), and NADPH (1.3 mM) in a total volume of 0.5 ml potassium phosphate buffer (25 mM, pH 7.5). Reactions were commenced with the addition of NADPH and shaken in a water bath open to the air at 37°C. At T = 0 and at five time points ranging to 40 min, aliquots (50 µl) were removed and added to termination mixtures containing internal standards as listed in Table 1. The samples were processed by extraction into methy t-butyl ether (3 ml), the aqueous layer was frozen in a dry ice-acetone bath, the organic solvent was decanted and evaporated under N2 at 30°C. The residue was reconstituted in 50 µl HPLC mobile phase A (see below). For methoxsalen samples, the work-up procedure consisted of precipitation of protein with CH3CN (100 µl), removal of precipitated materials by centrifugation, and analysis of the supernatant by HPLC-mass spectrometry (MS).
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Equilibrium Dialysis.
Drugs (1.0 µM) were mixed with human liver microsomes (at protein
concentrations used for the respective metabolic incubations), MgCl2 (3.3 mM) and potassium phosphate buffer (25 mM; pH 7.5). The mixtures were subjected to equilibrium dialysis versus
buffer/MgCl2 at 37°C using a Spectrum apparatus
(Spectrum Industries, Los Angeles, CA) as per instructions of the
manufacturer. Spectra-Por no. 4 membranes, with molecular mass cutoff
of 12 to 14 kDa, were used and the cells were rotated at 20 rpm
for 5 h. (These dialysis conditions had been previously shown to
give equilibrium for this dialysis apparatus; Obach, 1997). Dialysis
experiments were done in triplicate. On completion of the dialysis
period, the microsome and buffer samples were removed, processed as
described above, and analyzed by HPLC-MS. Microsome samples (50 µl)
were mixed with control buffer (100 µl), and buffer samples (100 µl) were mixed with control microsomes (50 µl) to yield an
identical matrix before sample work-up. Drug recovery through the
dialysis procedure was determined by analyzing samples of the mixtures
that were not subjected to dialysis, and recovery values were 86% or greater.
HPLC-MS Analysis. The HPLC-MS system consisted of a Hewlett- Packard 1100 quaternary gradient HPLC pump with membrane degasser (Hewlett-Packard, Palo Alto, CA), a CTC PAL autoinjector (Leap Technologies, Carrboro, NC), and a PE-Sciex API 100 single quadrupole mass spectrometer (Sciex, Thornhill, Ontario, Canada) with a turbo ionspray interface. There were various mobile phases used for the different drugs as listed in Table 1. Mobile phase system 1 consisted of 20 mM acetic acid (adjusted to pH 4 with NH4OH) and CH3CN used at various percentages of organic solvent (as listed in Table 1). System 2 consisted of 5 mM NH4OAc (pH unadjusted) and CH3CN at various percentages as listed in Table 1. The column used was a Phenomenex Luna C18 narrow bore column (2.5 × 50 mm) with a 3-µm particle size (Phenomenex, Torrance, CA). The flow rate was 0.5 ml/min and the mobile phase composition was held isocratically for each analyte. The injection volume was 25 µl.
The effluent was split with approximately 0.25 ml/min introduced into the turbo ionspray source of the mass spectrometer. Source parameters (e.g., orifice voltage, temperature, gas flow rates, etc.) were individually optimized for each drug, and the molecular ion (either M + H+ or M
H
,
depending on the orifice polarity) was followed for each compound and
internal standard in the selected ion monitoring mode.
Calculations.
In the determination of the in vitro t1/2, the
analyte/ISTD peak height ratios were converted to percentage
drug remaining, using the T = 0 peak height ratio
values as 100%. The slope of the linear regression from log percentage
remaining versus incubation time relationships (k) was used in
the conversion to in vitro T1/2, values by
in vitro T1/2 = 0.693/k. Conversion to in
vitro CL'int (in units of ml/min/kg) was
done using the following formula (Obach et al., 1997):
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Results |
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Top
Abstract
Introduction
Experimental Procedures
Results
Discussion
References
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The use of HPLC-atmospheric pressure ionization-MS was an important tool in the gathering of these metabolic lability and microsomal binding data. The selectivity and sensitivity of this instrumentation permitted facile quantitation of a wide variety of drug structures. Chromatographic methods were developed for each compound using the same column and only two types of mobile phases, with virtually the only customization required for each compound being determination of an optimal percentage of organic modifier (CH3CN) to effect elution of drug and internal standard within a reasonable run time.
