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Volume 32 Number s5
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HPLC and UHPLC
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Recent Developments in HPLC and UHPLC May 20194
Recent Developments inHPLC and UHPLC
6 Looking To The Future
Gert Desmet
An introduction from the guest editor of this special supplement from LCGC Europe revealing recent
developments in high performance liquid chromatography (HPLC) and ultrahigh-pressure liquid chromatography
(UHPLC).
8 Vacuum-Jacketed Columns: Maximum Efficiency, Easy Deployment Without Oven, and Improved LC–MS
Performance
Fabrice Gritti
This article describes how a user-friendly vacuum-jacketed column (VJC) has been designed without the need of
large internal diameter vacuum chamber and low- and high-vacuum pumps.
14 Recycle Reversed-Phase Liquid Chromatography to Achieve Separations Based on One H/D
Substitution on Aromatic Hydrocarbons
Kazuhiro Kimata, Tsunehisa Hirose, Eisuke Kanao, Takuya Kubo, Koji Otsuka, Ken Hosoya, Kohei Yoshikawa,
Eiichiro Fukusaki, and Nobuo Tanaka
The discrimination mechanism of H/D isotopic species is discussed based on the dispersion interactions of a
CH/CD group of the solute with the stationary phase as well as the mobile phase.
22 Progress in Peak Processing
M. Farooq Wahab, Garrett Hellinghausen, and Daniel W. Armstrong
A brief overview of the advantages and limitations of recently introduced mathematical procedures such as the
Fourier deconvolution of extracolumn effects, iterative curve fitting, multivariate curve resolution, modified power
law, and use of first and second derivatives in enhancing resolution.
29 Optimization of MS-Compatible Mobile Phases for IEX Separation of Monoclonal Antibodies
Evelin Farsang, Amarande Murisier, Krisztián Horváth, Olivier Colas, Alain Beck, Davy Guillarme, and Szabolcs
Fekete
The aim of this study was to understand the impact of ionic strength, buffer capacity, and pH-response on the
retention behaviour and peak shape of monoclonal antibody (mAb) species.
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5www.chromatographyonline.com
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Daniel W. Armstrong
University of Texas, Arlington, Texas, USA
Günther K. Bonn
Institute of Analytical Chemistry and
Radiochemistry, University of Innsbruck,
Austria
Deirdre Cabooter
Department of Pharmaceutical and
Pharmacological Sciences, University of
Leuven, Belgium
Peter Carr
Department of Chemistry, University
of Minnesota, Minneapolis, Minnesota,
USA
Jean-Pierre Chervet
Antec Scientific, Zoeterwoude, The
Netherlands
Jan H. Christensen
Department of Plant and Environmental
Sciences, University of Copenhagen,
Copenhagen, Denmark
Danilo Corradini
Istituto di Cromatografia del CNR, Rome,
Italy
Hernan J. Cortes
H.J. Cortes Consulting,
Midland, Michigan, USA
Gert Desmet
Transport Modelling and Analytical
Separation Science, Vrije Universiteit,
Brussels, Belgium
John W. Dolan
LC Resources, McMinnville, Oregon,
USA
Anthony F. Fell
Pharmaceutical Chemistry,
University of Bradford, Bradford, UK
Attila Felinger
Professor of Chemistry, Department of
Analytical and Environmental Chemistry,
University of Pécs, Pécs, Hungary
Francesco Gasparrini
Dipartimento di Studi di Chimica e
Tecnologia delle Sostanze Biologicamente
Attive, Università “La Sapienza”, Rome,
Italy
Joseph L. Glajch
Momenta Pharmaceuticals, Cambridge,
Massachusetts, USA
Davy Guillarme
School of Pharmaceutical Sciences,
University of Geneva, University of
Lausanne, Geneva, Switzerland
Jun Haginaka
School of Pharmacy and Pharmaceutical
Sciences, Mukogawa Women’s
University, Nishinomiya, Japan
Javier Hernández-Borges
Department of Chemistry
(Analytical Chemistry Division),
University of La Laguna
Canary Islands, Spain
John V. Hinshaw
Serveron Corp., Beaverton, Oregon,
USA
Tuulia Hyötyläinen
VVT Technical Research of Finland,
Finland
Hans-Gerd Janssen
Van’t Hoff Institute for the Molecular
Sciences, Amsterdam, The Netherlands
Kiyokatsu Jinno
School of Materials Sciences, Toyohasi
University of Technology, Japan
Huba Kalász
Semmelweis University of Medicine,
Budapest, Hungary
Hian Kee Lee
National University of Singapore,
Singapore
Wolfgang Lindner
Institute of Analytical Chemistry,
University of Vienna, Austria
Henk Lingeman
Faculteit der Scheikunde, Free University,
Amsterdam, The Netherlands
Tom Lynch
Analytical consultant, Newbury, UK
Ronald E. Majors
Analytical consultant, West Chester,
Pennsylvania, USA
Debby Mangelings
Department of Analytical Chemistry and
Pharmaceutical Technology, Vrije
Universiteit, Brussels, Belgium
Phillip Marriot
Monash University, School of Chemistry,
Victoria, Australia
David McCalley
Department of Applied Sciences,
University of West of England, Bristol, UK
Robert D. McDowall
McDowall Consulting, Bromley, Kent, UK
Mary Ellen McNally
DuPont Crop Protection, Newark,
Delaware, USA
Imre Molnár
Molnar Research Institute, Berlin, Germany
Luigi Mondello
Dipartimento Farmaco-chimico, Facoltà
di Farmacia, Università di Messina,
Messina, Italy
Peter Myers
Department of Chemistry,
University of Liverpool, Liverpool, UK
Janusz Pawliszyn
Department of Chemistry, University of
Waterloo, Ontario, Canada
Colin Poole
Wayne State University, Detroit,
Michigan, USA
Fred E. Regnier
Department of Biochemistry, Purdue
University, West Lafayette, Indiana, USA
Harald Ritchie
Advanced Materials Technology, Chester,
UK
Koen Sandra
Research Institute for Chromatography,
Kortrijk, Belgium
Pat Sandra
Research Institute for Chromatography,
Kortrijk, Belgium
Peter Schoenmakers
Department of Chemical Engineering,
Universiteit van Amsterdam, Amsterdam,
The Netherlands
Robert Shellie
Deakin University, Melbourne, Australia
Yvan Vander Heyden
Vrije Universiteit Brussel, Brussels,
Belgium
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10% Post
Consumer
Waste
I am delighted
to present to you
key developments
in the field of
ultrahigh-pressure
liquid
chromatography
(UHPLC) and
high performance
liquid
chromatography
(HPLC) by four leading researchers.
With their contributions, they give
an overview of the concepts and
visions that will, at least in my opinion,
dominate the future research in column
technology in HPLC and UHPLC.
One vision that gets progressively
more and more supported by the
literature is the fact that the current
column hardware is no longer
adequate to maintain the very high
efficiencies and small peak volumes
produced by the high-quality
particles (ever smaller, with an ever
narrower particle size distribution)
and high-quality packings (obtained
by ever more optimized packing
procedures) that have now become
state-of-the-art. The inadequacy
of the current column hardware is
the main theme in the contribution
by Fabrice Gritti, revealing how the
current bulky column design and the
way our columns are installed in our
instruments are unsuited to remove
the viscous friction heat one can
expect when using state-of-the-art
columns at their top speed. Having
removed a major fraction of the
excess thermal mass plaguing the
current column format, and having
developed an ingenious permanent
vacuum enclosure solution to let the
column operate in a near-perfect
adiabatic mode, 95% of the maximum
expected efficiency could be
achieved.
