Which Human Metabolites Have We MIST ... - ACS Publications

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Chem. Res. Toxicol. 2009, 22, 280–293

Which Human Metabolites Have We MIST? Retrospective Analysis, Practical Aspects, and Perspectives For Metabolite Identification and Quantification in Pharmaceutical Development Laurent Leclercq,*,† Filip Cuyckens,† Geert S. J. Mannens,† Ronald de Vries,† Philip Timmerman,† and David C. Evans‡ Department of Drug Metabolism and Pharmacokinetics, Global Preclinical DeVelopment, Johnson & Johnson Pharmaceutical Research and DeVelopment, A DiVision of Janssen Pharmaceutica N.V., Turnhoutseweg 30, B-2340 Beerse, Belgium, and Johnson & Johnson Pharmaceutical Research & DeVelopment, L.L.C., Raritan, New Jersey ReceiVed NoVember 13, 2008

With the recent publication of the FDA guidance on metabolites in safety testing (MIST), a reflection is provided that describes the impact of this guidance on the processes of drug metabolite identification and quantification at various stages of drug development. First, a retrospective analysis is described that was conducted on 12 human absorption, metabolism, and excretion (AME) trials with the application of these MIST criteria. This analysis showed that the number of metabolites requiring identification, (semi)-quantification, and coverage in the toxicology species would substantially increase. However, a significant proportion of these metabolites were direct or indirect conjugates, a class of metabolites that was specifically addressed in the guidance as being largely innocuous. The nonconjugated metabolites were all covered in at least one toxicology animal species, with no need for additional safety evaluation. Second, analytical considerations pertaining to the efficient identification of metabolites are discussed. Topics include software-assisted detection and structural identification of metabolites, the emerging hyphenation of ultraperformance liquid chromatography (UPLC) with radioactivity detection, and the various ways to estimate metabolite abundance in the absence of an authentic standard. Technical aspects around the analysis of metabolite profiles are also presented, focusing on precautions to be taken in order not to introduce artifacts. Finally, a tiered approach for metabolite quantification is proposed, starting with quantification of metabolites prior to the multiple ascending dose study (MAD) in humans in only specific cases (Tier A). The following step is the identification and quantification of metabolites expected to be of pharmacological or toxicological relevance (based on MIST and other complementary criteria) in selected samples from the MAD study and preclinical studies in order to assess metabolite exposure coverage (Tier B). Finally, a metabolite quantification strategy for the studies after the MAD phase (Tier C) is proposed. Contents 1. Introduction 2. Retrospective Analysis 3. Analytical Considerations 3.1. Metabolite Identification Using Accurate Mass Measurements and Software-Assisted Approaches 3.2. Metabolite Separation Using the Hyphenation of UPLC with Radioactivity Detection 3.3. Estimation of Metabolite Abundance in Cold Samples, Including FIH Samples 3.4. Is What You See Really There? 4. Recommended Strategy for Metabolite Quantification 4.1. Tier A 4.2. Tier B 4.2.1. Identification 4.2.2. Estimation of Abundance 4.2.3. Decision Flowchart for the Identification and Quantification of Relevant Metabolites Criterion 1: MIST Threshold Criterion 2: Metabolites Formed by Conjugation Criterion 3:PharmacologicalActivity Index (PAI) Criterion 4: Absolute Metabolite Levels 4.2.4. Some Considerations in the

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Quantification of Selected Metabolites in Preclinical and Clinical Samples to Accurately Determine Coverage 4.3. Tier C 4.4. Metabolite Levels Obtained from the Human AME Study: How Should They Be Used Regarding the Bioanalytical Quantification of Metabolites? 5. Perspective

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1. Introduction 285 286 286 286 286 286 288

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The Food and Drug Administration (FDA) has recently issued a guidance on metabolites in safety testing (the so-called MIST1 * Corresponding author. Tel: +32/(0)14 607094. Fax: +32/(0)14 605110. E-mail: [email protected]. † Johnson & Johnson Pharmaceutical Research and Development. ‡ Johnson & Johnson Pharmaceutical Research & Development, L.L.C. 1 Abbreviations: AUC, area under the curve; AME, absorption metabolism excretion; PAI, pharmacological activity index; AMS, accelerator mass spectrometry; CLND, chemiluminescent nitrogen detection; EPIC, elucidation of product ion connectivity; FIH, first-in-human; GLP, good laboratory practices; ICP-MS, inductively coupled plasma-mass spectrometry; MAD, multiple ascending dose; MIST, metabolites in safety testing; NOAEL, no observed adverse effect level; QRM, qualified research method; SAD, single ascending dose; HPLC, high performance liquid chromatography; UPLC, ultraperformance liquid chromatography; NMR, nuclear magnetic resonance; NAPQI, N-acetyl-p-benzoquinoneimide.

10.1021/tx800432c CCC: $40.75  2009 American Chemical Society Published on Web 01/30/2009

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guidance), making recommendations to industry on when to identify and characterize drug metabolites whose nonclinical toxicology needs to be evaluated (http://www.fda.gov/OHRMS/ DOCKETS/98fr/FDA-2008-D-0065-GDL.pdf). The guidance indicates that metabolites identified only in human plasma or metabolites present at disproportionately higher levels in humans than in any of the test animal species should be considered for additional safety assessment. The guidance further describes that metabolites that can raise a potential safety concern are those having a systemic exposure in humans greater than 10% of parent drug at steady state. This guidance followed an earlier publication of the U.S. pharmaceutical industry in assessing the role of drug metabolites as potential mediators of toxicity following the administration of a new drug product (1), which itself was debated in the literature (2-5) prior to the issuance of the MIST guidance in early 2008 by the FDA. The challenges associated with conducting toxicology studies on metabolites have also been well described in the literature (3, 5, 6).

2. Retrospective Analysis

One interpretation of the MIST guidance is that it challenges the pharmaceutical industry to deploy tools, translated in the broader context of either analytical methodologies or the chronology of development studies, to obtain a quantitative understanding of metabolites in humans at the earliest stages in drug development. The options that exist in this regard include estimation of metabolite abundance in human plasma samples from the phase I single and multiple ascending dose studies and consideration of performing the human absorption, metabolism, and excretion (AME) study (7) earlier than is currently the case, which is typically in phase II clinical development. An important objective is to provide guidance on when to quantify metabolites in clinical studies and toxicology studies conducted under good laboratory practices (GLP) to demonstrate metabolite exposure coverage in humans. In order to provide a perspective on the potential impact of the MIST guidance on drug development workload and timelines, we first present data from a retrospective analysis of human AME studies for 12 Johnson & Johnson (J&J) compounds conducted in-house for which we, post factum, have evaluated how the new guidance could have impacted the number and nature of metabolites requiring additional quantification/evaluation. In addition, this review focuses on several key methodological aspects for an efficient identification of metabolites in preclinical species and in humans, including recent hardware and software developments in the field, emerging hyphenation of ultraperformance liquid chromatography (UPLC) with radioactivity detection, various ways to estimate metabolite abundance in the absence of an authentic standard, and precautions to be taken in order not to introduce artifacts during the analysis of plasma samples. Finally, a scientific rationale is proposed around which human metabolites should be considered for further quantification in toxicology and clinical studies as well as advice on the most efficient timing and level of bioanalytical method validation needed. A tiered approach is proposed in order to ensure a balance between investment of resources and stage of development of the compound, while safeguarding the right level of quality to enable good decision-making. In an attempt to define those metabolites, this document also addresses the potential need to characterize metabolites based on their absolute abundance, chemical structures, and potency, in a philosophy close to the one adopted by Smith and Obach in their Seeing through the MIST paper (3).

