LC TROUBLESHOOTING
What is the point of the column
dead time?
PERSPECTIVES IN
MODERN HPLC
Myths in UHPLC
LCGC TV
Video interviews with experts
Go to: http://goo.gl/VRJL7n
March 2014
Volume 17 Number 1
www.chromatographyonline.com
For quantitative LCÐMS analysis
Detecting and Eliminating Matrix Effects
ES393329_LCA0314_CV1.pgs 02.26.2014 01:21 ADV blackyellowmagentacyan
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ES392501_LCA0314_CV2_FP.pgs 02.25.2014 16:45 ADV blackyellowmagentacyan
3
Editorial P olicy:
All articles submitted to LC•GC Asia Pacific
are subject to a peer-review process in association
with the magazine’s Editorial Advisory Board.
Cover:
Original materials: nadia
Columns13 LC TROUBLESHOOTING
Column Dead Time as a Diagnostic Tool
John W. Dolan
What good is that big, ugly peak at the beginning of the
chromatogram?
16 PERSPECTIVES IN MODERN HPLC
Myths in Ultrahigh-Pressure Liquid Chromatography
Michael W. Dong
The advent of ultrahigh-pressure liquid chromatography (UHPLC)
and its successful commercialization in the last few years has
brought forth a modern high performance liquid chromatography
(HPLC) platform capable of higher speed, resolution, precision, and
sensitivity. Currently, all major HPLC manufacturers have some
type of low-dispersion UHPLC products with upper pressure limits
ranging from 15,000 to 19,000 psi (1000 to 1300 bar) on the market.
This installment describes a number of popular myths or half-truths
in UHPLC and provides data that contradict or even repudiate some
of these commonly held beliefs.
25 MS — THE PRACTICAL ART
Mass Spectrometry for Natural Products Research:
Challenges, Pitfalls, and Opportunities
Nadia B. Cech and Kate Yu
As applied to natural products research, mass spectrometry (MS) is
fraught with challenges and pitfalls. Here is an account of strategies
to conduct effective research despite these obstacles.
Departments34 Products
36 Application Notes
COVER STORY5 Strategies for the Detection and Elimination of Matrix
Effects in Quantitative LC–MS Analysis
Amitha K. Hewavitharana, Swee K. Tan, and P. Nicholas Shaw
Currently available methods for the detection of matrix effects
in liquid chromatography–mass spectrometry (LC–MS) are
tedious and complex; therefore, a simpler method is required.
Although there are no methods to completely eliminate matrix
effects, the most well-recognized
technique available to correct for
matrix effects is that of internal
standardization using stable
isotope–labelled versions of the
analytes. As this method can
prove expensive, an alternative
method of correction is likely to
be useful. In this study, a simple
method based on recovery is
assessed for the detection of matrix
effects. Two alternative methods
for the rectif cation of matrix effects
in LC–MS are also assessed:
Standard addition and the coeluting
internal standard method.
March | 2014
Volume 17 Number 1
www.chromatographyonline.com
ES392780_LCADI0314_003.pgs 02.25.2014 21:16 ADV blackyellowmagentacyan
4 LC•GC Asia Pacific March 2014
The Publishers of LC•GC Asia Pacific would like to thank the members of the Editorial Advisory Board
for their continuing support and expert advice. The high standards and editorial quality associated with
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LCGC Asia Pacific provides troubleshooting information and application solutions on all aspects
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Pablo – CEU, Madrid, Spain
Brian A. BidlingmeyerAgilent Technologies, Wilmington,
Delaware, USA
Günther K. BonnInstitute of Analytical Chemistry and
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of Minnesota, Minneapolis, Minnesota, USA
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Netherlands
Jan H. ChristensenDepartment of Plant and Environmental
Sciences, University of Copenhagen,
Copenhagen, Denmark
Danilo CorradiniIstituto di Cromatografia del CNR, Rome,
Italy
Hernan J. CortesH.J. Cortes Consulting,
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Gert DesmetTransport Modelling and Analytical
Separation Science, Vrije Universiteit,
Brussels, Belgium
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USA
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Pennsylvania, USA
Anthony F. FellPharmaceutical Chemistry,
University of Bradford, Bradford, UK
Attila FelingerProfessor of Chemistry, Department of
Analytical and Environmental Chemistry,
University of Pécs, Pécs, Hungary
Francesco GasparriniDipartimento di Studi di Chimica e
Tecnologia delle Sostanze Biologica-
mente Attive, Università “La Sapienza”,
Rome, Italy
Joseph L. GlajchMomenta Pharmaceuticals, Cambridge,
Massachusetts, USA
Jun HaginakaSchool of Pharmacy and Pharmaceutical
Sciences, Mukogawa Women’s
University, Nishinomiya, Japan
Javier Hernández-BorgesDepartment of Analytical Chemistry,
Nutrition and Food Science University of
Laguna, Canary Islands, Spain
John V. HinshawServeron Corp., Hillsboro, Oregon, USA
Tuulia HyötyläinenVVT Technical Research of Finland,
Finland
Hans-Gerd JanssenVan’t Hoff Institute for the Molecular
Sciences, Amsterdam, The Netherlands
Kiyokatsu JinnoSchool of Materials Sciences, Toyohasi
University of Technology, Japan
Huba KalászSemmelweis University of Medicine,
Budapest, Hungary
Hian Kee LeeNational University of Singapore,
Singapore
Wolfgang LindnerInstitute of Analytical Chemistry,
University of Vienna, Austria
Henk LingemanFaculteit der Scheikunde, Free University,
Amsterdam, The Netherlands
Tom LynchBP Technology Centre, Pangbourne, UK
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Wilmington, Delaware, USA
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Victoria, Australia
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University of West of England, Bristol, UK
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Mary Ellen McNallyDuPont Crop Protection,Newark,
Delaware, USA
Imre MolnárMolnar Research Institute, Berlin, Germany
Luigi MondelloDipartimento Farmaco-chimico, Facoltà
di Farmacia, Università di Messina,
Messina, Italy
Peter MyersDepartment of Chemistry,
University of Liverpool, Liverpool, UK
Janusz PawliszynDepartment of Chemistry, University of
Waterloo, Ontario, Canada
Colin PooleWayne State University, Detroit,
Michigan, USA
Fred E. RegnierDepartment of Biochemistry, Purdue
University, West Lafayette, Indiana, USA
Harald RitchieTrajan Scientific and Medical. Milton
Keynes, UK
Pat SandraResearch Institute for Chromatography,
Kortrijk, Belgium
Peter SchoenmakersDepartment of Chemical Engineering,
Universiteit van Amsterdam, Amsterdam,
The Netherlands
Robert ShellieAustralian Centre for Research on
Separation Science (ACROSS), University
of Tasmania, Hobart, Australia
Yvan Vander HeydenVrije Universiteit Brussel,
Brussels, Belgium
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ES392728_LCA0314_004.pgs 02.25.2014 21:14 ADV blackyellowmagentacyan
5www.chromatographyonline.com
KEY POINTS
• LC–MS is now the predominant technique for
qualitative analysis in biological matrices.
• Matrix effects are a major challenge when analysing
biological matrices.
• An efficient strategy to detect and reduce matrix
effects is proposed.
High performance liquid chromatography (HPLC)
coupled to mass spectrometry (MS) has become the
predominant analytical method for the quantitative
determination of analytes in biological matrices because
of its high specificity, sensitivity, and throughput (1–3).
However, matrix effects have become a major concern in
quantitative liquid chromatography–mass spectrometry
(LC–MS) because they detrimentally affect the accuracy,
reproducibility, and sensitivity (3). Matrix effects occur
when compounds that are coeluted with the analyte
interfere with the ionization process in the MS detector,
thereby causing ionization suppression or enhancement
(2–7). Compounds with high mass, polarity, and basicity
are possible candidates to cause matrix effects (4–8).
However, the mechanisms involved in matrix effects
have not been fully explored. One of the proposed
theories to explain matrix effects is that the coelution of
interfering compounds, especially basic compounds, may
deprotonate and neutralize the analyte ions and, thus,
reduce the formation of protonated analyte ions (2,4).
Another theory postulates that less-volatile compounds
may affect the efficiency of droplet formation and reduce
the ability of charged droplets to convert into gas-phase
ions (2–4,8). In addition, matrix effects may also be caused
by high viscosity interfering compounds that could possibly
increase the surface tension of the charged droplets and
reduce the efficiency of droplet evaporation (2,4,6).
Several methods have been proposed for the
detection and assessment of matrix effects, including
post-extraction spike and post-column infusion methods.
The post-extraction spike method evaluates matrix effects
by comparing the signal response of an analyte in neat
mobile phase with the signal response of an equivalent
amount of the analyte in the blank matrix sample spiked
post-extraction. The difference in response determines the
extent of matrix effect (2,3,9). The major drawback of this
method is that for endogenous analytes such as metabolites
(for example, creatinine) blank matrix (urine or plasma) is
not available. The post-column infusion method assesses
matrix effects qualitatively. A constant flow of analyte is
infused into the HPLC eluent, followed by injection of the
blank sample extract. A variation in signal response of the
infused analyte caused by coeluted interfering compounds
indicates ionization suppression or enhancement (3,10).
By identifying the ionization suppression or enhancement
regions of the chromatogram, analytical methods can be
developed to eliminate matrix effects by preventing the
elution of the analyte peak in regions where matrix effects
occur. However, the process of post-column infusion is time-
consuming and requires additional hardware, and it is not
appropriate for multianalyte samples. Considering these
drawbacks of existing methods, we propose a simple, fast,
and reliable method to detect matrix effects that can be
applied to any analyte including endogenous compounds
such as creatinine and to any matrix without requiring any
additional hardware.
To obtain accurate and reliable LC–MS data, several
methods have been suggested to reduce or eliminate
Amitha K. Hewavitharana, Swee K. Tan, and P. Nicholas Shaw, School of Pharmacy, The University of Queensland,
Brisbane, Australia.
Currently available methods for the detection of matrix effects in liquid chromatography–mass spectrometry (LC–MS) are tedious and complex; therefore, a simpler method is required. Although there are no methods to completely eliminate matrix effects, the most well-recognized technique available to correct for matrix effects is that of internal standardization using stable isotope–labelled versions of the analytes. As this method can prove expensive, an alternative method of correction is likely to be useful. In this study, a simple method based on recovery is assessed for the detection of matrix effects. Two alternative methods for the rectif cation of matrix effects in LC–MS are also assessed: Standard addition and the coeluting internal standard method.
Strategies for the Detection and Elimination of Matrix Effects in Quantitative LC–MS Analysis
ES393233_LCA0314_005.pgs 02.26.2014 00:05 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 20146
Hewavitharana et al.
matrix effects. Matrix effects can be reduced simply by
injecting small amounts of samples or by diluting samples
(11,12). However, this approach can only be feasible when
the sensitivity of the assay is very high (12). Methods to
reduce or eliminate matrix effects include optimizing sample
preparation to remove interfering compounds from the
samples (1,9,10,13), changing chromatographic parameters
to avoid coelution of analytes and interfering compounds
(4,14–17), and changing MS conditions to reduce the
occurrence of matrix effects in the ion source. However,
these methods are not without their limitations. Most of the
sample cleanup methods fail to remove impurities that are
similar to the analyte and, hence, likely to be coeluted with
the analyte (11,18). Modifying chromatographic conditions
can be time-consuming, and some of the additives used in
the mobile phase to improve separation have been found to
suppress the electrospray signal of the analytes (3,4,9,15).
Furthermore, even when the sample is devoid of coeluted
substances, trace impurities present in the mobile phase can
significantly suppress the analyte peak (19).
It is clear from the above that matrix effects in LC–MS
cannot be completely eliminated. Therefore, the only
option available is the rectification of data to eliminate
the matrix effects. Calibration techniques such as the
external-matched standards method, the echo-peak
technique, and the most commonly used approach, the
internal standard method, have been developed to correct
the data (15,18–21). However, these calibration techniques
also have their drawbacks. For example, the matrix-matching
technique requires many blank matrices and appropriate
blank matrices are not always available for the preparation
of external standards (9,11,18,23). It is also impossible to
match the matrix of the calibration standards with each of the
samples exactly, as each sample has coeluting, interfering
compounds that are thereby exposed to a different extent of
ionization suppression (18). Echo-peak does not compensate
for matrix effects completely because both standard and
analyte peaks are not eluted at the exact same retention time
(11). The stable isotope–labelled internal standards (SIL-IS)
approach is the best available option but it is expensive
and standards are not always commercially available for the
analyte of interest (4,9,23).
The standard addition method for correcting matrix effects
is widely used in spectrophotometric analysis, especially in
atomic spectroscopy (24–27). However, this method is less well
documented with other analytical techniques and currently there
is no record of its practical use in compensating matrix effects
in LC–MS. Standard addition does not require a blank matrix
and is therefore appropriate for compensating matrix effects
for any analyte including endogenous metabolites in biological
fluids (20,28,29). In this study, we investigated the possibility
of using the method of standard addition in routine LC–MS
analysis to compensate for matrix effects and to thereby obtain
improved data. We also investigated the use of a coeluting
structural analogue of the analyte as the internal standard as
an alternative to the expensive and often unavailable stable
isotope–labelled internal standard for correcting matrix effects.
Although coeluting structural analogue compounds are used
to extend the linear range of calibration curves (30), there have
been no reports of their use in compensating matrix effects in
routine LC–MS analysis. In addition, we report a simple and
effective method for the detection as well as the correction of
matrix effects in routine LC–MS. All studies were carried out
using a creatinine assay applied to human urine samples.
ExperimentalInstrumentation: Separation of compounds was achieved
with an Agilent 1100LC binary pump and Agilent 1100
autosampler (Agilent Technologies). The HPLC column used
was a 150 mm × 2.1 mm, 4-µm dp Cogent Diamond-Hydride
100A column (MicroSolv Technology). For detection and
quantification, an API 3000 tandem mass spectrometer
equipped with a turbo ion spray interface and the software
program Analyst 1.5 (Applied Biosystems) was used.
Materials: Creatinine, creatinine-d3 (2-amino-1-
[trideuteriomethyl]imidazolidin-4-one), and cimetidine (N′′-
cyano-N-methyl-N′-[2[(5-methyl-1H-imidazol-4-yl)methylthio]-
ethyl]guanidine) were purchased from Sigma. HPLC-grade
acetonitrile and deionized water from a Milli-Q water system
(Millipore) were used for mobile-phase preparation. Human
urine samples were obtained from volunteers.
Chromatographic Conditions: Separations were carried
out at an ambient temperature of approximately 25 °C. The
flow rate was 200 μL/min. The injection volume was 10 μL.
Mobile-phase A consisted of deionized water containing 0.1%
(v/v) formic acid, and mobile-phase B comprised 0.1% (w/v)
formic acid in acetonitrile.
Table 1: The percent recoveries of creatinine in five human
urine samples. C0 is the known concentration of creatinine
standard added to sample, 6 mM; C1 is the concentration of
creatinine measured in the unspiked urine sample; and C2
is the concentration of creatinine measured after spiking (to
a final spiked concentration of 6 mM). Recovery calculation
procedure is explained in the results and discussion section.
Sample C0 (mM) C2 (mM) C1 (mM) Recovery (%)
1 6.000 10.693 6.762 65.52
2 6.000 11.141 7.101 67.24
3 6.000 11.486 7.590 64.94
4 6.000 11.624 7.934 61.49
5 6.000 12.521 8.279 70.69
Mean 65.98
SD 3.36
4.00E+07
3.00E+07
2.00E+07
1.00E+07
-0.015 -0.01 0.005 0.01-0.001
Creatinine concentration (mM)
Sig
nal re
spo
nse
00.00E+00
Figure 1: Standard addition method. A three-point calibration
line is extrapolated to zero response to estimate the original
concentration of creatinine present in the urine sample.