Nobuo Tanaka, still pushing to break
chromatographic records and achieve
record efficiencies, shows in his
contribution that column technology
should not be restricted to the
conventional single-column paradigm,
but that multicolumn systems (one of
my favourite topics) can be conceived
to increase the versatility, speed,
and efficiency of the analysis. More
specifically, Tanaka et al. used
recycled chromatography to produce
ultrahigh-efficiencies, capable of
separating aromatic hydrocarbons
based on the difference of one
H/D substitution down to a relative
retention ratio of α = 1.008.
Next to hardware—another of my
pet topics to emphasize how our
instruments (and columns) could
be empowered by adding much
more intelligence than is the case
today. While software and artificial
intelligence are literally revolutionizing
our world as we speak, little or no
effort is being made to incorporate
these concepts in the area of
chromatography. However,
as advocated by Dan Armstrong
and his team, chromatography is an
area that can highly benefit from a
variety of digital signal processing
techniques. Signal processing is
fully accepted in other areas, such
as spectroscopy, but has barely
been explored to its full potential
in chromatography, certainly not
at a commercial level. The authors
illustrate this by providing a
comprehensive overview of recent
data analysis algorithms that can be
used to enhance the signal-to-noise
ratio (S/N) and even separation
resolution. The techniques they
describe are easy to program and
the authors see no reason why they
would not be incorporated in future
instrument software versions.
It is also written in the stars that
more and more attention will be paid
to tighter connection between LC and
mass spectrometry (MS) instruments,
both in terms of an advanced
hardware integration as well as a
better chemical integration. While
the former is one of the prominent
aspects of the contribution of Gritti
(see the part on the integrated
column/electrospray ionization
[ESI] probe), the contribution of
Szabolcs Fekete, Davy Guillarme, and
co-workers addresses the second
issue. Considering the very timely
application of the ion-exchange
separation and MS detection of
monoclonal antibodies (mAbs) and
related products, they show how
an in-depth and systematic study
of recently proposed MS-friendly
buffers, such as ammonium
acetate and ammonium carbonate
or bicarbonate, can be used to
understand and optimize the impact
of ionic strength, buffer capacity,
and pH-response on the retention
behaviour and peak shape of mAb
species.
In conclusion, I am convinced
the high-level contributions in this
LCGC supplement show column
technology has not fully matured yet
as some believe. On the contrary,
there is still a large progression
margin and many challenges ahead.
For example, there is a growing
field of (potential) applications in
the life sciences where LC is still
too slow and does not offer enough
separation capacity. Improvements in
column technology will be the key to
overcome these limitations. Hopefully
one day, these improvements will
lead to an era where LC finally
offers the same efficiency as gas
chromatography (GC), a wish and a
vision once formulated by Pat Sandra.
Looking To The FutureGert Desmet, Vrije Universiteit Brussel, Department of Chemical Engineering, Brussels, Belgium
An introduction from the guest editor of this special supplement from LCGC Europe revealing recent
developments in high performance liquid chromatography (HPLC) and ultrahigh-pressure liquid
chromatography (UHPLC).
Gert Desmet
Recent Developments in HPLC and UHPLC May 20196
7www.chromatographyonline.com
As an alternative to conventional packed bed nano-liquid
chromatography (LC) columns that are frequently used in bottom-up
proteomics research, PharmaFluidics offers micromachined nano-LC
chip columns known as micro Pillar Array Columns (μPAC™). The
inherent high permeability and low “on-column” dispersion obtained
by the perfect order of the separation bed makes μPAC™-based
chromatography unique. The peak dispersion originating from
heterogeneous flow paths in the separation bed is eliminated (no
A-term contributions) and therefore components remain much
more concentrated during separation, resulting in unprecedented
separation performance. The free-standing nature of the pillars also
leads to much lower backpressure, which allows a high operational
flow rate flexibility with exceptional peak capacities.
Complementary to its landmark 200-cm-long column, which is ideally suited to perform comprehensive proteome research,
a 50-cm-long μPAC™ column is now available that can be used in a more routine research setting. With an internal volume
of 3 μL, this column is perfectly suited to perform high-throughput analyses with shorter gradient solvent times (30-, 60-, and
90-min gradients) and it can be used over a wide range of flow rates, between 100 and 2000 nL/min. Recently performed
experiments with 500 ng of HeLa cell digest indicate that an increase in protein identifications up to 50% and a gain of 70%
in peptide identifications can be achieved when comparing the 50-cm μPAC™ column to the current state-of-the-art in packed
bed columns that are used in routine operations. LC pump pressures needed to operate these classical columns at a flow rate of
300 nL/min ranged between 200 and 300 bar, whereas only 40 bar was needed to operate the 50-cm μPAC™ column at the
same conditions.
To support the use of the analytical μPAC™ columns, a micromachined trapping column was developed with matching
stationary phase support morphology. The online preconcentration of analytes onto low volume trapping columns is a
commonly used injection strategy in nano- and microbore LC–tandem mass spectrometry (MS/MS) analysis of complex peptide
mixtures. Compared to direct injection onto the analytical column, a sample trapping approach provides several advantages. By
effectively desalting and preconcentrating the analytes of interest onto the trap column, analytical column lifetime and workflow
throughput can be improved. The trapping configuration allows effective removal of sample matrix components, such as salts,
detergents, and contaminants, which can interfere with downstream MS analysis, thereby increasing analytical column lifetime
and at the same time improving detection sensitivity and the spectral quality that is generated for a certain sample. It also allows
dilute samples to be loaded at a much higher flow rate than is feasible when working with a direct injection approach, which has
a positive effect on the LC–MS/MS duty cycle.
However, apart from providing sample cleanup and preconcentration, it is also of paramount importance to maintain
chromatographic performance when combining an analytical column with a trapping column. A poor combination will result in
reduced chromatographic performance, which will in turn affect the quality of the data generated. Trapping column dimensions
and surface chemistry have to be selected carefully to match the analytical column that is used. Typically, trapping columns
with a capacity factor slightly lower than the analytical column will result in the best chromatographic performance as analytes
will experience a second preconcentration or refocusing event when eluted onto the analytical column. This eliminates the
detrimental effect of preanalytical column connections or void volumes on the separation efficiency. Compared to conventional
packed bed nano-LC columns, the stationary phase support of the μPAC™ columns consists of superficially porous silicon pillars
that have been modified with a hydrophobic ligand (octadecyl or C18), and therefore they have a slightly lower capacity factor.
Consequently, combining a μPAC™ analytical column with a μPAC™ Trapping column will result in the best chromatographic
performance.
More information can be found in the technical notes about the 50-cm μPAC™ analytical column and μPAC™ Trapping
column. Technical and application notes about the products can be found at the website at the following link: https://www.
pharmafluidics.com/news-and-media/
1PharmaFluidics, Technologiepark-Zwijnaarde 82, B-9052 Ghent, Belgium
Routine Proteome Analyses Using μPAC™ Nano-LC ColumnsGeert Van Raemdonck, Katrien Vanhonacker, Jeff Op de Beeck, and Paul Jacobs1
Advertisement Feature
In a recent article published in a
special supplement of LCGC (1), the
development of a chromatographic
column that can be operated under
strict adiabatic conditions (2) was
described. The main objective was
to maintain the intrinsic resolution
power of chromatographic columns
when operating them under
extreme experimental conditions
involving undesirable thermal
effects, and leading to inevitable
losses in column performance.
These applications cover analyses
by either ultrahigh-pressure
liquid chromatography ([UHPLC],
severe eluent heating [3–8]) or by
low-density fluid chromatography
at high temperatures and low
backpressures, such as supercritical
fluid chromatography ([SFC],
Joule-Thomson decompression,
severe eluent cooling [9]).
