FEATURE pubs.acs.org/ac
Analytical Strategies for Assessment of Human Metabolites in Preclinical Safety Testing Strategies for the detection, identification, and quantification of in vivo drug metabolites from nonradiolabeled studies are reviewed with an emphasis on the utilization of accurate-mass-based data mining tools. New approaches to the determination of coverage of human drug metabolites in preclinical species without using radiolabeled drugs or synthetic standards are also discussed. Shuguang Ma and Swapan K. Chowdhury Merck Research Laboratories
D
etection, identification, and quantification of human metabolites represent an important component of drug metabolism studies in the drug development process. The primary objective is to assure that metabolites of a development candidate do not pose a safety risk to humans. This is typically achieved by administering the drug to animals and evaluating its safety. The animals in safety testing at the highest no-adverse-effect dose level must be adequately exposed to human metabolites. It is also important to identify any pharmacologically active major human metabolite that may contribute to the pharmacodynamics of the drug or contribute to potential drug-drug interaction with coadministered drugs. In February 2008, the U.S. Food and Drug Administration (FDA) issued a formal guidance on criteria for metabolites in safety testing (MIST).1 Three statements in the FDA guidance are worth noting: 1) human metabolites that can raise a safety concern are those formed at >10% of parent drug's systemic exposure at steady state; 2) generally, metabolites identified only in human plasma or metabolites present at disproportionately higher levels in humans than in any of the animal test species should be considered for safety assessment; and 3) performing human in vivo metabolic evaluation as early as possible during the drug development process is strongly recommended. The International Conference on Harmonisation (ICH) subsequently r 2011 American Chemical Society
defined the level of a metabolite that would be required for nonclinical safety testing as 10% of the total drug-related exposure.2 Traditionally, human metabolites are identified and quantified in a single-dose radiolabeled (14C) study during Phase II clinical development or after proof-of-concept is achieved. The information generated from the 14C-absorption, metabolism and excretion (AME) study in humans not only appears to be inadequate to assess metabolism at steady state, but also is generated much later in the development programs and may delay large scale clinical trials if any human metabolite requires safety evaluation. A recent appealing alternative approach to adequately comply with the regulatory authority's guidelines is to assess metabolism in early drug development with Phase I rising multiple dose (RMD) studies in humans. Plasma samples from these studies can be used for metabolite identification and quantification at steady state, thus allowing for an early estimation of whether any human metabolite that exceeds 10% of the total drug-related material has been adequately tested in safety evaluation experiments. Later in a radiolabeled study in humans an assessment can be made if all major metabolites have been appropriately detected and characterized adequately in earlier studies. The high sensitivity and selectivity of LC/MS have made it the analytical tool of choice for the detection, identification, and quantification of drug metabolites. In this Feature, we provide an overview of the most recent advances in detection and characterization of drug metabolites with emphasis on the application of high resolution MS (HRMS) techniques, discuss the implications of the recently issued regulatory guidelines on MIST in the drug development process, and highlight current practices in assessing human metabolite coverage in preclinical safety species.
