In vitro assays using liver subcellular fractions or suspended hepatocytes for characterizing the metabolism of drug candidates play an integral role in the optimization strategy employed by medicinal chemists. However, conventional in vitro assays have limitations in their ability to predict clearance and generate metabolites for lowturnover (slowly metabolized) drug molecules. Due to a rapid loss in the activity of the drug-metabolizing enzymes, in vitro incubations are typically performed for a maximum of 1 hour with liver microsomes to 4 hours with suspended hepatocytes. Such incubations are insufficient to generate a robustmetabolic response for compounds that are slowly metabolized. Thus, the challenge of accurately estimating low human clearance with confidence has emerged to be among the top challenges that drugmetabolismscientists are confrontedwith today. In response, investigators have evaluated novel methodologies to extend incubation times andmore sufficiently measuremetabolismof low-turnover drugs. These methods include plated human hepatocytes inmonoculture, and a novel in vitromethodology using a relay of sequential incubations with suspended cryopreserved hepatocytes. In addition, more complex in vitro cellular models, such asHepatoPac (Hepregen, Medford, MA), a micropatterned hepatocyte-fibroblast coculture system, and the HmREL (Beverley Hills, CA) hepatic coculture system, have been developed and characterized that demonstrate prolonged enzyme activity. In this review, the advantages and disadvantages of each of these in vitro methodologies as it relates to the prediction of clearance and metabolite identification will be described in an effort to provide drug metabolism scientists with the most up-to-date experimental options for dealing with the complex issue of low-turnover drug candidates.

In vitro assays using liver subcellular fractions or suspended hepatocytes for characterizing the metabolism of drug candidates play an integral role in the optimization strategy employed by medicinal chemists. However, conventional in vitro assays have limitations in their ability to predict clearance and generate metabolites for low-turnover (slowly metabolized) drug molecules. Due to a rapid loss in the activity of the drug-metabolizing enzymes, in vitro incubations are typically performed for a maximum of 1 hour with liver microsomes to 4 hours with suspended hepatocytes. Such incubations are insufficient to generate a robust metabolic response for compounds that are slowly metabolized. Thus, the challenge of accurately estimating low human clearance with confidence has emerged to be among the top challenges that drug metabolism scientists are confronted with today. In response, investigators have evaluated novel methodologies to extend incubation times and more sufficiently measure metabolism of low-turnover drugs. These methods include plated human hepatocytes in monoculture, and a novel in vitro methodology using a relay of sequential incubations with suspended cryopreserved hepatocytes. In addition, more complex in vitro cellular models, such as HepatoPac (Hepregen, Medford, MA), a micropatterned hepatocyte-fibroblast coculture system, and the HµREL (Beverley Hills, CA) hepatic coculture system, have been developed and characterized that demonstrate prolonged enzyme activity. In this review, the advantages and disadvantages of each of these in vitro methodologies as it relates to the prediction of clearance and metabolite identification will be described in an effort to provide drug metabolism scientists with the most up-to-date experimental options for dealing with the complex issue of low-turnover drug candidates.

Inhibition of a drug-metabolizing enzyme by the reversible interaction of a drug with the enzyme, thus decreasing the metabolism of another drug, is a major cause of clinically significant drug-drug interactions. This chapter defines the four reversible mechanisms of inhibition exhibited by drugs: competitive, noncompetitive, uncompetitive, and mixed competitive/noncompetitive. An in vitro procedure to determine the potential of a drug to be a reversible inhibitor is also provided. Finally, a number of examples of clinically significant drug-drug interactions resulting from reversible inhibition are described.