Experimental evidence was shown
that if a chromatographic column is
fully embedded in a large cylindrical
chamber (6-cm internal diameter
[i.d.] and 25-cm-long) in which a high
vacuum (air pressure ~ 10-5 Torr)
is applied, and if all surface area
(column and vacuum chamber)
are wrapped with a thin aluminium
foil, then the maximum expected
performance of the column was
systematically achieved regardless
of the intensity of the thermal effects
(10–12). The advantage of placing
chromatographic columns in a strict
adiabatic environment was then
established.
However, these “proof-of-concept
adiabatic” columns are highly
impractical: routine high-throughput
LC analyses cannot comply with
adopting a heavy (~ 5 kg) stainless
steel (SS) vacuum chamber,
complex accessories related to
vacuum technology (among others,
a low-vacuum oil pump and a
high-vacuum turbomolecular pump),
extremely long setup times (half a
day) to assemble and connect the
whole column/vacuum chamber/
instrument system, and a one night
equilibration time to degas the
vacuum chamber (volume ~ 4 litres)
down to 10-5 Torr. Efforts are
first needed towards reducing
the large volume of the vacuum
chamber while keeping both the oil
and turbomolecular pumps. This
was successfully achieved with
a 2.0-cm i.d. cylindrical chamber
after reduction of the thermal mass
of both column endfittings, and
sealing the SS chamber with two
insulating PEEK side flanges (1). The
small chamber (volume ~ 0.05 litre)
still provided the column a strict
adiabatic environment, heat transfer
by convection was even eliminated
at normal air pressure (760 Torr), and
the maximum expected performance
was achieved again (1). Yet, this
new column hardware still required
the use of cumbersome accessories
such as vacuum pumps.
The first part of this article
describes how a quasi-adiabatic
thermal environment can be achieved
for a standard chromatographic
column free from any vacuum
equipment. This new column
assembly (or column hardware) is
Fabrice Gritti, Waters Corporation, Milford, Massachusetts, USA
This article demonstrates how a user-friendly vacuum-jacketed column (VJC) has been designed
without the need of a large internal diameter vacuum chamber and low- and high-vacuum pumps.
Efficiency tests show that the VJC cannot be run under strict adiabatic condition because of the
small residual heat loss at both ends of the VJC, but 95% of the maximum expected efficiency is
achieved. It is also shown that the VJC can be advantageously directly deployed to any optical
detector, which avoids the need for extracolumn tubing, and omits the need for a column oven to
operate at high temperatures. Finally, this work describes how to improve the coupling between
the eluent preheater, the chromatographic column, and the electrospray ionization (ESI) probe for
mass spectrometry (MS) detection. Besides a 30% gain in column efficiency under extreme viscous
heating conditions (10 Watt/m), the VJC-MS probe eliminates most of the post-column sample
dispersion of conventional liquid chromatography (LC)–MS systems. Overall, the experimental peak
capacities measured for a 2.1 × 100 mm column packed with sub-2-μm particles and placed in the
VJC-MS probe are doubled with respect to standard LC–MS systems.
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Vacuum-Jacketed Columns: Maximum Efficiency, Easy Deployment Without Oven, and Improved LC–MS Performance
Recent Developments in HPLC and UHPLC May 20198
called the vacuum-jacketed column
(VJC). In the second and third
part of the article, it is reported
how users can take advantage of
the VJC in terms of i) easy column
deployment to any optical detectors
free from the cumbersome use of
a column oven while still running
the chromatographic analyses
at high temperatures, and ii)
improved LC–mass spectrometry
(MS) performance when efficiently
coupling (zero post-column
dispersion) the mobile phase
preheater, the chromatographic
column, and the electrospray
ionization probe for MS detection.
From the Impractical
“Proof-of-Concept Adiabatic”
Column to a User-Friendly VJC
The complex but ideal
assembly required to prepare a
chromatographic column under strict
adiabatic environment has been
described in previous works (1,2,10–
12). The external surface area of the
column and the internal surface area
of the large internal diameter (6 cm)
stainless steel vacuum chamber are
first wrapped with aluminium foil to
eliminate most of the heat transfer
by electromagnetic radiation and the
air pressure in the vacuum chamber
was reduced to 10-5 Torr. Under
such conditions, it was demonstrated
experimentally that such adiabatic
column hardware was fully
successful, but it remained highly
impractical for routine analyses
(1,2,10–12).
In a first step towards designing a
user-friendly adiabatic environment
for the chromatographic column,
75% of the thermal mass at both
the inlet and outlet SS endfittings
of a 2.1 mm × 100 mm column was
removed. In addition, two 2-cm i.d.
disk-shaped PEEK side flanges
(with circular rubber O-rings) were
placed at the very ends of the
column to seal it against a small
2-cm i.d. SS cylindrical vacuum
chamber (still connected to a
turbomolecular pump). This new
design of the column hardware still
ensures full adiabatic conditions
and delivers maximum column
performance (N = 33,800 at air
pressure of 10-5 Torr) under severe
chromatographic conditions
(10 Watt/m viscous heating power).
Figure 1: Assembly of the VJC (right image and drawing), which does not
require any complex vacuum equipment. A cylindrical metal sleeve (left
image) including an open vacuum slit (10-7 Torr, 100-μm thickness) wraps the
chromatographic column (middle image) over its entire length. Note that a
fraction of the external surface area of two bulky stainless steel endfittings is
still in contact with the external environment.
SS cylindricalmetal jacket
VacuumJacketedColumn
Standard column
100 μmThick
Vacuumslit
(10-7 Torr) (VJC)
Figure 2: Plot of the experimental column efficiency versus the air pressure
imposed in a large 6-cm-i.d. vacuum chamber placing the column in fully
adiabatic conditions. Benefit of the VJC (filled blue circles) with respect to the
same but punctured VJC (the high vacuum in the 100-μm slit is disrupted,
filled brown circles). At 1 Torr and 760 Torr the relative efficiency losses
with respect to the fully adiabatic column (filled green circles) are only 3%
and 7%, respectively, for the VJC against 15% and 25% for the punctured
VJC. Experimental conditions: flow rate 0.7 mL/min, ΔP = 850 bar (frictional
heating power 10 Watt/m), isocratic mobile phase: 70:30 acetonitrile–H2O (v/v),
Teluent = laboratory temperature.
1.05
Hexanophenone
VJC column
VJC column punctured
Adiabatic column
1.00
0.95
vacu
um
N/N
0.90
0.85
0.80
0.75
1.E-06 1.E-05 1.E-04 1.E-03 1.E-02
Housing Air Pressure (Torr)
1.E-01 1.E-00 1.E+01 1.E+02 1.E+03
9www.chromatographyonline.com
Gritti
In contrast to the large 6-cm i.d.
vacuum chamber, at atmospheric
pressure or 760 Torr (N = 24,000),
heat transfer by convection mode
was eliminated for the small 2-cm
i.d. chamber (N = 28,800) because
the distance between the external
column wall and the internal
chamber wall is smaller than 1 cm
(10,13). However, the success of the
column assembly still relies on the
presence of the cumbersome oil and
turbomolecular vacuum pumps.
Therefore, in a second step, efforts
were directed towards recreating a
strict adiabatic environment for the
column by getting rid of both the oil
and turbomolecular vacuum pumps.
As shown in Figure 1, this could be
achieved by wrapping nearly the
entire column length with a special
cylindrical metal jacket. This jacket
is less than 1-mm thick and includes
across its thickness a thin (exactly
100-μm thick) open slit that spreads
over its entire surface area. The metal
jacket was supplied by Concept
group, LLC. The thin metal sleeve
was first annealed above its melting
point in order to permanently remove
the dissolved gases (H2) and a high
vacuum (<10-5 Torr) was applied in
the thin open slit, which was finally
sealed. The metal jacket acts as a
perfect thermal barrier between its
internal and external volume space.