A retrospective analysis was performed on a set of 12 J&J compounds with data from the human AME trials. These studies were of single dose design and represented the first type of study where a quantitative understanding of metabolite abundance in plasma (and other matrices) was obtained through classical radiochemical profiling techniques, a qualitative understanding of drug metabolism in humans being undertaken shortly after initial introduction of the first dose in single ascending dose (SAD) and multiple ascending dose (MAD) trials. The compounds were selected to cover some chemical diversity (see physicochemical properties in Table 1). In view of the recently published FDA MIST guidance, the focus was on the number, nature, and levels of circulating drug metabolites and their abundance as compared to the parent compound. The same analysis was performed following the criteria as described in the FDA’s initial draft Guidance for Industry: Safety Testing of Drug Metabolites (June 2005) (http://www.fda.gov/OHRMS/ DOCKETS/98fr/2005d-0203-gdl0001.pdf), where major metabolites were defined as those metabolites above 10% of systemic exposure (circulating radioactivity). No circulating metabolites exceeded the 10% parent threshold for 3 out of the 12 compounds and therefore did not trigger further evaluation. For the remaining nine compounds, between two and five metabolites circulated at levels above 10% of the parent area under the curve (AUC), resulting in a total of 28 metabolites exceeding the criterion. It should be noted that the majority of the circulating metabolites were those formed by conjugation (nine were direct conjugates of the parent drug and nine were conjugates of metabolites formed by oxidation), and the remaining 10 were metabolites formed by oxidation. The circulating glucuronides were either N- or O-glucuronides; no acyl glucuronides were detected. The metabolites were tested in vitro against target and nontarget receptors and enzymes. Four metabolites formed by oxidation showed target pharmacological activity and five were inactive, and for one, it was unknown. One metabolite was equipotent to the parent (benzimidazole oxidation of compound H), two were 2 to 5 times less active (N-desmethyl metabolite of compound F and N-desmethyl metabolite of compound K), and one was 250 times less active (beta-oxidation of compound L). Only one direct N-glucuronide (of compound K) was tested in vitro for its target pharmacological activity and was found to be inactive. By comparison, following the criterion of the earlier draft guidance published in 2005, a total of 15 major circulating metabolites would have been identified for 8 of the 12 compounds, i.e., circulating at levels above 10% of the circulating radioactivity (sum of parent and metabolites). Furthermore, classification of these 15 metabolites indicated that 6 metabolites were direct conjugates of the parent drug, 4 were conjugates of metabolites formed by oxidation, and 5 were metabolites formed by oxidation. Next to estimating the abundance of the drug metabolites in human plasma, their coverage in the toxicology species should be evaluated. When retrospectively comparing the drug metabolite profiles in humans with those from AME studies performed in animals (both single dose studies), all drug-related metabolites formed by oxidation that were observed in human plasma were covered in at least one toxicology animal species for all 12 compounds. However, there were some small discrepancies, but they were all for nonalerting conjugates: for compound H, the two N-glucuronides of the parent were not covered in rats and dogs, but they were in rabbits; for compound J, the glucuronide of an alicyclic hydroxylated metabolite was

MW, molecular weight; c log P, calculated log P; TPSA, total polar surface area; HBD, number of hydrogen bond donors; HBA, number of hydrogen bond acceptors. b NA, not applicable.

489.43 366.37 K L

a

2 N-desmethyl (M3) desimidazole (M15) 0 13 1 N-gluc parent 2 N-oxide beta-oxidation 15 27 52 sum 0 -0.996 2/5 1/5 65.84 71.78

405.48 J

4.22 4.54

3 gluc arom OH gluc alicycl OH O-gluc parent (8) 2 acid (14) O-gluc parent (7) 3 0.884 3/5 70.95

308.79 381.44 H I

3.5

2 N-gluc parent (6) benzimidazole oxidation (9) 1 O-dealkylation (3) 1 N-gluc parent (4) 2 sulfate O-dealkylation (2) OH sulfate (4) 50 38 0.14 -0.747 1/4 1/4 46.5 80.7

415.57 G

3.24 4.99

2 two gluc OH (2 + 2′) 16 1 1/5 77.07

420.52 215.65 366.46 287.39 426.54 324.8 A B C D E F

4.2

MW

1.86 0.96 5.75 1.03 0.95 2.49

64.3 72.55 97.42 41.93 84.39 61.42

0/1 3/4 2/6 1/4 1/7 0/6

0 0.033 0.003 0.105 0 0

95 95 48 48 97 36

0 0 0 2 O-gluc parent (5) glucuronide O-desmethyl (2) 0 2 N-desmethyl (10) N-gluc of desmethyl in combination with N-oxidation (2) 3 OH (A(2 + 2′)) two O-gluc parent (3 and 3′)

0 0 2 N-gluc parent (15) dehydrogenation (27) 0 0 1 N-gluc desmethyl (4)

NAb NA 15 in dog; 27 in mouse and dog 5 in rat and dog; 2 in rat NA 10 in dog, rabbit and rat; 2 in dog and rat; 4 in dog and rat A(2 + 2′) in dog and rat; (3 and 3′) in dog and rat; (2 + 2′) in dog 4 in rabbit; 6 in rabbit; 9 in dog and rat 2 in rat and monkey; 4 in rat; 3 in rat and monkey 14 in rat and dog; 7 in dog and rat; gluc arom OH in dog; alicycl OH (aglycon) in rat; 8 in dog and rat M3 in rat and dog; M15 in rat both are covered in rat

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compound

c log P

TPSA (Å2)

HBD/ HBA

charge pH 7.4

parent as % of TR AUC

total number and nature of metabolites potentially requiring monitoring based on 10% total radioactivity (metabolite code within parentheses)

additional metabolites and nature of additional metabolites potentially requiring monitoring based on 10% parent (metabolite code within parentheses)

coverage in toxicology species (see previous columns for metabolite codes)

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basic physicochemical propertiesa

Table 1. Comparison of the Number and Nature of Circulating Metabolites Falling above the 10% Threshold of Total Radioactivity or of Parent Systemic AUC

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not covered in rats and dogs; however, the corresponding aglycone was found in the rat; for compound K, the Nglucuronide of the parent was not covered in both rats and dogs, but it was in rabbits. In conclusion, application of the new MIST guidance to the analysis of a limited set of 12 compounds resulted in an almost doubling of the number of relevant metabolites versus the previous draft guidance (28 above 10% of parent as opposed to 15 above 10% of circulating radioactivity). However, some of these metabolites, e.g., nonalerting conjugates, fall into a different category of less safety concern and thus the lesser need for further evaluation. The metabolites formed by oxidation were covered in at least one toxicology species. Although this retrospective analysis has limitations, namely, data were from single dose human AME-trials and only a limited number of compounds were evaluated, it nonetheless substantiates the conclusion that more work is required for metabolite identification, semiquantification, and proof of coverage in the toxicology species upon implementation of the MIST guidance. Main challenges are to be expected for those compounds with low levels of circulating parent, especially if this results from high metabolic clearance. However, cases where additional safety evaluation with a metabolite is needed are expected to be rare.