In vitro T1/2 data in pooled human liver microsomes for the 29 compounds examined are listed in Table 3. Metabolic lability of this set of compounds spanned a wide range, the most stable compound being warfarin (in vitro T1/2 was immeasurably long at a microsomal protein concentration of 10 mg/ml), and the most labile being diclofenac, propafenone, and midazolam (scaled CL'int values of 160 ml/min/kg or greater). Within each general class of compounds (weak bases, weak acids, and neutral compounds), intrinsic clearance values spanned a broad range. Bases ranged from low intrinsic clearance values of 3.4 and 4.6 ml/min/kg for quinidine and clozapine, respectively, to high intrinsic clearance values of 122 and 166 ml/min/kg for verapamil and propafenone, respectively. Intrinsic clearance values for acids ranged from less than 0.52 ml/min/kg for warfarin and 0.90 and 0.94 ml/min/kg for tolbutamide and amobarbital, respectively, up to 189 ml/min/kg for diclofenac. Intrinsic clearance values for the neutral compounds ranged from 1.6 ml/min/kg for alprazolam to 160 ml/min/kg for midazolam.
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The extent of microsomal binding was determined for each compound using a microsomal protein concentration equal to that used in the metabolic incubations (Table 3). Because different protein concentrations were used, the compounds cannot be rank ordered with regard to extent of binding to microsomes. The values ranged from no binding to approximately 90% bound. Furthermore, those compounds that exhibited the greatest extent of binding were not necessarily those in which the microsomal protein concentration was highest. In general, the weak bases demonstrated greater binding to microsomes, despite the fact that microsomal concentrations used for the bases were, on average, lower than those used for the neutral and acidic compounds.
A summary of human blood clearance predictions from the in vitro data is presented in Table 4 and predicted clearance values are plotted versus actual clearance values in Fig. 2. Equations for both the well-stirred and the parallel-tube models of hepatic extraction were applied under three variations: disregarding all binding values (Table 4, eqs. 1 and 4), including only blood binding (Table 4, eqs. 2 and 5), and including both blood and in vitro microsome binding (Table 4, eqs. 3 and 6). Overall accuracy values, determined as described in Experimental Procedures, are listed in Table 5. For all compounds examined (n = 29), average fold error values were just over 2-fold in the cases in which either no binding values were considered or all binding values were considered. The most accurate method was the use of the parallel-tube model with both blood and microsome binding incorporated (average fold error of 2.13). Using only the blood binding value in either model of hepatic extraction yielded very poor predictions of human clearance. When subsets of compounds were considered, some differences as to which were the most accurate methods were observed. For weak bases and neutral compounds, disregarding all binding in either model of hepatic extraction yielded the best agreement between actual human clearance values and those projected from in vitro intrinsic clearance data. However, for the acidic compounds, the most accurate clearance prediction methods incorporated both blood and microsome binding. Figure 3 contains plots of accuracy of predicted clearance values using the six equations versus the respective human clearance values.
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Discussion |
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Top
Abstract
Introduction
Experimental Procedures
Results
Discussion
References
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The prediction of human pharmacokinetic parameters for new
chemical entities has become an important approach in the drug discovery process. For any given drug discovery approach, hundreds of
compounds will satisfy potency objectives, however few can be examined
in humans. New chemical entities require extensive investigation and
investment of resources prior to administration to humans, and
therefore it is desirable to be able to exclude compounds from this
process that would be expected to exhibit unsatisfactory human
pharmacokinetic properties. Recently, several investigators have
described various methods and approaches whereby human pharmacokinetic
parameters can be predicted from in vitro and/or preclinical
pharmacokinetic data (Hoener, 1994; Gobburu and Shelver, 1995; Lave et
al., 1997a,b; Obach et al., 1997; Kuhnz and Gieschen, 1998; Sarver et
al., 1997; Mahmood, 1998a,b). These methods vary in complexity and the
amount of data required for accurate predictions.
One of the simplest methods described to predict human clearance is the
use of human hepatic microsomal lability data, termed the in vitro
T1/2 approach (Obach et al., 1997). In this
method, one incubates the test compound with human liver microsomes in the presence of appropriate cofactors (NADPH for CYP catalyzed reactions) and measures the first-order rate of consumption of the test
compound. This rate is converted to an in vitro
CL'int value, scaled-up to reflect
CL'int in vivo, and inserted into a model of
hepatic extraction. A high degree of success was previously reported
for this particular approach, however the number of test compounds was
low, and a majority of the compounds were of a similar physicochemical
class (lipophilic amines). One of the objectives of the present
experimentation was to further test the in vitro T1/2 approach with more compounds and
greater structural diversity.
The use of hepatic microsomes in the prediction of clearance requires
acceptance of several assumptions and caveats: 1) metabolic clearance
is the major mechanism of clearance (i.e.,
CLmetabolism 
CLrenal + CLbiliary + CLother); 2)
the liver is the major organ of clearance (i.e.,
CLhepatic 
CLall other
organs); 3) oxidative metabolism predominates over other
metabolic routes such as direct conjugative metabolism, reduction,
hydrolysis, etc.; 4) rates of metabolism and enzyme activities in vitro
are truly reflective of those that exist in vivo. Additionally, a tenet
of the well-stirred and parallel-tube models of hepatic extraction is
that the unbound concentration of drug in the plasma is equal to the
unbound concentration in the hepatocyte. Therefore, facilitated
transport processes that could possibly be responsible for drug uptake
or drug extrusion from hepatocytes are not accounted for in these
models. The in vitro T1/2 approach has two
additional inherent assumptions that are not required if intrinsic
clearance were determined using the more rigorous approach of
calculating intrinsic clearance from enzyme kinetic data (i.e.,
Vmax/KM). These
are: 1) the substrate concentration employed (1.0 µM in the case of
this report) is well below the apparent KM
for substrate turnover, and 2) there is no significant product
inhibition, nor is there any mechanism based inactivation of enzyme.