The column is simply slit into the
sleeve. The final column-jacket
assembly is the user-friendly VJC.
Regarding performance, Figure 2
compares for the same compound
(n-hexanophenone) the efficiency
of the VJC to that of the same but
punctured VJC (after drilling a hole
through the entire thickness of the
metal jacket in order to disrupt the
high vacuum in the open slit and
annihilate the effectiveness of the
thermal barrier). Both the VJC and
the punctured VJC were placed in
the large internal diameter (6 cm)
vacuum chamber and the air pressure
was increased stepwise from
10-6 Torr to atmospheric pressure
(1 atm). As expected, under extreme
chromatographic conditions (flow
rate: 0.7 mL/min, ΔP = 850 bar,
eluent: 70:30 acetonitrile–H2O (v/v),
heat friction power = 10 Watt/m),
the efficiency of the punctured VJC
drops by 14% from 10-6 Torr to
1 Torr (heat loss by diffusion mode
through air being now permitted)
and by another 13% from 1 Torr to
760 Torr (heat losses by both diffusion
and convection modes being now
permitted). In contrast, for the intact
VJC column, these relative efficiency
losses are measured at only 3%
and 4%, respectively. In conclusion,
the user-friendly VJC column is not
operated under a strict adiabatic
environment, but its performance
remains very close to that of the strictly
adiabatic but impractical column.
This is confirmed from infrared
(IR) thermal images that show the
difference in heat leaks between
the VJC and the punctured VJC.
Overall, for the sake of reproducibility,
four different VJC columns
(2.1 mm × 100 mm, packed with
sub-2-μm particles) were embedded in
the large (6-cm i.d.) vacuum chamber
and their efficiency measured at
air pressures of 760 Torr (heat can
then be exchanged by convection
and diffusion modes), 5 Torr (heat
can be exchanged by diffusion
mode only), and 10-5 Torr (heat
can only be exchanged by residual
electromagnetic radiations). The
efficiencies were also measured when
placing the VJC inside the oven of the
UHPLC instrument (similar to 5 Torr
air pressure in the vacuum chamber,
heat can be only exchanged by
diffusion). All the efficiency results
were measured for six small analytes
(uracil, acetophenone, propiophenone,
butyrophenone, valerophenone, and
n-hexanophenone). On average, the
efficiencies of the four VJCs measured
at 760 Torr (representing laboratory
still-air thermal environment) are equal
to 96% (column 1), 96% (column 2),
94% (column 3), and 97% (column 4)
of the maximum theoretical efficiency
(100% at 10-5 Torr air pressure). These
results reveal that the user-friendly
VJC operates under quasi-adiabatic
conditions; the small relative loss
(close to -5%) in column efficiency was
a result of residual heat leaks at the
hot column outlet endfitting, which is
not fully insulated by the thin vacuum
slit embedded in the metal jacket
(Figure 1).
Figure 3: Deployment of the VJC directly at the cell of an optical UV–vis
detector. Note that the inlet eluent temperature set by the active eluent
preheater (Teluent), the temperature imposed at the surface of the inlet endfitting
by the inlet end nut heater (Tinlet nut), and the temperature imposed at the
surface of the outlet endfitting by the outlet end nut heater (Toutlet nut) can be
controlled independently.
Outletendnutheater
Inletendnutheater
Teluent, T Tinlet nut, outlet nut,are independently controlled
T Tinlet nutToutlet nut eluent
Activeeluent
preheater
Recent Developments in HPLC and UHPLC May 201910
Gritti
Easy Deployment of VJCs to Optical Detectors
at High Temperatures Without Oven
As demonstrated in the previous section, the VJC behaves
nearly identically to the fully adiabatic column when the
eluent temperature is set at room temperature. The heat
leaks causing the 5% loss in column efficiency under
extreme viscous heating conditions (10 Watt/m) are located
at the outlet endfitting. Similar experiments were performed
at elevated eluent temperatures (from room temperature
to 90 ºC imposed by the active eluent preheater of the
UHPLC instrument) while the VJC column was placed
outside the column oven directly connected the inlet
port of the UV detection cell (see Figure 3). Because the
column is operated under quasi-adiabatic conditions,
the use of a temperature oven is no longer required, and
VJCs are then easily deployed directly to the inlet port
of any detector (UV, refractive index [RI], fluorescence)
while reducing post-column dispersion. The comparison
between the gradient performances (peak capacity) of the
VJC column (2.1 mm × 100 mm, packed with sub-2-μm
particles, 0.7 mL/min, 1–99% acetonitrile gradient in
3 min) when it is deployed directly to the UV cell and
when it is placed in the UHPLC oven was investigated
at different temperatures of the entering mobile phase.
Remarkably, despite a reduction in pressure drop and
frictional heating, the performance of the deployed VJC is
2–15% lower than that of the same VJC in the instrument
oven when increasing the inlet temperature from 25–60
ºC. This loss in gradient performance is now caused by
the increasing intensity of the heat leaks from both the hot
inlet and outlet endfittings of the deployed VJC to the cool
laboratory air (the laboratory temperature is set at 25 ºC). A
practical solution to this loss in gradient performance was
proposed as shown in Figure 3: two small heaters were
placed on the inlet and outlet end nuts of the column. The
temperatures Teluent of the eluent entering the column (set
by the active preheater of the UHPLC instrument), Tinlet nut
of the inlet end nut (set by one small heater), and Toutlet nut
of the outlet end nut (set by the second small heater) are
controlled independently. For instance, Figure 4 shows the
relative change in gradient peak capacity (with respect to
that of the VJC placed in the oven, Teluent = Toven = 40 ºC,
no heaters added) when keeping Teluent at 40 ºC, the inlet
heater passive (“Ioff”), and stepwise increasing Toutlet
nut from the laboratory temperature (“Ooff”) to 40, 50,
60, 70, 80, and 90 ºC. The experimental results reveal
two poor Tinlet / Tinlet nut / Toutlet nut combinations: 40 ºC /
off / off or 40 ºC. This can be easily explained from a
qualitative viewpoint by estimating the intensity of the
heat leaks at both column ends. The deployed VJC can
be segmented into three zones: the inlet and outlet zones
(about 1.5-cm-long) where heat leaks occur and the
middle zone (7-cm-long for a 10-cm-long column), which
is well thermally insulated. Accordingly, for the temperature
combination 40 ºC / off / off or 40 ºC, the radial heat flux
in both the inlet and outlet zones is oriented in the same
direction: from the inlet column centre (40 ºC + ΔTinlet
from frictional heating, yellow colour) to the laboratory air
(25 ºC, white colour) and from the outlet column centre
(40 ºC + ΔToutlet > 40 ºC + ΔTinlet from heat friction, red
colour) to the laboratory air (25 ºC, white colour). As a
result, the peak widths are adversely affected at both
column ends (relative peak capacity losses of -15% and
-9% for Toutlet nut = off and 40 ºC, respectively). Note that
in the thermally insulated middle zone, heat leaks are
always negligible and they do not contribute much to
peak width enlargement besides the usual and expected
sample dispersion along packed beds in the absence of
radial temperature gradients. In contrast, the temperature
combinations 40 ºC / off / 60 ºC or 70 ºC (green colour
frame) are very close to the optimum combination (40 ºC /
off / 65 ºC) for a relative peak capacity gain of +7%; in this
case, the heat flux direction is reversed from the inlet to
the outlet zone. It is still from the inlet column centre, but
it is now from the laboratory air (60–70 ºC, white colour) to
the column centre (40 ºC + ΔToutlet < 60 ºC) at the outlet as
a result of the “high enough” temperature applied by the
heater to the outlet end nut. The initial peak deformation
occurring at the inlet is then partially compensated at the
column outlet. However, if Toutlet nut becomes too large
(for example, 90 ºC), overcompensation occurs and
the change in peak capacity returns to negative. This
compensation phenomenon is similar to that previously
reported in SFC using low-density mobile phases (carbon
dioxide above 100 ºC, outlet pressure below 100 bar)
where the temperature of the inlet eluent was controlled
independently from that of the column oven (11).
