Small molecule quantification by liquid chromatography-mass spectrometry for metabolites of drugs and drug candidates

Article abstract of DOI:10.1124/dmd.111.040865

Identification and quantification of the metabolites of drugs and drug candidates are routinely performed using liquid chromatography-mass spectrometry (LC-MS). The best practice is to generate a standard curve with the metabolite versus the internal standard. However, to avoid the difficulties in metabolite synthesis, standard curves are sometimes prepared using the substrate, assuming that the signal for substrate and the metabolite will be equivalent. We have tested the errors associated with this assumption using a series of very similar compounds that undergo common metabolic reactions using both conventional flow electrospray ionization LC-MS and low-flow captive spray ionization (CSI) LC-MS. The differences in standard curves for four different types of transformations (O-demethylation, N-demethylation, aromatic hydroxylation, and benzylic hydroxylation) are presented. The results demonstrate that the signals of the substrates compared with those of the metabolites are statistically different in 18 of the 20 substrate-metabolite combinations for both methods. The ratio of the slopes of the standard curves varied up to 4-fold but was slightly less for the CSI method. Copyright

Full text of DOI:10.1124/dmd.111.040865

Supplemental material to this article can be found at:
http://dmd.aspetjournals.org/content/suppl/2011/09/21/dmd.111.040865.DC1.html
0090-9556/11/3912-2355–2360$25.00
DRUG METABOLISM AND DISPOSITION
Copyright © 2011 by The American Society for Pharmacology and Experimental Therapeutics
DMD 39:2355–2360, 2011
Vol. 39, No. 12
40865/3733858
Printed in U.S.A.
Small Molecule Quantification by Liquid Chromatography-Mass
□
S
Spectrometry for Metabolites of Drugs and Drug Candidates
Upendra P. Dahal, Jeffrey P. Jones, John A. Davis, and Dan A. Rock
Department of Chemistry, Washington State University, Pullman, Washington (U.P.D., J.P.J.); and Department of
Pharmacokinetics and Drug Metabolism, Amgen Inc., Seattle, Washington (J.A.D., D.A.R.)
Received May 25, 2011; accepted September 21, 2011
ABSTRACT:
Identification and quantification of the metabolites of drugs and pray ionization LC-MS and low-flow captive spray ionization (CSI)
drug candidates are routinely performed using liquid chromatog- LC-MS. The differences in standard curves for four different types
raphy-mass spectrometry (LC-MS). The best practice is to gener- of transformations (O-demethylation, N-demethylation, aromatic
ate a standard curve with the metabolite versus the internal stan- hydroxylation, and benzylic hydroxylation) are presented. The re-
dard. However, to avoid the difficulties in metabolite synthesis, sults demonstrate that the signals of the substrates compared with
standard curves are sometimes prepared using the substrate, as- those of the metabolites are statistically different in 18 of the 20
suming that the signal for substrate and the metabolite will be substrate-metabolite combinations for both methods. The ratio of
equivalent. We have tested the errors associated with this assump- the slopes of the standard curves varied up to 4-fold but was
tion using a series of very similar compounds that undergo com-
mon metabolic reactions using both conventional flow electros-
slightly less for the CSI method.
Introduction
results depend on the initial substrate concentration used in the anal-
ysis. For example, Peng et al. (2010) applied the substrate depletion
method at two different concentrations to determine the stability of a
series of compounds with different affinities. It was found that at the
higher concentration (25 ␮M), high-affinity/high-clearance com-
pounds gave very low substrate depletion values, whereas at the lower
concentration (1 ␮M), most of the high-affinity compounds were
extensively metabolized.
A second method correlates the amount of substrate disappearance
with metabolite appearance. This correlation works for single-metab-
olite quantification but is more problematic when multiple metabolites
are formed from a substrate. When multiple metabolites are observed,
this method can be used if one assumes that two metabolites give
similar signals in the mass spectrometer.
