BIOM/PHAR 275 - Dalvie Paper 1 Chem. Res. Toxicol. 2009, 22, 357–368 357

Assessment of Three Human in Vitro Systems in the Generation of Major Human Excretory and Circulating Metabolites

Deepak Dalvie,*,† R. Scott Obach,‡ Ping Kang,† Chandra Prakash,‡,| Cho-Ming Loi,† Susan Hurst,‡ Angus Nedderman,§ Lance Goulet,† Evan Smith,† Hai-Zhi Bu,†,⊥ and Dennis A. Smith§ Pharmacokinetics, Dynamics and Metabolism, Pfizer Global Research and DeVelopment, San Diego California 92121, Pfizer Global Research and DeVelopment, Groton Connecticut 06340, and Pfizer Global Research and DeVelopment, Sandwich, Kent, United Kingdom

ReceiVed NoVember 14, 2008

An early understanding of key metabolites of drugs is crucial in drug discovery and development. As a result, several in vitro models typically derived from liver are frequently used to study drug metabolism. It is presumed that these in vitro systems provide an accurate view of the potential in vivo metabolites and metabolic pathways. However, no formal analysis has been conducted to validate their use. The goal of the present study was to conduct a comprehensive analysis to assess if the three commonly used in vitro systems, pooled human liver microsomes, liver S-9 fraction, and hepatocytes, adequately predict in vivo metabolic profiles for drugs. The second objective was to compare the overall capabilities of these three systems to generate in vivo metabolic profiles. Twenty-seven compounds in the Pfizer database and 21 additional commercially available compounds of diverse structure and routes of metabolism for which the human ADME data was available were analyzed in this study to assess the performance of the in vitro systems. The results suggested that all three systems reliably predicted human excretory and circulating metabolite profiles. Furthermore, the success in predicting primary metabolites and metabolic pathways was high (>70%), but the predictability of secondary metabolites was less reliable in the three systems. Thus, the analysis provides sufficient confidence in using in vitro systems to reliably produce primary in vivo human metabolites and supports their application in early discovery to identify metabolic spots for optimization of metabolic liabilities anticipated in humans in vivo. However, the in vitro systems cannot solely mitigate the risk of disproportionate circulating metabolites in humans and may need to be supplemented with metabolic profiling of plasma samples from first-in-human studies or early human radiolabeled studies.

Introduction is warranted for metabolites present in humans but absent or present in disproportionately lower levels in toxicology species. Drug biotransformation plays a pivotal role at all stages of Thus, it is important to identify the principal metabolites in discovery and development of new candidates (1, 2). At the humans and evaluate the species differences in drug metabolism lead optimization stage (early discovery stage), metabolite between the animals used in safety assessment and humans as identification can aid drug design efforts on several fronts (1). early as possible. The most important one is the identification of major metabolites In most cases, principal metabolites are identified by perform- and therefore the metabolically labile positions on the molecule ing radiolabeled absorption, distribution, metabolism and excre- for compounds rapidly metabolized. This can lead medicinal tion (ADME1) studies where 14Cor3H analogues of drugs are chemists to the design of more stable analogues. In the drug administered to humans and animals, and biological fluids are development stage, an effort toward gathering knowledge of evaluated for a comprehensive and quantitative profile of the in vivo metabolic profile of a drug in humans and preclinical metabolites. However, such studies are resource-intensive and species has recently gained more importance since the publica- thus not routinely feasible in drug discovery or early develop- tion of position papers on drug metabolites in safety testing ment. Although in vivo animal studies offer a possibility to (3-6) as well as the recently issued FDA guidance (7). This predict human in vivo metabolites, in vivo profiles of drug guidance recommends that human circulating metabolites metabolites do not always overlap between humans and animals. exceeding 10% of the parent should be present in equal or In vitro systems, especially the human-derived reagents, are greater quantities in at least one of the preclinical species used commonly used to study human drug metabolism and identify in toxicological assessment (8). Direct safety testing in animals the principal metabolites preferably prior to the human radiolabel ADME study (9-14). It is generally believed that hepatocytes * To whom correspondence should be addressed. Pharmacokinetics, Dynamics and Metabolism Department, Pfizer Global Research and and subcellular fractions (liver microsomes or S-9 fractions Development, Science Center Drive, San Diego, CA 92121. Phone: (858) 622-8016. E-mail: deepak.dalvie@pfizer.com. 1 Abbreviations: NADPH, nicotinamide adenine dinucleotide phosphate; † Pfizer Global Research and Development, San Diego, CA. UDPGA, uridine-5′-diphosphoglucuronic acid; SAM, S-methyladenosyl- ‡ Pfizer Global Research and Development, Groton, CT. methionine; PAPS, 3-phosphoadenosine-5′-phosphosulfate; ADME, absorp- § Pfizer Global Research and Development, Sandwich, Kent, U.K. tion, distribution, metabolism, and excretion; S-9 fraction, 9000g supernatant | Current address: Biogen Idec, Cambridge, MA 02142. of a liver homogenate; phase 1, oxidation, reduction, and hydrolysis; phase ⊥ Current address: 3D BioOptima Co., Ltd., Suzhou, China 215104. 2, conjugation.

10.1021/tx8004357 CCC: $40.75  2009 American Chemical Society Published on Web 01/15/2009 BIOM/PHAR 275 - Dalvie Paper 1 358 Chem. Res. Toxicol., Vol. 22, No. 2, 2009 DalVie et al.

Scheme 1. Structures of 27 Compounds from the Pfizer Database Included in the Analysis

when fortified with appropriate cofactors) provide an accurate Materials and Methods view of the principal metabolites and metabolic pathways for Materials. The 27 compounds used in the analysis were obtained most compounds. Of these three systems, the hepatocytes from the sample inventories of Pfizer, Inc. The other 21 com- presumably offer the most realistic system to that which is mercially available compounds were obtained from four different present in vivo with regard to the complement of drug sources. The structures of all compounds are shown in Schemes 1 metabolizing enzymes in a more natural cellular environment. and 2. Droloxifene citrate was obtained from the Pfizer compound The S-9 fraction also contains a large complement of drug bank. Zaleplon and zileuton were purchased from Sequoia Research metabolizing enzymes derived from the endoplasmic reticulum Products, Ltd. (Pangbourne, U.K.). Paroxetine hydrochloride and and cytosol but must be supplemented with cofactors needed Tadalafil were purchased from AK Scientific Inc. (Mountain View to support these enzymes (e.g., NADPH, UDPGA, SAM, PAPS, CA). Raloxifene hydrochloride, zomepirac sodium, diclofenac acetyl coenzyme A, etc.). Liver microsomes represent the most sodium, lamotrigine, dapsone, minoxidil, mirtrazapine, S-amino- frequently used in vitro drug metabolism system (by virtue of gluthetimide, salbutamol, sulindac, citalopram hydrobromide, zonisa- its simplicity and convenience), but it also possesses a limited mide sodium, haloperidol, nefazadone hydrochloride, ketotifen complement of drug metabolizing enzymes, albeit this includes fumarate, suprofen, NADPH, SAM, UDPGA, PAPS, alamethicin, the very important cytochrome P450 enzymes (9, 10). and acetyl coenzyme A were obtained from Sigma-Aldrich (St. It is important to select a system that can reliably predict all Louis, MO). Human liver microsomes, pooled from 53 individual donors, and human liver S-9, pooled from 10 individual donors biotransformation pathways of a drug. Although various rat were prepared by BD-Gentest (Woburn, MA). Cryopreserved subcellular fractions and cells have been compared previously human hepatocytes were obtained from In Vitro Technologies and to semiquantitatively predict drug metabolism (15), a systematic were pooled from three donors. Other reagents were obtained from analysis on the ability of human in vitro systems to predict common commercial suppliers and were of HPLC-grade or better. human in vivo metabolites has never been performed. Further- In Vitro Incubations. All compounds were incubated with more, there is little experimental data to compare the predictive pooled human liver microsomes, human liver S-9 fraction, and performance of hepatocytes, S-9 fractions, and liver microsomes. human hepatocytes. Liver microsomal incubations consisted of Thus, an analysis was conducted to assess if human hepatocytes, substrate (10 µM), microsomes (1 mg/mL), NADPH (1.3 mM), S-9 fractions, and liver microsomes adequately predict known UDPGA (5 mM), alamethicin (10 µg/mL), and MgCl2 (3.3 mM) human in vivo metabolites and metabolic routes for drugs. The in 1.0 mL potassium phosphate buffer (100 mM; pH 7.4). second objective was to compare the overall capabilities of the Microsomes were incubated on ice with alamethicin for 15 min three systems to generate in vivo human metabolite profiles. prior to use. Incubations were commenced with the addition of ° To this end, human in vivo ADME data from 27 compounds NADPH and UDPGA and were maintained at 37 C in a shaking water bath open to the air. The reaction was quenched with 5 mL from the Pfizer database and 21 additional commercially of CH3CN after 60 min. Controls were made in which a mixture available drugs were compared to in vitro data generated by containing everything except NADPH and UDPGA was added to incubating the compounds with pooled human hepatocytes, S-9 CH3CN (5 mL), followed by the addition of NADPH/UDPGA. The fractions, and liver microsomes to assess the ability of these precipitate was removed by centrifugation (1800g) for 5 min, the systems to produce the same metabolites as those observed in supernatant was decanted into a 15 mL conical glass tube, and humans in vivo. The aim was to gain a level of confidence in the liquid was evaporated under a stream of N2 at 35 °C. The the ability of in vitro systems to provide useful predictions of resulting residue was reconstituted in 0.15 mL of water containing the human in vivo metabolite profile, prior to dosing to humans. 0.1% HCOOH and 10% CH3CN for HPLC-UV-MS/MS analysis. BIOM/PHAR 275 - Dalvie Paper 1 Human Excretory and Circulating Metabolites Chem. Res. Toxicol., Vol. 22, No. 2, 2009 359