Improved LC–MS Hyphenation with Zero
Post-Column Dispersion
Most applications in LC analyses involve MS detection.
100 YEARS OF
ADVANCING SCIENCE
New columns include:
�� IMMOBILISED CHIRAL COLUMNS
��NEW POLYMERIC ACHIRAL
COLUMNS
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11www.chromatographyonline.com
Gritti
The column outlet is usually
connected to the ionization MS
probe of the mass spectrometer.
This causes post-column sample
dispersion after the sample zone has
eluted through long (up to 60–70 cm)
and large internal diameter (100–
125 μm) connecting tubes. This
significantly affects the efficiency
and gradient peak capacity of the
column. For example, for a 2.1 mm
× 100 mm column packed with
sub-2-μm particles and a standard
LC–MS interface (column à divert/
infusion valve à ESI probe), the
post-column sample dispersion is
as large as 4.3 μL2 while the column
dispersion (k = 1 at elution) is only
1.8 μL2. This means that only 30%
and 55% of the expected column
efficiency and peak capacity,
respectively, can be observed
after MS detection. A solution to
that problem was proposed by
coupling the chromatographic
column directly to the ESI probe as
shown in Figure 5. The conventional
ionization probe is extended to a
vacuum-jacket assembly, which
accommodates the chromatographic
column into the cylindrical insulating
metal jacket. The assembly is then
closed with a cap containing a
newly designed eluent preheater. In
addition, the MS probe conserves
all its functionalities by designing
a T-junction between the column
outlet, the side tubing (used for
MS lock-spray, calibration, direct
infusion, make-up flow, and
diversion), and the ESI probe
tube.
The proof-of-concept of this new
research prototype LC–MS interface
was tested regarding the separation
of small molecules (acetaminophen,
propranolol, diltiazen,
sulfadimethoxone, verapamil,
reserpine, and terfenadine) under
challenging (10 Watt/m frictional
heating) gradient chromatographic
conditions (2.1 × 100 mm column,
packed with sub-2-μm particles,
gradient: 1–99%B in 3 min, A: 0.1%
formic acid, B: 0.1% formic acid
in acetonitrile, flow rate: 0.7 mL/
min, inlet temperature 40 ºC, ESI+).
The MS chromatograms recorded
for the standard LC–MS system
(standard column, 60 cm × 100 μm
+ 75 cm × 125-μm connecting
tubes between the column outlet
and the ESI probe) and the new
VJC-ESI probe interface are shown in
Figure 6. The observed relative gain
in peak capacity was +110%. This
is illustrated in the peak shape of
the compound diltiazem in the right
insert in Figure 6. This relative gain is
explained by i) the nearly complete
Figure 5: Improved LC–MS coupling (VJC-MS probe) between the eluent
preheater, the chromatographic column, and the ESI probe for maximum
performance when MS detection is required. The standard LC column is
wrapped by a metal insulating sleeve placed in the extension of the ESI probe,
which is capped by a small size eluent preheater. Note the presence of the
T-junction joining together the column outlet, a divert tube (for various MS
functionalities indicated in the legend), and the ESI probe.
Easy-to-use
Place thecolumn
in the VJ
Cap withthe eluentpreheater
• Lock-Spray
• Calibration
• Infusion
• Post column addition
• Diversion
Keep multi-functionality
of the ionization probe
Figure 4: Experimental relative change of the peak capacity of the deployed
VJC as a function of the temperature imposed at the outlet endfitting, from Toutlet
nut = “off” (not imposed, passive heater), 40, 50, 60, 70, 80, and 90 ºC (from
left to right). The inlet eluent temperature is set constant at Teluent = 40 ºC and
the temperature at the inlet endfitting is not imposed (“off” or passive heater).
The reference performance is for the VJC in the Acquity oven at 40 ºC. Gradient
experimental conditions: column: 2.1 × 100 mm, 1.6-μm 90Å Cortecs-C18
(Waters), gradient: 1–99%B in 3 min, A: 0.1% formic acid, B: 0.1% formic acid in
acetonitrile, flow rate: 0.7 mL/min. Sample mixture: acetaminophen, propranolol,
diltiazen, sulfadimethoxone, verapamil, reserpine, and terfenadine. Note the
optimum VJC performance when the direction of the heat flux is reversed
from the inlet to the outlet of the column (temperature compensation–peak
refocusing effect) for Toutlet nut ~ 65 ºC.
Teluent
Ioff, Ooff
Pe
ak
Ca
pa
city
Ch
an
ge
(%
)
4
-1
-6
-11
-16
Reference “0”VJC in oven
Poor combination
Optimum combination
Ioff, O40
Temperature scale
C H
TempProfile
BandProfile
Ioff, O50 Ioff, O60 Ioff, O70 Ioff, O80 Ioff, O90
=40˚C
Recent Developments in HPLC and UHPLC May 201912
Gritti
elimination of the post-column
dispersion up to the T-junction and ii)
the improved gradient performance
of the quasi-adiabatic VJC with
respect to the standard column
placed in a standard oven.
Conclusion
This article has demonstrated
that it is possible to design an
easy-to-use (regular size) column
under quasi-adiabatic environment
without depending on cumbersome
vacuum accessories (oil pump,
turbomolecular pump, stainless
steel tubes, rubber O-rings), which
make routine LC and SFC analyses
impractically long. In order to
achieve this, the chromatographic
column is simply wrapped in a
cylindrical insulating metal sleeve
acting as a thermal barrier between
its inside and outside faces: this
defines the VJC.
Experimental results show that
95% of the maximum expected
column efficiency is achieved when
operating under intense frictional
heating conditions (10 Watt/m)
at room temperature. The 5%
efficiency loss is caused by the
residual heat leaks at both column
ends. In addition, the VJC can
be easily deployed to any optical
detectors without highly dispersive
post-column connecting tubes
and the constraining presence of
the column oven manager when
operating the column at elevated
temperatures. Performance even
superior to that of the same VJC
but placed in a conventional oven
can be achieved by independently
fixing the temperature of the inlet
and outlet endfittings to sharpen and
minimize the peak width at exactly
the detection point.
Most importantly and for routine
applications involving MS detection,
a nearly zero-dispersion VJC-ESI
probe interface was designed. The
advantages of the VJC-ESI probe
interface are twofold: i) maximum
column performance of the VJC
under extreme viscous heating
conditions (as a result of the
presence of the insulating
metal sleeve) and ii) significant
reduction of the post-column
sample dispersion of a standard
LC–MS system (by getting rid of
unnecessarily long and wide
internal diameter connecting
tubes). Overall, the peak
capacity of narrow-bore columns
(2.1 mm × 100 mm long) packed
with sub-2-μm particles is more than
doubled with respect to the classical
LC–MS configuration.
Acknowledgements
The author would like to thank Mike
Fogwill, Martin Gilar, Jason Hill,
Joseph A. Jarrell, Wade Leveille,
and Joseph Michienzi (Waters
Corporation, Milford, Massachusetts,
USA) for their constant technical
contributions, fruitful discussions,
and suggestions for this research
project.
References(1) F. Gritti, LCGC North America 36(s6),
18–23 (2018).
(2) F. Gritti, M. Gilar, and J. Jarrell, J.