Pharmacokinetic studies are now routinely performed using liquid
chromatography-mass spectrometry (LC-MS). When pharmacoki-
netic studies are required for metabolites, one of the major problems
with the use of LC-MS is that the signal varies, depending on the
compound. For LC-MS analysis, the best practice is to construct a
standard curve using a synthesized metabolite of the substrate and an
internal standard. The problem associated with this is the difficulty of
metabolite synthesis. Synthesis of the metabolites requires good syn-
thetic skills and, most importantly, time. Furthermore, sometimes the
metabolites are too complex to be readily amendable to synthesis.
Given these factors, it is not surprising that many studies use alter-
native methods to determine the amount of a given metabolite
produced.
By far, the simplest method is to assume that the substrate gives a
signal that is equivalent to that of the product (Koudriakova et al.,
When a synthetic standard of a metabolite is not available, alter-
native methods can be used to approximately quantify small mole-
cules by mass spectrometry. Most often, a substrate disappearance 1998; Shebley et al., 2009; Liu et al., 2010; Kamdem et al., 2011) and
method is used to determine the stability of a potential drug. With use
of this method, the percentage change in substrate over time can be
monitored at one or two different concentrations. Low turnover rates
or high affinity can lead to large errors in the substrate depletion
method because the change in signal is very small. Furthermore, the
to monitor signal from the product. This assumption makes metabolite
quantification using a mass spectrometer convenient, but it is not clear
when, or if, it is correct. This study tests the errors associated with this
assumption using a series of very similar compounds for which we
have synthesized metabolites using both low-flow captive (CSI) and
conventional flow (ESI) interfaces. The captive spray ionization tech-
nique takes advantage of a commercially available “plug and play”
ionization source requiring minimal setup and maintenance. This
technique operates at optimal flow rates of 250 nl/min to 50 ␮l/min,
between those of nanospray ionization (Ͻ500 nl/min) and conven-
tional flow ESI (50–1500 ␮l/min). We include the synthesis under the
Materials and Methods to illustrate the effort required to make a
This work was supported by the National Institutes of Health National Institute
of General Medical Sciences [Grant GM84546].
Article, publication date, and citation information can be found at
http://dmd.aspetjournals.org.
doi:10.1124/dmd.111.040865.
□S The online version of this article (available at http://dmd.aspetjournals.org)
contains supplemental material.
ABBREVIATIONS: LC, liquid chromatography; MS, mass spectrometry; CSI, captive spray ionization; ESI, electrospray ionization; TLC, thin-layer
chromatography.
2355

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DAHAL ET AL.
was evaporated in a rotary evaporator to obtain an oil. Flash chromatography
with dichloromethane as an eluent was used to obtain the purified product in
49% yield. 1H NMR (CDCl3): ␦ 2.67 (s, 3H), 7.83 (dd, J ϭ 5.0, 1.5, 1H), 8.91
(d, J ϭ 5.0 Hz, 1H), 9.31 (d, J ϭ 1.5 Hz, 1H); ESI-MS: [M ϩ H]ϩ ϭ 123.2.
O-Demethylation. The O-demethylation of the substrate was performed as
described previously (McOmie et al., 1968; Gao and Portoghese, 1996) with
the modifications as needed (Scheme 2a). To the 10-ml solution of substrate
(0.36 mmol) in dichloromethane, boron tribromide (1 M in CH2Cl2, 1.1 mmol)
was added over 2 min with stirring. The reaction mixture was stirred for 45 min
at room temperature. The reaction was quenched by saturated sodium carbon-
ate to adjust to pH 8 and was extracted with dichloromethane (three 30-ml
extractions). The combined organic phase was washed with water. The solvent
was removed under reduced pressure, and the residue was partially purified by
flash chromatography using the hexanes-dichloromethane-methanol solvent
system. The product was further purified by crystallization in hexanes-dichlo-
romethane. The NMR and mass spectral data for characterization of metabo-
lites are presented in the supplemental data.
synthetic metabolic standard, which must be balanced against the
errors in assuming that substrate and metabolite give the same MS
signal.