’ METABOLITE DETECTION Molecular ions of drug-related components are traditionally detected by analyzing the test and control samples in parallel using LC/MS followed by comparison of the extracted ion chromatograms based on the predicted gains and losses in potential metabolites’ molecular masses relative to that of the parent drug. MS/MS approaches to reveal the molecular ions of Published: April 20, 2011 5028
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Table 1. Common Phase I and Phase II biotransformation pathways and their corresponding elemental composition, accurate m/z, and mass defect changes. Adapted with modification from refs. 11 and 12. Elemental Metabolic reaction R-79Br f R-OH R-X-C2H5 f R-X-H (X = N, O, S)
Description
Composition change Br þ OH C2H4
oxidative debromination deethylation
Mass defect m/z change change (mDa) 61.9156 28.0313
84.4 31.3
R-35Cl f R-OH
oxidative dechlorination
Cl þ OH
17.9661
33.9
Hydroxylation þ dehydration
hydroxylation þ dehydration
H2
2.0157
15.7 15.7
R-CH2-OH f R-CHO; R-CHOH-R’ f R-CO-R’
primary/secondary alcohols to aldehyde/ketone
H2
2.0157
R-F f R-OH
oxidative defluorination
F þ OH
1.9957
4.30
R-CHNH2-R’ f R-CO-R’
oxidative deamination to ketone
NH3 þ O
1.0316
31.6
R-CH2OCH3 f R-COOH
demethylation and oxidation to acid
CH4 þ O
0.0364
36.4
R-CH2-NH2 f R-CH2-OH R-CO-R’ f R-CHOH-R’
oxidative deamination to alcohol reduction (ketone to alcohol)
NH þ O þ H2
0.9840 2.0157
16.0 15.7
R-CH2-R’ f R-C(O)-R’
oxidation (methylene to ketone)
H2 þ O
13.9793
20.7
R-XH f R-X-CH3 (X = N, O, S)
N, O, S methylation
þ CH2
14.0157
15.7
R-H f R-OH
hydroxylation
þO
15.9949
5.10
R-NH-R’ f R-NOH-R’; RR’R”N f RR’R”NO
second/third amine to hydroxylamine/N-oxide
þO
15.9949
5.10
R-S-R’ f R-SO-R’; R-SO-R’ f R-(O)S(O)-R’
thioether to sulfoxide, sulfoxide to sulfone
þO
15.9949
5.10
2 hydroxylation
2 hydroxylation
þ O2
31.9898
10.2
RR’S f RR’SO2 R-CH=CH-R’ f R-CH(OH)-CH(OH)-R’
thioether to sulfone dihydroxylation (alkenes to dihydrodiols)
þ O2 þ H2O2
31.9898 34.0055
10.2 5.50 10.6
R-NH2 f R-NHCOCH3
acetylation
þ C2H2O
42.0106
3 hydroxylation
3 hydroxylation
þ O3
47.9847
15.3
R-COOH f R-CONHCH2COOH
glycine conjugation
þ C2H3NO
57.0215
21.5
ROH f R-OSO3H
sulfation
þ SO3
79.9568
43.2
R-H f R-OSO3H
hydroxylation and sulfation
þ SO4
95.9517
48.3
R-COOH f R-CONHCH(CH2SH)-COOH
cysteine conjugation
þ C3H5NOS
103.0092
9.20
R-COOH f R-CONH-CH2CH2SO3H R-CH2-R’ f RR’-CH-SCH2CH(NH2)-COOH
taurine conjugation S-cysteine conjugation
þ C2H5NO2S þ C3H5NO2S
107.0041 119.0041
4.10 4.10
R-COOH f R-CO-SCH2CH(NHCOCH3)COOH
S-N-acetylcysteine conjugation
þ C5H7NO2S
145.0197
19.7
RR’-CH2 f RR’-CH-SCH2CH(NHCOCH3)-COOH
N-acetylcysteine conjugation
þ C5H7NO3S
161.0147
14.7
R-OH f R-O-C6H11O5
glucosidation
þ C6H10O5
162.0528
52.8
R-OH f R-O-C6H9O6
glucuronide conjugation
þ C6H8O6
176.0321
32.1
2 hydroxylation þ 2 SO3
2 hydroxylation þ 2 sulfation
þ S2O8
191.9035
96.5
R-H f R-O-C6H9O6
hydroxylation þ glucuronide
þ C6H8O7
192.0270
27.0
R-COOH f R-CO-SG (GSH = Glutathione) þ GSH 2H
S-acyl-glutathione conjugates GSH conjugation
þ C10H15N3O5S þ C10H15N3O6S
289.0732 305.0682
73.2 68.2
þ GSH
GSH conjugation
þ C10H17N3O6S
307.0838
83.8
þ GSH þ O 2H
oxidation þ GSH conjugation
þ C10H15N3O7S
321.0631
63.1
Epoxidation þ GSH
epoxidation þ GSH conjugation
þ C10H17N3O7S
323.0787
78.8
þ 2 C6H8O6
2 glucuronide conjugation
þ C12H16O12
352.0642
64.2
unexpected metabolites have focused on the use of a precursor ion scan (PIS) and a constant neutral loss scan (CNLS). The PIS detects all molecular entities that form a common product ion without previous knowledge of the molecular ions of metabolites, whereas CNLS is often used when a drug and its metabolites have a common structural feature that is lost in MS/MS experiments as neutral species, such as glucuronides, sulfates, and glutathione conjugates. However, should a metabolite undergo a modification that alters the mass of the fragment ion or neutral fragment used in the PIS or CNLS experiments, the metabolite will not be detected.3 In addition, metabolites within in vivo samples are typically present in very low levels and with a large excess of endogenous background. Detection of human metabolites from non-radiolabeled RMD studies with traditional LC/MS analysis,