Scheme 2. Structures of 21 Additional Commercially Available Compounds

Incubations in S-9 fractions (2 mg/mL) were conducted in 20% at 8 min; 40% at 35 min; and 90% at 42 min. It was maintained phosphate buffer (pH 7.4) in the same manner as that described at 90% B for 45 min and then decreased to 1% in the next 5 min. above, except that other cofactors were also included: SAM (0.1 The column was allowed to equilibrate at 1% B for 5 min prior to mM), PAPS (0.1 mM), and acetyl coenzyme A (1 mM). the next injection. The HPLC effluent going to the mass spectrom- Human hepatocyte (∼750,000 cells/mL) incubations were per- eter was directed to waste through a divert valve for the initial 5 formed in Williams E medium in a total volume of 1 mL using 10 min after sample injection. µM substrate concentrations. Incubations were conducted at 37 °C Mass spectrometric analyses were performed on a LTQ mass under a gas mixture of 5% CO2/95% O2. The reaction was quenched spectrometer or LCQ deca XP ion trap mass spectrometer (Thermo with 5 mL of CH3CN, at 2 h. Control incubations contained only Fisher Scientific, Waltham, MA). Both mass spectrometers were the hepatocytes and the media (lacking substrate), and were treated operated in a positive ion mode and were equipped with an in a similar manner. The samples were processed as described electrospray ionization source. The source parameters for the LTQ above. were source potential, 4.5 kV; capillary potential, 2 V; source HPLC-UV-MS/MS Analysis. Analyses of incubation samples temperature, 350 °C. For the Deca XP ion trap, the values for ESI for 48 compounds were performed by using either of the two were capillary temperature, 270 °C; spray voltage, 4.0 kV; capillary analytical systems. The first system consisted of a Finnigan Surveyer voltage, 4.0 V. Both mass spectrometers were operated in a data- HPLC injector (Thermo Fisher Scientific, Waltham, MA.), a HP- dependent scanning mode to MS3. The normalized collision energy 1100 quaternary gradient pump, and a HP-1100 diode array detector for the data dependent scanning was 30-40%. (Agilent Technologies, Palo Alto, CA) in line with a mass In Vivo Data. All compounds were previously examined in spectrometer. The chromatography was performed using Polaris human ADME studies in which radiolabeled material was admin- C18 (4.6 × 250 mm; 5 µm; Varian, Lake Forest, CA) column. istered to humans using the dosing route intended for clinical use. The mobile phase consisted of 0.1% HCOOH (A) and CH3CN (B), In vivo metabolite data for compounds from the Pfizer database and was delivered at a flow rate of 0.8 mL/min. The gradient was acquired from study reports on file at Pfizer. The list of consisted of 5% B for 5 min followed by a linear gradient to 80% metabolites and their percentages in human circulation (expressed B at 50 min. This was followed by a 10 min re-equilibration of the as % of circulating radioactivity) and excreta (expressed as % of column at 95% A. The effluent was passed through the diode array dose) are shown in Table 1A. Mean data were used for this analysis. detector operated in the wavelength range of 200 to 400 nm. This Only those metabolites that constituted at least 10% of the total was followed by introduction, at a split, of approximately 20 to 1, circulating radioactivity or 10% of the dose in excreta (defined as into the source of the mass spectrometer. major metabolites) were considered for further evaluation. The second system consisted of an HP-1100 autoinjector, HP- Since very few compounds showed phase 2 metabolites (formed 1100 binary gradient pump, and a HP-1100 diode array detector via conjugation reactions) in the excreta and plasma of humans, in (Agilent Technologies, Palo Alto, CA). Chromatography was the Pfizer database, 21 additional compounds for which phase 2 achieved on a Kromasil C4 100A column (3.5 µm, 150 × 2.0 mm, metabolites were observed in the human excreta and in circulation Phenomenex, Torrance, CA) by reverse phase chromatography at as major metabolites were selected from the literature and added ambient temperature. The mobile phase consisted of 0.1% formic to the set. In vivo metabolic data for commercially available acid (solvent A) and acetonitrile (solvent B), and was delivered at compounds was acquired from literature reports (Table 1B). Only 0.2 mL/min. The initial composition of solvent B was maintained the metabolites that were reported in excess of 10% of dose in the at 1% for 5 min and then increased in a linear manner as follows: excreta or 10% of the radioactivity in circulation were considered. BIOM/PHAR 275 - Dalvie Paper 1 360 Chem. Res. Toxicol., Vol. 22, No. 2, 2009 DalVie et al.