ES393232_LCA0314_006.pgs 02.26.2014 00:05 ADV blackyellowmagentacyan
7www.chromatographyonline.com
Hewavitharana et al.
B over the first 20 min, and then maintained at 50% B for
1 min, beforeve returning to 90% B from 21 to 24 min. The
percentage of B was maintained at 90% B for 10 min for
re-equilibration before the next sample was injected. The total
run time was 24 min.
Coeluting Internal Standard Method: An isocratic elution
method was used for elution. The mobile-phase composition
was 55% B over 10 min. The total run time was 10 min.
MS Conditions: Creatinine, creatinine-d3, and cimetidine
were detected in a positive multiple reaction monitoring
(MRM) mode in which the transitions of m/z 113.9 to 44.0,
m/z 117.0 to 47.0, and m/z 252.8 to 95.1 were monitored,
respectively. The MS and electrospray ionization parameters
were optimized and the analyzer settings were as follows:
ion spray voltage (IS) 5000 V; entrance potential (EP)
10 V; orifice/declustering potentials (DP) 26 V (creatinine),
36 V (creatinine-d3), and 36 V (cimetidine); ring/focusing
Detection of Matrix Effects and Standard Addition Method:
Gradient elution was used for analyte separation. The
mobile-phase composition was varied from 90% B to 50%
20
Cre
ati
nin
e c
on
cen
trati
on
Neg
ativ
e co
ntrol
Posit
ive
contr
ol
Stan
dard a
ddition (a
)
Stan
dard a
ddition (b
)
15
10
5
0
(a) (b)
S
N
N
NH
NH
O
NH
N
N
NH2
HN
Figure 2: Comparison of approaches using negative
control (external standard calibration), positive control
(SIL-IS), standard addition (a) with extrapolated calibration
line and standard addition (b) with equation. Detailed
explanation in text.
Figure 3: Chemical structures of (a) creatinine and
(b) cimetidine.
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LC•GC Asia Paciàc March 20148
Hewavitharana et al.
0.001, 0.003, 0.006, 0.01, 0.015, and 0.02 mM in 90% (v/v)
acetonitrile. A calibration curve of the ratio of the peak area
of creatinine and the peak area of creatinine-d3 versus
creatinine concentration was then plotted to determine the
concentration of the above five aliquots of 1000-fold diluted
urine.
For the standard addition method, two levels of additions
were prepared. A 0.003 mM addition was prepared by
mixing 10 μL of 10-fold diluted filtered urine, 30 μL of 0.1 mM
creatinine, 900 μL of acetonitrile, and 60 μL of water to make
the final volume up to 1000 μL. The 0.006 mM addition was
prepared by mixing 10 μL of 10-fold diluted filtered urine,
60 μL of 0.1 mM creatinine, 900 μL of acetonitrile, and 30 μL
of water to make the final volume up to 1000 μL. The zero
addition was prepared in the same manner but water was
added in place of the 0.1 mM creatinine solution. A calibration
plot of peak area of creatinine versus the added creatinine
concentration was plotted for each sample and the original
concentration of creatinine in five samples were estimated by
extrapolating the line of best fit to zero peak area.
A negative control experiment was also carried out by
running five samples containing no internal standard and
determining their concentration using the calibration curve
prepared in the section titled “Detection of matrix effects
using recovery” above.
Comparison of the Stable Isotope–Labelled Internal Standard
and Coeluting Internal Standard Methods: Standard
solutions for the SIL-IS method were prepared by mixing
10 μL of 0.05 mM creatinine-d3, 900 µL of acetonitrile,
appropriate volumes of 1.0, 0.1, and 0.01 mM creatinine,
and deionized water to make the final volume up to 1000 μL.
The final concentration of creatinine-d3 in all standards was
0.0005 mM and the concentrations of creatinine were 0.0001,
0.0003, 0.0005, 0.001, 0.003, and 0.006 mM in 90% (v/v)
acetonitrile.
Standard solutions for the coeluting internal standard
method were prepared by using 0.05 mM cimetidine in place
of creatinine-d3 and by following the procedure mentioned
in the previous section to obtain similar final concentrations
of creatinine and the internal standard. A calibration curve
of the ratio of the peak area of creatinine and peak area of
the internal standard versus creatinine concentration were
then plotted for each internal standard to calculate the
concentrations of creatinine in seven aliquots of 1000-fold
diluted urine. A negative control experiment was run in the
same manner as described in the previous section.
Results and DiscussionDetection of Matrix Effects: To eliminate matrix effects,
they should be assessed during early analytical method
development. In this paper, we propose a simple and
reliable method, recovery value, to estimate the extent
of matrix effects in biological matrices analysis. In the
recovery value test, the original concentration of creatinine
in each of the five urine samples was determined using
a calibration plot of the peak area of creatinine versus
creatinine concentration. Each of the urine samples was
then spiked with a known concentration of standard, 0.006
mM creatinine, and the total creatinine concentrations of the
spiked samples were calculated using the same calibration
curve. The percent recovery was determined by using the
following formula:
potentials (FP) 140 V (creatinine), 250 V (creatinine-d3), and
140 V (cimetidine); collision energy (CE) 29 V (creatinine,
creatinine-d3, and cimetidine); collision exit potential (CXP)
8 V (creatinine, creatinine-d3, and cimetidine). Curtain gas
(CUR), nebulizer gas (NEB), and the collision gas (CAD)
flows were kept at 12, 8, and 4, respectively (in arbitrary units
used in the instrument). The temperature of the ion spray was
300 °C. A dwell time of 150 ms was used for all transitions.
Resolution of both Q1 and Q3 were 1 amu.
Preparation of Urine Samples: Human urine samples were
prepared by filtering a small amount of the urine through a
0.22-μm polytetrafluoroethylene (PTFE) filter (Millipore).
1000-Fold Dilution of Urine Samples: The filtered urine
sample was diluted 10-fold by mixing 30 μL of filtered urine
and 270 μL of deionized water. This was followed by 100-fold
dilution by mixing 900 μL of acetonitrile, 10 μL of internal
standard (0.05 mM creatinine-d3 or 0.05 mM cimetidine),
10 μL of the diluted filtered urine sample, and 80 μL of
deionized water to a total volume of 1000 μL.
Preparation of Standard Solutions and Quantifcation:
Stock solutions (1.00 mg/mL) of creatinine, deuterated
creatinine, and cimetidine were prepared in deionized water
and stored at -20 °C.
Detection of Matrix Effects Using Recovery: Standard
solutions were prepared by mixing appropriate volumes
of 1.0, 0.1, and 0.01 mM creatinine, 900 μL of acetonitrile
and deionized water to a final volume of 1000 μL. The
concentrations of standards were 0.0001, 0.0003, 0.0005,
0.001, 0.003, 0.006, 0.01, 0.015, and 0.02 mM of creatinine
in 90% (v/v) acetonitrile. A calibration curve of peak area of
creatinine versus creatinine concentration was obtained.
Creatinine was added to five filtered urine samples
and then diluted 1000-fold to a final added creatinine
concentration of 0.006 mM. The creatinine concentrations of
spiked and unspiked urine were determined using the above
calibration plot to calculate the assay recovery.
Comparison of the Stable Isotope–Labelled Internal Standard
and Standard Addition Methods: Standard solutions for the
SIL-IS method were prepared by mixing 10 μL of 0.05 mM
creatinine-d3, 900 μL of acetonitrile, appropriate volumes
of 1.0, 0.1, and 0.01 mM creatinine, and deionized water to
make the final volume up to 1000 μL. The final concentration
of creatinine-d3 in all standards was 0.0005 mM and the
concentrations of creatinine were 0.0001, 0.0003, 0.0005,
Cimetidine
Creatinine
Time (min)
(a) (b)
Time (min)
Creatinine
Creatinine-d3
Figure 4: Chromatograms of (a) creatinine with coeluting
internal standard, cimetidine (0.005 mM) and (b) creatinine
with coeluted SIL-IS, creatinine-d3 (0.005 mM).
ES393231_LCA0314_008.pgs 02.26.2014 00:05 ADV blackyellowmagentacyan
9www.chromatographyonline.com
Hewavitharana et al.
When percent recovery is closer to 100 (within normally
expected error of ±3 standard deviations [SD]), it can be
deduced that there is minimal or no matrix effect. A value
of percent recovery less than 100 indicates that ionization
suppression is present and a value greater than 100
indicates ionization enhancement.
As presented in Table 1, the mean percentage of
recoveries for the five urine samples was 65.98%. This
result shows that the matrix effects profoundly affected
the urine samples and thus degraded the accuracy of the
quantitative LC–MS analysis. A high percentage of matrix
effects was anticipated because the sample cleanup for
the urine samples involved only filtration and dilution in
this experiment and there was no mechanism in place to
compensate for the resulting matrix effects. Furthermore,
the results of subsequent experiments described later in
this paper confirm the presence of matrix effects. The use
of the recovery value to detect matrix effects is simple
and cost-effective since it requires only two LC–MS runs.
Also, it is ideal for the determination of the concentration
of analytes in biological fluids that have complex matrices
since it does not require a blank matrix and only involves the
addition of analytes into the same sample matrix. Thus, it is
certainly an easier and simpler method when compared to
other methods available such as post-column infusion and
post-extraction spike method, as discussed above. Unlike
post-column infusion, which assesses the matrix effects in
a qualitative manner, the recovery value method provides
quantitative data.
% recovery = × 100%Concentration of analyte recovered, C
2–C
1
Concentration of analyte added, C0
[1]
where C0 is the added known concentration of standard;
C2 is the concentration of analyte in final solution after spiking
with known concentration of standard; and C1 is the original
concentration of analyte in initial solution.
Cre
ati
nin
e c
on
cen
trati
on 20
25
15
10
5
0
Neg
ativ
e co
ntrol
Posit
ive
contr
ol
Coelutin
g inte
rnal
stan
dard
Figure 5: Comparison of approaches using negative control
(external standard calibration), positive control (SIL-IS), and
coeluting internal standard. The coeluting internal standard is
cimetidine. Detailed explanation in text.
ES393227_LCA0314_009.pgs 02.26.2014 00:05 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201410
Hewavitharana et al.
(positive control), 1.42 (standard addition using extrapolative
calibration curve), and 1.083 (standard addition using
equation).
The mean concentration values of creatinine obtained
from using the standard addition method are very close
to those from the SIL-IS method and therefore it may be
considered as a potential alternative to the expensive and
less versatile SIL-IS method. Standard addition involves the
addition (spiking) of an analyte or a mixture of analytes into
the sample and measuring the analyte concentrations before
and after spiking. The spiking and subsequent analysis can
be done once or twice depending on whether the equation or
calibration curve option is used. There is no need for running
many standards for the construction of calibration curves for
each analyte. Thus, standard addition is especially suited for
multicomponent analysis and when there are few samples
to analyze. Furthermore, when the matrix is complex and the
sample cleanup procedure is lengthy and matching matrix
in calibration standards is impossible, standard addition
provides a simple and effective alternative.
Although the SIL-IS method is generally regarded as the
best option available for correcting matrix effects in LC–MS,
a coeluted internal standard also suppresses the analyte
signal and is therefore not the ideal option (9). Because the
same concentration of SIL-IS is added to all standards and
samples, in some samples the SIL-IS concentration may be
significantly higher than that of the analyte, and therefore
the extent of suppression by the IS may be significant.
When considering this, the standard addition method can
be considered as being closest to ideal since there is no
additional suppression and the matrix has not been changed
in the process of analysis. Other than the additional workload
required (which may not be the case in multicomponent
analysis) the method of standard addition can be considered
as the optimal route to providing the most accurate data.
Furthermore, it is a very practical and inexpensive option,
because SIL-IS are not available for many analytes, and
those that are available are very expensive.
Comparison of Stable Isotope–Labelled Internal Standard and Coeluting Internal Standard MethodsA SIL-IS has physicochemical properties and will undergo
ionization processes that are almost identical to those of
the analyte of interest. Therefore, a SIL-IS is eluted at the
same retention time as the analyte and experiences the
same extent of matrix effects (30,32–34). However, SIL-IS is
very expensive and not always commercially available (4).
Structural analogue internal standards that have chemical
structures, physicochemical properties, and retention
times that are sufficiently close to SIL-IS may provide a
cheaper and more readily available alternative to SIL-IS
in compensating for matrix effects. Although structural
analogues have been used in LC–MS (34) and were
demonstrated to be capable of increasing the linearity of the
method, both accuracy and precision were not improved
as compared to the SIL-IS method. This was because the
structural analogue used was not coeluted with the analyte of
interest, which resulted in variable exposures to matrix effects
in MS. A number of compounds such as hypoxanthine,
xanthine, and quinine dihydrochloride monohydrate have
been used as internal standards for creatinine analysis
(35,36). The majority of such compounds are not eluted
Comparison of Stable Isotope–Labelled Internal Standard and Standard Addition MethodsBecause of its proven accuracy and reproducibility, the SIL-IS
method was chosen as the reference (or positive control)
method and was thus compared with other methods. In
the comparison of SIL-IS and standard addition methods,
the SIL-IS method was run and analyzed using the same
chromatographic and MS conditions as that of the standard
addition method. The calibration curve of the peak area ratio
of creatinine to that of creatinine-d3 (SIL-IS) versus creatinine
concentration was linear with an R2 value of 0.989 up to a
concentration of 0.015 mM creatinine.
Although standard addition is a method that is widely
used in atomic spectrophotometric analysis, it has not been
so used in quantitative LC–MS analysis. In this study, we
investigated the possibility of using standard addition to
rectify matrix effects in LC–MS. Three runs were performed
for each sample (as described in the experimental section),
but there was no need for calibration standards; that is, each
sample has its own calibration curve with three points. A
calibration plot obtained for a typical sample in this study
is shown in Figure 1. The calibration line was extrapolated
to zero response to estimate the original concentration of
creatinine in the urine sample.
If the linear range of the calibration of analyte is already
established, and the concentration of analyte in both the
added and original samples fall within that range, an easier
alternative approach for standard addition is to do only one
addition and calculate the only unknown in the following
equation, the original analyte concentration in the sample:
[2]=[X]
i
[S]+[X]i
IX
IS+X
where [X]i is the original concentration of analyte in initial
solution, [S] is the known concentration added, IX is the
signal response from initial solution, and I(S+X) is the signal
response from the final solution after the addition of a known
concentration of standard.
Ellison and Thompson (31) highlighted that standard
addition can only be precise and feasible when the
analytical calibration curve is linear throughout the targeted
concentration range. From our earlier experiments it has
been demonstrated that the calibration curve for creatinine
was linear to a concentration of 0.015 mM, and we were
therefore able to estimate the original concentration of analyte
in the samples by using equation 2.
Figure 2 illustrates the results of the comparison of
methods: The mean concentrations of creatinine were
7.534 mM (negative control), 12.356 mM (positive control,
SIL-IS), 13.424 mM (standard addition using extrapolated
calibration curve), and 13.266 mM (standard addition
using equation). As described in the experimental section,
in the negative control experiment no method was
used to compensate for matrix effects. The accuracies
of the methods, shown as the percent error (deviation
from the mean value of reference method, SIL-IS) were
39.0% (negative control), 8.64% (standard addition using
extrapolative calibration curve), and 7.36% (standard addition
using equation). The precision values of the methods,
presented as relative standard deviations (using five
replicates in each case) were 0.612 (negative control), 0.450
ES393229_LCA0314_010.pgs 02.26.2014 00:05 ADV black
11www.chromatographyonline.com
Hewavitharana et al.