PIS, or CNLS methods is a challenging task because of the low detection sensitivity of PIS and CNLS and the use of low resolution mass spectrometers, which are not efficient at differentiating drug-related ions from background ions based on their nominal masses. Recent advances in the performance of high-resolution mass spectrometers such as TOF, FT ion cyclotron resonance, and FT Orbitrap instruments have transformed metabolite detection and identification. With high mass accuracy (at sub-millidalton) determined at ultrahigh resolution (>50,000), drug-related ions can be easily discriminated from isobaric matrix ions because of differences in elemental composition that lead to differences in the exact masses. As a result, new computational data-mining tools, such as mass defect filter (MDF),4,5 retention-time-shift-tolerant 5029
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Figure 1. TIC and radiochromatogram of rat plasma following a single oral dose of 15 mg kg1 14C-loratadine. A) Unprocessed TIC; B) TIC after background subtraction; C) TIC after background subtraction and noise reduction; D) TIC after isotope filtering; and E) radiochromatogram (CPM = counts per minute). Reprinted from ref. 8 with permission of John Wiley and Sons Limited and ref. 9.
background subtraction,68 and isotope pattern filtering,9 have been developed to perform objective searching/filtering of accurate-mass-based LC/MS data to facilitate metabolite detection. These have recently become popular for profiling metabolites in non-radiolabeled clinical and animal toxicology studies. MDF. Mass defect is defined as the difference between the exact m/z value and the nominal m/z value. Mass defect profiles of endogenous components in biological matrices were compared with those of 115 commonly prescribed drugs, and a clear separation in mass defects was observed.10 The mass defects of common Phase I and Phase II metabolites typically fall within 50 mDa of the parent drug (Table 1);11,12 therefore, LC/HRMS data can filter out matrix-related interference ions whose mass defects lie outside of this window. Zhang et al. demonstrated that the metabolite profile of a dog bile sample was dramatically simplified with the majority of interference ions removed by mass defect filtration and therefore facilitated the detection of drug metabolites.4 Metabolite reactions such as N-, O-dealkylation and hydrolyses that occur at an internal site result in cleavage of the drug molecule into small fragments with appreciable changes
in mass defects from the parent. These metabolites may be filtered out by the normal MDF technique. To overcome this potential shortcoming, an improved MDF approach that employed both drug and core structure filter templates (e.g., heteroatom dealkylated metabolites and hydrolysis metabolites) was developed for the detection of various classes of metabolites with mass defects similar to or significantly different from those of the parent drugs.13,14 The combination of MDF with MSE (where E represents elevated collision energies)15 and semi-automated software (Mass-Metasite)16 with a Q-TOF mass spectrometer was effective for identification of in vitro and in vivo metabolites. Post-acquisition processing with MDF eliminated endogenous interference ions, which simplified data interpretation and greatly enhanced the detection of metabolites. Mass-Metasite uses information from MS/MS data and the predicted site of metabolism to assign metabolite structures. The major drawback of MDF is that a wider mass defect window of 70100 mDa or multiple MDF templates are necessary when searching for uncommon metabolites; as a consequence, MDF-processed ion chromatograms often include multiple false-positive peaks. 5030
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Figure 2. A) Unprocessed and B) isotope filtering processed mass spectra of the peak at retention time (Rt) 12.2 min in Figure 1D. Reprinted from ref. 9.