Table 1 A: List of Metabolites Observed for 27 Compounds from the Pfizer Database and Their Percentages in Human Circulation (Expressed As % of Circulating Radioactivity) and Excreta (Expressed As % of Dose) Observed in Human Radiolabel ADME Studiesa primary or Ph1or secondary % of circulating compound name in vivo human metabolite(s) Ph 2 metabolites % in excreta radioactivity references gemcabene gemcabene glucuronide 2 1 44 avasimibe dehydrogenated avasimbe 1 1 16 hydroxy avasimbe 1 1 10 pagoclone hydroxy pagaclone 1 1 65 axitinib hydroxy axitinb 1 1 11 sulfoxide 1 1 16 axitinib glucuronide 2 1 50 capravirine sulfoxide of hydroxycapravirine 1 2 11 16 N-oxide of hydroxycapravirinesulfone 1 2 10 CJ-13610 sulfoxide 1 1 36 17 sulfone 1 2 21 10 traxoprodil methoxy sulfate 2 2 40 28 17 methoxy glucuronide 2 2 10 18 CP-122721 glucuronide of desmethyl 122721 2 2 56 18 glucuronide of n-dealkylated metabolite 2 2 27 14 glucuronide of desmethylhydroxy122721 2 2 11 salicylic acid derivative 1 2 25 tofimilast dihydroxy325366 1 2 23 14 ring opened 325366 metabolite 1 2 33 cleaved 325366 1 2 13 lasofoxifene glucuronide of lasofoxifene 2 1 22 19 capromorelin carboxylic acid of capromorelin 1 2 11 14 O-debenzyl hydroxycapromorelin 1 2 12 carboxylic acid of N-demethyl-O-debenzylcapromorelin 1 2 12 N-demethyl-O-debenzyl hydroxycapromorelin 1 2 15 torcetrapib bistrifluorobenzoic acid 1 2 50 50 20 7-trifluromethylquinaldic acid 1 2 29 63 CP-533536 hydroxy 533536 1 1 70 sulfate conjugate of 533536 2 1 11 CP-547632 N-dealkyl-547632 1 1 15 zoniporide hydroxyzoniporide 1 1 52 64 zoniporide carboxylic acid 1 2 17 celecoxib acid metabolite of celecoxib 1 2 73 20 21 glucuronde of celecoxib 2 2 15 CP-690550 hydroxy 690550 1 1 20 12 dihydroxy 690550 1 2 12 12 glucuronide of hydroxy 690550 2 2 11 13 ziprasidone ziprasidone sulfoxide 1 1 18 69 22 S-methyldihydroziprasidone 2 2 18 69 N-dealkylziprasidone S-oxide 1 2 11 21 N-dealkylsiprasidone sulfone 1 2 11 21 sunepitron hydroxysunepitron 1 1 17 61 23 trovafloxacin trovafloxacin acylglucuronide 2 1 13 22 24 linezolid carboxylic acid of cleaved linezolid 1 2 45 25 carboxylic acid of cleaved linezolid 1 2 10 sunitinib N-dealkyl metabolite 1 1 32 21 irinotecan M-11 (APC, ring opened acid) 1 2 11 26 delavirdine N-dealkyldelavirdine 1 1 46 25 depyridinyldelavirdine) 1 1 38 valdecoxib glucuronide conjugate of valdecoxib 2 2 23 27 valdecoxib N-glucuronide 2 1 20 eplerenone 6-hydroxy eplerenone 1 1 32 16 28 6,21-dihydroxy eplerenone 1 2 21 maraviroc hydroxymethyl maraviroc 1 1 13 29 N-dealkylcleaved maraviroc 1 2 22 N-dealkylhydroxymethyl maraviroc 1 2 11

B: In Vivo Metabolic Data for 21 Additional Commercially Available Compoundsb primary or % of circulating compound in vivo human metabolite secondary % in excreta metabolites references droloxifene droloxifene glucuronide 1 13 37 30 raloxifene raloxifene 4′glucuronide 1 major major 31-33 raloxifene 6-glucuronide 1 major major zomepirac zomepirac acylglucuronide 1 57 23 34 diclofenac 4′-hydroxy glucuronide 2 20 - 30 major 35 3′-hydroxy-4′-methoxy diclofenac 2 major lamotrigine lamotrigine 2-N-glucuronide 1 76 36 lamotrigine 5-N-glucuronide 1 10 dapsone N-hydroxy dapsone glucuronide 2 major 37 N-acetyl dapsone 1 major major S-aminogluthetimide N-acetylaminoglutethimide 1 4.0 - 25 9to48 38 minoxidil minoxidil glucuronide 1 56 major 39 mirtazapine 8-Hydroxymirtazapine glucuronide 2 40 major 40 mirtazapine glucuronide 1 25 major zaleplon hydroxyzaleplon glucuronide 2 major 62 41 zileuton zileuton glucuronide 1 80 major 42 citalopram citalopram glucuronide 1 12 major 43 zonisamide reduced zonisamide glucuronide 3 15 44 BIOM/PHAR 275 - Dalvie Paper 1 Human Excretory and Circulating Metabolites Chem. Res. Toxicol., Vol. 22, No. 2, 2009 361

Table 1. Continued B: In Vivo Metabolic Data for 21 Additional Commercially Available Compoundsb primary or % of circulating compound in vivo human metabolite secondary % in excreta metabolites references reduced zonisamide sulfate 3 50 sulindac glucuronide of sulfone metabolite 2 35 45 glucuronide of sulindac 2 22 paroxetine paroxetene methoxycathecol glucuronide 2 17 46 tadafil methoxycathecol sulfate 2 15 tadalafil tadafil methoxycatechol glucuronide 2 major 44 47 tadafil catechol glucuronide 2 major tadalafil methoxycathecol 2 major haloperidol haloperidol glucuronide 1 major major 48 nefazadone N-dealkylation alcohol glucuronide 2 6.0 -15 49 ketotifen quaternary glucuronide 1 major major 50 suprofen acyl glucuronide 1 62 major 51 salbutamol salbutamol sulfate 1 major 52 a Primary metabolite, a metabolite from a single biotransformation reaction from the parent; secondary metabolite, a metabolite from two or more biotransformation reactions from the parent. Ph 1, phase 1; metabolites were generated from oxidation, reduction, and hydrolysis reactions. Ph 2, phase 2; metabolites were generated from conjugation. b Phase 2 metabolites were observed as major metabolites in the excreta and in circulation for the selected compounds.

For some compounds, quantitative amounts of metabolites were following incubation with hepatocytes (Table 4), whereas 21 not reported. In these instances, the metabolites reported as major (44%) and 16 (33%) compounds produced a complete metabolic were used in the analysis and are listed as major in Table 1B. profile in incubations with microsomes and S-9 fractions, Identification of Metabolites in in Vitro Incubations. Me- respectively (Table 4). tabolites formed in the in vitro incubations were identified using Next, the ability of the in vitro systems to predict primary UV and total ion chromatograms. The UV chromatograms were reconstructed using the wavelength maxima of each of the parent (produced from a single biotransformation reaction from the compounds. These were then compared to UV chromatograms of parent drug) and secondary (produced from two or more the corresponding control incubations (reconstructed using the same reactions from the parent) metabolites was evaluated. The 48 wavelength maxima as that of the incubation sample). The UV compounds were categorized into two groups on the basis of peaks that were only present in the chromatograms of the incubation the primary and secondary metabolites observed in the excreta mixture but absent in the controls and showed a positive signal in and in circulation in vivo. Of the 32 compounds that showed the total ion chromatogram were identified as potential metabolites. primary metabolites in the excreta and in the circulation of If the compound lacked a suitable chromophore, the total ion humans, 66 to 69% successfully produced a complete in vivo chromatogram peak response was considered. No attempt was made metabolic profile following incubation with the three systems to quantify the peaks (due to a lack of synthetic standards), but the (Table 4). However, when in vitro profiles for the 25 compounds detection of the peak in the UV chromatogram and its corresponding match with the peak in the total ion chromatogram was considered showing secondary metabolites in humans were examined, the a success. Table 2 shows the human metabolites detected in the S-9 and microsomal incubations showed only 12 to 36% success, hepatocytes, S-9, and microsomal incubations for all compounds. while the hepatocyte incubations showed better success in the Assessment of Success in Predicting in Vivo Human prediction (56%) of all secondary human metabolites (Table Metabolites by in Vitro Systems. The capability of in vitro systems 4). to predict all human in vivo metabolites (excretory plus circulating The analysis was also performed by dividing the 48 com- metabolites) and only circulating metabolites was evaluated. The pounds according to the type of reactions, i.e., phase 1 success of the hepatocytes, S-9, and the microsomal incubations (constituting oxidation, reduction, and hydrolysis) and phase 2 in producing in vivo human metabolites was determined as follows. (glucuronidation, sulfation, acetylation, and methylation) reac- Following incubation, the in vitro metabolic profile generated for each compound by the three systems was compared to its respective tions. Of the 22 compounds that showed phase 1 metabolites in vivo human metabolic profile (Table 3). Identification of all in in vivo, 50% produced all phase 1 metabolites following vivo metabolites in the in vitro system was considered to be a 100% incubations with hepatocytes and S-9 fractions, while the success success. The success in the prediction of in vivo metabolites by in microsomal incubations was about 32% (Table 4). Similarly, each in vitro system was then determined by normalizing the when the 32 compounds showing phase 2 metabolites in vivo number of compounds that effectively produced a complete in vivo were analyzed, the success rate of the hepatocytes and S-9 was metabolic profile relative to the total number of compounds. (Some 53%, and the microsomes generated a complete phase 2 compounds showed a 25 to 67% success, indicating identification metabolite profile for only 38% of the compounds (Table 4). of a partial in vivo metabolite profile in the in vitro experiment. The analysis was further extended to gauge the relatively low However, this was not included in the analysis.) success rate (only 50%) in predicting phase 1 and phase 2 metabolites by in vitro systems. Thus, the groups of compounds Results showing primary metabolites and secondary metabolites in vivo Prediction of All Human Metabolites by in Vitro were further classified into smaller groups that showed only Systems. The metabolite information gathered from the in vitro primary and secondary phase 1 metabolites or primary and experiments for all 48 compounds was correlated with human secondary phase 2 metabolites (Figure 1A and B). This analysis metabolite profiles observed in vivo to determine the reliability revealed that all three systems produced in vivo primary phase of in vitro systems to predict in vivo metabolite profiles. The 1 metabolites for 71% of the compounds (Figure 1A). In data set was first evaluated to predict the success of the three contrast, the success in producing secondary phase 1 metabolites in vitro systems in generating all human in vivo metabolites was 57% in hepatocyte incubations and 36 and 14% in S-9 and (excretory plus circulating) observed in humans. Of the 48 microsomal incubations (Figure 1A). compounds that were analyzed for all human metabolites, 26 A similar assessment of compounds that showed primary and (54%) compounds produced a complete metabolite profile secondary phase 2 metabolites in vivo indicated that the three BIOM/PHAR 275 - Dalvie Paper 1 362 Chem. Res. Toxicol., Vol. 22, No. 2, 2009 DalVie et al.