Overall, clearance for these 29 compounds was generally underestimated
using any of the six approaches, giving credence to the notion that the
aforementioned assumptions are not completely valid in many cases. It
should be noted that the 29 compounds chosen for this examination
represent a set for which other clearance mechanisms (e.g., renal
clearance, nonoxidative clearance, etc.) are known to be less important
than hepatic oxidative metabolic clearance, but some are known to fall
outside the scope of the aforementioned assumptions, e.g., nonhepatic
metabolism of dexamethasone (Diederich et al., 1996; Tomlinson et al.,
1997), nonoxidative components of metabolic clearance such as the
reductive metabolism of warfarin (Moreland and Hewick, 1975) or
glucuronidation of ibuprofen (Rudy et al., 1991), and product
inhibition of diltiazem (Sutton et al., 1997).
A second objective of this work was to further explore the potential
importance of nonspecific reversible binding of substrate in microsomal
incubations in the prediction of human clearance from in vitro
intrinsic clearance data. Earlier work suggested that this phenomenon
was important in the projection of clearance of imipramine and
propranolol from hepatic microsomal CL'int
(Obach, 1997). The present experimentation sought to examine this
possibility for a larger number of compounds of wider physicochemical properties.
Two models of hepatic clearance were examined (well-stirred and parallel-tube models) under three different variations each: 1) assuming that no binding parameter has impact on clearance (Table 4, eqs. 1 and 4); 2) incorporating binding to blood constituents only (Table 4, eqs. 2 and 5); and 3) incorporating both blood and microsome binding (Table 4, eqs. 3 and 6). When considering all 29 compounds, either disregarding all binding or including both blood and microsome binding yielded the most accurate projections of human clearance (Table 5). However, when the three different classes of compounds were examined separately, it appeared that disregarding all binding yields the most accurate projections of human clearance for basic and neutral compounds but not for the acidic compounds. For the acidic compounds, human clearance projections were most accurate when including all binding parameters.
For the basic compounds, disregarding any potential impact of blood and microsome binding yielded accurate projections of human clearance. However, when the free-fraction in blood was accounted for, clearance of many of the basic compounds, especially those highly bound to blood proteins, were markedly underpredicted (e.g., chlorpromazine, amitriptyline, desipramine, imipramine, diltiazem, quinidine, and clozapine). Many of these compounds were substantially bound to microsomes; thus incorporation of both blood and in vitro unbound fractions again yielded more accurate projections of human clearance. However, clearance for some of the basic compounds were still underprojected (e.g., amitriptyline and diltizem). Diphenhydramine was substantially underprojected in all cases; the reason for this is not known. Also, an interesting case was that of ketamine, in which the extent of binding to microsomes was greater than that to blood proteins.
For neutral compounds, disregarding all binding in the projection of clearance from in vitro intrinsic clearance yielded some overprojections (e.g., diazepam, midazolam, and triazolam), however disregarding all binding yielded the greatest degree of accuracy. As with the basic compounds, inclusion of blood protein binding led to underprojections of clearance (e.g., steroids, diazepam, methoxsalen, and zolpidem), whereas including both blood and microsome binding yielded some improvements in clearance projections (e.g., triazolam, diazepam, and prednisone).
Disregarding all binding yielded marked overprojections of human clearance for many of the acidic drugs (e.g., diclofenac, ibuprofen, tenidap, tenoxicam, and warfarin), whereas including only the unbound fraction in blood yielded underprojections (e.g., diclofenac, warfarin, tenidap, and ibuprofen). Binding to microsomes was not substantial in the cases of many of the acidic drugs, so that improvements in the projection of clearance that were observed when including both blood and microsome binding for basic and neutral compounds, were not observed for all of the acidic drugs (e.g., diclofenac, tolbutamide, and ibuprofen). However, some of the acidic drugs demonstrated significant microsome binding and clearance projections were improved with the inclusion of this parameter (e.g., tenoxicam, tenidap, and amobarbital).
It should be recognized that the phenomenon of nonspecific binding to microsomes in in vitro metabolic incubations is not necessary reflective of the in vivo situation. The inclusion of the unbound fraction of drug in the in vitro incubation matrix is necessary so that the in vitro and in vivo situations can be directly reconciled via a common parameter: unbound intrinsic clearance. Drug that is sequestered in microsomes in vitro is presumed to be unavailable for direct interaction with metabolizing enzymes, just as drug that is bound to plasma proteins and tissue macromolecules in vivo is presumed to be unable to be directly acted on by drug metabolizing enzymes. In both cases, drug molecules must first dissociate from the nonspecific binding sites before they can bind to, and be metabolized by, drug metabolizing enzymes.