ConclusionsMatrix effects represent a major problem affecting the
accuracy of LC–MS analysis. In this study, a simple method
has been introduced for the qualitative and quantitative
detection of matrix effects. This method is based on
recovery calculations; if the responses are within a
previously established linear range the same data can be
used to quantify the analyte using the standard addition
method. This means that the detection and correction of the
matrix effects may be both achieved in a simple single-step
process.
Two alternatives to the SIL-IS method, the standard
addition method and the coeluting internal standard
method, were evaluated for their capacity to correct for
matrix effects. The standard addition method proved
to be a viable alternative and is likely to be a better
alternative in terms of accuracy and versatility. It could
also be a superior method in terms of time and labour for
multicomponent analysis using LC–MS such as pesticide
analysis. However, the coeluting internal standard method
as applied using cimetidine for the creatinine assay was
found to be less effective in correcting for the matrix effect.
A better structural analogue with similar chemical structure,
physicochemical properties, and ionization properties may
have produced better results. Because this approach was
significantly better than the negative control, it could well
be a good alternative for alleviating matrix effects if the
appropriate compound is used as the coeluting internal
standard.
sufficiently close to the retention time of creatinine and,
in addition, some of these compounds may be present in
human body fluids and are therefore unsuitable as internal
standards for urine samples.
In this study, we used cimetidine as an internal standard
(Figure 3) because it is a cheaper and more versatile alternative
to SIL-IS (37), and it is not endogenously present in human
urine. The chromatographic conditions were altered to a final
composition of 55:45 acetonitrile–water in isocratic mode to
produce coelution of creatinine and cimetidine and, hence,
a similar matrix effect. As shown in Figure 4, the two peaks
of creatinine and cimetidine overlapped to a very significant
degree using these conditions. The coeluting internal standards
method was then compared to the SIL-IS method, and the
results are shown in Figure 5. The mean concentrations of
creatinine for the negative control, SIL-IS, and coeluting internal
standard methods were 9.68 mM, 19.18 mM, and 14.7 mM,
respectively. The percent differences from the SIL-IS method
(positive control) were 49.4% (negative control) and 23.3%
(coeluting internal standard). The precision values of the
methods, expressed in terms of relative standard deviation
(using five replicates in each case) were 0.258 (negative
control), 0.561 (reference), and 0.852 (coeluting internal
standard). Although the coeluting internal standard method
using cimetidine lacks accuracy when compared with the SIL-IS
method, the results were still much improved when compared
to the method without internal standard. It is clear that the use of
compounds that are merely coeluted with the analyte of interest
is insufficient for optimal compensation of matrix effects.
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ES393288_LCA0314_011.pgs 02.26.2014 00:27 ADV blackyellowmagentacyan
LC•GC Asia Pacific March 201412
Hewavitharana et al.
20. G. Ouyang and J. Pawliszyn, Anal. Chim. Acta 627, 184 (2008).
21. L. Alder, S. Luderitz, K. Lindtner, and H.-J. Stan, J. Chromatogr. A
1058, 67 (2004).
22. A.K. Hewavitharana, Crit. Rev. Anal. Chem. 39, 272 (2009).
23. M. Stüber and T. Reemtsma, Anal. Bioanal. Chem. 378, 910
(2004).
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25. S. Altinöz and D. Tekeli, J. Pharm. Biomed. Anal. 24, 507 (2001).
26. P. Koscielniak, J. Kozak, and M. Wieczorek, J. Anal. At. Spectrom.
26, 1387 (2011).
27. P. Koscielniak, J. Kozak, M. Wieczorek, and M. Herman, Anal. Lett.
44, 411 (2011).
28. P. Kośõcielniak and J. Kozak, Crit. Rev. Anal. Chem. 36, 27
(2006).
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Thomas Karnes, J. Chromatogr. B 875, 333 (2008).
31. S.L.R. Ellison and M. Thompson, Analyst 133, 992 (2008).
32. L.G. Freitas, C.W. Götz, M. Ruff, H.P. Singer, and S.R. Müller, J.
Chromatogr. A 1028, 277 (2004).
33. X. Zhao and C.D. Metcalfe, Anal. Chem. 80, 2010 (2008).
34. K. Lanckmans, S. Sarre, I. Smolders, and Y. Michotte, Rapid
Commun. Mass Spectrom. 21, 1187 (2007).
35. Y. Zuo, C. Wang, J. Zhou, A. Sachdeva, and V.C. Ruelos, Anal. Sci.
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(2003).
Amitha K. Hewavitharana, Swee K. Tan, and P. Nicholas
Shaw are with the School of Pharmacy at The University of
Queensland in Brisbane, Australia. Direct correspondence to:
References
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Andre, and B. Le Bizec, Anal. Chim. Acta 529, 129 (2005).
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25, 881 (2006).
8. T.M. Annesley, Clin. Chem. 49, 1041 (2003).
9. H. Trufelli, P. Palma, G. Famiglini, and A. Cappiello, Mass Spectrom.
Rev. 30, 491 (2011).
10. R. Bonfiglio, R.C. King, T.V. Olah, and K. Merkle, Rapid Commun.
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Fernández-Alba, J. Chromatogr. A 1218, 7634 (2011).
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Spectrom. 18, 49 (2004).
14. C. Cote, A. Bergeron, J.N. Mess, M. Furtado, and F. Garofolo,
Bioanalysis 1, 1243 (2009).
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ES393601_LCA0314_012.pgs 02.26.2014 04:10 ADV blackyellowmagentacyan
13www.chromatographyonline.com
LC TROUBLESHOOTING
Often considered a necessary evil,
the first peak in a chromatogram
can be a useful diagnostic tool for
troubleshooting liquid chromatographic
(LC) separations. Most people I
encounter refer to this as the column
dead time peak, abbreviated t0.
However, it has a wide variety of other
names: Junk peak, garbage peak,
solvent front, or hold-up time, with
tM as the most common alternative
abbreviation. This represents the time
it takes something to go through the
LC column that does not interact with
the column. A corresponding dead
volume (or hold-up volume), VM, is
the volume of mobile phase inside the
column. This volume comprises both
the volume of mobile phase between
the packing particles (the interstitial
volume) and the volume within the
particles (the pore volume). We’ll see
that t0 can be a useful diagnostic tool
to identify potential problems with an
LC method.
Measuring t0If we want to use the column dead time
as a tool, we need to be able to identify
it. Most LC detectors will generate a
peak at t0, the most obvious exception
being the mass spectrometric
detector (liquid chromatography–mass
spectrometry [LC–MS]). Therefore,
the chromatogram usually has a peak
similar to the first baseline disturbance
in Figure 1. If the sample is very clean
and has minimal unretained material, a
small baseline disturbance as shown
in Figure 1(a) may appear. More
commonly, there is sufficient unretained
material to generate a large, off-scale
peak (Figure 1[b]). Although there are
more exact measurement techniques
for t0, such as injection of D2O, most
of us just use the retention time of the
peak. I prefer to pick a measurement
that is easy to reproduce, because
most of the time an estimate of t0 is
sufficient. For Figure 1(a), this is the
point the disturbance crosses the
baseline, noted by the arrow. Because
a large unretained peak usually is off
scale so that the top of the peak may
be inconvenient to locate, I usually pick
the point where the peak rises from
the baseline (arrow in Figure 1[b]). Of
course the retention time reported by
the data system is another convenient
measurement of the dead time.
To confirm a measured value of
t0 or to determine it if there is no
corresponding disturbance in the
baseline, as with LC–MS, we can
estimate the column dead volume, VM,
and convert it to t0. If you are using
a 4.6-mm i.d. column, VM can be
estimated as follows:
VM ≈ 0.01L [1]
where VM is in millilitres and L is in
millimetres. Thus, for a 150 mm ×
4.6 mm column, VM = 0.01 × 150 mm
= 1.5 mL. For columns of other internal
diameters, you can use
VM ≈ 0.5Ldc2/1000 [2]
where dc is the column internal
diameter in millimetres. For a 50 mm
× 2.1 mm column, VM ≈ 0.5 ×
50 × 2.12/1000 = 0.11 mL. Either
of these estimates is good to within
approximately ±10% for columns
packed with totally porous particles.
These estimates are based on the
assumption that VM represents ~65%
of the volume of an empty column and
that about half of this volume is inside
the particles and half is between the
particles.
The column dead time is simply the
column volume divided by the flow rate,
F (in millilitres per minute):
t0 = VM/F [3]
Using t0 as a Diagnostic ToolI regularly use t0 to help diagnose
problems submitted to me by readers.
Here are some of the ways this can be
useful.
Verify the Unretained Peak: I find
that it is useful to check to be sure
that the presumed t0 peak is in the
right place. For this, simply calculate
t0 using equation 1 or 2 and 3, then
compare this to the observed peak
in the chromatogram. For example,
if the chromatogram of Figure 1(b)
was obtained with a 150 mm ×
4.6 mm column operated at 2 mL/
min, t0 ≈ 1.5 mL/2 mL/min = 0.75 min.
This agrees with the observed peak
at approximately the same retention
time. If the calculated and measured
values of t0 differ by more than ~20%,
it is advisable to try to figure out why.
Several possibilities are discussed
below.
t0 Larger Than Expected: If t0 is
larger than expected, the most likely
cause is a flow-related problem. For
isocratic methods, the retention time
of retained peaks should change by
the same proportion as t0 when the
flow rate is changed. If, for example,
the above case had an observed t0
of 1.0 min, the retained peaks should
increase by 1.0/0.75 = 1.33-fold. If this
is confirmed, check for flow-related
problems. Larger than expected values
of t0 indicate a drop in the flow rate.
Always check the most obvious case
first — is the flow rate set properly? It
should also be obvious that historical
retention data should be consulted
Column Dead Time as a Diagnostic ToolJohn W. Dolan, Walnut Creek, LC Resources, California, USA.
What good is that big, ugly peak at the beginning of the chromatogram?
ES392734_LCA0314_013.pgs 02.25.2014 21:14 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201414
LC TROUBLESHOOTING
to be sure an abnormality in t0 really exists.
Assuming that the flow rate is set correctly, the most likely causes of a flow rate problem are leaks, air bubbles, and problems with the check valves or pump seals. A secondary symptom may be low pressure, depending on how far off t0 is. If leaks are not obvious, I would open the pump purge valve and run 5 mL or so of solvent to waste from each flow channel in use. Thus, if you are using a two-pump high-pressure mixing system, purge both pumps; if it is a low-pressure mixing system, purge
each solvent line. This should remove any bubbles from the system. If the problem persists, carefully check each fitting in the pressurized flow stream for leaks. Sometimes the fine point of a twisted laboratory wipe or facial tissue can be used to probe the fittings for possible leaks. If leaks in the flow stream are not found, a pump problem is most likely.
If you are using acetonitrile as one of the solvents and the pump does not have active check valves, it is possible for the inlet check valves to stick. This is from the formation of polymers on the surface of the check-valve seat, and usually can be corrected by sonicating the check valves for a few minutes in methanol. A more detailed discussion of this can be found in an earlier “LC Troubleshooting” column (1). The outlet check valves can also leak if they become contaminated. If you have a pump with outlet check valves, sonicating them may help. If you choose to sonicate the check valves, be careful that you know how they are assembled, in case they come apart in the process. Worn pump seals can also leak, resulting in a lower than expected flow rate. Check the maintenance log for the pump. If the seals haven’t been replaced in the past year, I would suggest replacing them. If the seals are newer, you can inspect the pump more closely for possible signs of leaking. Most pumps have a hole or drain tube below the pump head behind the check valves, where any leakage from the seals will exit. Look for signs of leakage, such as visible liquid or white deposits of buffer residue. Replace the pump seals if there is any question of its integrity.t0 Smaller Than Expected: If the observed t0 peak comes out
earlier than expected, one of two possibilities exists. The easier to address is a mistake in the flow rate setting so that the flow rate is too high. Although I suppose it is possible, I have never heard of a pump or controller software failure that resulted in excessive flow rates, so operator error is the most likely source of a flow-related problem.
With the most common forms of LC (reversed phase, normal phase, ion exchange, ion pairing, and so forth), t0 should be the first disturbance in the chromatogram. If something is eluted earlier than the expected retention of t0, the compounds may be excluded from the pores of the packing material. The exception to this is in size-exclusion chromatography, where everything should come out before t0. If a molecule is restricted from entering the packing pores, it only has access to the volume of column between the particles (the interstitial volume), which I mentioned at the beginning was approximately half of the total solvent volume, or 30–35% of the column volume if the total dead volume is ~65%. I’ve seen the interstitial volume quoted as 40% of the column volume (2), but in either case, a significant portion of the dead volume is inside the particles. If sample molecules are restricted from entering the pores of the packing, they will be eluted before t0. Such peaks may be confused with the true t0 peak, because we are used to assigning the first peak in the chromatogram as t0.
Two common causes of restricted access to the pores exist. The most obvious is that the molecules are too large to enter the pores. A rule of thumb is that the pore diameter should be 3–4 times the hydrodynamic radius of the molecule. Most analytical columns have pores in the 8–12 nm (80–120 Å) range, which will accommodate molecules of up to ~10,000 Da. Above this size, for example proteins, larger-pore columns, with 30–40 nm pores, are used. Thus, if your sample is a pharmaceutical product, although the analyte of interest may be <1000 Da, the formulation may contain polymers or other excipients in excess of 10,000 Da that may be excluded from the pores. In some cases, dimers or other aggregations of sample molecules can result in a material that is stable enough to
1 2Time (min)
(a) (b)
3 4 2Time (min)
4
0 5 10
Base
Base
(a) (b)
Neutral Neutral
Acid
Acid
0 5 10
Figure 1: Examples of t0 peaks (arrows). (a) Chromatogram with little unretained material; (b) large peak normally observed at t0.
Figure 2: Illustration of exclusion of a charged molecule from packing pores. (a) No ion pairing reagent present, charged base is poorly retained, acid is retained; (b) with ion pairing reagent, charge on particle surface attracts base, increasing its retention, but repels the acid, excluding it from the pores. See text for details. Adapted from reference 3.
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15www.chromatographyonline.com
LC TROUBLESHOOTING
chromatograph, but too large to enter the pores. Any of these
large molecules may be eluted before the column dead
volume.
A second cause of restricted access to the pores is that
chemical repulsion between a sample molecule and the pore
may exist. An example of this is illustrated in Figure 2 (3). In
Figure 2(a), a sample of adrenaline (base), benzyl alcohol
(neutral), and naphthalene sulphonate (acid) is separated
on a C18 column with a methanol–buffer mobile phase at
pH 6. Adrenaline has a pKa of 8.55, so it will be fully ionized
under these conditions, and naphthalene sulphonate has a
pKa of <1, so it will also be ionized. Adrenaline is unretained
because in its charged form it is very polar and not retained.
On the other hand, naphthalene sulphate has sufficient
nonpolar nature that it is well retained, even though it is
ionized. With the addition of 14 mM octane sulphate as an
ion-pairing reagent, the results of Figure 2(b) are obtained.
Here, the ion-pairing reagent is assumed to be immobilized
on the surface of the C18 stationary phase, creating an in
situ ion-exchange surface with a negative charge that is
used to retain the positively charged adrenaline. However,
although the pH of the mobile phase was not changed, you
can see that the naphthalene sulphonate is now unretained.
This is because the pores now contain a net negative charge
that repels the negatively charged naphthalene sulphonate.
You can imagine a similar situation where a pore with a
net charge would repel a sample molecule of opposite
charge, resulting in exclusion from the particles and elution
before the column dead time. This phenomenon, called
ion exclusion, is occasionally observed in ion-exchange
chromatography. Any chemical change in the pore surface
that repels sample molecules will have the same result.