Table 2. Human Metabolites Detected in Incubations with Pooled Human Hepatocytes, S-9 Fraction, and Liver Microsomesa metabolites detected in compound studied in human ADME in vivo human metabolite hepatocytes S-9 fractions microsomes gemcabene glucuronide no no no avasimibe dehydrogenated avasimbe no no no hydroxyavasimbe no no no pagoclone hydroxypagoclone yes yes no axitinib hydroxyaxitinb yes yes yes sulfoxide yes yes yes glucuronide yes no no capravirine sulfoxide of hydroxycapravirine yes yes yes N-oxide of hydroxycapravirinesulfone yes yes no CJ-13610 sulfoxide yes yes yes sulfone yes yes yes traxoprodil methoxy sulfate yes no no methoxy glucuronide yes no no CP-122721 glucuronide of desmethyl 122721 no no no glucuronide of N-dealkylated metabolite yes yes yes glucuronide of desmethylhydroxy 122721 no no no salicylic acid derivative no no no tofimilast dihydroxy 325366 no yes yes ring opened 325366 yes no no cleaved 325366 no no no lasofoxifene glucuronide of lasofoxifene yes yes yes capromorelin carboxylic acid of capromorelin no no no O-debenzyl hydroxycapromorelin no no yes carboxylic acid of N-demethyl-O-debenzylcapromorelin no no no N-demethyl-O-debenzyl hydroxycapromorelin no no no torcetrapib bistrifluorobenzoic acid no no no 7-trifluoromethylquinaldic acid no yes yes CP-533536 hydroxy 533536 yes yes yes sulfate conjugate of hydroxy 533536 no no no CP-547632 N-dealkyl-547632 no no no zoniporide hydroxyzoniporide yes yes yes zoniporide carboxylic acid yes no no celecoxib acid metabolite of celecoxib yes yes no glucuronde of celecoxib yes no no CP-690550 hydroxy 690550 no no yes dihydroxy 690550 yes yes no glucuronide of hydroxy 690550 no no no ziprasidone ziprasidone sulfoxide yes yes yes S-methyldiydro ziprasidone yes yes no N-dealkylziprasidone S-oxide no no no N-dealkylsiprasidone sulfone no no no sunepitron hydroxysunipetron yes yes yes trovafloxacin trovafloxacin acyl glucuronide yes yes yes linezolid carboxylic acid of cleaved linezolid no no no carboxylic acid of cleaved linezolid no no no sunitinib N-dealkyl metabolite yes yes yes irinotecan M-11 (APC, ring opened acid) yes yes yes M17 (SN-38), active metabolite no no yes delavirdine N-dealkyldelavirdine yes yes yes depyridinyldelavirdine) no no no valdecoxib glucuronide conjugate of hydroxyvaldecoxib yes yes no N-glucuronide of valdecoxib yes no no eplerenone 6--hydroxyeplerenone yes yes yes 6,21-dihydroxyeplerenone yes no no maraviroc hydroxymethylmaraviroc yes yes yes N-dealkylcleavedmaraviroc no no no N-dealkylhydroxymethylmaraviroc no no no droloxifene droloxifene glucuronide yes yes yes raloxifene raloxifene 4′glucuronide yes yes yes raloxifene 6-glucuronide yes yes yes zomepirac zomepirac acylglucuronide yes yes yes diclofenac 4′-hydroxy glucuronide yes yes yes 3′-hydroxy-4′-methoxy diclofenac no no no lamotrigine lamotrigine 2-N-glucuronide no no no lamotrigine 5-N-glucuronide no no no dapsone N-hydroxy dapsone glucuronide no no no N-acetyl dapsone no yes no S-aminogluthetimide N-acetylaminoglutethimide no yes no minoxidil minoxidil glucuronide yes yes yes mirtazapine 8-hydroxymirtazapine glucuronide yes yes yes mirtazapine glucuronide no yes yes zaleplon hydroxyzaleplon glucuronide yes yes no zileuton zileuton glucuronide yes yes yes citalopram citalopram glucuronide yes no no zonisamide reduced zonisamide glucuronide no no no reduced zonisamide sulfate no no no sulindac glucuronide of sulfone metabolite no no no glucuronide of sulindac no no no paroxetine paroxetene methoxycathecol glucuronide yes yes no tadafil methoxycathecol sulfate yes yes no tadalafil tadafil methoxycatechol glucuronide yes yes no tadafil catechol glucuronide yes no no tadalafil methoxycathecol yes no no haloperidol haloperidol glucuronide yes no no BIOM/PHAR 275 - Dalvie Paper 1 Human Excretory and Circulating Metabolites Chem. Res. Toxicol., Vol. 22, No. 2, 2009 363

Table 2. Continued metabolites detected in compound studied in human ADME in vivo human metabolite hepatocytes S-9 fractions microsomes nefazadone N-dealkylation alcohol glucuronide no no no ketotifen quaternary glucuronide yes yes yes suprofen acyl glucuronide yes yes yes salbutamol salbutamol sulfate no no no a The UV peaks that were only present in the chromatograms of the incubation mixture but absent in the controls and showed a positive signal in the total ion chromatogram were identified as potential metabolites. In some cases where the UV peak was not observed, the peak observed in the total ion chromatogram was used to identify the metabolite.