In summary, these data support two conclusions. First, the liver microsomal in vitro T1/2 approach can be a suitable approach to measure in vitro CL'int, which can be scaled up to the in vivo situation and used in the prediction of human clearance. This is provided that several caveats, outlined above, are taken into consideration. Second, the measurement of nonspecific binding to microsomes under conditions used in the measurement of in vitro T1/2, and inclusion of these unbound fraction values in the prediction of clearance from in vitro intrinsic clearance data appears to be a more broadly applicable approach than either disregarding all binding or including only blood binding parameters. However, it may be the case for some classes of compounds (especially basic compounds), that disregarding all binding values yields more accurate predictions of human clearance.
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Footnotes |
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Received April 9, 1999; accepted July 30, 1999.
Send reprint requests to: R. Scott Obach, Ph.D., Drug Metabolism Department, Candidate Synthesis, Enhancement, and Evaluation, Central Research Division, Pfizer, Inc., Groton, CT 06340. E-mail: obachr{at}pfizer.com
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Abbreviations |
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Abbreviations used are: CL'int, intrinsic clearance; fu(mic), unbound fraction in microsomal incubation mixtures; fu(blood), unbound fraction in blood; Q, hepatic blood flow; ISTD, internal standard.
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References |
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Top
Abstract
Introduction
Experimental Procedures
Results
Discussion
References
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)-, and (±)-verapamil after intravenous administration.
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S. S. De Buck, V. K. Sinha, L. A. Fenu, M. J. Nijsen, C. E. Mackie, and R. A. H. J. Gilissen Prediction of Human Pharmacokinetics Using Physiologically Based Modeling: A Retrospective Analysis of 26 Clinically Tested Drugs Drug Metab. Dispos., October 1, 2007; 35(10): 1766 - 1780. [Abstract] [Full Text] [PDF]
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R. Kitamura, Y. Yamamoto, S. Nagayama, and M. Otagiri Decrease in Plasma Concentrations of Antiangiogenic Agent TSU-68 ((Z)-5-[(1,2-Dihydro-2-oxo-3H-indol-3-ylidene)methyl]-2,4-dimethyl-1H-pyrrole-3-propanoic acid) during Oral Administration Twice a Day to Rats Drug Metab. Dispos., September 1, 2007; 35(9): 1611 - 1616. [Abstract] [Full Text] [PDF]
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M. G. Soars, K. Grime, J. L. Sproston, P. J. H. Webborn, and R. J. Riley Use of Hepatocytes to Assess the Contribution of Hepatic Uptake to Clearance in Vivo Drug Metab. Dispos., June 1, 2007; 35(6): 859 - 865. [Abstract] [Full Text] [PDF]
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S. S. De Buck, V. K. Sinha, L. A. Fenu, R. A. Gilissen, C. E. Mackie, and M. J. Nijsen The Prediction of Drug Metabolism, Tissue Distribution, and Bioavailability of 50 Structurally Diverse Compounds in Rat Using Mechanism-Based Absorption, Distribution, and Metabolism Prediction Tools Drug Metab. Dispos., April 1, 2007; 35(4): 649 - 659. [Abstract] [Full Text] [PDF]
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N. Picard, N. Djebli, F.-L. Sauvage, and P. Marquet Metabolism of Sirolimus in the Presence or Absence of Cyclosporine by Genotyped Human Liver Microsomes and Recombinant Cytochromes P450 3A4 and 3A5 Drug Metab. Dispos., March 1, 2007; 35(3): 350 - 355. [Abstract] [Full Text] [PDF]
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H. S. Brown, M. Griffin, and J. B. Houston Evaluation of Cryopreserved Human Hepatocytes as an Alternative in Vitro System to Microsomes for the Prediction of Metabolic Clearance Drug Metab. Dispos., February 1, 2007; 35(2): 293 - 301. [Abstract] [Full Text] [PDF]
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M. J. Zaya, R. N. Hines, and J. C. Stevens Epirubicin Glucuronidation and UGT2B7 Developmental Expression Drug Metab. Dispos., December 1, 2006; 34(12): 2097 - 2101. [Abstract] [Full Text] [PDF]