Chromatogr. A 1456, 226–234
(2016).
(3) H.-J. Lin and Sc. Horvath, Chem. Eng.
Sci. 36, 47–55 (1981).
(4) F. Gritti and G. Guiochon, Anal. Chem.
80, 5009–5020 (2008).
(5) F. Gritti and G. Guiochon, Anal. Chem.
80, 6488–6499 (2008).
(6) J. Kostka, F. Gritti, G. Guiochon, and
K. Kaczmarski, J. Chromatogr. A 1217,
4704–4712 (2010).
(7) K. Kaczmarski, F. Gritti, J. Kostka, and
G. Guiochon, J. Chromatogr. A 1216,
6575–6586 (2009).
(8) D. Poe and J. Schroden, J. Chromatogr.
A 1216, 7915–7926 (2009).
(9) F. Gritti, M. Gilar, and J. Jarrell, J.
Chromatogr. A 1444, 86–98
(2016).
(10) F. Gritti, M. Fogwill, M. Gilar, and J.
Jarrell, J. Chromatogr. A 1468, 217–227
(2016).
(11) F. Gritti, M. Fogwill, M. Gilar, and J.
Jarrell, J. Chromatogr. A 1472, 107–116
(2016).
(12) T.L. Bergman, A.S. Lavine, F.P.
Incropera, and D. Dewitt, Fundamentals
of Heat and Mass Transfer, (John Wiley
and Sons, Hoboken, New Jersey, USA,
7th ed., 2011).
(13) G.D. Raithby and K.G.T. Hollands, in
Advances in Heat Transfer (Academic
Press, New York, New York, USA, vol.
11, 1975), pp. 265–315.
Fabrice Gritti is a Principal
Consulting Scientist at Waters
Corporation, Milford, Massachusetts,
USA. He received his Ph.D. in
chemistry and physics of
condensed matter from the
University of Bordeaux in France,
in 2001 and worked with Georges
Guiochon as a research scientist
until 2014 at the University of
Tennessee Knoxville, USA. His
research interests involve liquid–
solid adsorption thermodynamics
and mass transfer in heterogeneous
media for characterization and
design optimization of new liquid
chromatography instruments and
columns. He has made fundamental
contributions to separation science
with over 30 seminars and tutorials,
50 keynote lectures, and 270
peer-reviewed publications.
Figure 6: Experimental evidence of the gain (+110%) in peak capacity for the
improved LC–MS coupling shown in Figure 6 (VJC-MS probe) with respect to
the gradient performance observed with a standard LC (i-class Acquity UPLC)
–MS (Xevo TQD MS) coupling. The VJC-ESI probe coupling allows for both
increased column performance under severe frictional heating conditions and
elimination of post-column sample dispersion. Same gradient experimental
conditions as those in Figure 4.
2.1 x 100 mm column
1.6μm 90Å CORTECS-C18
Gradient: 1-99%B in 3 min
A: 0.1% FA, B: 0.1% FA in ACN
Flow rate: 0.7 mL/min
Temp: 40˚C
ESI+
AcetaminophenPropranololDiltiazenSulfadimethoxone,VerapamilReserpineTerfenadine
Sample:VJC-MS Probe
Diltiazem
Pc+110%
Standard LC–MS
Time (min)
Time (min)
13www.chromatographyonline.com
Gritti
The separation of isotopic compounds
has often been performed to
demonstrate the high efficiency of a
chromatographic system, and also
to satisfy scientific curiosity about
how small the difference can be for
two kinds of solutes to be separated
by liquid chromatography (LC), the
presence of only one deuterium (D) or
its position in a molecule.
The separation of hydrogen isotopic
compounds illustrated the advances
of C18 silica columns (1,2,3,4,5,6,7).
Other examples include separations
based on isotopic chirality created by
the presence of deuterium in place
of hydrogen (8,9). The differentiation
of H/D-isotopically chiral molecules
indicates the presence of specific
interactions involving the isotopic
substituents on the chiral centre,
as suggested by mechanistic
studies on CH/CD discrimination by
reversed-phase high performance
liquid chromatography (HPLC)
(10,11,12,13).
In order to generate large plate
counts, a long column or a series
of several columns (6,14,15) and/
or recycle chromatography
(3,4,5,7,8,9,14,15) have been used
for isotopic separations. Solutes can
be recycled through the column by
simply connecting the detector outlet
tube to the pump inlet (Figure 1[a])
(8,9) or by alternate column recycle
chromatography (ACRC, Figure 1[b])
(3,4,5,7,14,15). The extracolumn
volume should be minimized in both
cases, and preferably a large column
should be used in Figure 1(a). In the
case of ACRC, the flow switching valve
should be operated when the solute
bands are located in the middle of a
column so the whole bands can be
held in one of the two columns as long
as possible.
In most cases, acetonitrile–
water mixtures were preferred to
methanol–water as a mobile phase
in reversed-phase mode because
the lower viscosity enables the use
of a long column and high flow rate,
which can generate large plate counts.
This approach can be justified when
the isotopic separation is used to
demonstrate the high efficiency of a
column or an instrument. In an attempt
to achieve the ultimate discrimination of
the isotopic compounds, however, the
separation conditions can be further
optimized to produce high selectivity
as well as high efficiency, as practiced
in routine HPLC applications.
Both stationary phase and
mobile phase participate in the
Recycle Reversed-Phase Liquid
Chromatography to Achieve
Separations Based on One H/D Substitution on Aromatic HydrocarbonsKazuhiro Kimata1, Tsunehisa Hirose1, Eisuke Kanao2, Takuya Kubo2, Koji Otsuka2, Ken Hosoya3, Kohei Yoshikawa4, Eiichiro
Fukusaki4, and Nobuo Tanaka4, 1Nacalai Tesque, Inc., Kaide-cho, Muko, Japan, 2Graduate School of Engineering, Kyoto University,
Katsura, Nishikyo-ku, Kyoto, Japan, 3Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Sakyo-ku,
Kyoto, Japan 4Department of Biotechnology, Graduate School of Engineering, Osaka University, Yamadaoka, Suita, Osaka, Japan
Ultrahigh-efficiency separations based on the presence of one deuterium in benzene, toluene, and
naphthalene were achieved by recycle chromatography using C18 silica columns. Larger isotopic
separation factors, α(H/D), were observed in methanol–water than in acetonitrile–water, when the
mobile phases provided similar retention factors (k), or similar methylene selectivity, α(CH2). Isotopic
resolutions between nondeuterated and perdeuterated aromatic hydrocarbons at long separation
times were estimated by using the plate counts obtainable by recycle operation as a function of a cycle
time, along with the retention factors and the separation factors experimentally observed. Methanol–
acetonitrile–water ternary mobile phases were predicted to provide the greatest resolution per unit time,
and actually enabled separation by the difference of one H/D substitution on the aromatic hydrocarbons
with α(H/D) = 1.008 or less, and also the differentiation of the isomers of monodeuterated toluene with
α(toluene-4-d/toluene-α-d) = 1.0016. The discrimination mechanism of H/D isotopic species is discussed
based on the dispersion interactions of a CH/CD group of the solute with the stationary phase as well as
the mobile phase.
Ph
oto
Cre
dit: c
hri
s/s
toc
k.a
do
be.c
om
Recent Developments in HPLC and UHPLC May 201914
discrimination between nondeuterated
and deuterated compounds in
reversed-phase HPLC (3,11,12,13).
While silica-based stationary
phases containing heavy atoms
(pentabromobenzyloxypropyl, PBB), or
poly-aromatic structures (pyrenylethyl,
PYE, and fullerene-bonded, C70),
resulted in an increased retention
and high H/D isotopic selectivity
(11,13), C18 stationary phases have
been used for the separation of one
or three-D-substituted benzenes
(3,4,5,6,7), owing to the high efficiency
and high selectivity for CH/CD in
aromatic compounds.