Table 3. Percentage Success of Hepatocytes, S9 Fractions, and Microsomes in Producing in Vivo Metabolites for Each Compound all human metabolites (g10% of total dose circulating metabolites (g10% of or total circulating drug-related material)a total circulating drug-related material)b % success % success compound studied number of number of in human ADME metabolites hepatocytes S9 fractions microsomes metabolites hepatocytes S9 fractions microsomes gemcabene 1 noned none none 0 n/ac n/a n/a avasimibe 2 none none none 2 none none none pagoclone 1 100 100 none 1 100 100 none axitinib 3 100 67 67 2 100 50 50 capravirine 2 100 100 50 0 n/a n/a n/a CJ-13610 2 100 100 100 2 100 100 100 traxoprodil 2 100 none none 2 100 none none CP-122721 4 25 25 25 3 33 33 33 tofimilast 3 33 67 33 3 33 67 33 lasofoxifene 1 100 100 100 1 100 100 100 capromorelin 4 none none 25 4 none none 25 torcetrapib 2 none 50 50 2 none 50 50 CP-533536 2 50 50 50 0 n/a n/a n/a CP-547632 1 none none none 1 none none none zoniporide 2 100 50 50 1 100 100 100 celecoxib 2 100 50 none 2 100 50 none CP-690550 3 33 33 67 3 33 33 50 ziprasidone 4 50 50 25 4 50 50 25 sunipetron 1 100 100 100 1 100 100 100 trovafloxacin 1 100 100 100 1 100 100 100 linezolid 2 none none none 0 n/a n/a n/a sunitinib 1 100 100 100 1 100 100 100 irinotecan 1 100 100 100 0 n/a n/a n/a delavirdine 2 50 50 50 1 100 100 100 valdecoxib 2 100 50 none 0 n/a n/a n/a eplerenone 2 100 50 50 1 100 100 100 maraviroc 3 33 33 33 2 none none none droloxifene 1 100 100 100 1 100 100 100 raloxifene 2 100 100 100 2 100 100 100 zomepirac 1 100 100 100 1 100 100 100 diclofenac 2 50 50 50 1 100 100 100 lamotrigine 2 none none none 0 n/a n/a n/a dapsone 2 none 50 none 1 none 100 none S-aminogluthetimide 1 none 100 none 1 none 100 none minoxidil 1 100 100 100 1 100 100 100 mirtazapine 2 50 100 100 2 50 100 100 zaleplon 1 100 100 none 1 100 100 none zileuton 1 100 100 100 1 100 100 100 citalopram 1 100 none none 1 100 none none zonisamide 2 none none none 0 n/a n/a n/a sulindac 1 none none none 0 n/a n/a n/a paroxetine 2 100 100 none 0 n/a n/a n/a tadalafil 3 67 67 none 1 100 100 none haloperidol 1 100 100 100 1 100 100 100 nefazadone 1 none none none 1 none none none ketotifen 1 100 100 100 1 100 100 100 suprofen 1 100 100 100 1 100 100 100 salbutamol 1 none none none 0 n/a n/a n/a a All human metabolites include metabolites in excreta plus circulation. b Circulating metabolites include metabolites in human plasma. c n/a, not applicable. d None, no metabolites observed. systems produced primary phase 2 metabolites 55% to 65% of sistent with those from the analysis of primary and secondary the time (Figure 1B). In contrast, the success in predicting metabolites and suggested that the lower success rate observed secondary phase 2 metabolites was lower and ranged from 4% in the assessment of phase 1 and phase 2 reactions was mainly in the microsomes to 28% in the hepatocytes with S-9 fractions due to underprediction of secondary metabolites by the in vitro predicting around 20% (Figure 1B). These results were con- systems. BIOM/PHAR 275 - Dalvie Paper 1 364 Chem. Res. Toxicol., Vol. 22, No. 2, 2009 DalVie et al.

Table 4. Success of Hepatocytes, S-9 Fractions, and Microsomes in Predicting All Human Metabolites Observed in Humansa hepatocytes S-9 fractions liver microsomes metabolite total number of compounds producing compounds producing compounds producing profile compounds showing in vivo metabolites in vivo metabolites (%) in vivo metabolites (%) in vivo metabolites (%) complete 48 26 54 21 44 16 33 primary 32 22 69 22 69 21 66 secondary 25 14 56 9 36 3 12 phase 1 22 11 50 11 50 7 32 phase 2 32 17 53 17 53 12 38 a The success in predicting in vivo metabolites was determined from the ratio of the number of compounds producing in vivo metabolites to the total number of compounds for which in vivo metabolites were observed, multiplied by 100.

Figure 1. Success of pooled human hepatocytes, liver S-9 fractions, and liver microsomes in predicting all human metabolites (excretory plus circulating). (Panel A) Phase 1, primary, and secondary metabolites. (Panel B) Phase 2, primary, and secondary metabolites. n is the number of compounds that showed phase 1 primary or secondary metabolites in the human ADME study. Values above the bar represent the % success of the number of compounds that produced in vivo metabolites in each of the in vitro systems. The percent success was determined from the ratio of the number of compounds that produced in vivo human metabolites in the respective in vitro system to the total number of compounds. Table 5. Success of Hepatocytes, S-9 Fractions, and Microsomes in Predicting Circulating Metabolites Only Observed in Humansa hepatocytes S-9 fractions liver microsomes metabolite number of compounds showing compounds producing compounds producing compounds producing profile in vivo metabolites in vivo metabolites (%) in vivo metabolites (%) in vivo metabolites (%) complete 37 24 65 22 59 17 46 primary 26 20 77 21 81 19 73 secondary 15 7 47 5 33 3 20 phase 1 17 10 59 10 59 10 59 phase 2 21 12 57 14 67 11 52 a The success in predicting in vivo metabolites was determined from the ratio of the number of compounds producing in vivo metabolites to the total number of compounds for which in vivo metabolites were observed, multiplied by 100.

Prediction of Human Circulating Metabolites by in the analysis of all human metabolites and suggested that the in Vitro Systems. In light of the FDA guidance, which primarily vitro systems could generate primary circulating metabolites focuses on circulating metabolites, the potential of in vitro observed in humans with better success compared to secondary systems to predict human circulating metabolites observed in metabolites. Evaluation of compounds in the phase 1 and phase vivo was evaluated. An approach similar to that described in 2 category revealed that all three systems produced phase 1 the analysis of all human metabolites was used. Thus, the circulating metabolites with 59% success (Table 5). However, success rate of in vitro systems to produce a complete circulating for compounds showing phase 2 circulating metabolites in vivo, metabolite profile was first determined. This was followed by the success rate ranged from 52 to 67% for the three in vitro prediction of the primary and secondary circulating metabolites systems (Table 5). as well as the phase 1 and phase 2 metabolites observed in vivo. As in the previous analysis, when the groups of compounds The analysis was conducted using 37 compounds since 11 of producing primary and secondary phase 1 circulating metabolites 48 compounds did not show any circulating metabolites in the and primary and secondary phase 2 circulating metabolites were ADME studies. analyzed, all in vitro systems were efficient in predicting primary The results revealed that hepatocyte, S-9, and microsomal phase 1 and phase 2 metabolites (75% for phase 1 metabolites incubations successfully predicted a complete circulating me- and 73 to 87% for phase 2 metabolites) (Figure 2A and B). tabolite profile for 59 to 65% of the compounds (Table 5). When However, correlation between secondary circulating metabolites an assessment to produce primary and secondary circulating observed in humans and their production in vitro in the three metabolites by in vitro systems was made, the hepatocytes, S-9 systems ranged from 11 to 22% for the phase 1 metabolites fractions, and the microsomes predicted the metabolic profile and 20 to 30% for phase 2 metabolites. for 77 to 81% of the compounds that showed primary Prediction of Primary Metabolic Pathways by in Vitro metabolites in vivo. In contrast, secondary circulating metabo- Systems. The success in predicting primary metabolic pathways lites were predicted with only 20 to 47% success in the three by the three in vitro systems was also evaluated in this study. systems (Table 5). This was consistent with the observation in The metabolic pathways for each compound were reconstructed BIOM/PHAR 275 - Dalvie Paper 1 Human Excretory and Circulating Metabolites Chem. Res. Toxicol., Vol. 22, No. 2, 2009 365