Gemcitabine (dFdC, 2',2'-difluorodeoxycytidine) is metabolized by cytidine deaminase (CDA) and deoxycytidine kinase (DCK), but the contribution of genetic variation in these enzymes to the variability in systemic exposure and response observed in cancer patients is unclear. Wild-type enzymes and variants of CDA (Lys27Gln and Ala70Thr) and DCK (Ile24Val, Ala119Gly, and Pro122Ser) were expressed in and purified from Escherichia coli, and enzyme kinetic parameters were estimated for cytarabine (Ara-C), dFdC, and its metabolite 2',2'-difluorodeoxyuridine (dFdU) as substrates. All three CDA proteins showed similar K(m) and V(max) for Ara-C and dFdC deamination, except for CDA70Thr, which had a 2.5-fold lower K(m) and 6-fold lower V(max) for Ara-C deamination. All four DCK proteins yielded comparable metabolic activity for Ara-C and dFdC monophosphorylation, except for DCK24Val, which demonstrated an approximately 2-fold increase (P < 0.05) in the intrinsic clearance of dFdC monophosphorylation due to a 40% decrease in K(m) (P < 0.05). DCK did not significantly contribute to dFdU monophosphorylation. In conclusion, the Lys27Gln substitution does not significantly modulate CDA activity toward dFdC, and therefore would not contribute to interindividual variability in response to gemcitabine. The higher in vitro catalytic efficiency of DCK24Val toward dFdC monophosphorylation may be relevant to dFdC clinical response. The substrate-dependent alterations in activities of CDA70Thr and DCK24Val in vitro were observed for the first time, and demonstrate that the in vivo consequences of these genetic variations should not be extrapolated from one substrate of these enzymes to another.

The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state (Vss). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis. In vivo and in vitro preclinical data were used to predict Vss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss. Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, for many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. It is recommended to initially use the in vitro tissue composition‐based equations to predict Vss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human Vss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Øie–Tozer, the rat –dog–human proportionality equation, and the lumped‐PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human Vss from several methods available in the literature. © 2011 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4074–4089, 2011

The objective of this study was to evaluate the performance of the Wajima allometry (Css‐MRT) approach published in the literature, which is used to predict the human plasma concentration–time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady‐state volume of distribution (Vss) were predicted by unbound fraction corrected intercept method (FCIM) and Øie–Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)–based approaches (TSrat‐dog) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css‐MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria. © 2011 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4111–4126, 2011

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration–time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (Cmax and AUC0‐t). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first‐pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro‐input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods. © 2011 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4127–4157, 2011

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.

Semagacestat is a functional γ-secretase inhibitor that has been shown to reduce the rate of formation of amyloid-β in vitro and in vivo. This study was conducted to characterize the disposition of semagacestat in humans. After a single 140-mg dose of [14C]semagacestat administered as an oral solution to six healthy male subjects, semagacestat was rapidly absorbed (Tmax ∼0.5 h) and eliminated from the systemic circulation (terminal t1/2 ∼2.4 h). The major circulating metabolites of semagacestat, M2 (hydrolysis of the amide bond proximal to the benzazepine ring) and M3 (benzylic hydroxylation of the benzazepine ring), accounted for approximately 27 and 10% of total radioactivity exposure, respectively, as calculated from relative area under the plasma concentration versus time curve from 0 to 24 h derived from the plasma radiochromatograms. The radioactive dose was almost completely recovered after 7 days postdose, with 87% of the dose in urine and 8% in feces. Unchanged [14C]semagacestat in urine accounted for approximately 44% of the dose, which indicates that renal excretion played an important role in elimination. Metabolites M2 and M3, with their related secondary metabolites, each accounted for approximately 20% of the dose in excreta. In vitro data indicate the formation of M3 is primarily mediated by CYP3A, with cDNA-expressed CYP3A5 approximately 2 times more efficient than CYP3A4 in forming M3. Thus, the relative content of CYP3A4 and CYP3A5 in humans will likely determine the formation clearance of M3 after exposure to semagacestat.