Using t0 to Check for “Good” ChromatographyAnother use I make of t0 is to check for the quality of the
separation, especially when readers submit problem
chromatograms for me to diagnose. For isocratic separations
(those with a constant mobile phase composition), the
retention factor, k, is calculated as:
k = (tR – t0)/t0 [4]
where tR is the retention time of the peak of interest. The
retention factor is a measure of the distribution of the
sample between the stationary phase and the mobile
phase. As an indicator of chromatographic quality, I like to
see 1 < k < 20, or better 2 < k < 10, as has been discussed
in past “LC Troubleshooting” columns (for example,
reference 4). If k < 1 is observed, the peak is likely to have
poor retention-time reproducibility and more likely than
strongly retained compounds to have interferences from the
tail of the t0 peak. The retention factor can be estimated by
using t0 as the unit of measure for retention and measuring
retention beginning at the observed value of t0. For the
chromatogram of Figure 1(b), this is done by dividing the
baseline up in units of t0 instead of minutes. The first peak
is eluted a little over 1 t0 unit past t0, so it has a k value of a
little more than 1. Similarly, the second and third peaks have
k values of a bit more than 2 and 3, respectively. Although
equation 4 does not apply to gradient elution, the general
principle of keeping the first peak away from t0 to avoid
interferences still holds. From this standpoint, I like to see
the first peak of interest in a gradient come off the column at
least 1 t0 unit past t0.
ConclusionsAlthough at first glance, the solvent peak at the beginning of
the chromatogram has no value, it can be a useful tool to help
diagnose problems with the chromatogram. We can compare
column dead time estimates with the observed retention time
of the t0 peak and get an idea of what might be going wrong.
When the first peak comes out after the expected retention
time, the problem is usually flow-related and most commonly
caused by a leak. Peaks that are eluted before the expected
retention t0 time are most likely excluded from the pores of
the column, because they are too large or are repelled from
the pores. So we can see that nothing (t0) really is a useful
diagnostic tool.
References(1) J.W. Dolan, LCGC North Am. 26(6), 532–538 (2008).
(2) U.D. Neue, HPLC Columns (Wiley-VCH New York, USA, 1997), p. 53.
(3) J.H. Knox and R.A. Hartwick, J. Chromatogr. 204, 3–21 (1981).
(4) J.W. Dolan, LCGC North Am. 25(7), 704–709 (2007).
John W. Dolan is vice president of LC Resources, Walnut Creek,
California, USA. He is also a member of LC•GC Asia Pacific’s
editorial advisory board. Direct correspondence about this
column should go to “LC Troubleshooting” LC•GC Asia Pacific,
4A Bridgegate Pavilion, Chester Business Park, Wrexham Road,
Chester, CH4 9QH, UK, or email the editor-in-chief, Alasdair
Matheson, at [email protected]
Contact your local distributoror visit www.ace-hplc.com
ACE, UltraCore, SuperC18 and SuperPhenylHexyl are trademarks of Advanced Chromatography Technologies Ltd
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ES392736_LCA0314_015.pgs 02.25.2014 21:14 ADV blackyellowmagentacyan
LC•GC Asia Pacià c March 201416
PersPectives in Modern HPLC
For five decades since the 1960s, the
pressure limits of high performance
liquid chromatography (HPLc)
systems remained stagnant at
6000 psi (400 bar). this pressure
limit was appropriate for the
column packings available at the
time, which continuously trended
towards smaller particle sizes (that
is, from 30, 10, 5, to 3 μm). there
appeared to be no concerted efforts
to increase the system pressure
ratings during that period with
the exception of an exploratory
study published in 1969 (1). the
“breakthrough” in ultrahigh-pressure
liquid chromatography (UHPLc)
came in 1997 with proof-of-concept
research by James Jorgenson (2) and
follow-on studies by Milton Lee (3).
these early studies demonstrated
spectacular performance (column
efficiency, N = 200,000 plates) at
very high pressures (>60,000 psi)
in research systems using capillary
columns. However, the impact of their
discoveries for typical practitioners
and for routine applications were
only possible after the debut of
commercial UHPLc equipment with
reliable autosamplers and gradient
capabilities
in 2004, Waters corporation
introduced the first UHPLc system —
the Acquity UPLc (Ultra-Performance
Lc) system with an upper pressure
limit of 15,000 psi (1000 bar) together
with Acquity UPLc columns (1.0 and
2.1 mm i.d.) packed with sub-2-μm
hybrid particles (4–8). Although
this pressure rating was modest
in comparison to that achieved
using the early research systems,
the new UHPLc system generated
considerable excitement and
established higher performance
benchmarks and expectations. these
early systems enjoyed immediate
acceptance in research applications
despite some initial concerns over
injection precision and other issues in
quality control (Qc) applications (7,9).
other manufacturers quickly followed
with their own UHPLc systems. By
2010, the transformation from HPLc to
UHPLc was essentially complete with
UHPLc product offerings available
from most major vendors. today, all
UHPLc systems have reduced system
dispersion and dwell volumes as well
as improved precision and sensitivity
(10).
the fundamentals, benefits,
potential issues, and best practices of
UHPLc are well documented (5–7,
9–15). some of the key benefits are as
follows:
• Faster analysis with good (or
acceptable) resolution — the
primary incentive for new users
in high-throughput screening,
liquid chromatography–mass
spectrometry (Lc–Ms), routine
testing, and method development.
• superiority in high-resolution
separations of complex samples —
peak capacities of 600–1000 are
now possible in a reasonable time
(<60 min under gradient conditions).
this capability is transformative
in life science research and
the analysis of complex
pharmaceuticals, filling an unmet
need for Qc applications (13–15).
• other benefits of UHPLc versus
conventional HPLc include
substantial solvent savings (5- to
15-fold), increased mass sensitivity
in Uv detection (3–10-fold), and
improved precision for both
retention times (2- to 3-fold) and
peak areas (<0.1% rsd).
in the last few years, UHPLc has
evolved from a scientific curiosity
for early adopters in research and
high-throughput screening into a
modern standard HPLc platform.
As the saga of UHPLc unfolded, a
number of myths or half-truths have
emerged. the goal of this column
instalment is to describe some of the
more interesting myths and provide
evidence to delineate or repudiate
these widely held misconceptions.
the myths:
• You don’t need an expensive
UHPLc system — high-temperature
Lc or core–shell columns will get
you there.
• viscous heating is a “huge” issue
for sub-2-μm particle columns.
• A 2.1-mm i.d., sub-2-μm column is
the best choice for UHPLc.
• Gold-plated fittings with double
ferrules are needed in UHPLc.
• A binary high-pressure mixing
pump is a “must”.
• UHPLc provides substantially
higher Uv sensitivity than
conventional HPLc.
• Method transfer between UHPLc
and HPLc is very easy (“a piece of
Myths in Ultrahigh-Pressure Liquid chromatography
The advent of ultrahigh-pressure liquid chromatography (UHPLC) and its successful commercialization in the last few years has brought forth a modern high performance liquid chromatography (HPLC) platform capable of higher speed, resolution, precision, and sensitivity. Currently, all major HPLC manufacturers offer some type of low-dispersion UHPLC products with upper pressure limits ranging from 15,000 to 19,000 psi (1000 to 1300 bar). This instalment describes a number of popular myths or half-truths in UHPLC and provides data that contradict or even repudiate some of these commonly held beliefs.
Michael W. Dong, Genentech, south san Fransisco, california, UsA.
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17www.chromatographyonline.com
PersPectives in Modern HPLC
technology. With overwhelming
increases in efficiency over fully
porous material demonstrated in initial
studies (+40% versus 1.7-μm particles
and >200% versus 3.0-μm fully
porous particles), its impact can be
transformative in modern HPLc.
today, the objections to UHPLc
versus high-temperature Lc or
core-shell columns by skeptics are
waning as UHPLc is becoming a
mainstream platform.
Viscous heating is a “huge” issue
for sub-2-μm particle columns:
A frictional heating phenomenon is
observed when the mobile phase
is pumped at a relatively high flow
rate and operating pressure through
columns packed with very small
particles. the heat generated is
cumulative, giving rise to longitudinal
thermal gradients along the
length of the column. the heat is
simultaneously dissipated through the
column wall, resulting in radial thermal
gradients and parabolic flow profiles
that cause band broadening. this is
a popular research topic with dozens
of papers already published (19–21).
capital investment. Along the same
line are comments that superficially
porous sub-3-μm (core–shell) material
is a more cost-effective alternative to
expensive UHPLc systems.
nowadays, most practitioners may
realize that these arguments are not
valid because UHPLc can be used in
combination with these approaches
(including two-dimensional [2d]
Lc) with superior results, as they
are options rather than alternatives
(13,14). the use of high-temperature
Lc above 60–70 °c is not viable for
thermally labile pharmaceuticals
and compounds (16,17). core–
shell (also known as fused core,
solid core, or superficially porous)
material is becoming the dominant
contender to totally porous material
for all applications (18). However, the
notion that core–shell columns will
lessen the need for UHPLc is less
compelling with recent introductions of
1.3- and 1.6-μm core–shell particles
that deliver ~400,000 plates/m (18).
i believe the availability of sub-2-μm
core–shell material represents an
exciting advancement in column
cake”) and method revalidation is
not needed.
• Lower-dispersion UHPLc systems
are better.
Dispelling Some Popular Myths in UHPLCYou don’t need UHPLC —
high-temperature LC or core–shell
columns will get you there: in
April of 2010, i was invited to a local
meeting to give a presentation on
UHPLc. the format turned out to
be a debate between two opposing
viewpoints on UHPLc versus
high-temperature Lc. i remembered
being surprised by some comments
that UHPLc was a marketing hype
invented by the vendors to extract
more money from the user. At
first, i thought that this conspiracy
theory was a joke but it turned
out to be serious. i also recalled
hearing this line of argument about
high-temperature Lc around 2006,
mostly from vendors in the “have not”
camps that indicated high column
temperatures will allow one to use
small-particle columns without a major
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When you are running your GC methods, sometimes you face difficulties.
You might have high levels of noise, or spiking peaks, or ghost peaks.
Or you might have decreasing retention times or irreproducible results.
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ES392753_LCA0314_017.pgs 02.25.2014 21:16 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201418
PersPectives in Modern HPLC
for columns packed with very small
particles (<1.5 μm), operation at very
high pressures (>800 bar), or with
a forced air oven and if there are
critical pairs sensitive to temperature
changes.
A 2.1-mm i.d., sub-2-μm column
is the best choice for UHPLC: the
most popular UHPLc column format
consists of 2.1-mm i.d. columns
packed with sub-2-μm particles;
these columns are particularly
well-matched to the first commercial
systems in 2004 (6). since then, there
has been a trend towards UHPLc
systems to accommodate existing
HPLc methods with larger-diameter
columns and a higher flow rate range
(>2 mL/min), a bigger column oven
(>150 mm), larger injection loops
(>20 μL), and larger internal diameter
connection tubing. these UHPLc
systems would have higher system
dispersion, a trade-off for better
flexibility and compatibility to existing
HPLc methods.
Figure 1 compares the isocratic
performance of three 50-mm long
columns of various diameters (2.1,
3.0, and 4.6 mm) packed with 1.8-μm
c18 particles with appropriate flow
adjustments. note that the 4.6-mm i.d
column displays significantly higher
column efficiencies (N = 12,860
versus 8170 for the 2.1-mm i.d.
column). this observation is in line
with the notion that the detrimental
“wall effect” is more pronounced
for narrower columns (4,8), as it
is exceedingly difficult to pack
narrow-bore columns with high
reduced plate heights. Figure 1 also
shows the effect of system dispersion
or extracolumn band-broadening
as lower column efficiencies (N)
are observed for early peaks (for
example, the first peak, which is
toluene). note that the extracolumn
effect is more severe for 2.1-mm i.d.
columns because of the smaller peak
volumes (6,8).
For most users, a strong case can
be made for 3-mm i.d. columns,
particularly in Qc applications. these
columns generally have higher
column efficiencies in comparison
to their 2.1-mm i.d. counterparts and
support practical flow rates of
0.6–1.5 mL/min. their use may
provide easier transitions for HPLc
users familiar with 4.6-mm i.d.
columns (15).
where HPLc method conditions are
transferred to UHPLc, this effect can
be partially mitigated by deliberately
setting the UHPLc methods to
a lower column temperature (for
example, 5 °c). Another viable
solution to reduce the effect of
longitudinal heating is to introduce
intermediate active cooling by
connecting shorter columns together
to form a longer column (22). this
longitudinal heating effect may
cause issues when transferring
methods, particularly across UHPLc
platforms from different vendors
and with varying types of column
ovens.
For most users, it is important
to acknowledge the existence of
viscous heating, however, it may not
be a serious practical issue except
these complex effects are dependent
on the type of column oven, particle
size, column length and diameter,
thermal conductivity of the mobile
phase, and flow rate.
it turns out that radial thermal
gradients are indeed problematic
when the column wall temperature
is controlled under isothermal
conditions (that is, in a water bath
or to some extent in a forced air
column oven). For still-air column
ovens, the longitudinal heating effect
is more serious and can increase
the temperatures at the end of the
column by 10 °c to 20 °c (19,20).
Although this does not cause band
broadening, it raises the average
temperature of the column, causing
lower retention and potential
selectivity changes. in situations
Figure 1: comparative chromatograms of efficiency performance of three 50-mm
long, 1.8-µm dp c18 columns of various inner diameters: (a) 2.1 mm, (b) 3.0 mm,
and (c) 4.6 mm. observed UsP column efficiencies, N, are labelled for peaks 1,
3, and 5. An Agilent 1290 UHPLc system was used in this evaluation. column:
Agilent Zorbax eclipse Plus c18 (50 mm, 1.8 μm); mobile phase: 70% methanol
in 0.1% formic acid in water; flow rate: (a) 0.5 mL/min at 35 °c, (b) 1.0 mL/min at
35 °c, and (c) 2.0 mL/min at 35 °c; detection: 250 nm at 80 points/s; pressure: (a)
570 bar, (b) 460 bar, (c) 520 bar; sample: 1.0 μL of test mix containing (in order of
peak appearance) toluene, ethylbenzene, propylbenzene, tert-butylbenzene, and
anthracene.
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19www.chromatographyonline.com
PersPectives in Modern HPLC
tend to be vendor-specific and are
dictated by pump designs (piston
volume, availability of variable
stroke volume) and the mixer type.
Quaternary low-pressure mixing
pumps have larger dwell volumes
(because mobile phases are selected
by a proportionating valve at low
pressure and pumped by a single
pump with mixing occurring inside
the pump). they are particularly
useful for method development. they
also have substantially lower price
tags because only a single pump is
needed to form gradient or for mobile
phase blending. Quaternary UHPLc
pumps are now available from all
major manufacturers and many have
dwell volumes less than 0.5 mL, which
are acceptable for most analyses by
UHPLc.
UHPLC provides substantially
higher UV sensitivity than
HPLC: reports on higher Uv
detection sensitivity with UHPLc
versus conventional HPLc can be
misleading. UHPLc using small
internal diameter columns have
often been reported to have much
higher sensitivity (peak heights).
this is because peak volumes are
proportional to column void volumes,
so a smaller column will produce a
much higher peak height for the same
sample amount injected. However,
when the sample amount is scaled to
the column volumes, both HPLc and
UHPLc should yield similar sensitivity,
provided detector noise and
flow-cell pathlengths are equivalent.
this is borne out by comparative
chromatograms shown in Figure 2
of a sample analyzed on a HPLc
system and an UHPLc system using
identical columns with a conventional
HPLc method. detailed analysis
shows the signal-to-noise ratio (s/n)
and gradient shifts to be comparable
on both systems. the operating
pressures were 160 and 200 bar,
respectively, a reflection of the smaller
internal diameter connection tubing of
the UHPLc system. retention times on
the UHPLc system were 0.8 to 1.4 min
lower because of its smaller dwell
volumes. More discussion on how
to mitigate this issue during method
transfers can be found in the next
section. note that an early eluted peak
(for example, M235) has ~30% higher
sensitivity (19 mAU in UHPLc versus
14 mAU in HPLc). this is a result of
fittings remain quite expensive. As
a result of these current offerings,
gold-plated nuts and double metallic
ferrules are no longer requirements for
UHPLc.