3. Analytical Considerations The drug metabolism information for a pharmaceutical compound essentially emerges over time, starting in discovery and continuing through the development phase. Within J&J, the first discovery studies generally consist of in vitro incubations of the nonradiolabeled drug with liver preparations coupled to the profiling of in vivo samples (e.g., plasma, excreta) in order to gain an initial understanding of the predictability of the in vitro models to the in vivo situation. These studies essentially contribute to the selection of the appropriate preclinical toxicology species, albeit this qualitative approach places a heavy emphasis on studies performed in liver microsome and hepatocyte preparations from human tissue in vitro. In drug development, the early availability of the radiolabeled compound presents the opportunity to repeat in vitro metabolism studies in a more quantitative manner, and to conduct in vivo absorption, metabolism, and excretion studies in animals in order to derive, as at least one output, in vitro-in vivo correlations. At this stage, we are also obtaining an understanding of the main excretion routes, clearance pathways, and circulating metabolites in relevant animal species prior to the first administration to humans. It is obvious that, prior to the conduct of the human mass balance study, there is reliance on obtaining drug metabolism data in humans from an analysis of samples obtained from early clinical studies performed using nonradiolabeled compounds (e.g., plasma and excreta from so-called “cold” studies) and comparing these data to those obtained in preclinical toxicology species. The next section is split in to four main themes that cover the whole metabolite profiling and identification paradigm: (1) how to most efficiently find metabolites and assign metabolite structures, making full use of the advantages of exact mass measurement in mass spectrometry, (2) how to most efficiently separate metabolites and quantify them, potentially using the emerging hyphenation of UPLC with radioactivity detection, (3) how to most efficiently estimate metabolite abundance in the absence of an authentic standard or radiolabeled material, and (4) how to ensure that appropriate precautions are taken in order not to introduce artifacts during the analysis of important plasma samples.

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3.1. Metabolite Identification Using Accurate Mass Measurements and Software-Assisted Approaches. The availability of user-friendly fast scanning high-resolution mass spectrometers has proven to be extremely useful in speeding up the process of metabolite identification, not withstanding the drawback linked to the high cost of some of those instruments. Significant efficiency gains have also been obtained by interfacing with software packages such as Metabolynx from Waters, which make full use of the high mass accuracy and specificity of those instruments and allow the automated extraction of metabolite peaks from the endogenous background using, a.o., the concept of mass defect filtering coupled to the comparison of the analyte sample with an appropriate control sample (8-12). This dual process increases the chances not to miss metabolites provided they show sufficient MS response; this is especially important for metabolites that cannot easily be predicted a priori by applying (combinations of) standard biotransformations to the parent compound (the so-called unexpected metabolites). Of note, some important future improvements are likely to occur in this area, with the integration of the concept of chemically intelligent metabolite peak extraction, making use of the chemical structure of the parent compound and the subsequent plausible metabolic cleavages to define metabolite-specific mass defect regions in order to reduce false positive results. This will also help to rationalize the possible elemental composition of the pseudomolecular ions by only considering losses of neutrals that can be explained on the basis of the parent drug structure (13). This methodology has proven to be extremely successful in our experience for the analysis of samples originating from discovery metabolism studies or, in general, for studies for which no radiolabeled compound is available (including frequent analysis of plasma, urine, and bile samples, and samples originating following incubation of the test compound with liver microsome and hepatocyte preparations). The same methodology can be successfully applied to the analysis of plasma samples from the first-in-human (FIH) study in order to rapidly evaluate the nature of circulating metabolites. Once tentative structures have been assigned to metabolites, using mass spectrometry and potentially supported by nuclear magnetic resonance (NMR) analysis, the next logical step would be the synthesis of an authentic standard, which would allow further assessment (e.g., quantification and assessment of pharmacological activity) within an appropriate time frame. The localization of sites of metabolic biotransformation by mass spectrometry analysis alone often remains a bottleneck (14), especially when the product ion mass spectra of the metabolite and the parent compound show little similarity, either because of divergent structures or because the specific site(s) of metabolism inducing changes in the fragmentation mechanisms. In most cases, the structural assignment of drug metabolites is heavily reliant on the ability of the analyst to assign and interpret experimental product ion data (including those from minor fragments) to propose a putative metabolite structure. Thus, there is a significant interest from the pharmaceutical industry in the development of software tools that would at least partially automate this step. Structural elucidation tools have been designed that are commercially available (MassFrontier from Thermo and MS Manager from ACD) and help rationalize the product ion spectra obtained by collisional activation of analytes whose structures are already known, taking advantage of the accurate mass measurement of fragment or product ions to reduce the number of possible hits. However, none of the currently available software packages are able to automatically and routinely localize the site of a biotransformation for drug