OBJECTIVE:To determine whether duloxetine is a substrate, inhibitor or inducer of cytochrome P450 (CYP) 1A2 enzyme, using in vitro and in vivo studies in humans.METHODS:Human liver microsomes or cells with expressed CYP enzymes and specific CYP inhibitors were used to identify which CYP enzymes catalyse the initial oxidation steps in the metabolism of duloxetine. The potential of duloxetine to inhibit CYP1A2 activity was determined using incubations with human liver microsomes and phenacetin, the CYP1A2 substrate. The potential for duloxetine to induce CYP1A2 activity was determined using human primary hepatocytes treated with duloxetine for 72 hours. Studies in humans were conducted using fluvoxamine, a potent CYP1A2 inhibitor, and theophylline, a CYP1A2 substrate, as probes. The subjects were healthy men and women aged 18-65 years. Single-dose duloxetine was administered either intravenously as a 10-mg infusion over 30 minutes or orally as a 60-mg dose in the presence or absence of steady-state fluvoxamine (100 mg orally once daily). Single-dose theophylline was given as 30-minute intravenous infusions of aminophylline 250 mg in the presence or absence of steady-state duloxetine (60 mg orally twice daily). Plasma concentrations of duloxetine, its metabolites and theophylline were determined using liquid chromatography with tandem mass spectrometry. Pharmacokinetic parameters were estimated using noncompartmental methods and evaluated using mixed-effects ANOVA. Safety measurements included vital signs, clinical laboratory tests, a physical examination, ECG readings and adverse event reports.RESULTS:The in vitro results indicated that duloxetine is metabolized by CYP1A2; however, duloxetine was predicted not to be an inhibitor or inducer of CYP1A2 in humans. Following oral administration in the presence of fluvoxamine, the duloxetine area under the plasma concentration-time curve from time zero to infinity (AUC(infinity)) and the maximum plasma drug concentration (C(max)) significantly increased by 460% (90% CI 359, 584) and 141% (90% CI 93, 200), respectively. In the presence of fluvoxamine, the oral bioavailability of duloxetine increased from 42.8% to 81.9%. In the presence of duloxetine, the theophylline AUC(infinity) and C(max) increased by only 13% (90% CI 7, 18) and 7% (90% CI 2, 14), respectively. Coadministration of duloxetine with fluvoxamine or theophylline did not result in any clinically important safety concerns, and these combinations were generally well tolerated.CONCLUSION:Duloxetine is metabolized primarily by CYP1A2; therefore, coadministration of duloxetine with potent CYP1A2 inhibitors should be avoided. Duloxetine does not seem to be a clinically significant inhibitor or inducer of CYP1A2; therefore, dose adjustment of CYP1A2 substrates may not be necessary when they are coadministered with duloxetine.

Genotype/phenotype analysis with human hepatocytes has identified a new inactive CYP2D6 allele, CYP2D6*56. Cryopreserved human hepatocytes from 51 livers were evaluated for CYP2D6 activity with dextromethorphan as the probe substrate. Hepatocyte lots that lacked CYP2D6 activity were further evaluated for CYP2D6 expression and known genetic variations, including CYP2D6*2, *3, *4, *5, *6, *7, *8, *9, *10, *11, *14, *15, *17, *18, *19, *20, *25, *26, *29, *30, *35, *40, *41, *43, and various multiple copy CYP2D6 alleles (*1xn, *2xn, and *4xn) by the AmpliChip CYP450 prototype microarray (Roche Molecular Systems, Inc., Branchburg, NJ). Two discrepancies were uncovered between the CYP2D6 genotype and activity by this approach. In one sample, a previously unreported 3201C 224 T transition in exon 7 resulted in Arg344(CGA) being replaced by a stop codon (TGA), resulting in a CYP2D6 enzyme lacking the terminal 153 amino acids. This allele was given the designation of CYP2D6*56 and the GenBank accession number DQ282162. The lack of CYP2D6 activity in cryopreserved hepatocytes and microsomes found in the second sample, despite a normal level of CYP2D6 expression and a genotype (*10/*1) predictive of normal CYP2D6 activity, was attributed to enzyme inactivation by an unknown metabolite. The identification and characterization of the CYP2D6*56 allele indicates that commercial cryopreserved human hepatocytes may provide a valuable means to rapidly identify genetic variations with functional relevance. This integrated approach of identifying alleles and examining allele relationships to gene expression and function could be of tremendous value to understanding the mechanism responsible for functional differences in gene variation. The commercial availability of human cryopreserved hepatocytes also makes this potential readily available to any who are interested in it, not just those with access to private liver banks.