Materials and Methods
Solvents and chemicals were purchased from Sigma-Aldrich (St. Louis,
MO), Thermo Fisher Scientific (Waltham, MA), EMD Chemicals (Gibbstown,
NJ), and Mallinckrodt Baker, Inc. (Phillipsburg, NJ). Solvents and chemicals
are used without purification. The 1H NMR spectra were obtained with a
300-MHz spectrometer equipped with a quad-detection probe (1H, 13C, 31P,
and 19F). The 1H-decoupled 13C NMR spectra were obtained at 75 MHz.
Conventional flow mass spectrometry was performed on a ThermoQuest
Surveyor coupled to a Thermo Finnegan LCQ Advantage ESI-MS system.
Low-flow mass spectrometry was performed on a Thermo Scientific Accela
(pump and autosampler) coupled to a Thermo Scientific LTQ XL system
equipped with a captive spray ionization source (Microchrom BioResources,
Inc., Auburn, CA).
N-Demethylation. The N-demethylation method developed by Acosta et al.
(1994) was used to obtain mono N-demethylation directly from the N-
dimethylated substrates (Scheme 2b). Iodine (0.27 mmol) was added to the
ice-chilled mixture of substrate (0.14 mmol), CaO (1.1 mmol) in tetrahydro-
furan (1.6 ml), and methanol (1.2 ml). The mixture was stirred at 0°C and
monitored by TLC. After 4 h, all of the reactant was consumed. The reaction
mixture was filtered, and filtrate was sequentially washed with 15% sodium
thiosulfate solution, water, and brine solution. The organic phase was dried
over magnesium sulfate, and solvent was evaporated in a rotary evaporator.
The product was separated by flash chromatography using a hexanes-dichlo-
romethane-methanol solvent system. The product was further purified by
crystallization in hexanes-dichloromethane. The NMR and mass spectral data
for characterization of metabolites are presented in the supplemental data.
4-((tert-Butyldimethylsilyloxy)methyl)aniline. tert-Butyldimethylsilylchlo-
ride (12.2 mol) and imidazole (13.4 mmol) in dichloromethane (20 ml) were
added to a solution of 4-aminobenzyl alcohol (12.2 mmol) in 20 ml of
dichloromethane. After stirring for 1 h at room temperature, the reaction
mixture was diluted with brine solution and then extracted with ethyl acetate.
The organic phase was dried over sodium sulfate and filtered. The solvent was
evaporated in a rotary evaporator to get the oil with 97% yield. 1H NMR
(CDCl3): ␦ 0.01 (s, 6H), 0.85 (s, 9H), 3.58 (s, 2H), 4.55 (s, 2H), 6.56 (d, J ϭ
8.6 Hz, 2H), 7.03 (d, J ϭ 8.5 Hz, 2H).
Benzylic Hydroxylation. Protected 4-aminobenzyl alcohol [4-((tert-bu-
tyldimethylsilyloxy)methyl)aniline] was reacted with appropriate acid chlo-
rides (the acid chlorides were prepared using the same method described under
Synthesis of Substrates) to get the protected benzylic alcohols, which, on
deprotection, yielded the desired products (Scheme 2c). The acid chloride (2
mmol) was dissolved in a 5:1 mixture of dichloromethane-triethylamine (5 ml),
and 4-((tert-butyldimethylsilyloxy)methyl)aniline (2 mmol) in 1 ml of dichlo-
romethane was added. The reaction mixture was stirred at room temperature
for 2 h. After 2 h, the solvent was removed by rotary evaporation, and crude
product was purified by flash chromatography (30 g of Silica Gel 60, 0.063–
0.200 mm) to get the protected benzylic alcohol using a hexanes-dichloro-
methane-methanol solvent system. The protected alcohol was dissolved in 20
Synthesis of Substrates. The substrates were synthesized using the method
described by Peng et al. (2008), with modifications as described previously.