metabolites, not withstanding recent publications in this respect (15-17). One novel approach that may offer a solution was the elucidation of the product ion connectivity (EPIC) algorithm (commercially available as MassFragment from Waters), where a database of all theoretically possible fragments of a compound is created using an exhaustive approach of breaking of all theoretical bonds (18). The resultant fragment database is then compared to the observed experimental ions. Each potential ion structure with an exact mass falling within a predefined range versus the observed accurate mass is assigned a score based on predefined criteria; the lower the score, the higher the probability that the proposed fragment represents the correct solution. This approach has the merit of providing an alternative concept (an exhaustive fragmentation of all bonds supplemented with chemical intelligence) as compared to a rule-based approach identifying only those bonds that are likely to break. These authors also indicated that the EPIC algorithm was able to suggest rationalizations for a substantially higher proportion of the observed product ions. In our experience, the MassFragment software does a very good job in terms of rationalizing structures of readily interpretable fragments, while for fragments involving more complex fragmentation mechanisms, the software delivers the correct solution as a first hit around 70% of the time. Additional chemical-intelligence-added postprocessing will be the key to increasing this success rate. In our laboratory, we have developed a novel approach that further extended the EPIC approach, referred to as IsoScore (19). The concept is based on the generation of all virtual regioisomers of a given biotransformation, followed by the virtual fragmentation of all regioisomers and the scoring of all virtual fragments versus the experimentally observed ions. Metabolite structures are finally ranked by their cumulative scores, showing their likelihood in relation to the experimental data. The approach also takes into account the level of similarity between the mass spectra of the metabolite and the parent compound. We have tested this concept with a high rate of success on a variety of metabolites for which the absolute structure was known. The data showed that in most cases, the correct regioisomer was identified as the first hit or as one of the most probable hits in cases where mass spectrometry could not discriminate between multiple regiosiomers. This work can be seen as a proof-of-concept showing the value of product ion scoring of a putative regioisomer of a given biotransformation in order to localize the most likely position of metabolism. The approach will benefit from input of additional chemical intelligence to the scoring function, in order to better differentiate likely and unlikely ion structures. Software routines such as Isoscore are considered by the authors as potentially extremely helpful to quickly rationalize biotransformations based on relatively straightforward fragmentation mechanisms or, in more complex cases, assist the analyst in identifying key product ions that can help discriminate between putative metabolism positions. However, the overall complexity surrounding the structural elucidation of drug metabolites by mass spectrometry is such that a complete automation of this task is clearly not a realistic or a desirable outcome. 3.2. Metabolite Separation Using the Hyphenation of UPLC with Radioactivity Detection. Since metabolites are structurally related entities and structural differences can be very minor, a complete separation between all drug-related compounds is a goal that is often difficult to achieve. However, only a complete separation allows the rigorous quantification of individual metabolites in a radioactive AME study and the estimation of whether a metabolite can fall above the 10% of parent drug steady state concentrations (Section 4.2.2). A typical

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Figure 1. (a) Radio-HPLC analysis of 350 µL of a dog urine sample, (b) Radio-UHPLC analysis of 350 µL of the same sample (23).

worst case but nonetheless frequent scenario is when isobaric metabolites (such as isomers of aromatic hydroxylation) cannot be chromatographically separated for downstream radiodetection. Such entities could even be considered as a single metabolite in cases where their LC/MS retention times are very similar, and the product ion spectrum does not provide clear evidence for the presence of more than one drug-related entity under the peak. Such situations can lead to the overestimation of metabolite abundance and could thus make metabolites become relevant with regard to MIST while they actually should not. Recently, UPLC instrumentation, withstanding higher backpressures inherent to the use of smaller particles, has become one of the most efficient solutions to improve chromatographic performance in many fields, including metabolite profiling and identification. In order for UPLC to replace high performance liquid chromatography (HPLC) as the ultimate separation technique for metabolite profiling, it would require interfacing with radioactivity detectors since most metabolism studies in drug development are conducted with a radiolabeled compound to allow mass balance determination as well as quantification of individual metabolites. However, the narrow peak widths typically obtained in UPLC separations appear intrinsically incompatible with the relatively high counting times needed for adequate sensitivity in radiochemical detection. An interesting approach to optimize sensitivity is to perform radioactivity counting off-line using a rapid fraction collection system in combination with a microplate scintillation counter (20). When collections are made in a scintillator embedded well plate, the radioactive fractions can be infused into the mass spectrometer in order to facilitate structure characterization of drug-related metabolites. This approach capitalizes on the collective merits of high intrinsic sensitivity (mass spectrometry and scintillation counting) and chromatographic resolution (UPLC). The main drawback is that fractions need to be dried down prior to radioactivity counting, making the system more prone to variable recoveries due to the presence of endogenous compounds in the biological matrix (20, 21). Also, the off-line approach is limited by the maximum number of well plates contained in the fraction collection system, is more labor

intensive and time-consuming, shows the potential for apolar compounds to adsorb onto the surface of the plate, and yields a relatively low number of data points for accurately describing the narrow chromatographic peak. When considering online coupling of HPLC with radioactivity detection, the counting time can be increased using a stop-flow technique (22). Although the method was shown to be up to 20 times more sensitive compared to traditional flow-through radiochemical detectors, its combination with UPLC is not optimal since the radio-UPLC analysis time with stop-flow would be longer than that for a traditional radio-HPLC analysis. In our laboratory, we have developed an efficient combination of UPLC using a conventional radiodetector (23). This combination was made possible by improving the online radioactivity detection as well as increasing the column loading and peak capacity for ultrahigh-pressure separations. The sensitivity of radioactivity detection was improved by the implementation of a variable scintillation flow via a simple modification to the classical online radiochemical detection setup, thereby also significantly reducing scintillation liquid consumption and radioactive waste production. A modification of the flow-through cell design, essentially by reducing the internal diameter of the tubing, further increased the sensitivity and resolution by decreasing peak tailing. The injection of relatively large volumes was made possible by the use of columns packed at ultrahigh pressure with 2.2 µm particles. Because of the reduced back pressure associated with using these larger particles, two 3 × 150 mm columns could be coupled, allowing 4-fold larger injection volumes and a 50% increase in theoretical plate number at a similar back pressure compared to that of a standard 2.1 × 150 mm UPLC column. The value of the methodology described was demonstrated by the analysis of in vitro and in vivo metabolism samples containing 3H- and 14C-labeled compounds compared with conventional radio-HPLC as illustrated in Figure 1 for a urine sample of a dog dosed with a 14C-labeled compound. This approach allows for a more efficient metabolite separation coupled to the ability to identify metabolites in samples with a low concentration of radioactivity.

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3.3. Estimation of Metabolite Abundance in Cold Samples, Including FIH Samples. It is clear that the emergence of LC/ MS in the early 1990s has greatly facilitated the conduct of routine in vitro and in vivo metabolism studies because of its superior specificity and sensitivity over other analytical techniques. It also became the preferred technique for the quantification of drugs and their metabolites in various biological matrices, including plasma. However, the intensity of the MS signal obtained with the Atmospheric Pressure Ionization sources depends strongly on the chemical structure analyzed, which necessitates the use of an authentic standard for quantification. However, since standards are not always available for metabolites at this early stage, alternative procedures must be developed to estimate metabolite abundance. Our preferred approach in this respect is based on the comparison of the MS peak areas of the metabolite in plasma with that of a sample spiked with known amounts of the radiolabeled metabolite (similar to the approach published by C. P. Yu et al. (24)). Several other analytical techniques do not require an authentic standard for quantification. The most straightforward detection technique generally found in combination with LC/MS is UV-visible detection. Metabolites can be quantified relative to the UV-visible absorption of the parent drug, provided that the chromophore offers sufficient selectivity, is not altered by metabolism, and the compounds are well separated from other drug-related entities and endogenous compounds. While UV detection is an easy and routine technique, these provisions all highlight why this approach has such high potential to be fraught with quantitative inaccuracies. Nuclear magnetic resonance (NMR) spectroscopy can be used to quantify metabolites since its signal intensity is proportional to the number of resonating nuclei and therefore independent of the structure. While 1H NMR could in theory be used for estimating metabolite abundance, it currently still lacks the selectivity needed for the analysis in the complex matrix of metabolism samples. 19F is not naturally present in biofluids so that 19F-NMR can be used to selectively detect metabolites derived from fluorine containing compounds. A good correlation with radioactive quantification has been demonstrated (25, 26), but the current limited sensitivity of the NMR technique remains the major drawback. Of note, the concentration of a partially purified metabolite in an NMR solvent can be determined, and this solution can thus be used in certain cases as an analytical standard to estimate metabolite abundance. The majority of drugs contain one or more nitrogen atom. Chemiluminescent nitrogen detection (CLND) gives a response proportional to the number of nitrogens in the structure and, therefore, can be used to estimate metabolite quantities if the number of nitrogen atoms in the structure is known. The response for N-N or NdN containing compounds is lower and less reliable so that for these compounds a calibration curve of the authentic standard is recommended (27, 28). Selectivity is a major issue with CLND because of numerous nitrogen containing endogenous compounds, but it has been demonstrated that CLND can be a valuable technique with appropriate sample cleanup and a good chromatographic separation (29). Inductively coupled plasma-mass spectrometry (ICP-MS) is another very interesting alternative technique showing a response independent of the chemical structure of the compound. Although limited to those elements that can be analyzed by ICPMS such as metals, Br, Cl, I, P, and S, many applications can be found where ICP-MS was employed in drug metabolism studies, as recently reviewed by Gammelgaard et al. (30). Considering its high specificity and sensitivity, ICP-MS could