Background Subtraction. A retention-time-shift-tolerant background subtraction (BgS) software was recently developed for extraction of drug metabolites in biological matrices.7,8 For each ion detected in the analyte file, the program searches the control file for that target ion. If such a target ion is present in the control file within the mass tolerance and retention-time-shift window, the maximal intensity of the ion is multiplied by a predefined scaling factor and subtracted from the intensity of the ion in the analyte file. This program was very effective in detecting metabolites in complex matrices.6 The key factor contributing to the effectiveness of this program is the use of accurate mass data coupled with retention-time-shift-tolerance to determine if an ion detected in the analyte file is present in the control file. Zhu and coworkers further improved the background subtraction program by adding a noise reduction algorithm (NoRA) to further reduce the residual ion noise after background subtraction by removing ion signals that are not consistent across many adjacent scans.8 NoRA is based on the concept that the noise observed in LC/MS data is usually random, whereas real analyte signals tend to be consistently present across multiple scans. For each ion at a given m/z in a certain scan, the program searches for that ion in adjacent scans (e.g., 7 scans). If the number of scans that contain such a signal reaches a certain threshold (e.g., 5 out of 7), the ion is considered real; otherwise, it is flagged as noise. BgS-NoRA effectively detected and identified the metabolites of loratadine in rat plasma (Figure 1). Intense matrix ions dominate the unprocessed total ion chromatogram (TIC) of rat plasma, whereas the drug-related ions are almost invisible (Figure 1A). Background subtraction alone significantly attenuated endogenous matrix signals; however, residual matrix peaks still remain as major signals in the TIC (Figure 1B). BgS-NoRA eliminated the majority of the matrix ions, leaving for the most part only drugrelated peaks (Figure 1C), which qualitatively correlate very well with the radiochromatogram (Figure 1E). BgS-NoRA has proven to be highly effective in reducing matrix ion signals in ion chromatograms from biological samples. The main advantage of background subtraction approach over the MDF technique is that there is no need for prior knowledge
of the molecular weights, structures, or fragmentation pathways of metabolites, thus facilitating the identification of both common and uncommon metabolites. BgS-NoRA is especially useful in metabolite profiling studies when radiolabeled drug is not available. Isotope Pattern Filtering (IPF). An accurate-mass-based IPF algorithm has been developed to facilitate the detection of drugderived materials that possess highly diagnostic isotopic patterns (e.g., chlorine- and bromine-containing compounds).9 The algorithm is strictly based on accurate masses for which the m/z differences of designated isotopic ion pairs (e.g., Mþ2:M or Mþ1:M, where M is the molecular ion, typically MHþ or [M-H] ions, and Mþ1 and Mþ2 are the isotopic counterparts) must fall into the predefined accurate mass tolerance window (e.g., 5 ppm) and at the same time satisfy the predefined relative abundance criteria. The inclusion of the Mþ1:M ion pair, together with spectral averaging prior to isotope filtering to reduce scan-toscan variability of ion intensities, further enhanced the specificity of this algorithm. The effectiveness of the IPF algorithm was demonstrated by metabolite identification of loratadine (containing one chlorine) in rat plasma.9 As shown in Figure 1D, most matrix ions were removed and the drug-related ions were revealed as major peaks after applying the IPF algorithm. The processed TIC is in excellent qualitative correlation with the radiochromatogram. The simplicity and cleanliness of the IPF-processed data allows easy detection of the molecular ion of drug-derived material. As an example, Figure 2 shows the mass spectra of the peak eluting at 12.2 min before and after IPF processing. The molecular ion of hydroxyldesloratadine metabolite at m/z 327.1254 was embedded among many other predominant endogenous ions and was barely detected in the unprocessed mass spectrum. In contrast, this ion was the only peak with no interference after applying IPF. The IPF algorithm is applicable to not only compounds containing recognizable natural isotopes (Cl, Br, etc.), but also an isotope pattern generated by combination of natural and stable isotope (2H, 13C, 15N, 18O, etc.) labeled drugs. Customdesigned isotopic clusters resulting from the mixture of natural 5031
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and synthetically enriched isotopes can greatly facilitate the detection and identification of metabolites. Unlike the background subtraction program, the IPF algorithm does not require background/control data, and there is no prior assumption of mass defect range as with the MDF approach. IPF complements the existing tool set for metabolite detection in nonradiolabeled samples in which a dosed drug contains natural or synthetically incorporated isotope pattern.