The general synthetic scheme for the synthesis is presented in Scheme 1.
Potassium hydroxide (842 mg, 15 mmol) and ethanol (5 ml) were placed in a
100-ml round-bottom flask equipped with a water condenser. The mixture was
stirred at 80°C to dissolve the potassium hydroxide. Isatin (5 mmol) was added
to the solution followed by dropwise addition of the appropriate ketone (5.5
mmol). The mixture was refluxed at 80°C for 2 days. Then the solvent was
evaporated using the rotary evaporator, and the residue was dissolved in 50 ml
of water. The aqueous phase was neutralized by dropwise addition of 1 N HCl
to pH ϳ6. The resulting solid was collected by vacuum filtration to get crude
quinoline carboxylic acid. The crude carboxylic acid was used directly to get
acyl chloride. The crude carboxylic acid was added to a 50-ml round-bottom
flask equipped with a water condenser, and 5 ml of undiluted thionyl chloride
was added, followed by reflux at 80°C. After 2 h, the excess thionyl chloride was
evaporated using a stream of argon to yield quinoline-4-carbonyl chloride. The
resulting solid was dissolved in a 5:1 mixture of dichloromethane-
triethylamine (10 ml) in a 50-ml round-bottom flask and appropriate amine
(5.5 mmol) was added. The reaction mixture was stirred at room temperature.
After 2 h, the solvent was evaporated by rotary evaporation and the product
was separated by flash chromatography using a hexanes-dichloromethane-
methanol solvent system. The product was further purified by crystallization in
hexanes-dichloromethane. The NMR and mass spectral data for characterization of
substrates are presented in the supplemental data. All compounds were pure on the
basis of NMR spectroscopy and TLC of the product after purification.
1-(Pyrimidin-4-yl)ethanone or 4-Acetylpyrimidine. 1-(Pyrimidin-4-yl)
ethanone was prepared as described by Easmon et al. (1992). Dropwise addition
of an aqueous solution of 70% tert-butylhydroperoxide (180 mmol) and an
aqueous solution (150 ml) of iron(II) sulfate heptahydrate (50 g, 180 mmol), in
separate addition funnels, over 30 min to a vigorously stirred ice-cold (10–
20°C) solution of sulfuric acid (4 M), acetaldehyde (8 g, 180 mmol), pyrim-
idine (2.4 g, 30 mmol), and CH2Cl2 (180 ml) yielded crude product. After
1.5 h, the organic phase was separated and extracted with dichloromethane
(three 100-ml extractions). The organic phase was then pooled, and the solvent
SCHEME 1. Synthesis of the quinoline carboxamide analogs as sub-
strate for CYP 3A4. EtOH, ethanol.

SMALL MOLECULE QUANTIFICATION
2357
SCHEME 2. Synthesis of the metabolites. RT,
room temperature; MeOH, methanol; THF, tet-
rahydrofuran; TBDMS, tert-butyldimethylsilyl;
TBAF, tetrabutylammonium fluoride.
ml of tetrahydrofuran, and 2 ml of 1 M tetrabutylammonium fluoride solution
in tetrahydrofuran. The reaction mixture was stirred, and the progress of the
reaction was monitored by TLC. The reaction was completed in 4 h, the
solvent was evaporated, and the product was purified by flash chromatography
(30 g of Silica Gel 60, 0.063–0.200 mm) using hexanes, dichloromethane, and
methanol in 9:10:1 ratio. The product was further purified by crystallization
using hexanes-dichloromethane. The NMR and mass spectral data for charac-
terization of metabolites are presented in the supplemental data.
Standard Curves. The conventional flow standard curves (analyte area/
internal standard area versus analyte concentration/internal standard concen-
tration) were constructed for the metabolites and the substrates against the
internal standard phenacetin using freshly prepared solutions. The assay solu-
tion contains 7.14 ␮M phenacetin and at least four different concentrations of
the substrate or metabolite. The substrate and metabolite concentrations were
within the linear detection limit of the LC-MS instrument. Data for the
source. The following source settings were optimized as generic settings using
compound 1 and were used for all subsequent data collection: sheath gas, 0
arbitrary units; auxiliary gas, 0 arbitrary units; spray voltage, 1 kV; capillary
temperature, 175°C; capillary voltage, 43 V; and tube lens voltage, 80 V.