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S. J. Godin, E. J. Scollon, M. F. Hughes, P. M. Potter, M. J. DeVito, and M. K. Ross Species Differences in the in Vitro Metabolism of Deltamethrin and Esfenvalerate: Differential Oxidative and Hydrolytic Metabolism by Humans and Rats Drug Metab. Dispos., October 1, 2006; 34(10): 1764 - 1771. [Abstract] [Full Text] [PDF]
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C. Lu, P. Li, R. Gallegos, V. Uttamsingh, C. Q. Xia, G. T. Miwa, S. K. Balani, and L.-S. Gan Comparison of Intrinsic Clearance in Liver Microsomes and Hepatocytes from Rats and Humans: Evaluation of Free Fraction and Uptake in Hepatocytes Drug Metab. Dispos., September 1, 2006; 34(9): 1600 - 1605. [Abstract] [Full Text] [PDF]
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T. Shimizu, K. Akimoto, T. Yoshimura, T. Niwa, K. Kobayashi, M. Tsunoo, and K. Chiba AUTOINDUCTION OF MKC-963 [(R)-1-(1-CYCLOHEXYLETHYLAMINO)-4-PHENYLPHTHALAZINE] METABOLISM IN HEALTHY VOLUNTEERS AND ITS RETROSPECTIVE EVALUATION USING PRIMARY HUMAN HEPATOCYTES AND CDNA-EXPRESSED ENZYMES Drug Metab. Dispos., June 1, 2006; 34(6): 950 - 954. [Abstract] [Full Text] [PDF]
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D. Hallifax and J. B. Houston BINDING OF DRUGS TO HEPATIC MICROSOMES: COMMENT AND ASSESSMENT OF CURRENT PREDICTION METHODOLOGY WITH RECOMMENDATION FOR IMPROVEMENT Drug Metab. Dispos., April 1, 2006; 34(4): 724 - 726. [Full Text] [PDF]
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V. Uchaipichat, P. I. Mackenzie, D. J. Elliot, and J. O. Miners SELECTIVITY OF SUBSTRATE (TRIFLUOPERAZINE) AND INHIBITOR (AMITRIPTYLINE, ANDROSTERONE, CANRENOIC ACID, HECOGENIN, PHENYLBUTAZONE, QUINIDINE, QUININE, AND SULFINPYRAZONE) "PROBES" FOR HUMAN UDP-GLUCURONOSYLTRANSFERASES Drug Metab. Dispos., March 1, 2006; 34(3): 449 - 456. [Abstract] [Full Text] [PDF]
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D. Hallifax, H. C. Rawden, N. Hakooz, and J. B. Houston PREDICTION OF METABOLIC CLEARANCE USING CRYOPRESERVED HUMAN HEPATOCYTES: KINETIC CHARACTERISTICS FOR FIVE BENZODIAZEPINES Drug Metab. Dispos., December 1, 2005; 33(12): 1852 - 1858. [Abstract] [Full Text] [PDF]
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J. J. Engtrakul, R. S. Foti, T. J. Strelevitz, and M. B. Fisher ALTERED AZT (3'-AZIDO-3'-DEOXYTHYMIDINE) GLUCURONIDATION KINETICS IN LIVER MICROSOMES AS AN EXPLANATION FOR UNDERPREDICTION OF IN VIVO CLEARANCE: COMPARISON TO HEPATOCYTES AND EFFECT OF INCUBATION ENVIRONMENT Drug Metab. Dispos., November 1, 2005; 33(11): 1621 - 1627. [Abstract] [Full Text] [PDF]
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M. Suzuki, Y. Li, P. C. Smith, J. A. Swenberg, D. E. Martin, S. L. Morris-Natschke, and K.-H. Lee ANTI-AIDS AGENTS 65: INVESTIGATION OF THE IN VITRO OXIDATIVE METABOLISM OF 3',4'-DI-O-(-)-CAMPHANOYL-(+)-CIS-KHELLACTONE DERIVATIVES AS POTENT ANTI-HIV AGENTS Drug Metab. Dispos., November 1, 2005; 33(11): 1588 - 1592. [Abstract] [Full Text] [PDF]
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R. J. Riley, D. F. McGinnity, and R. P. Austin A UNIFIED MODEL FOR PREDICTING HUMAN HEPATIC, METABOLIC CLEARANCE FROM IN VITRO INTRINSIC CLEARANCE DATA IN HEPATOCYTES AND MICROSOMES Drug Metab. Dispos., September 1, 2005; 33(9): 1304 - 1311. [Abstract] [Full Text] [PDF]
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C. Giuliano, M. Jairaj, C. M. Zafiu, and R. Laufer DIRECT DETERMINATION OF UNBOUND INTRINSIC DRUG CLEARANCE IN THE MICROSOMAL STABILITY ASSAY Drug Metab. Dispos., September 1, 2005; 33(9): 1319 - 1324. [Abstract] [Full Text] [PDF]
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N. Treijtel, H. van Helvoort, A. Barendregt, B. J. Blaauboer, and J. C. H. van Eijkeren THE USE OF SANDWICH-CULTURED RAT HEPATOCYTES TO DETERMINE THE INTRINSIC CLEARANCE OF COMPOUNDS WITH DIFFERENT EXTRACTION RATIOS: 7-ETHOXYCOUMARIN AND WARFARIN Drug Metab. Dispos., September 1, 2005; 33(9): 1325 - 1332. [Abstract] [Full Text] [PDF]
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 L. K. Kamdem, F. Streit, U. M. Zanger, J. Brockmoller, M. Oellerich, V. W. Armstrong, and L. Wojnowski Contribution of CYP3A5 to the in Vitro Hepatic Clearance of Tacrolimus Clin. Chem., August 1, 2005; 51(8): 1374 - 1381. [Abstract] [Full Text] [PDF]