’ METABOLITE IDENTIFICATION LC/MS/MS and LC/MSn are the most common techniques used in metabolite identification. Because the structure and molecular weight of the parent drug is known, the change in m/z can provide clues as to the nature of structural modification in a metabolite. The site(s) of biotransformation can often be localized to a certain region of the molecule based on comparison of the shift in m/z value of a characteristic metabolite fragment ion (MS/MS or MSn) to the parent drug and its corresponding fragmentation pattern. When coupled with HRMS, the elemental composition of the metabolite can be easily discerned. Although MS approaches such as LC/MS and LC/MS/MS are by far the most widely used technique for metabolite identification, they do not always provide sufficient information for unequivocal structural elucidation of metabolites. NMR spectroscopy is an excellent complementary tool for deriving detailed structural information once preliminary data on the nature of the metabolic change is available from LC/MS. Static NMR analysis is often performed on a metabolite at high purity and thus requires time-consuming isolation and purification steps to generate a suitable sample from in vivo or in vitro experiments. The direct coupling of LC with NMR eliminates the need for extensive sample purification steps and therefore increases its capability of solving structural problems in complex mixtures.17 Recently, Penner et al. reported an excellent example of identification of novel metabolites of SCH 486757 (m/z 440, C24H24N3OCl2), a Nociceptin/Orphanin FQ peptide receptor agonist, from a non-radiolabeled Phase I clinical study.18 The LC/MS analysis of human plasma detected two major circulating metabolites, M27 (m/z 392) and M34 (m/z 406), each representing 38% of the LC/MS response of the parent drug. Similar to the parent compound, MS/MS spectra of M27 and M34 contained a characteristic ion at m/z 235 (Figure 3), indicating that the bis(2-chlorophenyl)methyl group is unchanged. Therefore, the modification (loss of 48 Da and 34 Da, respectively) occurred at the tropane-pyrimidine moiety for both metabolites. Accurate mass analysis showed that the most probable elemental compositions of the two metabolite ions were C22H16N3Cl2 (m/z 392.0715) and C23H18N3Cl2 (m/z 406.0872), so the elemental composition changes relative to the parent drug were C2H8O and CH6O, respectively. M34 appeared to contain an additional CH2 group. Because LC/MS/MS data for M27 and M34 provided only limited structural information (Figure 3), a H/D exchange LC/MS experiment was conducted by replacing hydrogen-containing salts and solvents in the mobile phase with fully deuteriumsubstituted ones.19 The mass spectra of the parent compound (SCH 486757) showed an increase of m/z by 2 Da, which is consistent with the exchange of the proton of the -OH group attached to the tropane moiety and the ionizing proton. Surprisingly, the m/z of the two metabolites remained unchanged, which implied that M27 and M34 did not have any exchangeable protons and did not require an ionizing proton, suggesting that
Figure 3. Collision-induced dissociation product ion spectra of parent drug (SCH 486757) and its two major human metabolites (M27 and M34).