MS/MS conditions were optimized for each compound to determine the
appropriate relative collision energy and product ion to use for selected
reaction monitoring analysis. Sample was delivered by flow injections (1 ␮l)
directly into the source, and data were collected and analyzed using Thermo
Scientific Xcalibur software (version 2.06). Linear regression analysis was
performed using the analyte/internal standard area ratio versus analyte con-
centration/internal standard concentration.
Results
To assess the equivalency of the signals between the substrate and
standard curve were generated by LC-MS/MS analysis using a ThermoQuest the metabolite, we synthesized 20 substrates for cytochrome P450
high-performance liquid chromatograph coupled with a Thermo Finnigan LCQ
Advantage system. The ions were generated by electrospray ionization and
were monitored in positive ion mode. The high-performance liquid chroma-
tography system used reverse-phase chromatography (Eclipse Plus C18 5-␮m,
150-mm length, 3.2-mm i.d. column; Agilent Technologies, Santa Clara, CA)
beginning with 85% mobile phase A (0.1% acetic acid in water) and 15%
mobile phase B (0.1% acetic acid in acetonitrile) with a linear gradient to 85%
of mobile phase B over 25 min. After 25 min, mobile phase B was reduced to
15% and run isocratically for 5 min.
The CSI standard curves used 12 concentration points (5–0.002441 ␮M)
and were constructed for the metabolites and the substrates in a 50:50 (v/v)
solution of acetonitrile and water with 0.1% formic acid containing 1 ␮M
testosterone as internal standard. At least nine different concentrations of the
substrate or metabolite were used for the curves to obtain a linear MS response.
Data for the standard curves were generated by LC-MS/MS analysis using a
Thermo Scientific Accela high-performance liquid chromatograph coupled
with a Thermo Scientific LTQ XL mass spectrometer. Positive ions were
generated by the low-flow electrospray ionization captive source. The high-
performance liquid chromatography system delivered a mobile phase using an
isocratic mixture of 50% mobile phase A (0.1% formic acid in water) and 50%
mobile phase B (0.1% formic acid in acetonitrile) at a flow rate of 100 ␮l/min.
The flow was reduced to 5 ␮l/min using a 1/20 splitter (Accurate Splitter, LC
3A4 and the corresponding metabolite for each substrate. Cytochrome
P450 3A4 is a major drug-metabolizing enzyme that metabolizes
approximately 50% of clinically used drugs (Ortiz de Montellano,
2005). The substrates are quinoline carboxamide analogs that have
been used as model compounds for cytochrome P450 kinetic assays
(Peng et al., 2008, 2010; Pearson et al., 2011). The metabolites
represent common transformations mediated by cytochrome P450
enzymes including O-demethylation, N-demethylation, aromatic hy-
droxylation, and benzylic hydroxylation. The structures of the syn-
thesized substrates and the metabolites are presented in Table 1. The
synthesized metabolites were confirmed to be the cytochrome P450
3A4 metabolites by comparing the retention time and fragmentation
patterns in LC-MS with those of the enzymatically formed metabo-
lites (data not shown).
The synthesis of a metabolite can be challenging. Synthesis of
the mono N-demethylated metabolite failed using several literature
procedures. For example, N-demethylation using N-iodosucci-
namide (Stenmark et al., 2000) and using the method developed by
Rosenau et al. (2004) did not work in our hands, although N-de-
methylation was eventually successfully performed for all the
Packings; Dionex, Amsterdam, The Netherlands) before introduction into the aniline substrates using the method of Acosta et al. (1994), which

2358
DAHAL ET AL.
using ESI, whereas compounds 3 and 11 show insignificant dif-
TABLE 1
ferences using CSI.