potentially overtake radioactivity detection as the method of choice for drug metabolism studies of metal-containing compounds. Whenever a radioactive compound is not available or cannot be used, the application of ICP-MS for halogen or sulfurcontaining drugs is also worthy of consideration. Particularly iodine and bromine-containing compounds can be measured with good sensitivity and selectivity. ICP-MS has shown a good correlation with radioactive metabolite profiling, especially when isotope dilution was used to counteract signal fluctuations from the gradient elution (31). Accelerator mass spectrometry (AMS) is used in drug metabolism research predominantly for 14C detection (32). The use of AMS has been reviewed recently by Tuniz et al. (33). Thanks to its extremely high sensitivity, samples can be accurately measured from humans dosed with radioactive amounts equivalent to the naturally occurring radiation in the adult human body (typically 3.7-7.4 kBq), allowing the use of a radiolabel earlier in drug development. In contrast with the previous alternative techniques already mentioned, synthesis of a 14C-labeled compound is required, and hyphenation with liquid chromatography is not yet available so that fractions need to be collected and analyzed off-line. Therefore, the technique is quite labor-intensive and expensive. The use of AMS in drug metabolism research will undoubtedly increase upon successful coupling of AMS with liquid chromatography (34). While most of these analytical techniques are not routinely used for metabolite quantification, they are worthwhile considering when specific challenges are encountered. Further development and optimization will be necessary to turn some of these techniques (especially ICP-MS and AMS) into real alternatives for routine metabolite quantification. 3.4. Is What You See Really There? Metabolite profiling in plasma presents some technical challenges. In the field of metabolite profiling, sample cleanup steps are purposefully limited (opposite to quantitative bioanalysis) in order to not miss metabolites or not to introduce artifacts (known as metabonates) (35, 36). Conversely, direct injection of plasma is complicated as the high protein content is not compatible with the organic solvent content of conventional HPLC eluents, and column loading with proteins is likely to affect column integrity and performance. Most investigators apply one or another way to facilitate protein precipitation (most widely used is mixing of plasma with 2-3-fold excess of acetonitrile), centrifugation of the protein pellet, collection of the supernatant, evaporation to dryness, and reconstitution in a solvent mixture compatible with HPLC injection. A big advantage is that this is also a sample concentration step (volume reduction), facilitating metabolite detection and identification. Nevertheless, one should always be aware not to lose drug or metabolites as a result of coprecipitation with the proteins (either included in the pellet or bound to proteins) or as a result of limited solubility. Highly water-soluble metabolites can have a decreased solubility in the presence of acetonitrile or in the solvent mixture that is used for HPLC injection. Obviously, losses of unknown metabolites can easily be overlooked during the analysis of plasma samples from clinical trials. An advantage of planning AME studies early is that a simple measurement of the radioactivity recovery at all sample preparation steps during the analysis of radiolabeled AME samples is a good indicator of metabolite recovery. Besides losing metabolites, the protein precipitation procedure can also introduce structural modifications to the parent drug or metabolites. Some examples are shown in Table 2. The most critical steps turn out to be the conditions of the evaporation step and the solvent selection prior to HPLC injection. There

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Table 2. Examples of Structural Modifications Following Plasma Precipitation with Acetonitrile, Evaporation, and Reconstitution structural group carbamate basic nitrogen ester acylglucuronide primary and secondary amine N-oxide carboxylic acid

conversion

extent of conversion

carbamate migration to an alcohol on a β-carbon (48) N-oxidation hydrolysis hydrolysis, acyl migration N-glycosylation

major

reduction esterification with an alcohol

minor to major minor to major

minor to major minor to major minor to major major

are also some other findings, which indicate that a metabonate originates from the sample pretreatment process: a constant metabolite/parent ratio over time (usually there is a time profile in the in vivo metabolite generation and elimination), the metabolite has not been detected in excreta or in in vitro systems, or the same component is also seen in blank plasma that was fortified with the parent drug and treated as the genuine samples prior to HPLC injection. Finally, the levels of the parent drug that are estimated with the metabolite profiling method should be compared with the levels that were measured with a quantitative bioanalytical method. Given the importance of circulating metabolites in the context of FDA’s MIST guidance, it is key that reliable data are generated. Metabolite level underestimation or overestimation as well as the generation of artifactual metabolites (metabonates) can have a big impact on the preclinical safety testing strategy.

4. Recommended Strategy for Metabolite Quantification Different types of quantitative data can be obtained, ranging from relative, to estimated, to absolute quantitative data. The different approaches are further defined in Table 3. In practice, a tiered approach is proposed, which is based on the pivotal concept that metabolites generally only become relevant for safety assessment once they have been observed above a particular level in human plasma following repeated exposure to the parent compound, as this situation is the closest to the context of future clinical use. These exposure data are typically available after completion of the MAD study. On this basis, the following is proposed: • Tier A: Perform quantification of metabolites before completion of the clinical MAD study only in specific cases with a strong scientific rationale using a qualified research method (QRM; see Table 3) for bioanalysis. • Tier B: Identify and estimate the abundance of circulating human metabolites after the MAD study has completed using analytical conditions (especially chromatographic conditions) comparable to those used for preclinical drug metabolism studies. Identify which metabolites can be of pharmacological or toxicological relevance in humans based on MIST and other complimentary criteria. Quantify these metabolites in a selection of samples from the MAD study and in a selection of samples from the main toxicology species using a QRM method (see Table 3). For metabolites where no reference compound is available, relative concentrations (see Table 3) can still be obtained. • Tier C: conduct a method validation for metabolites that need to be quantified in studies after Tier B. This concept is further detailed and exemplified in the following sections:

4.1. Tier A. There are obvious cases where the estimation of metabolite abundance and/or quantification of a metabolite is needed at an early stage (pre-MAD). This is typically the case when: • A compound shows unexpected low plasma levels. A qualitative check for the presence of metabolites can reveal whether extensive metabolism can explain this phenomenon or if the explanation for limited plasma exposure resides somewhere else (e.g., low absorption). This situation is moretypicallyencounteredduringdiscoveryleadoptimization. • The amount and/or in vitro potency (target or nontarget) of a given metabolite is high. In this case, quantification of the metabolite in a selection of samples from FIH studies is warranted, as the pharmacology of the metabolite could potentially influence the interpretation of, for example, proof-of-concept or the safety profile of the compound. An example of when this can be encountered is when the pharmacology target of interest is a subtype receptor of a family of highly homologous pharmacophores such that a product of metabolism has a different (competing or additive, but nevertheless potent) receptor binding profile. • When there is a lack of correlation between the in vitro and in vivo drug metabolism profiles in preclinical species, yet the metabolism of the candidate drug in human liver microsome or hepatocyte preparations indicates the potential for a metabolite to have a disproportionately high level in humans relative to the intended toxicology species. If the medical benefit-risk associated with this compound is such that there is a strong desire to advance this compound into humans, then quantification of the metabolite in (a selection of) samples from FIH and toxicity studies is warranted. 4.2. Tier B. Within Tier B, 4 different phases can be distinguished: (i) identification, (ii) estimation of abundance, (iii) applying a decision flowchart for future work, and (iv) quantification. These 4 phases are detailed below. 4.2.1. Identification. A qualitative assessment of which metabolites are circulating in human plasma is conducted after the completion of the single ascending dose (SAD) and the multiple ascending dose (MAD) studies using analytical conditions comparable to the ones used for preclinical metabolism studies. Typically, metabolite identification is performed using HPLC/UPLC coupled to high-resolution mass spectrometry. Metabolites are identified using software-assisted approaches allowing an unbiased identification of their molecular weights and elemental composition (Section 3.1.). The identification of metabolites in the human sample is also greatly facilitated by the monitoring of important metabolites previously observed in vitro (human and preclinical species) or in vivo (in preclinical species). Identification of drug metabolites in human samples is generally most efficient using samples from the high dose SAD study, as those samples are likely to contain the highest level of metabolites and will facilitate their characterization and detection. 4.2.2. Estimation of Abundance. The abundance of the identified metabolites can be estimated in a selection of samples from the MAD study at a dose level as close as possible to the anticipated clinical dose. The use of multiple time points (at minimum Tmax, 2xTmax, and 4xTmax) is recommended to allow the calculation of systemic exposure (AUC) for individual metabolites. Our preferred approach for estimating metabolite abundance in the absence of an authentic standard is based on the comparison of the MS peak area of the plasma metabolite with

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Chem. Res. Toxicol., Vol. 22, No. 2, 2009 287 Table 3. Methodologies for Metabolite Quantification description

definition

validated bioanalytical method (internal standard needed)

• A bioanalytical method that has been validated according to the current FDA Guidelines on method validation (http://www.fda.gov/ CDER/GUIDANCE/4252fnl.pdf). • When this type of method is used, validated quantitative results are obtained. • Stability of the analytes is assessed in detail (blood stability, plasma stability, freeze/thaw stability, long-term stability).

qualified research method (QRM) (internal standard needed)

• A bioanalytical method that has been developed and is applied using the batch acceptance criteria as described in the current FDA guidelines. (http://www.fda.gov/CDER/GUIDANCE/4252fnl.pdf). • No a priori method validation, but in the study, validation is conducted for this type of method, which minimizes the workload of setting up and writing up the assay considerably. • When this type of method is used, qualified quantitative results are obtained. • Stability of the drug candidate in the matrix is assessed (scientific practical approach, aligning the level of detail of stability work with the stage of development of the drug and using scientific considerations (e.g., esters in rodents...)). • The quality of the data is comparable with a validated method because the same analytical batch acceptance criteria are used. • The amount of work involved in setting up and applying a QRM method is significantly less than that for a validated method.

quantification using radioactivity detection

• When direct quantification is done using LC/RAD, then quantitative results are obtained. • Because of the large dynamic range observed for radioactivity detection, large amounts of radioactivity can be injected, resulting in a large signal-to-noise ratio and, thus, very good precision and accuracy. However, the radioactivity levels in samples from metabolism studies conducted in humans are generally limited and spread over more than one peak. Therefore, a confidence level of ( 15% is acceptable around LOQ, while a better accuracy can be expected for larger peaks. In addition to the injection of QC samples, it is good practice to calculate the recovery of radioactivity following sample preparation and LC separation.

estimated concentrations (internal standard needed if QRM or validated bioanalytical method is used)

• If quantitative results are obtained but the bioanalytical procedures and/or batch acceptance criteria as described in the FDA Guidelines are not followed, the results are defined as estimated concentrations. • If quantification is performed in an indirect way, for instance by using the response ratio between LC/UV and LC/MS or between LC/RAD and LC/MS for quantification (as discussed in detail in Section 3.3), the results are defined as estimated concentrations. Also, the concentration of a partially purified metabolite in an NMR solvent can be determined, and this solution can be used as a standard to estimate metabolite concentrations.

relative concentrations

• Even if no reference compound is available, relative concentrations can still be reported. Using this approach, it is possible to compare exposure between different species in a relative way in order to demonstrate coverage of metabolite exposure in the main toxicology species compared to man for those metabolites that can raise a safety concern according to the MIST guidance. Importantly, coverage information used via this methodology should be used only if metabolite peak areas significantly differ between human and animals; otherwise, a more precise measurement would be necessary for adequate comparison. • Precautions need to be taken to ensure that the absolute response remains the same during the analysis of the samples from the different studies and species (e.g., repeatedly injecting an incurred sample). Also, some tests need to be done to ensure that matrix effects are comparable between the different species. • Stability tests in plasma and blood are performed using incurred samples, using the same scientific practical approach as for QRMs.

the MS peak area of the same metabolite for which the absolute concentration is known based on its radioactivity response (24). While the use of the relative MS versus radioactivity response is a very elegant way to make a good quantitative estimate of metabolites in FIH samples, a few points of particular note need to be kept in mind in order to make this approach successful: (i) the FIH plasma samples should be mixed with the appropriate blank reference matrix, and the radioactive reference sample

should be mixed with blank human plasma in order to neutralize matrix effects; (ii) it is important to ensure that metabolites are analyzed in the linear range of the MS detector; (iii) all metabolites need to be identified, not only those that are expected (a major advantage of an initial unbiased software-assisted identification of metabolite peaks); (iv) the radioactive peaks in the preclinical samples should be chromatographically separated so that the radioactive peak used for the calculation