Figure 2. Success of pooled human hepatocytes, liver S-9 fractions, and liver microsomes in predicting circulating human metabolites. (Panel A) Phase 1, primary, and secondary metabolites. (Panel B) Phase 2, primary, and secondary metabolites. n is the number of compounds that showed phase 1 primary or secondary metabolites in the human ADME study. Values above the bar represent the % success of the number of compounds that produced in vivo metabolites in each of the in vitro systems. The percent success was determined from the ratio of the number compounds that produced in vivo human metabolites in the respective in vitro system to the total number of compounds. Table 6. Success of in Vitro Systems to Identify Primary circulating metabolites. The objective was to address two Metabolic Pathways for the Compounds in the Pfizer questions. Can we make reliable extrapolations of in vivo human Database metabolites from in vitro models, and how does this impact primary clearance pathway discovery and development strategies for new candidates? The clearance pathway observed in cryopreserved human hepatocytes, human liver S-9 fractions, S-9 and human liver microsomes are frequently used in a lead compound primary hepatocytes fraction microsomes optimization and development stage to assess the metabolism gemcabene glucuronidation no no no of compounds in humans. Therefore, these three systems were avasimibe N/A n/a n/a n/a assessed for their predictive performance. pagoclone hydroxylation yes yes no axitinib oxidation yes yes yes All three in vitro systems adequately predicted (33 to 54%) capravirine hydroxylation yes yes yes all human metabolites (excretory plus circulating metabolites). CJ-13610 S-oxidation yes yes yes As expected, hepatocytes and the S-9 fraction (the systems with traxoprodil hydroxylation yes yes yes CP-122721 O-demethylation yes yes yes a more complete complement of drug metabolizing enzymes) tofimilast ring oxidation no yes yes were more successful in generating metabolites comprising 10% lasofoxifene glucuronidation yes yes yes or more of total circulating drug-related material or total dose capromorelin O-dealkylation no yes yes torcetrapib N-decarbamoylation no yes yes since many metabolites require enzymes not present in liver CP-533536 hydroxylation yes yes yes microsomes for their generation. Given the importance of the CP-547632 excretion of parent n/a n/a n/a circulating metabolites from a regulatory perspective, the zoniporide hydroxylation yes yes no primary focus of the study was to assess the success of in vitro celecoxib hydroxylation yes yes yes CP-690550 excretion of parent n/a n/a n/a systems in predicting human circulating metabolites. As in the ziprasidone reduction yes yes no case of all human metabolites, the hepatocytes, S-9, and sunipetron hydroxylation yes yes yes microsomal incubations produced a complete circulating me- trovafloxacin glucuronidation no yes yes linezolid hydroxylation yes yes yes tabolite profile reasonably well (46 to 65%). The trend of the sunitinib N-deethylation yes yes yes success rate for the three systems was similar to what was irinotecan decarbamoylation no no yes observed for the prediction of all human metabolites (i.e., delavirdine N-dealkylation yes yes yes g > valdecoxib hydroxylation yes yes yes hepatocytes S-9 fractions microsomes). eplerenone hydroxylation yes yes yes The results revealed that there was a distinct difference in maraviroc hydroxylation yes yes yes the generation of primary and secondary metabolites in vitro success rate (%) 75a 92a 83a especially with circulating metabolites. All three in vitro systems predicted primary in vivo metabolites with greater frequency a The % success rate for the primary metabolic pathways was than secondary in vivo metabolites, suggesting that the in vitro determined from the ratio of the number of positives in the three in vitro systems to the total number of primary pathways (24) for the 27 systems were more reliable in predicting metabolites that are compounds. formed via one biotransformation reaction compared to the ones that are formed via two or more reactions in vivo. On the basis from the excretory metabolite data, and the most predominant of this analysis, it was presumed that underprediction of metabolic reaction in these pathway trees was identified. This complete metabolic profiles in the in vitro systems was most was only done for the first 27 compounds obtained from the likely due to the poor success of these systems to generate Pfizer database for which a definitive percentage of excreted secondary metabolites. The lower frequency in the generation metabolites was available (Table 1A). The correspondence of secondary metabolites may be attributed to the limitation that between the in vitro metabolites and the primary in vivo in vitro incubations can only be conducted for a relatively short metabolite was compared. The primary metabolic pathway was duration. In contrast, in vivo human metabolism experiments correctly identified 75, 92, and 83% of the time by hepatocytes, usually involve the collection of excreta or plasma samples over S-9, and microsomes, respectively (Table 6). several days so that the residence time of drug-related material is much greater than in in vitro experiments, increasing the likelihood of sequential metabolic reactions. Furthermore, the Discussion overall metabolic capacity of in vitro systems is lower rela- This study evaluated the capability of standard in vitro drug tive to that in vivo. In vivo, the amount of drug relative to metabolism experiments to predict in vivo human excretory and the total amount of drug metabolizing enzymes is much less BIOM/PHAR 275 - Dalvie Paper 1 366 Chem. Res. Toxicol., Vol. 22, No. 2, 2009 DalVie et al.

than that in typical in vitro systems. It should be noted that suggests that while the in vitro incubation provides useful initial all of the above points are just speculations. The bottom line characterization of the metabolic fate for a compound, there is is that production and detection of secondary metabolites in still a reasonable likelihood of missing major circulating vivo are more complex. Thus, the prediction of which metabolite(s) from in vitro incubations. The implication of this metabolites will be major in circulation is complicated by is a potential delay in safety evaluation of unidentified major metabolites potentially having diverse distribution properties metabolites in preclinical toxicology species and consequently when compared to the parent or other circulating metabo- the delay in initiating phase III trials or product registration. lites. Different strategies can potentially be deployed to mitigate Although reasonable success was achieved in predicting the risk of finding disproportionate circulating metabolites in primary in vivo metabolites by in vitro systems, some com- humans late in the development process. For instance, consid- pounds failed to generate these metabolites in vitro. This could eration can be given to conduct radiolabeled ADME studies in be ascribed to the poor turnover of these compounds and human and in toxicology species relatively early in the develop- therefore the inability to detect the metabolites after a given ment cycle. This approach will overcome the shortcoming of incubation time. Alternatively, the experimental conditions could underpredictability by in vitro systems and allow ample time also affect the production of metabolites in the in vitro systems. to resolve any issues regarding disproportionate metabolites, if All experiments in this analysis were performed under incuba- present. However, because of the potential limitations on tion conditions that are normally used to identify metabolites radiolabel synthesis and a desire to minimize the use of in a discovery setting, without complete optimization of the radioactivity, such studies are traditionally conducted late in incubation systems for a compound. It is possible that increasing the development phase. Alternatively, characterization of cir- substrate or protein concentration could help in the generation culating metabolites can be accomplished by profiling plasma of these metabolites and therefore increase the success rate. samples generated from first-in-human studies following the However, the effect of increasing the protein concentration or administration of a single dose or multiple doses of a develop- even the compound concentration on the success rate was not ment candidate. With the advent of high resolution mass evaluated in this study. spectrometers, one can detect circulating metabolites fairly Finally, the success of in vitro systems to predict major in accurately without the use of radiolabel material. This approach vivo metabolic pathways was also evaluated in this study. In provides a first hand look at the in vivo metabolic profile in vitro experiments using human-derived systems have been used humans at an early stage of development and possibly allows to assess metabolic liabilities in humans in early discovery or the identification of metabolites that might be missed in the in lead optimization. Therefore, the success in identifying the vitro experiment (e.g., secondary metabolites). A caution here primary metabolic pathway of the drug by the in vitro systems is that the data obtained is nonquantitative and thus cannot be is also important, especially for compounds that are eliminated used to replace the radiolabeled ADME studies in humans. mainly via metabolism. The results of this analysis suggested However, early knowledge of human circulating metabolites that the primary metabolic pathways were predicted 75 to 92% gained from such a metabolite scouting experiment, coupled of the time by the three systems. with metabolic profiling using in vitro systems, especially the hepatocytes, can provide useful information for risk assessment The findings of this study suggest that one can make reliable and guide the strategy regarding the appropriate timing to extrapolation of in vivo human metabolites from in vitro conduct the radiolabel ADME study. metabolite identification experiments. However, the impact of In conclusion, the results of this study highlight the strengths the information generated from these in vitro systems may vary and weaknesses of the three in vitro systems with regard to depending upon the stage of candidate development. In lead generating metabolite profiles that are relevant in vivo. In optimization stage, the primary goal is to identify metabolic general, systems with a more complete complement of enzymes liabilities using in vitro systems and enable the design of such as hepatocytes and S-9 fractions (with appropriate cofac- molecules that exhibit optimal ADME properties. This is readily tors) perform better than liver microsomes. The analysis provides addressed by elucidating primary metabolites and metabolic sufficient confidence in the use of in vitro systems to reliably pathways of a compound. Reasonable success in identifying produce primary in vivo human metabolites and validates their primary human metabolites and metabolic pathways by the three application in early discovery to identify metabolic spots that in vitro systems, as illustrated in this study, supports the use of could help minimize metabolic liabilities in humans in vivo. these systems in capturing these metabolites that may affect The study also illustrates relatively low success in the prediction the metabolic stability of the compound. Although all three in of secondary metabolites and substantiates the observation that vitro systems are valuable, the hepatocytes or S-9 fractions the metabolic profiles in vivo could be more complex than those supplemented with multiple cofactors are more appropriate and produced in vitro. This implies that in vitro systems alone cannot preferable since they contain a larger complement of drug mitigate the risk of disproportionate circulating metabolites in metabolizing enzymes relative to microsomes, consequently humans and may need to be supplemented by in vivo metabolic providing a more complete representation of the metabolic profiling of first-in-human plasma samples or an early human profile. Of the two, the S-9 fractions are cost-effective and radiolabeled ADME study. possibly more beneficial in a discovery setting since the experimental setup with S-9 fractions is simple and amenable Acknowledgment. We thank Dr. Natilie Hosea for critically to high throughput metabolite identification. reviewing this manuscript. In the development stage, the primary focus is to identify all major metabolites in humans as early as possible and compare References the metabolic profile with that observed in nonclinical toxicology (1) Baillie, T. A. (2007) Metabolism and toxicity of drugs. Two decades species. From a regulatory perspective, more emphasis is placed of progress in industrial drug metabolism. Chem. Res. Toxicol. 21, on the identification of major human circulating metabolites, 129–137. (2) Lin, J. H., and Lu, A. Y. H. (1997) Role of pharmacokinetics and which if disproportionate, may require assessment of their safety metabolism in drug discovery and development. Pharmacol. ReV.49, in nonclinical toxicology species. The analysis presented here 403–449. BIOM/PHAR 275 - Dalvie Paper 1 Human Excretory and Circulating Metabolites Chem. Res. Toxicol., Vol. 22, No. 2, 2009 367