Structures of the cytochrome P450 3A4 substrates and metabolites used for the
study and the standard curve slope ratio of metabolite to substrate
The ratio of the slope of the metabolite to that of the substrate is
presented in Table 1. The largest slope difference was 4.0 for com-
pounds 7 and 7M using ESI. Whereas some of the metabolites gave
a higher signal relative to that of the substrate for a given concentra-
tion, this was not consistently true. For example, the slope of the
metabolite 18M was almost half that of the substrate 18 for ESI,
whereas this is true for the majority of the CSI results. These results
illustrate that the determination of the metabolite concentration using
substrate to construct the standard curve can yield a predicted con-
centration of the metabolite up to 4-fold lower than the actual con-
centration to approximately 2-fold higher than the actually metabolite
concentration.
Slope Ratio
Substrate
R2
Metabolite
R2
R1
Substrate
Metabolite
ESIa
CSIb
OCH3
N(CH3)2
H
1
2
OH
1M
2M
0.7
0.9
1.5
2.6
1.7
2.5
4.0
2.5
2.4
1.3
2.1
2.2
1.3
0.4
0.9
0.5
3.6
0.7
1.2
0.5
1.9
0.5
0.9
0.7
NH(CH3)
OH
3
3M
CH3
4
CH2OH
OH
4M
Discussion
OCH3
N(CH3)2
H
5
5M
It has been proposed that low-flow LC-MS techniques such as
nanospray may provide less variability in ionization (Benetton et al.,
2003; Hop et al., 2005; Ramanathan et al., 2011) (see below) than
regular LC-MS. To test this hypothesis we also compared standard
curves generated using CSI LC-MS. The results are shown in Table 1,
and the overall variability is less than that for normal-flow LC-MS. To
look at the absolute deviations associated with the difference in
signals from parent and metabolite, we calculated the average of the
absolute differences of the ratio of the metabolite and parent signals
from 1 (unsigned average difference). If the ratio was a fraction, it was
inverted and subtracted from 1. For example, the slope ratio for CSI
of compound 5 is 0.3; inverting this number gives 3.3, which has an
absolute error from perfect agreement of 2.3. The unsigned average
difference of the substrate/metabolite ratios from 1 indicates that for
normal LC-MS the error is 1.2, whereas for captive spray LC-MS it is
0.8, the largest error being 3.0 for compound 7 for LC-MS and 2.3 for
compounds 5 and 14 for captive spray LC-MS.
The metabolic pathways for this series of compounds cover ben-
zylic hydroxylation, aromatic hydroxylation, N-dealkylation, and O-
dealkylation, which are some of the most commonly observed meta-
bolic transformations. It is of interest to determine whether some
metabolic transformations give more similar metabolite/substrate
slope ratios than others. Although we expected to see consistent
deviation for each metabolic pathway as a result of pKa differences or
changes in lipophilicity in the parent versus metabolite, this was not
the case. The results did not show a consistent trend for signal
differences between substrate and metabolite with respect to different
metabolic transformations using either ESI or CSI. Using ESI as an
example, the ratio of the slopes of the metabolite to that of the
substrate for O-demethylation for five different substrates, 1, 5, 9, 13,
and 17, are 0.7, 1.7, 2.4, 2.3, and 3, respectively, with the unsigned
average difference in the slopes being 1.2. Likewise, for N-demeth-
ylation of substrates 2, 6, 10, 14, and 18, the slope ratios are 0.9, 2.5,
1.3, 2.7, and 0.6, respectively, with the unsigned average difference in
the slopes being 0.85. For aromatic oxidation, all the slopes are
positive with the average difference in slopes from 1 being 1.7,
whereas for benzylic hydroxylation, the unsigned average difference
in the slopes from 1 is 1.2. These observations indicate that the signal
of a metabolite can be higher or lower than that of the substrate and
that none of the different metabolic transformations give a consis-
tently better agreement in the slopes. This result does not appear to be
the case for carboxylic acid metabolites (Hop et al., 2005; Valaskovic
et al., 2006; Ramanathan et al., 2007) or for the loss of an amine by
6
NH(CH3)
OH
6M
7
7M
CH3
8
CH2OH
OH
8M
OCH3
N(CH3)2
H
9
9M
10
11
12
NH(CH3)
OH
10M
11M
12M
CH3
CH2OH
OCH3
N(CH3)2
H
13
14
15
OH
13M
14M
15M
2.3
2.7
3.1
2.4
3.0
1.6
3.0
0.8
1.2
1.2
NH(CH3)
OH
CH3
16
17
18
19
20
CH2OH
OH
16M
17M
18M
19M
20M
OCH3
N(CH3)2
H
NH(CH3)