A binary high-pressure mixing
pump is a “must”: Low dwell volume
is advantageous to reduce gradient
delay time for fast gradients (6–8).
Binary high-pressure mixing pumps
have inherently low dwell volumes
(because different mobile phases are
pumped by two different pumps and
mixing is external to the pumps). they
are preferred for high-throughput
screening and Lc–Ms applications.
it was quickly found that some
mixing volumes, provided by external
mixers, are needed for efficient
solvent blending to reduce baseline
perturbations in Uv detection (7,11).
the optimum mixing volumes required
for high-sensitivity Uv detection
Gold-plated àttings with double
ferrules are needed in UHPLC:
reliable fittings for UHPLc column
connections and leak-free operation
at high pressures were found to
be problematic during the early
days of UHPLc. Gold-plated nuts
(to prevent seizing of the threads)
and double ferrules were used in
first-generation UHPLc fittings. they
have fixed insertion depths and
are not universally compatible with
columns from different manufacturers.
currently, many choices are available,
including reusable fittings for
finger-tight or wrench-tight operation
that can be resealed many times with
a pressure rating up to 20,000 psi.
some examples found to be
convenient and reliable are opti-Lok
from optimize technologies, vHP-320
from idex-Upchurch, and viper from
thermo Fisher/dionex, though these
Figure 2: comparative chromatograms of a retention marker solution for a
multi-chiral drug spiked with expected impurities on (a) an HPLc (Agilent 1200
with a quaternary pump) and (b) an UHPLc system (Agilent 1290 with a binary
pump). column: 150 mm × 4.6 mm, 3.0-μm dp Ace-3-c18; mobile-phase A:
20 mM ammonium formate, pH 3.7; mobile-phase B: acetonitrile with 0.5% formic
acid; gradient: 5–15% B in 5 min, 15–40% in 25 min, 40–90%B in 3 min, total run
time = 42 min; flow rate: 1.0 mL/min at 30 °c; detection: 280 nm; sample: 10 μL of
a retention time marker test mix containing the drug substance at 0.5 mg/mL spiked
with expected impurities. note that the noise and gradient shift were found to be
comparable for the two chromatograms. the operating pressure was found to be
at 160 and 200 bar, respectively. the retention times for the sample components for
the UHPLc system were found to be 0.8 to 1.4 min lower than those from the HPLc
system because of the lower system dwell volume (0.3 mL versus 1.0 mL).
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LC•GC Asia Paciàc March 201420
PersPectives in Modern HPLC
the lower dwell volume and system
dispersion of the UHPLc system.
in UHPLc, innovative flow
cell designs with total internal
reflectance allow the construction of
smaller-volume flow cells (0.5–2 μL)
with the same 10-mm pathlength
as a standard HPLc flow cell
(8–10 μL). this design concept
has also led to the development
of extended-pathlength flow cells
(such as 25–60 mm) to enhance
detector sensitivity (6,23). Figure 3
shows comparative chromatograms
of the same sample injected on
an UHPLc system with a standard
(10-mm) flow cell (Figure 3[a]) and
on the same UHPLc system with
an extended-pathlength flow cell
(60-mm) (Figure 3[b]). note that
while the noise was found to be
similar (AstM noise of 25 μAU), the
signals (peak heights) were six times
higher on the extended flow cell,
as was expected. However, these
extended-pathlength flow cells may
be less useful for impurity analysis
using area percent calculations
because detector signal saturation
can easily occur on the main peak
(8). they also have higher dispersion
and are therefore more compatible
with larger internal diameter
columns. nevertheless, they can be
advantageous for impurities testing
based on external standardization,
determination of trace genotoxic
impurities (24), and cleaning
verification applications of highly
potent compounds (25).
Method transfer between UHPLC
and HPLC is “a piece of cake”
and method revalidation is
unnecessary: Method transfer is
the formal process of demonstrating
that a validated method, developed
or validated in one laboratory, can
be properly executed by another
laboratory operating under a good
manufacturing practice (GMP)
environment. this ensures that
accurate, quality data can be
generated in the latter (21). Formal
method transfer between two different
HPLc systems is typically not
needed unless they are deemed “not
equivalent.” there are three scenarios
for “method transfers” between HPLc
and UHPLc: same HPLc method on
different types of equipment (HPLc
versus UHPLc); newly developed
UHPLc methods “back transfer”
to HPLc conditions; and existing
(legacy) HPLc methods to UHPLc
methods.
Same HPLC methods on different
types of equipment (HPLC and
UHPLC, the simplest case): For
laboratories having both HPLc and
UHPLc equipment, it would be ideal
if equivalent results using the same
HPLc method could be obtained
on both types of equipment. As
demonstrated in the previous section,
results are fairly equivalent with the
exception of retention time shifts
because of the smaller dwell volumes
of a typical UHPLc (~0.3 mL for
UHPLc versus ~1.0 mL for HPLc).
this can be remedied by several
means: increasing the dwell volume
of UHPLc system by using a larger
external mixer (probably not very
practical); building an initial isocratic
segment into the HPLc method
and allowing the user to adjust the
duration of this segment in the method
(generally preferred); or using optional
simulation software available on some
chromatography data systems to
simulate the performance of various
equipment by automatic method
adjustments (26,27).
“Back transferring” or “translating”
UHPLC methods to HPLC method
conditions (in method development
labs): Many laboratories prefer to use
UHPLc for rapid method development
including column and mobile phase
screening and method optimization
(5–7,21) and then “back transfer”
the optimized UHPLc methods to
HPLc conditions using longer column
with larger particles via geometrical
scaling. the approach is typically
used to support global manufacturing
operations since UHPLc may not be
available universally. case studies
for method transfer processes are
available elsewhere (7,21,28,29).
Method transfer from HPLC to UHPLC
(for GMP operation): the primary
driver to purchase UHPLc equipment
is the ability to perform faster analysis
with “good” resolution. A 2–3-fold or
greater reduction in analysis time,
while maintaining similar resolution,
is readily achievable using UHPLc.
For instance, a 15-min method using
a 150 mm × 4.6 mm column packed
with 5-μm particles can theoretically
be performed on a 50 mm × 2.1 mm
column packed with 1.7-μm particles
with equivalent column efficiency
in 5 min (10). even faster analysis
(1.5 min or a ninefold increase) is
possible if optimum flow rates are
used (optimum linear velocity is
inversely proportional to particle size).
A geometrical scaling approach is
typically used to accomplish such
transfers (29).
some ground rules for method
scaling between HPLc and UHPLc:
column length is scaled to particle
size keeping the column length to
particle size ratio the same; flow
rate is scaled to cross-sectional
area of the column (also inversely
proportional to the particle size if
optimum flow can be used); gradient
time is scaled to column length; and
flow rate and injection volume are
scaled to column void volume. one
important requirement is that the new
UHPLc column used must contain
identical bonded phase materials to
eliminate any selectivity differences.
Also, mobile phases used should be
identical (type of buffer, strength,
pH, and organic modifier). details
on this geometrical scaling are
available from the Pharmacopeial
Forum (29) and calculator programs
are available at various vendors’
websites (Waters, Agilent, and
thermo Fisher/dionex) and other
sources (30).
For validated HPLc methods, there
were numerous discussions on what
constitutes a method adjustment
versus a method change, and at what
point a method revalidation is needed
(21,31). the current consensus
appears to be that a partial method
validation (including specificity,
intermediate precision, linearity, and
robustness) should be considered as
well as a demonstration of method
equivalency between the two methods
(7,21,28,31). this process may be
straightforward for simple assays
but can be challenging for complex
samples (15,21), particularly for Qc
methods for commercial products.
Lower-dispersion UHPLC systems
are better — some pros and cons:
this statement may not be a myth
because lower system dispersion is
always considered to be better (6,8).
Low system dispersion systems are
desirable because they allow the use
of smaller columns without efficiency
loss. However, there are also some
important caveats and trade-offs.
Lower dispersion is achieved by a
ES392748_LCA0314_020.pgs 02.25.2014 21:16 ADV blackyellowmagenta
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LC•GC Asia Paciàc March 201422
PersPectives in Modern HPLC
and UHPLc systems (6,32). it should
be noted that UHPLc systems have
substantially lower dispersion than
convention HPLc systems and larger
sample loop or flow cell, switching
valve, and connection tubing all
contribute to system dispersion or
bandwidth.
it is useful to realize that system
dispersion before the column
(injector, loop, switching valve)
is generally less important since
most high-resolution analyses
are conducted under gradient
conditions (since sample bands are
refocused at the top of the column).
Post-column dispersion (tubing from
column to detector and detector
flow cell or mass spectrometer
source) is more critical because
it will broaden separated bands.
nevertheless, post-column tubing
and detector Uv flow cells can easily
be changed in some cases. note that
a low-dispersion kit to reduce system
bandwidth is often available from
some vendors (such as Agilent) (33).
Also, the use of small sample loops
(<20 μL), column ovens (<200 mm),
and connection tubing (<0.003 in.
i.d. which generates substantial back
pressure at flow rates greater than
1 mL/min) in some low-dispersion
systems, can be less compatible
with legacy HPLc methods. so,
lower system dispersion is a good
thing for demanding applications for
maximum performance — but may
lead to some sacrifice in system
convenience and flexibility for routine
analysis with diverse methods.
Summary and Conclusionsthis instalment addresses eight
popular myths in UHPLc and provides
evidence and references to delineate
and repudiate some of these beliefs.
Here is a summary of the conclusions:
• UHPLc is complementary to
high-temperature Lc and core–
shell columns and can be used by
itself or in combination with these
approaches.
• viscous heating is not a “huge”
practical issue for sub-2-μm particle
columns using still-air ovens under
“normal” operating conditions.
• A 2.1-mm i.d., sub-2-μm column
is a common column for UHPLc;
however, a strong case can be
made for 3-mm i.d. columns,
particularly for Qc applications.
a compilation of comparative
system dispersion measurements
(5σ bandspread or instrumental
bandwidth) of a number of HPLc
reduction of the volume of sample
fluidic path (that is, sample loop,
switching valve, connection tubing,
and flow cell). table 1 shows
Figure 3: comparative chromatograms of a retention marker solution for a
multi-chiral drug spiked with expected impurities on (a) an UHPLc system with a
standard 10-mm Uv flow cell (Agilent 1290 with a binary pump) and (b) the same
UHPLc system with an extended-pathlength flow cell (60 mm). HPLc method
conditions are identical to those in Figure 2 except that the injection volume is
2 μL. An AstM noise of 24 μAU was found for both chromatograms. the limits of
quantitation (LoQ) were found to be 0.05% and 0.01% for (a) and (b), respectively, at
2 μL injection. note that an LoQ of 0.002% was found for (b) using a 10-μL injection
though the main peak would saturate the detector signal.
Table 1: comparative data on system dispersion (5σ band spread) of various HPLc and
UHPLc systems. data courtesy of Waters corporation.
System Band Spread (µL) (5σ)
shimadzu UFLc 41
Agilent 1200 28
shimadzu nexera (with microbore flow cell) 26
Agilent 1290 (configured for dual column) 23
thermo Accela 21
Agilent 1290 (configured for single column) 20
dionex Ultimate 3000 17
Waters Acquity UPLc H-class with column manager 12
Waters Acquity UPLc H-class with column heater 9
Waters Acquity UPLc (with 1-µL loop) 8
Waters Acquity UPLc i-class Fnt (flow through needle) 7.5
Waters Acquity UPLc i-class FL (fixed loop) 5.5
Ftn = flow through needle, FL = fixed loop (1 μL). note that a low-dispersion kit to reduce
system bandwidth is often available from some vendors (for example, Agilent) (33).
ES392755_LCA0314_022.pgs 02.25.2014 21:16 ADV blackyellowmagentacyan
Please fill in ovals as shown: ●1. I am employed by: (fill in ONE only)0001 ◯ Private Industry0002 ◯ University/College0003 ◯ Government0004 ◯ Hospital0005 ◯ Medical Center0006 ◯ Research Lab/Institute/Foundation0007 ◯ Independent Analytical Lab0008 ◯ Utility Company0220 ◯ Other (please specify) ___________________________
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LC•GC Asia Paciàc March 201424
PersPectives in Modern HPLC
(20) F. Gritti and G. Guiochon, Anal. Chem.
80, 5009–5020 (2008).
(21) B. debrus, e. rozet, P. Hubert, J.-L.
veuthey, s. rudaz, and d Guillarme in
UHPLC in Life Sciences, d. Guillarme,
J.-L. veuthey, and r.M. smith, eds.
(royal society of chemistry Publishing,
cambridge, United Kingdom, 2012), pp.
67–98.
(22) K. Broeckhovn, J. Billen, M. verstraeten,
K. choikhet, M. dittmann, G. rozing,
and G. desmet, J. Chromatogr. A 1217,
2022–2031 (2010).
(23) A. Gratzfeld-Huesgen, Agilent
technologies, 2012, 5991-0115en.
(24) A. teasdale, ed., Genotoxic Impurities:
Strategies for Identification and Control
(Wiley, Hoboken, new Jersey, UsA,
2011).
(25) M.W. dong, e.X. Zhao, d.t. Yazzie, c.c.
Gu, and J.d. Pellett, Amer. Pharm. Rev.
15(6), 10–17 (2012).
(26) M. dittmann, K. choikhet, P. stemer,
and K. Witt, “Method transfer Between
UHPLc and HPLc: issues and
solutions,” presented at Pittcon 2011,
Atlanta, Georgia, UsA, 2011.
(27) Agilent 1290 infinity Lc with intelligent
system emulation technology, Agilent
technologies, 20135990-8670en,
2013.
(28) G. vanhoenacker, F. david, P. sandra,
B. Glatz, and e. naegele, Agilent
Applications notes, 5990-3981 en,
2009.
(29) U.d. neue, d. Mccabe, v. ramesh, H.
Pappa, and J. deMuth, Pharmacopeial
Forum 35(6), 1622–1626, 2009.
(30) d. Guillarme, http://www.unige.ch/sci-
ences/pharm/fanal/lcap/telechargement-
en.htm.
(31) M. swartz and i. Krull, LCGC North Am.
24(8), 480–490 (2006).
(32) s. Fekete, i. Kohler, s. rudaz, and d.
Guillarme, J. Pharm. Biomed. Anal.,
in press, http://dx.doi.org/10.1016/j.
jpba.2013.03.012.
(33) J.J. stankovich, F. Gritti, P.G. stevenson,
and G. Guichon, J. Sep. Sci. 36(17),
2709–2717 (2013).
Michael W. Dong is a senior scientist
in small Molecule drug discovery at
Genentech in south san Francisco,
california, UsA. He is responsible
for new technologies, automation
and supporting late-stage research
projects in small molecule analytical
chemistry and Qc of small molecule
pharmaceutical sciences. He holds a
Phd in analytical chemistry from the
city University of new York, UsA, and
a certificate in Biotechnology from U.c.
santa cruz, UsA. He has conducted
numerous courses on HPLc/UHPLc,
pharmaceutical analysis, HPLc method
development, drug development
process and drug quality fundamentals.
He is the author of Modern HPLC for
Practicing Scientists and a co-editor of
Handbook of Pharmaceutical Analysis
by HPLC. He is a member of the
editorial advisory board of LCGC North
America.
data collected here stemmed from
equipment and columns available at
the time of evaluation and may not
be representative of those currently
available. the opinions expressed
in this article are solely those of the
author and bear no reflection on
those of LCGC Asia Pacific or other
organizations.