(3) Baillie, T. A., Cayen, M. N., Fouda, H., Gerson, R. J., Green, J. D., Fagerness, P. E., Stryd, R. P., Peng, G. W., and Shobe, E. M. (2001) Grossman, S. J., Klunk, L. J., LeBlanc, B., Perkins, D. G., and Shipley, Pharmacokinetics, metabolism, and excretion of linezolid following L. A. (2002) Drug metabolites in safety testing. Toxicol. Appl. an oral dose of [14C] linezolid to healthy human subjects. Drug Metab. Pharmacol. 182, 188–196. Dispos. 29, 1136–1145. (4) Smith, D. A., and Obach, R. S. (2005) Seeing through the MIST: (26) Miller, L. L., Baker, D. S., Pesheck, C. V., and Lord, R. S., III (2000) Abundance versus percentage. Commentary on metabolites in safety Pharmacokinetics, metabolism, and excretion of irinotecan (CPT-11) testing. Drug Metab. Dispos. 33, 1409–1417. following I. V. infusion of [14C ]CPT-11 in cancer patients. Drug (5) Smith, D. A., and Obach, R. S. (2006) Metabolites and safety: What Metab. Dispos. 28, 423–433. are the concerns, and how should we address them? Chem. Res. (27) Yuan, J. J., Yang, D.-C., Zhang, J. Y., Bible, R., Jr., Karim, A., Toxicol. 19, 1570–1579. and Findlay, J. W. A. (2002) Disposition of a specific cyclooxy- (6) Prueksaritanont, T., Lin, J. H., and Baillie, T. (2006) Complicating genase-2 inhibitor, valdecoxib, in human. Drug Metab. Dispos. 30, factors in safety testing of drug metabolites: kinetic differences between 1013–1021. generated and preformed metabolites. Toxicol. Appl. Pharmacol. 217, (28) Cook, C. S., Berry, L. M., Bible, R. H., Hribar, J. D., Hajdu, E., and 143–152. Liu, N. W. (2003) Pharmacokinetics and metabolism of [14C]e- (7) Food and Drug Administration (2008) Guidance for Industry: Safety plerenone after oral administration to humans. Drug Metab. Dispos. Testing of Drug Metabolites. FDA, Rockville, MD. 31, 1448–1455. (8) Davis-Bruno, K. L., and Atrakchi, A. (2006) A regulatory perspective (29) Abel, S., Russell, D., Whitlock, L. A., Ridgway, C. E., Nedderman, on issues and approaches in characterizing human metabolites. Chem. A. N. R., and Walker, D. K. (2008) Assessment of the absorption, Res. Toxicol. 19, 1561–1563. metabolism and absolute bioavailability of maraviroc in healthy male (9) Brandon, E. F. A., Raap, C. D., Meijerman, I., Beijnen, J. H., and subjects. Br. J. Clin. Pharmacol. 65 (Suppl. 1), 60–67. Schellens, J. H. M. (2003) An update on in vitro test methods in human (30) John, B. A., Brodie, R. R., Baldock, G. A., McBurney, A., Chasseaud, hepatic drug biotransformation research: pros and cons. Toxicol. Appl. L. F., Jank, P., and Von Nieciecki, A. (2002) Pharmacokinetics and Pharmcol. 189, 233–246. metabolism of the anti-oestrogen droloxifene in female human subjects. (10) Pelkonen, O., and Raunio, H. (2005) In vitro screening of drug Xenobiotica. 32, 699–713. metabolism during drug development: can we trust the predictions. Expert Opin. Drug Metab. Toxicol. 1, 49–59. (31) Forgue, S. T., Rudy, A. C., Knadler, M. P., Basson, R. P., Nelson, J. E., Henry, D. P., and Allerheiligen, S. R. (1996) Raloxifene (11) Ekins, S. Ma¨enpa¨a¨, J. and Wrighton S. A. (1999) In Vitro Metabolism: pharmacokinetics in healthy postmenopausal women. Pharm. Res. 13, Subcellar Fractions, in Handbook of Drug Metabolism (Woolf, T. F., 429. Ed.) pp 363-399, Marcel Dekker, Inc. New York. (12) Vermeir, M., Annaert, P., Mamidi, R. N. V. S., Roymans, D., (32) Ni, L., Allerheiligen, S. R., Basson, R., Knadler, M. P., Latz, J., Geiser, Meuldermans, W., and Mannens, G. (2005) Cell-based models to study J, Lantz, R., Rash, J., and Henry, D. P. (1996) Pharmacokinetics of hepatic drug metabolism and enzyme induction in humans. Expert raloxifene in men and postmenopausal women volunteers. Pharm. Res. Opin. Drug Metab. Toxicol. 1, 75–90. 13, 430. (13) Plant, N. (2004) Strategies for using in vitro screens in drug (33) Allerheiligen, S., Geiser, J., Knadler, M. P., Basson, R., Miller, J., metabolism. Drug DiscoVery Today 9, 328–336. Lantz, L., Rash, J., and Draper, M. (1996) Raloxifene (RAL) (14) Pelkonen, O., Turpeinen, M., Uusitalo, J., Rautio, A., and Raunio, pharmacokinetics and the associated endocrine effects in premeno- H. (2005) Prediction of drug metabolism and interactions on the pausal women treated during the follicular, ovulatory, and luteal phases basis of in vitro investigations. Basic Clin. Pharmacol. Toxicol. of the menstrual cycle. Pharm. Res. 13, S430. 96, 167–175. (34) O’Neill, P. J., Yorgey, K. A., Renzi, N. L., Jr., Williams, R. L., and (15) De Graaf, I. A. M., Van Meijeren, C. E., Pektas, F., and Koster, H. J. Benet, L. Z. (1982) Disposition of zomepirac sodium in man. J. Clin. (2002) Comparison of in vitro preparations for semi-quantitative Pharmacol. 22, 470–476. prediction of in vivo drug metabolism. Drug Metab. Dispos. 30, 1129– (35) Schneider, W., and Degen, P. H. (1981) Simultaneous determination 1136. of diclofenac sodium and its hydroxy metabolites by capillary column (16) Bu, H.-Z., Pool, W. F., Wu, E. Y., Raber, S. R., Amantea, M. A., and gas chromatography with electron-capture detection. J. Chromatogr. Shetty, B. V. (2004) Metabolism and excretion of capravirine, a new 217, 263–271. non-nucleoside reverse transcriptase inhibitor, alone and in combination (36) Doig, M. V., and Clare, R. A. (1991) Use of thermospray liquid with ritonavir in healthy volunteers. Drug Metab. Dispos. 32, 689– chromatography-mass spectrometry to aid in the identification of 698. urinary metabolites of a novel antiepileptic drug, Lamotrigine. (17) Johnson, K., Shah, A., Jaw-Tsai, S., Baxter, J., and Prakash, C. (2003) J. Chromatogr. 554, 181–189. Metabolism, pharmacokinetics, and excretion of a highly selective (37) Zuidema, J., Hilbers-Modderman, E. S., and Merkus, F. W. (1986) N-methyl-D-aspartate receptor antagonist, traxoprodil in human cy- Clinical pharmacokinetics of dapsone. Clin. Pharmacokinet. 11, 299– tochrome P450 2D6 extensive and poor metabolizers. Drug Metab. 315. Dispos. 31, 76–87. (38) Dalrymple, P. D., and Nicholls, P. J. (1988) Metabolism profiles and (18) Colizza, K., Awad, M., and Kamel, A. (2007) Metabolism, pharma- excretion of [14C]aminoglutethimide in several animal species and man. cokinetics, and excretion of the substance P receptor antagonist CP- Xenobiotica 18, 75–81. 122721 in humans: structural characterization of the novel major (39) Gottlieb, T. B., Thomas, R. C., and Chidsey, C. A. (1972) circulating metabolite 5-trifluoromethoxy salicylic acid by high- Pharmacokinetic studies of minoxidil. Clin. Pharmacol Ther. 13, performance liquid chromatography-tandem mass spectrometry and 436–441. NMR spectroscopy. Drug Metab. Dispos. 35, 884–897. (40) Delbressine, L. P., Moonen, M. E., Kaspersen, F. M., Wagenaars, (19) Prakash, C., Johnson, K. A., and Gardner, M. J. (2008) Disposition G. N., Jacobs, P. L., Timmer, C. J., Paanakker, J. E., van Hal, H. J., of lasofoxifene, a next-generation selective estrogen receptor and Voortman, G. (1998) Pharmacokinetics and biotransformation of modulator, in healthy male subjects. Drug Metab. Dispos. 36, 1218– mirtazapine in human volunteers. Clin. Drug InVest. 15, 45–55. 1226. (20) Dalvie, D., Chen, W., Zhang, C., Vaz, A. D., Smolarek, T. A., (41) Wu, W. H., Dicioccio, A. T., Demaio, W., Amorusi, P., Tonelli, A. P., Cox, L. M., Lin, J., and Obach, R. S. (2008) Pharmacokinetics, and Mayer, P. R. (1996) Mass balance and pharmacokinetics of Pharm. Res. metabolism and excretion of torcetrapib a cholesteryl ester transfer zaleplon, a non-benzodiazepine sedative/hypnotic, in man. 13 protein inhibitor, in humans. Drug Metab. Dispos. 36, 2185–2198. , S487. (21) Paulson, S. K., Hribar, J. D., Liu, N. W. K., Hajdu, E., Bible, R. H., (42) Braeckman, R. A., Granneman, G. R., Rubin, P. R., and Kestenson, Jr., Piergies, A., and Karim, A. (2000) Metabolism and excretion of J. W. (1989) Phanmacokinetics and metabolism of the new 5-lipoxy- [14C]celecoxib in healthy male volunteers. Drug Metab. Dispos. 28, genase inhibitor A-64077 after a single oral administration in man 308–314. (abstr.). J. Clin. Pharmacol. 29, A22. (22) Prakash, C., Kamel, A., Gummerus, J., and Wilner, K. (1997) (43) Dalgaard, L., and Larsen, C. (1999) Metabolism and excretion of Metabolism and excretion of a new antipsychotic drug, ziprasidone, citalopram in man: identification of O-acyl- and N-glucuronides. in humans. Drug Metab. Dispos. 25, 863–872. Xenobiotica 29, 1033–1041. (23) Prakash, C., Cui, D., Baxter, J. G., Bright, G. M., Miceli, J., and Wilner, (44) Ito, T., Yamaguchi, T., Miyazaki, H., Sekine, Y., Shimizu, M., Ishida, K. (1998) Metabolism and excretion of a new anxiolytic drug S., Yagi, K., Kakegawa, N., Seino, M., and Wada, T. (1982) candidate, CP-93,393, in healthy male volunteers. Drug Metab. Dispos. Pharmacokinetic studies of AD-810, a new antiepileptic compound. 26, 448–456. Phase I trials. Arzneim. Forsch. 32, 1581–1586. (24) Dalvie, D. K., Khosla, N., and Vincent, J. (1997) Excretion and (45) Duggan, D. E., Hare, L. E., Ditzler, C. A., Lei, B. W., and Kwan, metabolism of trovafloxacin in humans. Drug Metab. Dispos. 25, 423– K. C. (1977) The disposition of sulindac. Clin. Pharmacol. Ther. 21, 427. 326–335. (25) Slatter, J. G., Stalker, D. J., Feenstra, K. L., Welshman, I. R., Bruss, (46) Haddock, R. E., Johnson, A. M., Langley, P. F., Nelson, D. R., Pope, J. B., Sams, J. P., Johnson, M. G., Sanders, P. E., Hauer, M. J., J. A., Thomas, D. R., and Woods, F. R. (1989) Metabolic pathway of BIOM/PHAR 275 - Dalvie Paper 1 368 Chem. Res. Toxicol., Vol. 22, No. 2, 2009 DalVie et al.