OH
0.6 N.D.
2.7
0.9
0.7
2.8
CH3
CH2OH
N.D., not determined.
a Slope (average) of the standard curve for metabolite versus internal standard divided by
slope (average) of the standard curve for substrate versus internal standard using ESI.
b Slope (average) of the standard curve for metabolite versus internal standard divided by
slope (average) of the standard curve for substrate versus internal standard using nanospray
ionization. Slopes are presented in the supplemental data.
uses calcium oxide and iodine in the presence of methanol (Scheme
2b). Attempts to perform benzylic hydroxylation directly on the
substrates were also unsuccessful. Therefore, the protected 4-ami-
nobenzyl alcohol was reacted with appropriate acid chloride to get
the protected benzylic alcohol, which, upon deprotection, yielded
the desired product (Scheme 2c).
Having the substrates and the metabolites in hand, we constructed
the standard curves for the substrates or the metabolites versus the
internal standard using conventional flow LC-MS methodology, and
CSI LC-MS. As an example, a representative pair of standard curves
for a substrate (compound 14) and a metabolite (compound 14M) are
presented in Fig. 1 using each type of methodology. The slopes vary
by a ratio of 2.7 for conventional flow and 3.0 for CSI. A graphical
representation of the slopes associated with the standard curves for all
20 of the substrates versus metabolites is presented in Fig. 2 for both
methods. To assess whether the difference in signal associated with
the substrate versus that of the metabolite was statistically significant,
we performed Student’s t tests for the slopes of the substrates and
those of the metabolites. The calculated independent, two-sample t
values and the slopes for substrate and metabolite pairs are presented
in Supplemental Tables S1 and S2. For 18 of the 20 pairs of substrates
and metabolites, the slopes are significantly different at the 95% N-dealkylation (Benetton et al., 2003), both of which consistently
confidence level. Two substrate/metabolite pairs (compound 10 show a lower signal for the metabolite. Indeed, the fact that all the
and compound 20) have insignificant differences in their slopes compounds tested have multiple nitrogen atoms and no other ioniz-

SMALL MOLECULE QUANTIFICATION
2359
FIG. 1. Representative standard curves. The curves were constructed by plotting the ratio of area of analyte to internal standard (IS) against the ratio of concentration of
analyte to IS. A, standard curve of substrate 14 versus IS using electrospray ionization. B, standard curve of metabolite 14M versus IS using electrospray ionization. C,
standard curve of substrate 14 versus IS using CSI. D, standard curve of metabolite 14M versus IS using CSI.
able group would lead one to conclude that this is a best case scenario 2007). Furthermore, a number of other methods to estimate exposure
and that one might expect larger deviations for metabolites that to a metabolite using corrected responses in LC-MS/MS (Vishwana-
introduce a negative charge or for the loss of nitrogen.
than et al., 2009; Gao et al., 2010; Ma et al., 2010; Tong et al., 2010;
Thus, it appears that the use of substrate to estimate the LC-MS Mutlib et al., 2011; Walker et al., 2011) or NMR based methods
signal, although convenient, leads to significant errors. In this regard, (Vishwanathan et al., 2009; Mutlib et al., 2011; Walker et al., 2011)
other methodologies show promise. For example, a number of inves- have been developed.