References(1) B.A. Bidlingmeyer, r.P. Hooker, c.H.
Lochmuller, and L.B. rogers, J. Sep.
Sci. 4(6), 439–446 (1969).
(2) J.e. Macnair, K.c. Lewis, and J.W.
Jorgenson, Anal. Chem. 69, 983–989
(1997).
(3) n. Wu, J.A. Lippert, and M.L. Lee, J.
Chromatogr. A 911, 1–12 (2001).
(4) U.d. neue, M. Kele, B. Bunner, A.
Kromidas, t. dourdeville, J.r. Mazzeo,
e.s. Grumbach, s. serpa, t.e. Wheat,
P. Hong, and M. Gilar, in Advances in
Chromatogr., s. Fanali, P.r. Haddad,
c. Poole, P. schoenmakers, and d.K.
Lloyd, eds. (elsevier/crc Press, Boca
raton, Florida, UsA, 2009), pp. 99–143.
(5) d. Guillarme, J.-L. veuthey, and r.M
smith (ed), UHPLC in Life Sciences
(royal society of chemistry Publishing,
cambridge, United Kingdom, 2012).
(6) K.J. Fountain and P.c. iraneta, in
UHPLC in Life Sciences, d. Guillarme,
J.-L. veuthey, and r.M smith, eds.
(royal society of chemistry Publishing,
cambridge, United Kingdom, 2012), pp.
283–311.
(7) M.W. dong, LCGC North Am. 25(7),
656–666 (2007).
(8) M.W. dong, Modern HPLC for Practicing
Scientists (Wiley, Hoboken, new Jersey,
UsA, 2006).
(9) n. Wu and A.M. clausen, J. Sep. Sci.
30, 1167–1182 (2007).
(10) d. Guillarme and M.W. dong, Amer.
Pharm. Rev. 16(4), 36–43 (2013).
(11) M.W. dong, in Chromatography: A
Science of Discovery, r.L. Wixom and
c.W. Gehrke, eds. (Wiley, Hoboken, new
Jersey, UsA, 2010), pp. 328–332.
(12) d.t.t. nguyen, d. Guillarme, s. rudaz,
and J.L. veuthey, J. Sep. Sci. 29,
1836–1848 (2006).
(13) d. Guillarme, J. ruta, s. rudaz, and
J.-L. veuthey, Anal. Bioanal. Chem. 397,
1069–1082 (2010).
(14) d. Guillarme, e. Grata, G. Glauser, J.-L.
Wolfender, J.-L. veuthey, and s. rudaz,
J. Chromatogr. A 1216, 3232–3243
(2009).
(15) M.W. dong, d. Guillarme, s. Fekete, r.
rangelova, J. richards, d. Prudhomme,
and n.P. chetwyn, J. Pharm. Biomed.
Anal. submitted.
(16) J. ruta, d. Guillarme, s. rudaz, and
J.L. veuthey, J. Sep. Sci. 33, 2465–2477
(2010).
(17) s. Heinisch, in UHPLC in Life Sciences,
d. Guillarme, J.-L. veuthey, and r.M.
smith, eds. (royal society of chemistry
Publishing, cambridge, United Kingdom,
2012), pp. 102–128.
(18) s. Fekete, e. oláh, and J. Fekete, J.
Chromatogr. A 1228, 57–71 (2012).
(19) L. nováková, J.-L. veuthey, and d.
Guillarme, J. Chromatogr. A 1218,
7971–7981 (2011).
• there are many excellent fittings
that can be used and resealed
many times with pressures up
to 20,000 psi. Gold-plated nuts
and double ferrules are not a
requirement.
• A binary high-pressure
mixing pump is preferred for
high-throughput separations,
though most major vendors also
offer quaternary low-pressure
mixing pumps with marginal
increases in dwell volumes.
• UHPLc systems do not provide
substantially higher sensitivity
in Uv detection with standard
10-mm long Uv flow cells.
However, 2–6-fold increases
are possible with the use of
extended-pathlength flow cells (25
to 60 mm long).
• Method transfer (translation)
between UHPLc and HPLc can be
challenging for complex methods.
A partial method revalidation is
a typical regulatory expectation
(or requirement) including
a demonstration of method
equivalency.
• Lower-dispersion UHPLc systems
are indeed better but expect some
sacrifice in flexibility with respect
to injection volumes, compatibility
to longer columns, and higher flow
rates required by routine analysis
with legacy HPLc methods.
Acknowledgementsthe author is grateful to sam Yang,
christine Gu, Mohammad Al-sayeh,
and eileen Zhao of Genentech;
Jim Jorgenson of the University of
north carolina; davy Guillarme and
szabolcs Fekete of the University
of Geneva; raphael ornaf of vertex
Pharmaceuticals; Ken Broeckhoven
of vrije Universiteit Brussel; tom
Waeghe of MacMod; John dolan
from Lc resources; Bill Barber from
Agilent technologies; Pam iraneta
and eric Grumbach of Waters; Joe
dicesare and Wilhad reuter of
Perkinelmer; and ross Woods of
the University of texas at Arlington.
it should be recognized that the
design of modern UHPLc equipment
constitutes many trade-offs between
system bandwidth, sensitivity, and
compatibility to conventional HPLc
methods, dwell volume, mixing
efficiency (Uv sensitivity), and
cost, flexibility, and reliability. the
ES392754_LCA0314_024.pgs 02.25.2014 21:16 ADV blackyellowmagenta
25www.chromatographyonline.com
MS – THE PRACTICAL ART
Among natural products chemists
there is a joke that goes like this:
Nuclear magnetic resonance (NMR)
spectroscopy is like your mother; she
knows what is good for you and tells
you what you need to hear. Mass
spectrometry (MS) is like your lover,
willing to say whatever you want to
hear whether it is true or not.
This joke reflects a common
attitude in natural products research;
NMR serves as a primary tool,
whereas MS is relegated to the task
of providing the molecular formulas
of pure compounds. Yet over the
past several decades, we have
witnessed astonishing growth in MS.
Electrospray ionization (ESI) has
enabled the analysis of biological
molecules previously deemed
intractable, and instruments that
offer astounding mass accuracy
are becoming routinely available.
Nonetheless, as applied to natural
products research, MS is still fraught
with challenges and pitfalls. What
follows is an account of the strategies
that my laboratory (and those of
colleagues and collaborators)
espouse to conduct effective
research despite these obstacles. In
this column, I have tried to paint an
honest picture of what we actually do
(and don’t do) in the laboratory rather
than what is theoretically possible.
As such, this account reflects my
own perspective and opinions. By no
means do I present it as the only (or
last) word on the topic.
MS for Structure ElucidationNatural products research is primarily
concerned with identifying useful
compounds from natural sources,
including plants, fungi, bacteria, and
marine organisms. These organisms
share the common characteristic of
being complex mixtures of thousands
of structurally diverse molecules
present at varying abundance (1).
(Note that reference 1 describes
a typical plant extract mixture in
which there are estimated to be 920
compounds theoretically detectable
by a given liquid chromatography
with ultraviolet absorbance
detection [LC–UV] method. These
represent only a subset of the
compounds present in the mixture.)
Natural products chemists seek
to unravel this complexity and
distill it to the key active elements
that contribute to a desired effect.
Thus, we isolate anti-inflammatory
compounds from marine sponges,
insecticidal compounds from fungi,
or antimicrobial compounds from
traditional plant-derived medicines.
Like many scientists educated in
the 1990s, I can thank Sean Connery
— or, more precisely, his character
in the film “Medicine Man” — for
my introduction to natural products
research. Those acquainted with
this classic may recall a scene
in which a cancer-curing natural
product mixture is injected into a
portable gas chromatography–mass
spectrometry (GC–MS) system,
which, having been transported
by canoe, miraculously operates
in the midst of the jungle on
generator-provided power. Within
seconds, the structure, including
stereochemistry, of a new molecule
responsible for the biological
activity of this mixture appears on a
blinking LED screen. Fantastic as it
seems even by 2013 standards, this
scenario could represent the holy
grail of natural products research.
Such an instrument — portable and
able to elucidate the structure of
components in a mixture without the
need for user intervention or pure
standards — would revolutionize our
field, not to mention all of chemistry
and biology. Regrettably, analytical
equipment of such awesome
Mass Spectrometry for Natural Products Research: Challenges, Pitfalls, and OpportunitiesNadja B. Cech1 and Kate Yu2, 1University of North Carolina Greensboro, North Carolina, USA, 2Waters Corporation, Milford,
Massachusetts, USA.
A common attitude in natural products research is that nuclear magnetic resonance (NMR) spectroscopy serves as a primary tool, whereas mass spectrometry (MS) is relegated to the task of providing the molecular formulas of pure compounds. Yet over the past several decades, we have witnessed astonishing growth in MS. Electrospray ionization has enabled the analysis of biological molecules previously deemed intractable, and instruments that offer astounding mass accuracy are becoming routinely available. Nonetheless, as applied to natural products research, MS is still fraught with challenges and pitfalls. Here is an account of strategies to conduct effective research despite these obstacles.
ES392801_LCADI0314_025.pgs 02.25.2014 21:18 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201426
MS – THE PRACTICAL ART
How GC–MS Is Applied for Unknown IdentiàcationGC–MS instruments are arguably
the best mass spectrometers
for identifying unknown small
molecules. Their effectiveness stems
from compatibility with electron
ionization (EI), which generates
highly consistent and searchable
fragmentation spectra. By searching
an unknown against spectral
libraries, one can generate candidate
structures in seconds. For example,
the National Institute of Standards
and Technology (NIST) standards
database contains over 200,000 EI
spectra. Despite the availability of
such excellent databases, however,
the limitations of EI hinder our ability
to identify unknown compounds. EI’s
harshness often makes detecting
the molecular ion difficult, and the
ability to identify compounds is
limited by information previously
entered into the database. Also, EI
spectra often do not enable isomers
to be distinguished. Therefore,
identifications made on the basis of
mass spectral databases are always
tentative and must be confirmed
with an orthogonal technique like
NMR. Even so, we can gain much
by using MS to obtain a tentative
structure before beginning the
isolation process. The MS result often
facilitates a decision about whether
isolation is worthwhile (on the basis of
a compound’s novelty) and simplifies
isolation and structure elucidation.
How Liquid Chromatography–Mass Spectrometry Is Applied for Unknown IdentiàcationMost biologically interesting
molecules, including the majority
of natural products, are nonvolatile
and, therefore, unsuitable for direct
analysis by GC–MS. Even more
problematic, most biologically
relevant matrices (including many
natural product extracts) are also
nonvolatile. ESI-MS can ionize
nonvolatile species, enabling direct
coupling between LC and MS. Its
application for this purpose has
become so commonplace that the
term LC–MS typically implies the
use of ESI. Before the advent of
electrospray, analysts routinely spent
much time and effort extracting and
derivatizing biological samples to
render them suitable for GC–MS
products research, where identifying
compounds is the most critical
goal, scientists expert in isolation
and NMR structure elucidation
dominate. Unfortunately, this reliance
on NMR has encouraged a bias
toward structurally interesting and
easily isolable compounds. Such
compounds often become the
major focus, even at the expense
of those that are more biologically
interesting. Also, as we shall later
see, combination effects (synergy)
may be overlooked in the isolation
process. Finally, to achieve pure
samples for NMR analysis requires
an intense isolation effort, one
often wasted when applied to
known compounds, particularly
commercially available ones. MS
can enable identification without
isolation, so it offers the potential to
resolve many of these issues. Still,
limitations loom, and they must be
addressed before we can consider
MS an optimal technique for
structure elucidation.
capability does not currently
exist. The tool that comes closest,
however, is the mass spectrometer.
Advantages and Disadvantages of MS as a Tool for Solving StructuresMolecular mass is an intrinsic
quality of any analyte undergoing
measurement that, by means of
MS, we can easily calculate a
priori. Despite this faculty, we mass
spectrometrists are not particularly
good at de novo structure elucidation
for unknown small molecules.
MS data provide an excellent
complement to NMR data for solving
unknown structures and are useful
for rapidly searching a database
to determine whether a compound
was previously identified. Those
benefits notwithstanding, NMR is
the far more effective technique
for solving unknown structures,
provided sufficient quantities of
purified material are available. It is
hardly surprising, then, that in natural
100(a)
(b)
4
1 23
56
7
7
8
NL:
1.08 x 109
Rela
tive a
bu
nd
an
ceA
bso
rban
ce (
mA
U)
90
80
70
60
50
40
30
20
10
1600
1400
1200
1000
800
600
400
200
0
0 2 4 6 8
0 2 4
4
6 8 10 12 14
Time (min)
Time (min)
0
Figure 1: Comparison of (a) LC–MS and (b) LC–UV chromatograms for the same
botanical extract. The peaks in the chromatogram represent a series of alkylamides
extracted from the plant Spilanthes acmella (11). The chromatogram in (a) is a base
peak chromatogram normalized to a total ion count of 1.08 × 109. Only two of the
eight compounds detectable by LC–MS can be detected with LC–UV.
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27www.chromatographyonline.com
MS – THE PRACTICAL ART
barrier today in the field of natural
products research. This barrier could
be surmounted if a comprehensive
and searchable database of MS–MS
spectra analogous to that available
for GC–MS data existed. Of the
databases that seek to fill this need,
none are perfect for natural products
applications. Currently, the best
databases for searching natural
product molecular formulas are
the Dictionary of Natural Products,
AntiMarin (University of Canterbury,
New Zealand), and SciFinder.
None of these are open-access or
completely comprehensive, nor do
they provide searchable MS–MS
spectra. Open-access, searchable
databases for small molecules do
exist — for example MassBank
(MassBank Project, Keio University,
Tsuruoka City, Yamagata, Japan)
and ChemSpider (Royal Society
of Chemistry). None, however,
are tailored specifically to natural
products. Admittedly, generating a
comprehensive database of natural
product MS–MS spectra would be
a far-from-trivial accomplishment.
Doing so would require empirical
measurements made from standards
of all molecules of interest.
Furthermore, such a database, if
compiled, would also need to address
issues of limited reproducibility
among different instruments. Only
through the combined efforts of many
researchers could such a natural
product database be compiled.
Moreover, those researchers would
need access to diverse natural
product standards. Finally, they would
require expertise in analyzing data
presented in numerous software
formats and in collecting and
interpreting those data on multiple MS
platforms.
It is tempting to propose that
the requirement of empirically
measured MS–MS spectra could
be circumvented by generating
predicted spectra from known
molecular structures. Indeed, excellent
databases of predicted fragment
spectra are available for peptides,
and the field of proteomics would not
exist in its current, advanced form
without these databases. Useful,
predicted MS–MS spectra for peptides
can be generated because when
these compounds are subjected to
collisionally induced dissociation, they
analysis. Nowadays, this process is
commonly skipped in favor of LC–MS,
for which sample preparation is much
simpler.
The approach for identifying
unknowns using LC–MS is
fundamentally different than that
employed for GC–MS. ESI yields
molecular or pseudomolecular ions
without appreciable fragmentation
for many analytes. Thus, coupling
ESI to an instrument with accurate-
mass capabilities (such as an
Orbitrap [Thermo Scientific] or
quadrupole time-of-flight [QTOF]
mass spectrometer) enables rapid
determination of the molecular formula
of interest. A caveat is that in-source
fragmentation (for example, the loss
of water) and adduct formation (for
example, with sodium or acetate
ions) may complicate the process of
identifying the molecular ion. Isotope
ratios inherent in the MS data can help
confirm correct formula assignment,
and various software packages for
deconvoluting MS and LC–MS data,
such as IntelliXtract (ACD/Labs),
ExactFinder (Thermo Scientific),
and Apex (Sierra Analytics), can
identify likely clusters and fragments
according to characteristic mass
differences. It is also possible (and
highly advisable) to manually inspect
mass-spectral data for characteristic
mass differences among peaks.