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K. Venkatakrishnan and R. S. Obach IN VITRO-IN VIVO EXTRAPOLATION OF CYP2D6 INACTIVATION BY PAROXETINE: PREDICTION OF NONSTATIONARY PHARMACOKINETICS AND DRUG INTERACTION MAGNITUDE Drug Metab. Dispos., June 1, 2005; 33(6): 845 - 852. [Abstract] [Full Text] [PDF]
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T. A. Esbenshade, G. B. Fox, K. M. Krueger, T. R. Miller, C. H. Kang, L. I. Denny, D. G. Witte, B. B. Yao, L. Pan, J. Wetter, et al. Pharmacological Properties of ABT-239 [4-(2-{2-[(2R)-2-Methylpyrrolidinyl]ethyl}-benzofuran-5-yl)benzonitrile]: I. Potent and Selective Histamine H3 Receptor Antagonist with Drug-Like Properties J. Pharmacol. Exp. Ther., April 1, 2005; 313(1): 165 - 175. [Abstract] [Full Text] [PDF]
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R. L. Walsky, R. S. Obach, E. A. Gaman, J.-P. R. Gleeson, and W. R. Proctor SELECTIVE INHIBITION OF HUMAN CYTOCHROME P4502C8 BY MONTELUKAST Drug Metab. Dispos., March 1, 2005; 33(3): 413 - 418. [Abstract] [Full Text] [PDF]
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R. P. Austin, P. Barton, S. Mohmed, and R. J. Riley THE BINDING OF DRUGS TO HEPATOCYTES AND ITS RELATIONSHIP TO PHYSICOCHEMICAL PROPERTIES Drug Metab. Dispos., March 1, 2005; 33(3): 419 - 425. [Abstract] [Full Text] [PDF]
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D. F. McGinnity, M. G. Soars, R. A. Urbanowicz, and R. J. Riley EVALUATION OF FRESH AND CRYOPRESERVED HEPATOCYTES AS IN VITRO DRUG METABOLISM TOOLS FOR THE PREDICTION OF METABOLIC CLEARANCE Drug Metab. Dispos., November 1, 2004; 32(11): 1247 - 1253. [Abstract] [Full Text] [PDF]
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Q. Chen, E. Tan, J. R. Strauss, Z. Zhang, J. E. Fenyk-Melody, C. Booth-Genthe, T. H. Rushmore, R. A. Stearns, D. C. Evans, T. A. Baillie, et al. Effect of Quinidine on the 10-Hydroxylation of R-Warfarin: Species Differences and Clearance Projection J. Pharmacol. Exp. Ther., October 1, 2004; 311(1): 307 - 314. [Abstract] [Full Text] [PDF]
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H. M. Jones and J. B. Houston SUBSTRATE DEPLETION APPROACH FOR DETERMINING IN VITRO METABOLIC CLEARANCE: TIME DEPENDENCIES IN HEPATOCYTE AND MICROSOMAL INCUBATIONS Drug Metab. Dispos., September 1, 2004; 32(9): 973 - 982. [Abstract] [Full Text] [PDF]
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N. Treijtel, A. Barendregt, A. P. Freidig, B. J. Blaauboer, and J. C. H. van Eijkeren MODELING THE IN VITRO INTRINSIC CLEARANCE OF THE SLOWLY METABOLIZED COMPOUND TOLBUTAMIDE DETERMINED IN SANDWICH-CULTURED RAT HEPATOCYTES Drug Metab. Dispos., August 1, 2004; 32(8): 884 - 891. [Abstract] [Full Text] [PDF]
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T. B. Andersson, E. Bredberg, H. Ericsson, and H. Sjoberg AN EVALUATION OF THE IN VITRO METABOLISM DATA FOR PREDICTING THE CLEARANCE AND DRUG-DRUG INTERACTION POTENTIAL OF CYP2C9 SUBSTRATES Drug Metab. Dispos., July 1, 2004; 32(7): 715 - 721. [Abstract] [Full Text] [PDF]
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B. Lalovic, B. Phillips, L. L. Risler, W. Howald, and D. D. Shen QUANTITATIVE CONTRIBUTION OF CYP2D6 AND CYP3A TO OXYCODONE METABOLISM IN HUMAN LIVER AND INTESTINAL MICROSOMES Drug Metab. Dispos., April 1, 2004; 32(4): 447 - 454. [Abstract] [Full Text] [PDF]
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R. S. Foti and M. B. Fisher IMPACT OF INCUBATION CONDITIONS ON BUFURALOL HUMAN CLEARANCE PREDICTIONS: ENZYME LABILITY AND NONSPECIFIC BINDING Drug Metab. Dispos., March 1, 2004; 32(3): 295 - 304. [Abstract] [Full Text] [PDF]
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 L. Di, E. H. Kerns, Y. Hong, T. A. Kleintop, O. J. Mc Connell, and D. M. Huryn Optimization of a Higher Throughput Microsomal Stability Screening Assay for Profiling Drug Discovery Candidates J Biomol Screen, August 1, 2003; 8(4): 453 - 462. [Abstract] [PDF]
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J.-S. Wang and C. L. DeVane INVOLVEMENT OF CYP3A4, CYP2C8, AND CYP2D6 IN THE METABOLISM OF (R)- AND (S)-METHADONE IN VITRO Drug Metab. Dispos., June 1, 2003; 31(6): 742 - 747. [Abstract] [Full Text] [PDF]