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Figure 2. Decision flowchart for the quantification of metabolites based on metabolite levels in the MAD and human mass balance study.

of the MS response only comprises one single metabolite; and (v) artifacts resulting from the loss or degradation of metabolites should be avoided. 4.2.3. Decision Flowchart for the Identification and Quantification of Relevant Metabolites. Once the abundance of human circulating metabolites has been estimated in samples from the MAD study, a decision has to be made on which metabolites will need to be quantified in toxicology species and which metabolites will potentially need to be quantified in downstream clinical studies. The decision criteria are incorporated in a decision flowchart (Figure 2) leading to 6 possible scenarios (A to F). Should the metabolite not be covered in preclinical species, a seemingly rare occurrence, some preliminary guidance is provided before the conduct of a toxicology study on the metabolite itself, along with the inherent issues associated with this approach (6). The genetic toxicology assays referred to here are the ones recommended by the FDA guidance on point mutation and chromosomal aberration. Also in the case when a human circulating metabolite is detected above the 10% criterion, but is not detected at sufficient levels in animal plasma, the metabolite can be monitored in the excreta of animals (urine, feces, and bile) as an alternative to ensure the animal has been exposed to appreciable levels of the metabolite. This is unambiguously stated in the FDA MIST guidance. Importantly, using alternate animal species for toxicology assessment should be considered in case the human metabolite investigated is not covered in the animal species initially selected. The decision criteria considered are described below. Criterion 1: MIST Threshold. The MIST criterion (metabolite AUC as compared to Parent AUC at steady state) is used as the first line criterion to define whether a metabolite should be further investigated. Of note, metabolites 0.25 (>25%) can be used as a framework within which the

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Figure 3. Overview of potentially reactive metabolites formed by conjugation.

quantification of pharmacologically active metabolites could be considered. The PAI index is defined as follows:

PAI/ )

metabolite AUC × parent AUC pharmacological activity of parent pharmacological activity of metabolite

The 25% value is based on an example previously published (3), where AUC data are corrected for plasma protein binding, and PAI values 25%), the quantification of the metabolite in clinical studies in addition to toxicity studies might be warranted. Similarly, for a drug metabolite whose structure is consistent with it having been formed via metabolic activation, the body burden of this metabolite is considered as the best measure of its potential risk. Since there are few if any toxicities associated with drugs dosed at the level of 10 mg/day or less that are mediated via the formation of reactive intermediates (drugs that are dosed less than 10 mg/day can show toxicity, but it is more probably associated with a suprapharmacologic mechanism), a body burden for a metabolite of 10 mg appears to represent a conservative threshold for considering potential implications of the metabolite to safety findings. Additional perspectives related to the profiling of human excreta are provided in Section 4.4. 4.2.4. Some Considerations in the Quantification of Selected Metabolites in Preclinical and Clinical Samples to Accurately Determine Coverage. 4.2.4.1. Study and Sample Selection. A typical study and sample selection that can be used to assess coverage can consist of a selection of study samples from the MAD study (e.g., 2 or 3 nonplacebo subjects at 2-3 different dose levels

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around the anticipated clinical dose) and a selection of study samples from repeated dose studies in the main toxicology species (rodent and nonrodent). Sample selection could be limited to samples from repeat dose studies of the longest duration available at the moment that the analyses are performed. The NOAEL dose is preferentially selected for these analyses. By running all of the above metabolite analyses as one analytical batch and by applying a QRM approach, these analyses can be performed quite efficiently, saving resources and thus costs. Even if no reference compound is available, relative concentrations can still be reported using a comparison of the MS peak areas between human and animal samples provided several precautions are taken as described in Table 3. 4.2.4.2. Stability Considerations. The metabolite analysis should include an assessment of stability in blood and in plasma on-the-bench for each species and for each metabolite that is quantified in order to ensure the metabolite’s stability. Storage stability in the freezer can also be relevant to assess. Storage stability testing can be initiated at the moment the reference compound of the metabolite becomes available. However, this might not cover the period between sampling and analysis because the reference compounds of the metabolites are not always available at the start of the study. In order to be able to initiate storage stability testing before availability of the reference compound, storage stability can also be initiated by spiking plasma with hepatocyte or microsomal incubations of the radiolabeled drug. 4.3. Tier C. In Tier C, a decision has to be made on how to strategically move forward with quantification of the metabolites in future preclinical and clinical studies. A situation where quantification of a metabolite will be required in all future toxicology and clinical studies is when a metabolite has a significant contribution to the total pharmacological activity of the drug (case D1,2 in the flowchart, Figure 2). There are also situations where it might be sufficient to limit the metabolite quantification to a few relevant clinical and preclinical studies (e.g., healthy volunteers, patient populations, interaction studies, and special populations that can yield a different exposure ratio of the metabolites), thus limiting the quantification to those studies that are needed to document the metabolite exposure. 4.4. Metabolite Levels Obtained from the Human AME Study: How Should They Be Used Regarding the Bioanalytical Quantification of Metabolites? The nature and levels of drugrelated metabolites in plasma from the human AME study (which used radiolabeled drug material) should be correlated with those data from the human SAD and MAD studies (conducted with nonradiolabeled drug material). Should any additional important metabolite (>10% of parent AUC) appear in this study that was not identified as such previously, it should be considered for further quantification using a QRM method. An early planning of the human AME trial is therefore important. From a drug safety evaluation perspective, metabolite profiling in excreta also provides information on the exposure of the body to potentially alerting structures (reactive metabolites or related downstream products). Those metabolites are generally suspected to trigger toxicity by their nonselective, irreversible, binding to macromolecules (e.g., to proteins or DNA; referred to as type B, C, and D toxicities (3)), which can lead to direct organ toxicity, immunoallergic reactions, mutagenicity, and carcinogenicity. The human mass balance study will generally be the first time the importance of such metabolites can be rigorously evaluated in humans, although indications for the