For example, formation of a sodium
cluster will result in a 22 Da mass
difference between the mass-to-
charge ratio (m/z) of the [M + H]+ ion
and the [M + Na]+ ion, and neutral
loss of water will result in a difference
of 18 Da. Assignments of molecular
formulas based on MS data alone,
however, should always be viewed
with caution. A common mistake is
to search structural databases using
the molecular formula of a fragment
or cluster ion, find nothing, and then
incorrectly conclude that the structure
was not previously reported.
Assuming that formulas are
correctly assigned, they can be
searched against those of known
compounds as a first stage in
identification. Useful though this
approach is, it doesn’t solve the de
novo structure-elucidation quandary.
Many molecules share the same
molecular formula and, consequently,
are indistinguishable by accurate
mass and isotope patterns alone.
For example, literally hundreds of
isobaric (same mass) flavonoids
have been identified from plants,
and searching a flavonoid molecular
formula in the Dictionary of Natural
Products (CRC CHEMnetBASE
database) or SciFinder (CAS, a
division of the American Chemical
Society) can yield countless hits.
The structure of the true analyte
may or may not be included among
these, depending on whether it was
previously reported and uploaded to
the relevant database. To assign the
correct structure from among isobaric
candidates, we need orthogonal
information. NMR data are best for
this purpose, assuming the availability
of sufficient purified analyte. Where
an analyte has not been purified,
applying tandem MS (MS–MS)
measurements is the next-best tool
for structure confirmation. The term
“MS–MS” refers to a two-stage MS
experiment whereby an ion of interest
(the precursor ion) is selected in the
first stage and then fragmented. The
masses of the fragments are then
measured, constituting the second
stage. Multiple methods of generating
fragment ions for MS–MS experiments
exist. Among the most common is
collisionally induced dissociation
(CID), wherein the precursor ion
collides with gas molecules and thus
fragments. When the fragments are
formed from high-energy collisions,
the technique is called higher-energy
collisional dissociation (HCD).
Many mass analyzers enable the
generation of MS–MS data, including
triple-quadrupoles, ion-trap, and
QTOF instruments. However, the
relative abundance and presence
or absence of particular fragments
can vary greatly by instrument.
Even for a single instrument, such
variation can arise from changing
parameter settings. Thus, it is
currently common practice to use
MS–MS spectra primarily as a tool to
compare standards with unknowns
under the same conditions on
the same instrument. In this way,
structural assignments can be made
by matching retention time and
MS–MS spectra. Unfortunately, the
availability of the required standards
is extremely limited, which restricts
the applicability of this approach.
Difficulty in assigning structure
based on LC–MS data is a critical
ES392802_LCADI0314_027.pgs 02.25.2014 21:19 ADV blackmagentacyan
LC•GC Asia Paciàc March 201428
MS – THE PRACTICAL ART
fragment consistently. Unfortunately,
the structural diversity of natural
product secondary metabolites
makes developing rules to predict
their MS–MS spectra difficult. Current
rule-based software packages for
this purpose (for example, Mass
Frontier [Thermo Scientific], ACD/
MS Fragmenter [ACD/Labs], and
MassFragment [Waters]) generate
hundreds of predicted fragments
with no indication as to which will be
observed experimentally. Thus, these
tools are truly useful only for assigning
observed fragment structures. A
comprehensive empirical database
of high-resolution mass spectra and
MS–MS spectra for diverse structural
classes of natural products might
enable improvements in the currently
available fragmentation predictors.
Selectivity in LC–MS Analysis of Natural ProductsIt is the selectivity of MS — its
tendency to generate radically
different responses for analytes with
different structures — that led to
its playful characterization among
natural products chemists as the
misleading mistress. Selectivity in
MS analyses is primarily dictated
by the type of ionization source
used. No truly universal ionization
technique exists for LC–MS. ESI,
the most commonly used ionization
technique, is selective for analytes
that contain an inherent positive
charge or that can be charged by
protonation, deprotonation, or adduct
formation. Among charged analytes,
an additional layer of selectivity is
introduced by the analyte’s nonpolar
character, with nonpolar, chargeable
analytes yielding the highest
response (2). Although many natural
products are ionizable by ESI, they
can vary widely in responsiveness.
Several common contaminants, such
as polymers and surfactants, yield
high signals and can suppress the
response of other analytes of interest.
LC–MS analysts must, therefore,
resist the tempting assumption that
the largest peaks in the total ion
current (TIC) chromatogram represent
the most important — or even the
most abundant — compounds in the
sample.
Despite issues of selectivity, it
is possible to compare the relative
abundance of the same compound
in different samples on the basis of
peak area in LC–MS chromatograms.
In many applications of natural
products chemistry — for example,
the evaluation of purity — being able
to compare the relative abundance
of different classes of compounds in
a single sample or multiple samples
would be desirable. Differences in
ionization efficiency prohibit such
comparisons on the basis of data
obtained by electrospray ionization.
Instead, an orthogonal detection
technique, where response is more
closely correlated with analyte
abundance, must be employed.
Two popular techniques for this
purpose are evaporative light-
scattering detection (ELSD) and
charged-aerosol detection (CAD).
These detection methods do not
replace LC–MS. They are far
less sensitive and do not provide
structural information. Nevertheless,
comparison of ELSD or CAD
chromatograms with those obtained
with LC–MS can help discern which
peaks correspond to the most
abundant compounds in a sample.
Although most LC–MS separations
employ ESI for ionization, other
ionization techniques are sometimes
used to provide complementary
information or detect small molecules
not ionizable with ESI. The most
common of these are atmospheric
pressure photoionization (APPI)
(3) and atmospheric pressure
chemical ionization (APCI). APCI
is harsher than ESI and may ionize
polar compounds that lack acidic
or basic functional groups. APPI
can be applied for the analysis of
some nonpolar species that are
not amenable to ESI analysis, and
studies suggest that this technique
is somewhat more universal than ESI
for small drug-like molecules (4,5).
On the other hand, APPI and APCI
source development has been less
a focus of instrument companies
than ESI sources, which are typically
Extract 1
F1 F2 F3 F4 F5 F6 F7 F8
F1 F2 F3 F4 F5 F6 F7 F8
Extract 2
Extract 3
Synergy testing
Synergy testing
Structure elucidation
Pure compound (synergist)
Extract with synergist
Separation (chromatography)
LC–MS
LC–MS
Figure 2: Schematic of synergy-directed fractionation. A series of extracts is
profiled using LC–MS to identify known compounds (if possible) and is subjected
to synergy assays. Extracts that demonstrate synergistic effects are subjected to
separation, and the fractions are then tested again to identify which fractions contain
synergists. This process is repeated iteratively until a pure compound is isolated in
sufficient quantity for structure elucidation.
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29www.chromatographyonline.com
MS – THE PRACTICAL ART
better designed. APPI also suffers
from practical limitations, including
the need to introduce dopant and the
expense of replacing source bulbs.
APCI can often achieve better linear
dynamic ranges than those possible
with ESI for some analytes, but APCI
typically yields slightly higher limits
of detection (6). Practically, the
need to switch sources and collect
copious quantities of data often
limits the ability to operate using
multiple ionization modes. For many
applications, collection of data in
the positive and negative ionization
modes using LC–MS with ESI is
sufficient. However, the possibility of
missing active compounds because
of the selectivity of this technique
should always be considered. Finally,
even with access to APCI, ESI, and
APPI, ionization is impossible for
some natural product molecules. A
need remains for better methods to
ionize small molecules that prove
intractable using existing techniques.
MS for Quantitative Analysis of Mixture ComponentsNatural products chemistry often
involves quantitative analysis.
Such analysis is necessary to
verify the solubility or extractability
of natural product molecules in
different solvents, compare levels
of bioactive compounds in different
raw materials, or measure toxins
or contaminants. MS is particularly
useful for such applications. It
can detect compounds present
in minute abundance (sub-parts-
per-billion levels) and resolve
molecules on the basis of their
m/z values. Indeed, because of
their exceptional sensitivity and
selectivity, triple-quadrupole mass
spectrometers operating in the
selected reaction monitoring (SRM)
mode have long been considered
the gold standard for the quantitative
analysis of trace mixture components.
Recently developed high-resolution
MS platforms, such as the LTQ
Orbitrap and Q Exactive systems
(both from Thermo Scientific), enable
quantitation based on accurate-mass
measurements of the molecular
ion. Such quantitation now rivals
the selectivity of triple-quadrupole
instruments (7,8).
Absolute quantitation with MS
requires pure standards. Although
some standards can be obtained
commercially (for example, from
SigmaAldrich or ChromaDex), in many
cases the compounds of interest
are unavailable. Options are to rely
on relative, rather than absolute,
quantitation or to synthesize or isolate
the standard of interest. Obtaining
natural product standards that are
sufficiently pure to accomplish
absolute quantitation is a major
challenge, one that applies even with
commercial standards. It is common
practice to report the purity of these
standards according to their response
in LC–UV chromatograms. Yet this
approach detects only contaminants
with UV chromophores. Quantitative
NMR can circumvent this problem
(9) but is not yet widely adopted as
a method to evaluate purity. Thus,
the accuracy of percent purity
claims for commercial standards is
questionable.
It is common practice when
performing quantitative analysis
via MS to distinguish coeluted
compounds by plotting selected ion
chromatograms. Here the phrase
“selected ion chromatogram” refers
to a plot of ion current versus time
for the m/z value of the compound
of interest. Such selected ion
chromatograms can be used to
plot calibration curves even for
compounds with identical retention
times. Chemists trained to perform
quantitative analysis using LC–UV
are driven absolutely mad by this
practice. They invariably cite the
concern of ion suppression (otherwise
referred to as matrix interference)
— that one ion may influence the
ionization of another, thereby skewing
the quantitative results.
If the goal of a quantitative analysis
is relative quantitation, and if the
matrix is consistent among samples,
ion suppression is not a major
concern. However, matrix suppression
can seriously undermine the
accuracy of measurements seeking
to determine absolute concentration.
The chromatographer’s solution to this
problem is to separate all components
of the mixture with baseline resolution
and quantify by LC–UV. However, for
most biologically relevant complex
mixtures, including complex natural
product extracts, the physical
limitations on chromatographic
resolution (10) make doing so
impossible in a single stage of
separation. In fact, what may in LC–
UV appear to be baseline separation
is merely baseline resolution of all
components detectable by the UV
detector, a fact revealed when LC–UV
and LC–MS data are compared for
the same sample (Figure 1).
When mixture components cannot
be fully resolved chromatographically
from others with similar absorbance,
or when they are present below the
limit of quantitation with a UV detector,
quantitation by LC–MS is often a valid
alternative to quantitation by LC–UV.
For such analyses, the extent of
matrix interference must be evaluated
by comparing the signal (peak area)
for standards in solvent versus
matrix. This approach is necessary
because matrix interference can be
caused by compounds that the mass
spectrometer cannot detect, such as
salts that wash off the column early in
the separation process. Thus, matrix
effects can occur even when the
mass spectrum appears to contain
only one compound. A number of
strategies can be used to address
matrix effects, including standard
addition and matrix matching. A
recent report by Kruve and Leito
(12) nicely demonstrates strengths
and limitations of these and other
approaches.
However tempting (particularly
for analytical chemists) is the
quest for absolute quantitation,
it may not always be necessary.
When standards are not available,
it is possible to compare relative
peak areas for a particular
sample component to draw useful
conclusions. When making such
comparisons, it is important to be
aware of biases that systematic
fluctuations in instrument response
may introduce. A common source
of such fluctuations is fouling of ion
optics over time, which may cause
the signal intensity to decrease slowly
during the analysis. Including control
standards at the beginning, middle
and end of a run can help identify this
problem. Changes in ion transmission
that result from tuning or cleaning can
also cause the absolute instrument
response to shift (consistently higher
or consistently lower) between runs.
For this reason, collecting all data
for a relative quantitation experiment
in a single analysis is advisable. If
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LC•GC Asia Paciàc March 201430
MS – THE PRACTICAL ART
doing so is impractical, data from the
same sample or standard analyzed
(with replicates) in each run should
be compared to ensure that the
response did not drift (beyond the
tolerance of random errors) between
analyses. If such drift does occur,
adjusting for it is extremely difficult.
Theoretically, comparing the peak
area of each analyte to that of an
internal standard can correct for
differences in instrument response
between runs. Practically, however,
this approach rarely seems to work,
perhaps because signal drift does
not occur consistently across the
chromatogram.
The Holy Grail of Natural Products Chemistry: Correlating Biological Activity with Chemical CompositionThe truly interesting questions in
natural products chemistry occur
at the intersection of chemistry
and biology. These questions seek
information about how natural
products can be used to treat
diseases, eliminate pests, produce
greener technologies, or generally
benefit the planet or humankind.
To address such questions, it is not
enough to simply determine what
is in a sample. Rather, we want
to answer the question “what are
the bioactive components of this
sample?” The following paragraphs
describe some of the traditional
methods for addressing this question,
their limitations, and potential areas
in which MS can help resolve those
limitations. (Here I should note that
biologists would pose a different
question as the Holy Grail of
natural-products research: What is
the mechanism of action for bioactive
natural products? MS certainly
can contribute something toward
investigating mechanistic questions
as well, but that topic extends beyond
the scope of this column instalment.)
Strengths and Limitations of
Bioactivity-Guided Fractionation
for Active Compound Identiàcation:
Natural products researchers
typically rely on bioactivity-guided
fractionation to identify the active
compounds in a mixture. To apply this
technique, a series of natural product
extracts (from plants, fungi, bacteria,
marine organisms, or other natural
sources) are screened against a
desired biological assay (cytotoxicity,
antimicrobial, insecticidal, and so
forth) After an extract is identified as
a good lead — one that evidences
the desired biological activity — it
is separated, usually by liquid–
liquid partitioning or column
chromatography. The resultant
fractions are then tested using the
same biological assay, and the active
fractions undergo further purification.
This process is repeated iteratively
until compounds of sufficient purity
for structure elucidation and more
in-depth evaluation of activity are
isolated.
It is highly undesirable, wasting
both time and resources, for the
bioactivity-guided fractionation
process to result in known
compounds with known activity.
Natural products chemists therefore
employ various “dereplication”
strategies to prevent the reisolation
of known compounds. The ideal
dereplication strategy would rapidly
identify all known compounds
in a mixture without any prior
purification. Both MS (13) and NMR
(14) strategies have been pursued
toward this goal. Dereplication by
MS is currently seriously limited
by the aforementioned lack of
searchable databases of LC–MS
data. To circumvent this problem,
many laboratories construct in-house
databases to enable the dereplication
of compounds commonly
encountered in their samples of
interest. For example, my collaborator
Nicholas Oberlies at the University of
North Carolina Greensboro developed
a 170-compound library with MS,
MS–MS, UV, and retention-time data
that can identify fungal secondary
metabolites previously isolated
by his laboratory (15). Using this
database, it is possible to rule out
~50% of the new fungal samples the
Oberlies laboratory extracts because
of the dominance of compounds
his laboratory has already isolated.
Similar in-house databases in
commercial and academic research
laboratories around the world hold
a treasure trove of information that
if combined into a single natural
products database, would be a
formidable tool indeed.
A major challenge associated
with bioactivity-guided fractionation
is the inherent assumption that
the bioactivity of a mixture can be
distilled to a single compound (or
series of compounds). Certainly,
historical precedent for such an
assumption exists. Key examples
include taxol, from yew bark, and
camptothecin, from the Chinese
tree Camptotheca acuminata (16).