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D. Projean, B. Baune, R. Farinotti, J.-P. Flinois, P. Beaune, A.-M. Taburet, and J. Ducharme IN VITRO METABOLISM OF CHLOROQUINE: IDENTIFICATION OF CYP2C8, CYP3A4, AND CYP2D6 AS THE MAIN ISOFORMS CATALYZING N-DESETHYLCHLOROQUINE FORMATION Drug Metab. Dispos., June 1, 2003; 31(6): 748 - 754. [Abstract] [Full Text] [PDF]
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Y. Naritomi, S. Terashita, A. Kagayama, and Y. Sugiyama Utility of Hepatocytes in Predicting Drug Metabolism: Comparison of Hepatic Intrinsic Clearance in Rats and Humans in Vivo and in Vitro Drug Metab. Dispos., May 1, 2003; 31(5): 580 - 588. [Abstract] [Full Text] [PDF]
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J. M. Margolis and R. S. Obach Impact of Nonspecific Binding to Microsomes and Phospholipid on the Inhibition of Cytochrome P4502D6: Implications for Relating in Vitro Inhibition Data to in Vivo Drug Interactions Drug Metab. Dispos., May 1, 2003; 31(5): 606 - 611. [Abstract] [Full Text] [PDF]
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Y. Y. Lau, E. Sapidou, X. Cui, R. E. White, and K.-C. Cheng Development of a Novel in Vitro Model to Predict Hepatic Clearance Using Fresh, Cryopreserved, and Sandwich-Cultured Hepatocytes Drug Metab. Dispos., December 1, 2002; 30(12): 1446 - 1454. [Abstract] [Full Text] [PDF]
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R. P. Austin, P. Barton, S. L. Cockroft, M. C. Wenlock, and R. J. Riley The Influence of Nonspecific Microsomal Binding on Apparent Intrinsic Clearance, and Its Prediction from Physicochemical Properties Drug Metab. Dispos., December 1, 2002; 30(12): 1497 - 1503. [Abstract] [Full Text] [PDF]
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S. Kumar, K. Samuel, R. Subramanian, M. P. Braun, R. A. Stearns, S.-H. L. Chiu, D. C. Evans, and T. A. Baillie Extrapolation of Diclofenac Clearance from in Vitro Microsomal Metabolism Data: Role of Acyl Glucuronidation and Sequential Oxidative Metabolism of the Acyl Glucuronide J. Pharmacol. Exp. Ther., December 1, 2002; 303(3): 969 - 978. [Abstract] [Full Text] [PDF]
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 C. J. Hackbarth, D. Z. Chen, J. G. Lewis, K. Clark, J. B. Mangold, J. A. Cramer, P. S. Margolis, W. Wang, J. Koehn, C. Wu, et al. N-Alkyl Urea Hydroxamic Acids as a New Class of Peptide Deformylase Inhibitors with Antibacterial Activity Antimicrob. Agents Chemother., September 1, 2002; 46(9): 2752 - 2764. [Abstract] [Full Text] [PDF]
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Y. Shibata, H. Takahashi, M. Chiba, and Y. Ishii Prediction of Hepatic Clearance and Availability by Cryopreserved Human Hepatocytes: An Application of Serum Incubation Method Drug Metab. Dispos., August 1, 2002; 30(8): 892 - 896. [Abstract] [Full Text] [PDF]
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R. S. Obach and A. E. Reed-Hagen Measurement of Michaelis Constants for Cytochrome P450-Mediated Biotransformation Reactions Using a Substrate Depletion Approach Drug Metab. Dispos., July 1, 2002; 30(7): 831 - 837. [Abstract] [Full Text] [PDF]
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C. Tang, Y. Lin, A. D. Rodrigues, and J. H. Lin Effect of Albumin on Phenytoin and Tolbutamide Metabolism in Human Liver Microsomes: An Impact More Than Protein Binding Drug Metab. Dispos., June 1, 2002; 30(6): 648 - 654. [Abstract] [Full Text] [PDF]
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M. G. Soars, B. Burchell, and R. J. Riley In Vitro Analysis of Human Drug Glucuronidation and Prediction of in Vivo Metabolic Clearance J. Pharmacol. Exp. Ther., April 1, 2002; 301(1): 382 - 390. [Abstract] [Full Text] [PDF]
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I. Nestorov, I. Gueorguieva, H. M. Jones, B. Houston, and M. Rowland Incorporating Measures of Variability and Uncertainty into the Prediction of in Vivo Hepatic Clearance from in Vitro Data Drug Metab. Dispos., March 1, 2002; 30(3): 276 - 282. [Abstract] [Full Text] [PDF]