both metabolites most likely possessed quaternary ammonium ions and the hydroxyl group on the tropane moiety was not present. The H/D exchange experiments thus provided important pieces of information in understanding the structure of the two novel metabolites. Based on the data from LC/MS/MS, accurate mass analysis, and most importantly, H/D exchange experiments, the structures of M27 and M34 were proposed as shown in Figure 3. These two compounds were synthesized, and NMR confirmed their structures. The m/z, retention time, and fragment ion spectra of the two metabolite standards matched those obtained from human plasma samples. M27 and M34 were formed through a loss of the C-C bridge from the tropane moiety followed by aromatization to pyridine with a tertiary ammonium ion. Metabolite identification is an integrated approach consisting of LC/HRMS, LC/MS/MS, H/D exchange, and NMR characterization. The example discussed above underscores the need for multiple approaches to determine the structure of metabolites. In some cases, chemical derivatization of metabolites are performed to 1) confirm the presence of certain functional groups, 2) enhance detectability by decreasing polarity, and 3) increase sensitivity by introducing ionizing groups.20 The flow diagram shown in Figure 4 highlights the typical metabolite identification approach adopted in today’s drug metabolism laboratories. When the drug is administered with a radiotracer (e.g., 14C or 3H), metabolites are detected and quantified using in-line LC/MS flow scintillation analysis or fraction collection followed by off-line scintillation counting. However, structural 5032
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Figure 4. Typical analytical strategies for drug metabolite detection and identification using HRMS from nonradiolabeled studies.
identification of metabolites is performed using the same approach discussed above.
’ SEMI-QUANTITATION OF DRUG METABOLITES FROM FIRST-IN-HUMAN STUDIES LC/MS technological advances in the last decade have greatly improved the analytical capabilities to detect and identify metabolites, but obtaining quantitative metabolite profiles from nonradiolabeled first-in-human studies remains considerably challenging. Metabolism results in structural changes that may appreciably alter the ionization efficiency of individual metabolite; therefore, it is necessary to first calibrate the mass spectrometric response of metabolites relative to that of the parent drug to semi-quantify the metabolite level in biological matrices. Various methods have been developed to determine a metabolite’s response factor.21 If a metabolite reference standard is available, the metabolite’s response factor can be directly determined by spiking the metabolite standard and the parent drug into plasma and measuring the peak area ratio by LC/MS. If the metabolite of interest is detected in a radiolabeled study in animals or in vitro microsomal or hepatocyte incubations, the response factor can be determined from the ratio of radioactivity response (metabolite/parent) to the corresponding MS response. The response factor can then be applied to determine the amounts of metabolites in human plasma samples.22 This is a cost effective means to identify if any metabolite requires MIST evaluation early in the clinical development programs. Nanospray ionization (NSI) has shown potential for quantification of drug metabolites without the need of radioisotopes or authentic reference standards. Recent studies suggest that the degree of variability in MS responses of drugs and metabolites is much smaller in NSI systems compared to that of conventional LC/MS systems.23-25 But because of the complexity of operation and lack of reproducibility, NSI systems have not been used extensively for metabolite quantitation in biological matrices. Another approach to obtain human metabolism information early in the development program is to administer a micro radiocarbon (14C) dose in conjunction with a therapeutic non-