Both were isolated by Wall and
Wani (17)using bioactivity-guided
fractionation, and both became
highly effective cancer drugs when
used in isolation (that is, without any
other components of the mixture). In
H3CO
H3CO
O
O
O
O
4
1: R = CH3, R' = CH
3
3: R = H, R' = CH3
2: R = CH3, R' = H
N+
R'
R
OH
OH
OCH3
Figure 3: Synergy-directed fractionation of the medicinal plant goldenseal
(Hydrastis canadensis) yielded the flavonoids sideroxylin (1), 8-desmethyl-
sideroxylin (2), and 6-desmethyl-sideroxylin (3). These compounds enhance the
antimicrobial activity of the alkaloid berberine (4) via efflux inhibition.
ES392799_LCADI0314_030.pgs 02.25.2014 21:18 ADV blackyellowmagentacyan
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LC•GC Asia Paciàc March 201432
MS – THE PRACTICAL ART
many cases, however, particularly
those involving botanical extracts,
all or part of the activity is lost in the
isolation process. Moreover, this loss
may not always be readily apparent.
Because mixture components are
purified and tested for activity at
increasingly higher concentrations
with each successive stage of
isolation, a perceived improvement in
activity upon purification may actually
be attributable to an increase in
concentration. To determine whether
a particular isolated compound
represents the entire activity of a
mixture, it is useful at the end of
a bioactivity-guided fractionation
experiment to compare the activity
of the pure component to that of
the original mixture at identical
concentrations. Such comparisons
require the concentration of the
bioactive compound to be determined
in the original mixture. Fortunately,
the bioactivity-guided fractionation
process itself produces the pure
standard needed to measure this
concentration.
If, as is often the case, the mixture
displays better activity than the
pure compounds, the explanation is
typically that some sort of synergy
is involved in the activity of the
mixture. Here synergy is defined
as a scenario in which the whole is
greater than the sum of its parts. The
underlying mechanisms for synergy in
complex mixtures, which have been
reviewed elsewhere, may include
several compounds interacting
at the same receptor, multiple
compounds targeting different
biological receptors, or situations
where the solubility of one compound
is improved by the presence of
another. This last case is surprisingly
common with in vitro assays of natural
products. Many natural products
chemists have encountered the
maddening experience of beginning
with a perfectly soluble (and
bioactive) mixture, and after multiple
painstaking steps of isolation ending
up with a pure compound that is
about as soluble (and bioactive) as
brick dust.
Recombining fractions from an
active extract is sometimes proposed
as a way to identify synergists. It is
not practical, however, to test the
vast number of fractions generated
by a bioassay-guided fractionation
experiment in combination. For
example, if such an experiment
generates 10 fractions in the first
stage of separation, those fractions,
taken two at a time, can be
combined in 45 different ways: n!/
(k!(n – 2)!, where n = 10 and k = 2.
To accomplish combination assays
of these fractions over a relevant
concentration range would require
an estimated 9000 assays. If at
least three stages of fractionation
are needed, the number of assays
required increases exponentially to
more than 1,000,000. Even this large
figure ignores the possibility that more
than two fractions may be required to
achieve synergy.
Why Isolate at All?: Given the
limitations of bioassay-guided
fractionation, it might seem advisable
to perform all biological assays
on mixtures, without isolation.
Unfortunately, correlating the
presence of components in a
complex mixture with its biological
activity is difficult. A too-common
scenario in the botanical medicine
literature involves testing a mixture
for some biological effect, analyzing
it for the presence of known “marker”
compounds, and then surmising
(hoping?) that these compounds
produce that effect. The choice of
marker compounds is often made
for practical reasons, such as the
availability of standards or the
ease with which they are detected.
Testing for the presence of these
marker compounds is prudent, to
authenticate the source material,
but does not of itself establish a
link between activity and chemical
composition. Sufficient statistical
power to sort the components
responsible for activity requires the
number of measurements of biological
activity to be at least as great as the
number of compounds present, which
is impossible if activity is tested for
a single mixture. Sufficient statistical
power theoretically can be achieved
if multiple mixtures containing a
range of concentrations of bioactive
molecules are tested. Carrying out
such testing, however, requires some
fractionation, so we find ourselves
back where we started.
Synergy-Guided Fractionation to
Address the “Kobayashi Maru” of
Natural Products: The preceding
section describes what appears to
be a no-win situation, a “Kobayashi
Maru” of natural products. (For the
enlightenment of those who are
not acquainted with Star Trek, the
“Kobayashi Maru” is a test in the
fictional Star Trek universe for which
there is no solution.) To investigate the
activity of mixtures makes identifying
the biologically active components
difficult, but isolating the components
from the mixture often results in loss
of activity because of the inability to
account for synergy. Our laboratory
has worked to develop strategies
to address this dilemma. Currently,
we are conducting several studies
for which the goal is to identify the
array of compounds responsible
for biological activity of botanical
medicines. To accomplish this goal,
we developed a modification of
the bioactivity-guided fractionation
approach that we refer to as
“synergy-guided fractionation” (18)
(Figure 2). This approach is similar
to bioactivity-guided fractionation,
except that fractions are tested in
a synergy assay where they are
combined with the original crude
extract or some isolated component
thereof. The synergy-guided
fractionation approach solves
the dilemma of generating an
exponentially increasing number of
combinations. It also ensures that all
compounds of interest are present
in any given biological assay. In
analytical chemistry terms, such
an experiment is essentially one of
standard addition, but it is performed
with a biological rather than a
chemical endpoint.
Our first case study of the
synergy-directed fractionation
approach involved applying it to
identify bioactive flavonoids from
the botanical medicine goldenseal
(Hydrastis canadensis) (Figure 3)
(18). These flavonoids were shown
to significantly enhance the
antimicrobial activity of the known
alkaloid berberine, also a constituent
of H. canadensis. We demonstrated
that the ability of the flavonoids to
act as efflux pump inhibitors caused
this enhancement. Importantly,
the flavonoids were inactive as
antimicrobial agents alone, and
a traditional bioactivity-guided
fractionation approach would,
therefore, have missed them. The
synergy assays that led to the
ES392804_LCADI0314_032.pgs 02.25.2014 21:19 ADV blackmagentacyan
33www.chromatographyonline.com
MS – THE PRACTICAL ART
isolation of flavonoids from H.
canadensis involved combining
extract fractions with purified
berberine. We are currently engaged
in more detailed studies to identify
additional synergists by testing
in combination with the crude H.
canadensis extract.
A Role for MS in Bioactivity-
Guided and Synergy-Guided
Fractionation?: Bioassay-guided
fractionation and synergy-directed
fractionation are time-consuming
processes inherently biased
toward isolable compounds. It
would be very desirable to develop
approaches to rapidly obtain a
more comprehensive understanding
of the relationship between the
composition and biological activity
of natural product mixtures. Toward
this end, we have used MS to track
compounds of interest throughout
the isolation process by their
measured m/z values and retention
times. This practice has proven
useful for verifying whether activity
corresponds with compounds already
reported as constituents of botanical
interest (so far, generally, it has not).
In addition, we have attempted,
albeit with limited success, to
use untargeted metabolomics to
determine which compounds are
unique to active fractions and to
then focus our isolation efforts on
those compounds. Theoretically, an
advantage of this approach is that
mass-guided fractionation (isolation
focused on a particular ion) is far
more efficient than bioassay-guided
fractionation (isolation based on a
particular biological activity). Yet
a number of practical challenges
prevent this approach from being
entirely effective. First, for complex
botanical extracts, a large number
of fractions are needed to resolve
mixture components sufficiently to
distinguish bioactive from inactive
compounds. It is difficult to identify a
biological assay that is inexpensive,
robust, and efficient enough for this
purpose. Second, fractionation may
again cause synergistic effects to be
overlooked. Finally, the possibility
remains that the most biologically
interesting molecules in a given
set of extract fractions are either
undetectable, because of the
selectivity of the mass spectrometer,
or masked by other mixture
components. The undetectable
nature of such molecules renders
fractionation based only on mass
somewhat unnerving.
For all these reasons, we have
yet to significantly improve the
bioactivity-directed fractionation
process by involving MS (beyond
the initial dereplication step).
Nonetheless, we expect that, with
improvements in methodology, this
will eventually become possible.
These improvements may include
the development of better databases
for natural product identification,
improved software methods for
correlating biological and MS data,
and more creative and robust
biological assays.
The FutureCurrently, many natural products
chemists still ignore MS in the
isolation process and instead
employ NMR data to facilitate
isolation of as many unique (and
structurally interesting) compounds as
possible. Given all of the challenges
and limitations addressed here,
this rejection of MS is perhaps
unsurprising. It is clear that the
mass spectrometer portrayed in
“Medicine Man,” one that can
simultaneously solve natural product
structures and identify those that
are biologically active, does not
yet exist. The development of such
a mythical instrument will demand
continued advances in the ion source
universality, instrument sensitivity,
dynamic range, resolving power,
and — most importantly — software
and database capabilities. Such
accomplishments will require
sustained collaborative efforts
involving (but not limited to) analytical
chemists, natural products chemists,
instrument and software developers,
and biologists. Nonetheless, as
I gauge how far our field has
progressed in recent decades, I am
convinced that these advances are
indeed possible, and that the future
of MS as a tool for natural products
research is unquestionably bright.
References(1) C.G. Enke and L.J. Nagels, Anal. Chem.
83(7), 2539–2546 (2011).
(2) N.B. Cech and C.G. Enke, Rev. Mass
Spectrom. 20(6), 362–287 (2002).
(3) D.B. Robb, T.R. Covey, and A.P. Bruins,
Anal. Chem. 72(15), 3653–3659 (2000).
(4) Y. Cai, D. Kingery, O. McConnell, and
A.C. Bach, Rapid. Commun. Mass
Spectrom. 19(12), 1717–1724 (2005).
(5) D.B. Robb and M.W. Blades, Anal.
Chim. Acta 627(1), 34–49 (2008).
(6) L.C. Herrera, J.S. Grossert, and L.
Ramaley, J. Am. Soc. Mass Spectrom.
19(12), 1926–1941 (2008).
(7) A. Kaufmann, P. Butcher, K. Maden,
S. Walker, and M. Widmer, Anal. Chim.
Acta 673(1), 60–72 (2010).
(8) A. Kaufmann, Anal. Bioanal. Chem.
403(5), 1233–1249 (2012).
(9) M. Weber, C. Hellriegel, A. Ruck, R.
Sauermoser, and J. Wuthrich, Accred.
Qual. Assur. 18, 91–98 (2013).
(10) J.M. Davis and J.C. Giddings, Anal.
Chem. 55(3), 418–424 (1983).
(11) S.S. Bae, B.M. Ehrmann, K.A. Ettefagh
and N.B. Cech, Phytochem. Anal. 21(5),
438–443 (2010).
(12) A. Kruve and I. Leito, Anal. Methods 5,
3035–3044 (2013).
(13) K.F. Nielsen and J. Smedsgaard, J.
Chrom. A 1002(1-2), 111–136 (2003).
(14) G. Lang et al., J. Nat. Prod. 71(9),
1595–1599 (2008).
(15) T. El-Elimat et al., J. Nat. Prod. in press
(2013).
(16) W.-L. Lee, J.-Y. Shiau and L.-F. Shyur,
Adv. Bot. Res. 62, 133–178 (2012).
(17) M.E. Wall and M.C. Wani, J.
Ethnopharmacol. 51(1-3), 239–254
(1996).
(18) H.A. Junio et al., J. Nat. Prod. 74(7),
1621–1629 (2011).
Nadja B. Cech, PhD, earned her BS
degree in chemistry from Southern
Oregon University in 1997, and her
PhD in Analytical Chemistry from the
University of New Mexico in 2001.
Her PhD training is in the area of
mass spectrometry, and for the last
14 years she has worked to apply
this expertise to solve challenging
problems in natural products
research. As a faculty member at
the University of North Carolina
Greensboro, Dr. Cech supervises a
research group of 12 students and
postdoctoral research associates. She
is the recipient of the 2011 Journal of
Natural Products Jack L. Beal Award,
for a paper detailing approaches to
study synergy in botanical medicines.
Dr. Cech is funded by the National
Institutes of Health on several projects
that involve the identification of
botanical products effective against
inflammation or infection.
“MS — The Practical Art” Editor
Kate Yu joined Waters in Milford,
Massachusetts, USA, in 1998.
She has a wealth of experience
in applying LC–MS technologies
to various application fields such
as metabolite identification,
metabolomics, quantitative
bioanalysis, natural products, and
environmental applications.
ES392800_LCADI0314_033.pgs 02.25.2014 21:19 ADV blackmagentacyan
LC•GC Asia Pacifi c March 201434
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36 LC•GC Asia Pacifi c March 2014
ADVERTISEMENT FEATURE
There has been a signif cant resurgence in the development of
antibody-drug conjugates (ADC) as target-directed therapeutic
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the degree of drug addition which directly impacts both potency and
potential toxicity of the therapeutic, and can have signif cant effects
on properties such as stability and aggregation. Determination of
DAR is, therefore, of critical importance in the development of novel
ADC therapeutics.
DAR is typically assessed by mass spectrometry (MALDI-TOF or
ESI-MS) or UV spectroscopy. Calculations based on UV absorption
are often complicated by similarities in extinction coeff cients of
the antibody and small molecule. Mass spectrometry, though a
powerful tool for Mw determination, depends on uniform ionization
and recovery between compounds — which is not always the case
for ADCs.
Here we present a method for DAR determination based on SEC–
MALS in conjunction with UV absorption and differential refractive
index detection. Figure 1 shows UV traces for two model ADCs;
molecular weights of the entire ADC complexes are determined
directly from light scattering data.
Component analysis is automated within the ASTRA 6 software
package by using the differential refractive index increments (dn/dc)
and extinction coeff cients, which are empirically determined for each
species or mined from the literature, to calculate the molar mass of
the entire complex as well as for each component of the complex.
In this example an antibody has been alkylated with a compound
having a nominal molecular weight of 1250 Da (Figure 2). Molar
masses of the antibody fractions are similar, which indicates
that the overall differences between the two formulations ref ect
distinct average DARs which are consistent with values obtained
by orthogonal techniques. Note that the molar mass traces for the
conjugated moiety represent the total amount of attached pendant
groups; the horizontal trends indicate that modif cation is uniform
throughout the population eluting in that peak.
Antibody Drug Conjugate (ADC) Analysis with SEC–MALS Wyatt Technology Corporation
Wyatt Technology Corporation6300 Hollister A venue, Santa Barbara, California 93117, USA
Tel: +1 (805) 681 9009 fax: +1 (805) 681 0123
Website: www.wyatt.com
Antibody-Drug Conjugate Analysis
(■) Mw of complex
(+) Mw of antibody
(x) Mw of conjugated drug
1.0x105
1.0x104
9.0 9.5 10.0 10.5 11.0 11.5 12.0Time (min)
Complex Antibody Drug
DAR
ADC1
ADC2
167.8 (±1.2%)
163.7 (±1.2%)
155.2 (±1.8%)
155.6 (±1.2%)
12.6
8.1 6.5
10.1
Mw (kDa)
Mo
lar
Ma
ss (
g/m
ol)
ADC1
ADC2
2.0x105
Molar mass vs. time
167.8 kDa
ADC1
ADC2
163.7 kDa1.8x105
1.6x105
1.4x105
1.2x105
Mo
lar
Ma
ss (
g/m
ol)
1.0x105
8.0x104
9.0 9.5 10.0 10.5Time (min)
11.0 11.5 12.0
Figure 2: Molar masses for the antibody and total appended drug are calculated in the ASTRA software package based on prior knowledge of each component’s extinction coeff cent and dn/dc, allowing determination of DAR based on a nominal Mw of 1250 Da for an individual drug.
Figure 1: Molar masses for two distinct ADC formulations are determined using SEC–MALS analysis.
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