Methanol–water mixtures
were reported to provide larger
H/D separation factors for the
isotopologues of benzene and toluene
than acetonitrile–water (3,11,12,13).
This article reports on the optimization
of the mobile phase composition,
including a methanol–acetonitrile–
water ternary mobile phase. The
application of the optimized conditions
to separate benzene and toluene
isotopologues with the difference of
one deuterium substitution with a C18
column system (150 mm × 4 = 60 cm
total length, 6-mm internal diamter
[i.d.]) will be described. The results
will be compared with those reported
earlier, including the recent paper (7),
while mechanistic interpretations for
CH/CD discrimination will be provided
in relation to the mobile phase
selection.
Experimental
Simple recycle chromatography
(Figure 1[a]) was performed
with conventional HPLC, LC-9A
(Shimadzu), equipped with a SPD-6A
UV detector (8-μL cell, operated at
254 nm) and a valve-loop injector.
The 20 μL sample loop of the injector
was bypassed during the recycle
operation. The ultrahigh-pressure
liquid chromatography (UHPLC)
instrument, Nexera (Shimadzu),
equipped with a pump LC-30AD and
a UV detector SPD-20A, was used
for the single-column experiment with
a 15 cm × 3 mm, 5-μm Cosmosil
5-C18-II column (Nacalai Tesque). A
series of four 15 cm × 6 mm, 5-μm
Cosmosil 5-C18-II columns (Nacalai
Tesque) were used for recycle
chromatography (the column dead
volume, Vm, 10.6 mL). Chromatographic
measurement was performed at
30 ºC. Deuterated compounds were
available from C/D/N Isotopes Inc. or
Sigma-Aldrich. Other chemicals were
obtained from Nacalai Tesque.
Results and Discussion
Plate Counts, Retention Factors,
and Separation Factors in Relation
to the Mobile Phase Composition:
Maximum plate counts, 11080 and
9900, were observed for benzene
in 45:55 acetonitrile–water at
chromatographic linear velocity,
u = 2.5 mm/s (0.6 mL/min), and in
45:55 methanol–water at 1.5 mm/s
(0.4 mL/min), respectively, with the
3-mm i.d. column. (Up to 25% larger
plate counts were observed with the
6-mm i.d. columns because of the
smaller contribution of the extracolumn
dispersion.)
A ternary mobile phase, 25:20:55
methanol–acetonitrile–water, provided
the van Deemter plot in between those
obtained for the binary mobile phases,
with maximum 10,840 theoretical
plates at 2 mm/s (0.5 mL/min). The
effect of the plate counts on resolution
(RS, equation 1) is not large compared
to the effects of the retention factor,
k, and the separation factor, α, in the
linear-velocity range 1.5–2 mm/s used
for recycle chromatography:
RS = (1/4) (√N) (α-1) [k/(k+1)] [1]
Figure 1: Schemes of (a) simple recycle chromatography, and (b) alternate
column recycle chromatography.
Figure 2: (a) Plot of the methylene group selectivity, α(CH2) (Δ, Ŷ, the right
y-axis), between toluene and benzene, and the isotopic separation factor,
α(H/D) (ż, Ɣ, the left y-axis), between nondeuterated and perdeuterated
benzene, against organic solvent content (%) of the mobile phase, in methanol–
water (open symbols) and acetonitrile–water (solid symbols). (b) Plot of
α(H/D) for benzene against α(CH2) in methanol–water (Δ, 30–70% methanol)
and acetonitrile–water (Ŷ, 25–70% acetonitrile). The retention factor, k, of
benzene-d6 is given beside each plot in methanol–water in square brackets
and in acetonitrile–water in round brackets.
15www.chromatographyonline.com
Tanaka et al.
Figure 2(a) shows the plot of
separation factors between toluene
and benzene (the right y-axis), α(CH2)
= (ktoluene/kbenzene), and α(H/D)
values (the left y-axis) observed for
nondeuterated and perdeuterated
benzenes against organic solvent
content. The α(H/D) and α(CH2)
are larger in methanol–water than
in acetonitrile–water at the same
organic content, and decrease
with the increase in organic solvent
content. For example, α(CH2) in 30:70
acetonitrile–water is similar to that
in 55:45 methanol–water, and the
retention factor of benzene-d6 in 35:65
acetonitrile–water is similar to that
observed in 45:55 methanol–water
(using uracil as a t0 marker).
Figure 2(b) shows the plot of α(H/D)
against α(CH2) with the retention factor
of benzene-d6 attached. The α(H/D)
in methanol–water are clearly larger
than those in acetonitrile–water when
α(H/D) are compared at similar α(CH2)
values, in spite of the smaller retention
factors in methanol–water, as reported
by Tchapla et al. (12). It is interesting to
note that α(H/D) values are different for
the mobile phases producing the same
α(CH2) value, indicating that CH and
CD are differentiated by the mechanism
that is different from typical hydrophobic
interactions causing certain increase in
retention by the addition of a methylene
group to the solute structure, and
the latter mechanism results in larger
α(H/D) in methanol–water than in
acetonitrile–water (Note that the plots
merge in water in the absence of the
organic solvent). This subject will be
discussed later. The shorter retention
allowing a faster recycle operation and
larger isotopic separation factors should
lead to easier H/D isotopic separations
in methanol–water than in acetonitrile–
water.
The plate counts obtainable in
recycle chromatography for a solute
can be considered in terms of the
effective column length, or the actual
column length multiplied by the
number of cycles. The number of
theoretical plates obtainable for each
1 h separation time, N(RC)/h, can be
calculated from the observed plate
counts, N(obs), by equation 2, where tR
is the total separation time in minutes:
N(RC)/h = N (obs)/(tR/60) [2]
N(RC)/h values plotted against tR
in Figure 3(a) indicate that the first
few cycles of recycle operation are
accompanied by a considerable
decrease in plate counts for each
hour as a result of the small band
width in an early stage of recycle
chromatography, particularly for
the conditions that resulted in small
retention factors. This is presumably a
result of the contribution of extracolumn
effects, including those of the detector
and the pump. After several cycles, the
decrease in N(RC)/h settled because
of the increase in the band width. The
larger the retention factor of a solute
(longer time for the first point of the
plot), the larger the plate counts that
were obtained for each cycle (data not
shown), and the smaller the decrease
in N(RC)/h that was observed with
increasing number of recycles.
The largest N(RC)/h was observed
for the higher flow rate, that is, for the
smallest cycle time, tC (time required
for one cycle, tC = t0[k + 1]), and for
the smallest retention factor, k. Thus,
a faster flow rate and shorter retention
are advantageous for generating large
numbers of theoretical plates, in spite
of the smaller number of theoretical
plates for each cycle. The plate counts
obtainable per unit time (h) at the
separation time of tR = 1000 min,
N(RC,1000 min)/h, were calculated by the
extrapolation of the last part of each
curve in Figure 3(a). The observed
column efficiency at 1000 min was
33,000–48,000 theoretical plates for
each cycle for different cycle times.
The plots of the obtained N(RC,1000 min)/h
against tC in Figure 3(b) were found
to be approximated by equation 3
with r2 = 0.86. This is actually a rough
estimate because the plate counts
were obtained at different flow rates
in a narrow range, and for different
solutes and mobile phases:
N(RC,1000 min)/h = 955000 tC-0.749 [3]
The N(RC,1000 min)/h values described
by equation 3 in Figure 3(b) were
Figure 3: (a) Numbers of theoretical plates that can be generated in 1 h
by recycle operation of the 60-cm column system, N(RC)/h, plotted against
the separation time, tR. Solute: benzene (B), toluene (T), and naphthalene
(N). Mobile phase organic solvent: acetonitrile (AN) and methanol (MA). (b)
Numbers of theoretical plates that can be generated in 1 h at the separation
time of 1000 min by recycle operation of the 60-cm column system,
N(RC,1000 min)/hour, plotted against the time for one cycle (tC).