tigators have used nanospray LC-MS to decrease the differences
In summary, we compared the slopes of the standard curves be-
between the signal from parent drug and metabolites (Benetton et al., tween substrates and metabolites with a common internal standard to
2003; Hop et al., 2005; Valaskovic et al., 2006; Ramanathan et al., test whether the signal is equivalent and whether the substrates can be
FIG. 2. Slopes of the standard curves for the
substrate and metabolite pairs. The standard
curves (analyte area/internal standard area ver-
sus analyte concentration/internal standard con-
centration) were prepared from the LC-MS
analysis using at least four concentrations of
substrate and metabolite. The error bar repre-
sents the S.D. All slopes except those for com-
pounds 10 and 20 are significantly different at
the 95% confidence limit. A, results for ESI. B,
results for CSI.

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used to quantify the metabolites in LC-MS. The results demonstrated
that the signals of the substrates compared with those of the metab-
olites are significantly different statistically for metabolite/substrate
pairs for O-demethylation, N-demethylation, aromatic hydroxylation,
and benzylic hydroxylation. The ratio of the signal for the metabolite
to that of the substrate was found to be up to 4-fold different, which
may be an unacceptable error unless you have very low or high
amounts of metabolite formed. The results also demonstrate that the
signal of the compound is an intrinsic property of the compound and
not related to any given metabolic pathway.
Ortiz de Montellano PR (2005) Cytochrome P450: Structure, Mechanism, and Biochemistry,
Kluwer Academic/Plenum Publishers, New York.
Pearson J, Dahal UP, Rock D, Peng CC, Schenk JO, Joswig-Jones C, and Jones JP (2011) The
kinetic mechanism for cytochrome P450 metabolism of type II binding compounds: evidence
supporting direct reduction. Arch Biochem Biophys 511:69–79.
Peng CC, Cape JL, Rushmore T, Crouch GJ, and Jones JP (2008) Cytochrome P450 2C9 type
II binding studies on quinoline-4-carboxamide analogues. J Med Chem 51:8000–8011.
Peng CC, Pearson JT, Rock DA, Joswig-Jones CA, and Jones JP (2010) The effects of type II
binding on metabolic stability and binding affinity in cytochrome P450 CYP3A4. Arch
Biochem Biophys 497:68–81.
Acknowledgments
We thank Carolyn Joswig-Jones for help formatting the figures.
Authorship Contributions
Participated in research design: Dahal, Jones, Davis, and Rock.
Conducted experiments: Dahal and Davis.
Contributed new reagents or analytic tools: Dahal, Jones, Davis, and Rock.
Performed data analysis: Dahal, Davis, and Jones.
Wrote or contributed to the writing of the manuscript: Dahal, Jones, Davis,
and Rock.
Ramanathan R, Raghavan N, Comezoglu SN, and Humphreys WG (2011) A low flow ionization
technique to integrate quantitative and qualitative small molecule bioanalysis. Int J Mass
Spectrom 301:127–135.
Ramanathan R, Zhong R, Blumenkrantz N, Chowdhury SK, and Alton KB (2007) Response
normalized liquid chromatography nanospray ionization mass spectrometry. J Am Soc Mass
Spectrom 18:1891–1899.
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chromatography-like setup. Org Lett 6:541–544.
Shebley M, Kent UM, Ballou DP, and Hollenberg PF (2009) Mechanistic analysis of the
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transfer, and uncoupling. Drug Metab Dispos 37:745–752.
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Tong W, Chowdhury SK, Su AD, and Alton KB (2010) Quantitation of parent drug and its
unstable metabolites by in situ coulometric oxidation and liquid chromatography-tandem mass
spectrometry. Anal Chem 82:10251–10257.
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Address correspondence to: Jeffrey P. Jones, Fulmer 406, Department of
Chemistry, Washington State University, Pullman, WA 99164-4630. E-mail:
jpj@wsu.edu