radioactive dose. Accelerator MS (AMS) uses a particle accelerator that measures long-lived isotopes, such as 3H, 14C, 35Cl, or 129 I, and can generate a radiocarbon profile of plasma extracts.26-28 AMS is extremely sensitive and can measure 14C at attomole levels, resulting in significantly lower radioactive doses required for humans. In principle, it is possible to administer multiple micro radiocarbon doses to humans to achieve steady state, although such studies have not been reported to date. Perhaps the greatest limitations in using AMS for high throughput analysis are that biological samples must be graphitized prior to analysis, which is a tedious and a slow process, and that no metabolite structural information is obtained. Encouragingly, exploratory work on coupling LC to AMS to allow biological samples to be introduced directly to AMS have been reported.29,30 Alternatively, a new ultrasensitive laser-based analytical technique, intracavity optogalvanic spectroscopy, which has extremely high sensitivity for detection of 14C-labeled CO2, has been developed. This technique, which is still very early in the development process for “real world” sample analysis, quantifies attomoles of 14C in sub-microgram samples and has potential to replace AMS for micro-radiotracer studies in drug development.31 NMR is a traditional analytical tool for definitive structural characterization. With recent improvements in sensitivity— especially the availability of cryoprobes—NMR is increasingly able to estimate the concentrations of drug metabolites present in biological matrices. A 1H NMR calibration standard curve generated from the parent drug or structurally similar compounds can provide reliable quantitative measurement of isolated metabolites.32 Vishwanathan and coworkers quantitatively determined reliable area-under-the-curve (AUC) exposure values of metabolites with NMR, and these values are in excellent agreement with those determined from traditional LC/MS/ MS.33 For drugs containing fluorine, 19F NMR has been used for selective detection and quantitation of metabolites,34 and the results were in good agreement with those from scintillation counting using radiolabeled drugs.35 The key advantage of this approach is the ability to selectively detect and quantify metabolites without the need for internal standards or administration of radiolabeled drug. 5033
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Figure 5. A schematic diagram for work procedures and proposed decision tree for assessing human metabolite coverage in preclinical safety testing.
’ QUANTITATIVE ASSESSMENT OF THE COVERAGE OF HUMAN METABOLITES IN SAFETY TESTING The “Hamilton” pooling approach, in which appropriate aliquots of the plasma samples obtained from different pharmacokinetic time points are pooled, yields one sample that has a concentration of a drug and metabolites proportional to the AUC and therefore can determine the systemic exposure of metabolites relative to that of the parent drug.36,37 If any one of the approaches described above can determine the MS response factor of the metabolite, the coverage of metabolites in preclinical safety species can be assessed from the animal-to-human exposure multiples (EM) using the equation: EM ¼
p m p ðA m a =A a ÞxRFa xAUCa p p m m ðA h =A h ÞxRFh xAUCh
ð1Þ
p where Am a is the peak area of metabolite in animal, Aa is the peak m area of parent in animal, RFa is the MS response factor of metabolite in animal, AUCpa is the parent AUC in animal, Am h is the peak area of metabolite in human, Aph is the peak area of
parent in human, RFm h is the MS response factor of metabolite in human, and AUCph is the parent AUC in human. Response factor of a metabolite is the ratio of the LC/MS response of equimolar amounts of the metabolite and the parent drug. Many drug candidates generate multiple metabolites in humans that should be evaluated if any metabolite exceeds the MIST threshold defined by regulatory guidelines. Generating a LC/MS response factor for every metabolite of interest is very labor intensive and in some cases, almost impossible. Recently, Gao et al. developed a method that eliminated the need for response correction in the assessment of exposure multiples of metabolites in preclinical species.38 This was accomplished by mixing an equal volume of dosed human plasma with blank animal plasma and vice versa, followed by analysis of the plasma extracts by LC/MS/MS. The researchers demonstrated that a quantitative comparison of metabolite levels in animals vs. humans is possible from the peak area ratios of the metabolites versus an internal standard from multiple reaction monitoring experiments.38 Because both plasma samples are essentially identical (50:50 human:animal), the effect of matrix difference 5034
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across species on the LC/MS response of a metabolite was eliminated, and the ratio of response factors becomes 1. Thus, the exposure multiple calculations can be simplified as: EM ¼
p p ðA m a =A a ÞxAUCa p p ðA m h =A h ÞxAUCh
ð2Þ
“Hamilton” pooling36 and peak area normalization to an internal standard produces the equation: AUCpa ðA pa =A IS a Þ p ¼ p ðA h =A IS AUCh hÞ
ð3Þ
IS where AIS a and Ah are the peak area of the internal standard in animal and human, respectively. Therefore, Equation 2 can be further simplified to:
EM ¼
IS ðA m a =A a Þ IS ðA m h =A h Þ
ð4Þ
Gao et al. have shown that if the peak area ratio of a metabolite in an animal species versus in humans is >2 (Equation 4), then it could be confidently established (p < 0.01) that the animal has greater exposure to the metabolite than humans.38 Ma and coworkers developed a similar approach in concept but using full scan HRMS. They demonstrated its utility in two development programs in which the major metabolites were also measured by validated LC/MS/MS. The exposure multiples of the parent and its metabolites were all within (15% of those obtained with validated LC/MS/MS measurements.39 The results thus clearly demonstrate that this method can reliably and quantitatively assess metabolite coverage in safety species (animals used in safety evaluation) in the absence of synthetic standards, radiolabeled compounds, or validated bioanalytical methods. The main advantage of using accurate mass full scan LC/MS is that both metabolite identification and reliable quantitative assessment of metabolite coverage in safety species can be obtained from a single LC/MS analysis. In addition, the full scan LC/MS analysis captures data on all metabolites; therefore, this approach is applicable to assess the coverage of any metabolite of interest. Furthermore, the method eliminates the need for a determination if a metabolite is >10% of total drug-related material because assessment of the coverage of all detectable metabolites can be obtained from a single full scan LC/HRMS analysis of plasma samples from humans and preclinical species. The regulatory guidance does not require the knowledge of the actual concentrations of circulating metabolites as long as metabolites are adequately exposed in any one of the preclinical safety species. Based on this notion, a schematic diagram illustrating a strategy in assessing human metabolite coverage in preclinical safety species has been developed (Figure 5).
’ CONCLUSIONS AND PERSPECTIVES The determination of the coverage of human metabolites in preclinical species used in safety testing early in the drug development process is becoming an important activity in today’s drug metabolism laboratories as a result of the recent FDA and ICH guidelines. With the advancement of LC/MS technology, in particular HRMS systems that provide reliable, high-throughput, accurate mass analysis and the development of “intelligent” data processing tools, the detection and identification of human metabolites from non-radiolabeled drug administrations, previously
thought to be impossible, are becoming a routine practice. MDF technology, background subtraction programs, and isotope filtering can detect and identify metabolites with ease from first-in-human studies; however, it remains considerably challenging to obtain quantitative metabolite profiles. Detection technologies that provide quantitative assessment of metabolite levels without a synthetic standard or radiolabeled metabolites are highly desirable. The recent publications by Gao et al.38 and Ma et al.39 that assess the coverage of metabolites from LC/MS/MS or full scan LC/HRMS analysis of plasma samples from humans and preclinical species fulfill that obligation in a high-throughput and cost effective manner. This by no means obviates the need for radiolabeled ADME studies in humans, which should be performed at an appropriate later time. The ADME studies will provide excretion and mass balance information and will affirm whether earlier studies detected all metabolites and that coverage was appropriate. The use of a microtracer (14C) in early human studies and detection, identification, and quantification of metabolites using a combination of LC/MS and AMS can be an alternative approach to determine MIST coverage early in the development programs but would be a very expensive early stage investigation.
’ BIOGRAPHY Shuguang Ma is a senior principal scientist at Merck Research Laboratories. His research interests include the development of novel LC/MS-based techniques for metabolite characterization and assessment of human metabolites in safety testing. Swapan K. Chowdhury is a director in the Drug Metabolism Department at Merck Research Laboratories. His current interest involves utilization of drug metabolism, transport, and pharmacokinetics data from in vitro and in vivo experiments to optimize lead candidates and select compounds for clinical development. Contact Chowdhury at Drug Metabolism and Pharmacokinetics, Merck Research Laboratories, 2015 Galloping Hill Road, Kenilworth, NJ 07033, USA; E-mail:
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