paroxetine in animals and man and the comparative pharmacological (50) Chen, X., Zhong, D., Liu, D., Wang, Y., Han, Y., and Gu, J. (2003) properties of its metabolites. Acta Psychiatr. Scand. 350 (Suppl), 24– Determination of ketotifen and its conjugated metabolite in human 26. plasma by liquid chromatography/tandem mass spectrometry: (47) Phillips, D. L., Smith, R. L., Patterson, B. E., Parker, N., Mitchell, application to a pharmacokinetic study. Rapid Commun. Mass M., Wheeler, W. J., and Watkins, V. S. (2004) Barbuch RJ. Spectrom. 17, 2459–2463. Metabolism and excretion of tadalafil in healthy men after oral (51) Mori, Y., Kuroda, N., Sakai, Y., Yokoya, F., Toyoshi, K., and Baba, administration of 100 mg [14C]tadalafil. AAPS J. 6, W5308. S. (1985) Species differences in the metabolism of suprofen in (48) Kudo, S., and Ishizaki, T. (1999) Pharmacokinetics of haloperidol: laboratory animals and man. Drug Metab. Dispos. 13, 239–245. an update. Clin. Pharmacokinet. 37, 435–456. (52) Lin, C., Li, Y., McGlotten, J., Morton, J. B., and Symchowicz, S. (49) Mayol, R. F., Cole, C. A., Luke, G. M., Colson, K. L., and Kerns, (1977) Isolation and identification of the major metabolite of albuterol E. H. (1994) Characterization of the metabolites of the antidepres- in human urine. Drug Metab. Dispos. 5, 234–238. sant drug nefazodone in human urine and plasma. Drug Metab. Dispos. 22, 304–311. TX8004357