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R. Singh, I-W. Chen, L. Jin, M. V. Silva, B. H. Arison, J. H. Lin, and B. K. Wong Pharmacokinetics and Metabolism of a Ras Farnesyl Transferase Inhibitor in Rats and Dogs: In Vitro-In Vivo Correlation Drug Metab. Dispos., December 1, 2001; 29(12): 1578 - 1587. [Abstract] [Full Text] [PDF]
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M. Hidestrand, M. Oscarson, J. S. Salonen, L. Nyman, O. Pelkonen, M. Turpeinen, and M. Ingelman-Sundberg CYP2B6 and CYP2C19 as the Major Enzymes Responsible for the Metabolism of Selegiline, a Drug Used in the Treatment of Parkinson's Disease, as Revealed from Experiments with Recombinant Enzymes Drug Metab. Dispos., November 1, 2001; 29(11): 1480 - 1484. [Abstract] [Full Text] [PDF]
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Y. Naritomi, S. Terashita, S. Kimura, A. Suzuki, A. Kagayama, and Y. Sugiyama Prediction of Human Hepatic Clearance from in Vivo Animal Experiments and in Vitro Metabolic Studies with Liver Microsomes from Animals and Humans Drug Metab. Dispos., October 1, 2001; 29(10): 1316 - 1324. [Abstract] [Full Text] [PDF]
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J. C. Kalvass, D. A. Tess, C. Giragossian, M. C. Linhares, and T. S. Maurer Influence of Microsomal Concentration on Apparent Intrinsic Clearance: Implications for Scaling in Vitro Data Drug Metab. Dispos., October 1, 2001; 29(10): 1332 - 1336. [Abstract] [Full Text] [PDF]
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A. Hemeryck, C. A. De Vriendt, and F. M. Belpaire Metoprolol-Paroxetine Interaction in Human Liver Microsomes: Stereoselective Aspects and Prediction of the in Vivo Interaction Drug Metab. Dispos., April 13, 2001; 29(5): 656 - 663. [Abstract] [Full Text]
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T. B. Andersson, H. Sjöberg, K.-J. Hoffmann, A. R. Boobis, P. Watts, R. J. Edwards, B. G. Lake, R. J. Price, A. B. Renwick, M. J. Gómez-Lechón, et al. An Assessment of Human Liver-Derived in Vitro Systems to Predict the in Vivo Metabolism and Clearance of Almokalant Drug Metab. Dispos., April 13, 2001; 29(5): 712 - 720. [Abstract] [Full Text]
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Y. Shibata, H. Takahashi, and Y. Ishii A Convenient In Vitro Screening Method for Predicting In Vivo Drug Metabolic Clearance Using Isolated Hepatocytes Suspended in Serum Drug Metab. Dispos., April 13, 2001; 28(12): 1518 - 1523. [Abstract] [Full Text]
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K. Venkatakrishnan, L. L. von Moltke, and D. J. Greenblatt Application of the Relative Activity Factor Approach in Scaling from Heterologously Expressed Cytochromes P450 to Human Liver Microsomes: Studies on Amitriptyline as a Model Substrate J. Pharmacol. Exp. Ther., April 1, 2001; 297(1): 326 - 337. [Abstract] [Full Text]
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D. F. McGinnity, A. J. Parker, M. Soars, and R. J. Riley Automated Definition of the Enzymology of Drug Oxidation by the Major Human Drug Metabolizing Cytochrome P450s Drug Metab. Dispos., November 1, 2000; 28(11): 1327 - 1334. [Abstract] [Full Text]
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S. Ekins and R. S. Obach Three-Dimensional Quantitative Structure Activity Relationship Computational Approaches for Prediction of Human In Vitro Intrinsic Clearance J. Pharmacol. Exp. Ther., November 1, 2000; 295(2): 463 - 473. [Abstract] [Full Text]
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R. S. Obach Metabolism of Ezlopitant, a Nonpeptidic Substance P Receptor Antagonist, in Liver Microsomes: Enzyme Kinetics, Cytochrome P450 Isoform Identity, and In Vitro-In Vivo Correlation Drug Metab. Dispos., September 1, 2000; 28(9): 1069 - 1076. [Abstract] [Full Text]
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K. Venkatakrishnan, L. L. von Moltke, R. S. Obach, and D. J. Greenblatt Microsomal Binding of Amitriptyline: Effect on Estimation of Enzyme Kinetic Parameters In Vitro J. Pharmacol. Exp. Ther., May 1, 2000; 293(2): 343 - 350. [Abstract] [Full Text]
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); neutral
compounds (
); acidic compounds (
). Lines signify 2-fold errors
between predicted and actual clearance values. Note the different
scales for B and E. Errors for tenidap and tenoxicam in A and D are
well off scale (5000-6000% error) and are not depicted.