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potential of a drug to undergo bioactivation in vivo can be assessed during the metabolism and excretion studies conducted in animal toxicology species (identification of (downstream products of) reactive intermediates in plasma and excreta, and assessment of covalent binding of radioactive material to plasma proteins) or by profiling urine from FIH SAD and MAD studies in addition to plasma. Those data can have an important impact on the timing and design of the human AME study using the radiolabeled compound. An important and difficult scientific question is how to define an appropriate risk assessment strategy for metabolites identified in human excreta that are downstream products of bioactivation of the parent drug? In the absence of understanding the link between drug adduction to proteins and the potential associated toxicological consequences mediated thereafter, the default position is to attempt to define an empirical body burden threshold for this class of metabolite in excreta. One might argue that this body burden could reasonably exist somewhere between 10 mg/day (3) and 400 mg/day (based on N-acetyl-p-benzoquinoneimide (NAPQI) generated from 4 g/day Tylenol 5, 39), but regardless of the specific threshold, several interesting challenges are faced in practice when making decisions on which metabolites in excreta ought be quantified. First, the structural characterization of all metabolites might become extremely resource intensive, especially in the case of a high dose compound metabolized into multiple minor metabolites. Second, our ability to determine whether a particular metabolite is derived from bioactivation might sometimes be inadequate, especially when its structure differs from that of the usual suspects (such as dihydrodiols or glutathione-derived conjugates). A simple case would be a phenolic intermediate, which can arise either from arene oxide formation followed by opening of the epoxide and dehydratation, or from direct oxidation. The decisions that need to be made regarding the safety assessment of metabolites in human excreta thus form a complex matter that will generally need to be handled on a case-by-case basis. The 10 mg body burden threshold for metabolites suspected to be linked to bioactivation (5) can be considered as a conservative starting point, although this threshold might trigger substantial unnecessary work in a significant number of cases. Future qualitative and quantitative work aimed at defining the mechanistic link between reactive metabolite formation, the nature of the proteins modified (40-43), and biological effect will help define appropriate thresholds above which those metabolites might become relevant for safety findings. This will, subsequently, help to define industry standards around the amount of analytical work needed with regard to their structural characterization and quantification.

5. Perspective During the drug discovery and development phases, a set of metabolism experiments are conducted, and a growing package of information is generated that describes the metabolic fate and clearance routes for a candidate drug. A proactive approach to identifying drug metabolites in liver preparations from animals and humans in vitro, and thereafter in vivo, is imperative in order to select the appropriate safety species to be used in toxicology studies. This will also help to avoid surprises at the level of circulating metabolites that may be disproportionately high in humans or have substructures potentially commensurate with mediating a safety concern. Clearly, a goal is to avoid the potential to form a metabolite that is unique to humans, and on the basis of our retrospective analysis of 12 J&J compounds, this has not occurred, and we believe is likely to be rare in the

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industry given the multiple preclinical safety species that are used in the development of a new drug candidate. An additional goal is to avoid the need to administer metabolites as discrete chemical entities to preclinical species to assess their toxicity, with all the potential complexity of this approach (3, 6). Since body-weight-corrected doses in animal toxicology studies are generally much greater than in humans, our experience is that the risk of forming disproportionate metabolites that are not covered in animal species is low and that the need for such studies will also be rare. Increased attention will therefore be needed to improve our ability to predict which metabolites detected early on might also be important in the systemic circulation, on the basis of animal findings (in vitro or in vivo) and physicochemical properties. The observation that circulating metabolites often are conjugates is not surprising as these are generally more water soluble and hence have a smaller volume of distribution. As mentioned in the guidance, conjugation reactions generally produce pharmacologically inactive products, thereby eliminating the need for further evaluation. A good understanding of the in vitro metabolite profile and the physicochemical properties affecting the solubility, distribution, and pharmacological activity of a metabolite should be part of the metabolite profiling and identification strategy in order to avoid surprises on important circulating human metabolites at the FIH stage or later in development. An important milestone in drug development is the FIH trial, not least for the generation of the FIH plasma samples to determine the pharmacokinetics but also as the first access point toward in vivo metabolite profiling, potentially triggering further bioanalytical work on human metabolites. Importantly, all attention should go to ensure an accurate and unbiased analysis of these invaluable FIH plasma samples (single and multiple dosing), which will become more straightforward and efficient with the help of the latest generation of hardware and software tools relying on exact mass measurements of metabolites. Although the FDA clearly indicates in their guidance that metabolite abundance should be studied at steady state, the single dose human radiolabeled AME trial definitely remains irreplaceable for a good understanding of the disposition of a drug in humans. The metabolite abundance can be quite different after single versus repeated doses, although the qualitative metabolite profile following a single dose usually is predictive for that following repeated doses. The advantage of the use of a radiolabel is that, in theory, all metabolites are clearly visible (as they contain the radiolabel), metabolite quantification is straightforward, and metabolite recovery issues can easily be identified. Here also, emerging technologies and hyphenation, in particular off-line counting and coupling of UPLC to radioactivity detection, will lead to better metabolite separation and quantification even for samples containing limited levels of radioactivity (e.g., plasma). A timely scheduling of the radiolabeled human AME trial should be embedded in the strategy. The MIST guidance represents a good framework within which the majority of drug metabolite issues, specifically in the context of their quantification in animals and humans, can be considered. There are some scenarios where the application of this guidance is challenging, and the industry continues to debate the rational balance between how resources are deployed and how a full assessment of drug safety is evaluated in the context of exposure to drug-related material. As examples, compounds that are extensively metabolized will circulate at relatively low concentrations in plasma, and many of the downstream me-


tabolites could represent >10% of the parent drug levels. For this class of compound, a focus on metabolites >10% of total radioactivity is recommended. When considering highly potent, low dose, compounds, the question of metabolite abundance versus percentage is relevant since, on the basis of the current guidance, chemically inert metabolites representing >10% of parent drug but representing a very low overall body burden and circulating in plasma at only very low absolute concentrations could receive unnecessary evaluation. A potential solution is that metabolites present at 10% of parent but represents a lower absolute body burden when compared to the high dose drug. We have attempted to accommodate these anomalies in our decision criteria (Figure 2) where factors such as absolute and relative abundance, pharmacological activity, and potential for reactive metabolite formation have been addressed. Much of what has been presented deals with direct application of the FDA’s MIST guidance in preclinical and clinical development, but the question of whether adoption of this guidance will truly improve the safety profile of drugs brought to market remains moot (5). Namely, what evidence exists that the assurance of the systemic cover of drug related metabolites will translate into an improved safety profile? While there is some logic to this line of reasoning, at worst, the guidance has essentially required pharmaceutical companies to front load their resources to address the minimal risk of forming disproportionate metabolites in order to guard against those instances for when it may occur. At best, derivation of systemic levels of drug metabolites that are of equal or superior pharmacologic potency allows some assessment of toxicity potential associated with a surprapharmacologic response. Consistent with the focus of the initial FDA guidance on molecules that mediate toxicity via a reactive intermediate pathway, however (e.g., acetaminophen, halothane, and felbamate), the majority of black box drugs have issues associated with reactive metabolite formation (44) and subsequent elimination in excreta, not disproportionate metabolite formation as assessed in plasma (5). A logical focus to consider in order to enhance the safety profile of drugs in the future is to continue to invest resources to understand the metabolic disposition of drugs in both plasma and excreta but with a view to minimizing the potential to form reactive metabolites during the lead optimization stage, and this already appears to be a widely adopted approach across the pharmaceutical industry (44-47). Acknowledgment. We thank all members of the Johnson & Johnson Center of Expertise on Metabolite Profiling & Identification and the Center of Expertise on Bioanalysis for their input in the discussions on the corporate strategy, as well as Dr. Ron Gilissen, Ph.D., for providing the data on the pharmacological activity of the metabolites described in the retrospective analysis section.

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