Figure 4: Separation factors for isotopologues plotted against methanol content
of the mobile phase. Total organic content of the mobile phase is indicated at
the upper right-hand corner of each line connecting the plots at the same total
organic content.
Recent Developments in HPLC and UHPLC May 201916
Tanaka et al.
used for estimating the resolution of two isotopologues
at long separation times in combination with the retention
factors and the separation factors experimentally observed.
The N(RC,1000 min)/h values calculated by equation 3 give a
conservative estimate of the resolution for a shorter time of
recycle operation than 1000 min, because the N(RC)/h values
for a shorter separation time are larger than N(RC,1000 min)/h,
as shown in Figure 3(a).
Figure 4(a) shows the plots of the isotopic separation
factor, α(H/D), for benzene–benzene-d6 against the
methanol content of the binary or ternary mobile phases
observed with a 6-mm i.d. column, while the total organic
solvent content is indicated in the upper right-hand corner of
the plots in the figure. The plots obtained at the same total
organic content are connected with a line. The separation
factors, α(H/D), (and the retention factors of benzene,
toluene, and naphthalene) monotonously decreased with
the increase in acetonitrile content of the mobile phase at
a constant total organic content in all cases. It should also
be noted that the isotopic separation factors, α(H/D) values,
are larger in methanol–water than in acetonitrile–water,
producing similar α(CH2), or similar retention factor, k, as
shown in Figure 2(b). Similar plots are made for toluene and
naphthalene isotopologues. When the acetonitrile content
in the ternary mobile phase is 20% or more, the pressure
limit of 30 MPa allows the flow rate of 2 mL/min through the
recycle system. With such a mobile phase composition, the
separation factors are closer to those in methanol–water, and
the sample band may be recycled at a high flow rate.
The resolution between a nondeuterated and
perdeuterated solute pair obtainable in 1 h, RS/h, at a long
recycle separation time, 1000 min, can be calculated by
equation 4 as a function of a retention factor, k, and a
separation factor, α(H/D), with the plate counts, N(RC,1000)/h,
where N in equation 1 is given by equation 3:
RS/h = (1/4) [√(955000 [t0(k+1)]–0.749)] (α-1) [k/(k+1)] [4]
RS/h values for benzene and benzene-d6 for the mobile
phases examined in Figure 4 were calculated from the
N(RC,1000)/h values in Figure 3(b) with the cycle time, tC,
and the separation factors shown in Figure 4. The optimum
mobile phases are predicted to be 25:20:55 or 30:15:55
methanol–acetonitrile–water for the separation of benzene
and benzene-d6, and 40:20:40 methanol–acetonitrile–water
for toluene and toluene-d8. Similarly, 45:20:35 methanol–
acetonitrile–water was predicted to be optimum for the
separation between naphthalene and naphthalene–d8. The
RS/h values calculated for nondeuterated and perdeuterated
solute pairs were 2.54, 2.94, and 3.34 for benzene, toluene,
and naphthalene, respectively, in the optimum ternary mobile
phases.
Separation of Benzene and Deuterated Benzenes Based
on One H/D Substitution: Figure 5 shows the separation
of isotopic benzenes, benzene-d6, -d5, -1,3,5-d3, -d, and
benzene, by recycle chromatography with the optimal mobile
phase composition, 25:20:55 methanol–acetonitrile–water.
The chromatogram in Figure 5(b) shows plate counts of
approximately 500,000, resulting in the separation of the
benzene isotopologues with a resolution greater than 1.0
for the difference of one H/D substitution in approximately
460 min. The resolution, RS/h = 2.54, obtainable in 1 h under
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17www.chromatographyonline.com
Tanaka et al.
the present conditions estimated
based on equation 4, indicates that
RS = 4.0 for benzene–benzene-d6, or
RS = 2.0 based on three deuterium
substitutions, can be obtained in
approximately 160 min with about
170,000 theoretical plates and α(H/D)
= 1.023. The results will be compared
with those from other studies.
Similar benzene–benzene-d
separation was achieved by van der
Wal in approximately 700 min with
55 cycles by ACRC with two 15-cm
columns in 20:80 acetonitrile–water
generating N = 200,000 and α(H/D)
= 1.049 (4). He intentionally used a
less selective mobile phase to illustrate
the efficiency of the instrument and
the column. The increases in k and
α(H/D) were observed during ACRC
operation, indicating the effect of
pressure on retention in the presence
of steep pressure gradient, 38
MPa/30-cm column length. Greater
pressure effects were observed for
nitrogen or oxygen isotopic separations
in ionization control mode (14,15).
Takeuchi and co-workers reported
RS of approximately 2.0 for benzene–
benzene-1,3,5-d3–benzene-d6 with
N = 230000 in about 600 min in
30:70 acetonitrile–water at 0.8 MPa by
ACRC using monolithic silica capillary
C18 columns (0.1 mm i.d., 45.5 and
44.0 cm) (5). In these examples, low
acetonitrile content mobile phases
were employed to obtain a large
α(H/D).
Recently, Gritti and co-workers
reported extremely fast separation
of benzene–benzene-1,3,5-d3–
benzene-d6 with RS of 2.0,
α(H/D) = 1.023, and k = 2.0 by
ACRC using two columns packed
with core–shell silica C18 particles
(15 cm × 3 mm, 2.7-μm) in 83 min in 22
cycles to generate N = 275,000, at a
flow rate of 0.4 mL/min under 5300 psi
(approximately 37 MPa/30 cm) (7).
The reported separation factor,
α(H/D) = 1.023, for the difference of
three deuterium atoms on benzene,
or α(H/D) = 1.046 for benzene–
benzene-d6 in 55:45 acetonitrile–water,
was much larger than that reported by
Tchapla and co-workers (12) and was
also observed in the present study.
Although the effect of pressure on
retention and separation factor was
not reported, the pressure gradient
was similar to that reported by van
der Wal (4). The polymeric C18 phase
employed could show larger α(H/D)
than the monomeric C18, especially
under high pressure (16), making
the system sensitive to the impact of
pressure.
The examples discussed in this
section indicate the importance of
optimization of mobile phase, and
the importance of understanding the
effect of pressure on solute retention
for ACRC using short, high efficiency
columns under steep pressure
gradient. In the present system, the
pressure effect was avoided with small
pressure gradient 30 MPa/60 cm and
by employing 5 μm, monomeric C18
silica particles. The use of a polymeric
C18 phase in combination with an
optimized mobile phase may further
facilitate the H/D isotopic separations.
Separations of Isotopologues
Based on One H/D Substitution
with Toluene and Naphthalene:
Figure 6(a) shows separations of
toluene-α-d and toluene-4-d from
toluene in 40:20:40 methanol–
acetonitrile–water. The results indicate
a very slight difference in retention
factor between toluene-α-d and
toluene-4-d. As shown in Figure 6(b),
the two monodeuterated toluene
isomers were differentiated based on
a separation factor of 1.0016 by the
long recycle operation generating
3,500,000–4,000,000 theoretical
plates in about three days. A slightly
larger H/D isotope effect for aromatic
CH/CD than aliphatic CH/CD was
reported previously (11,12). Figure 6(c)
shows the separation between
Figure 6: (a) Separation of monodeuterated toluene and toluene. Mobile phase:
40:20:40 methanol–acetonitrile–water. Flow rate: 2 mL/min. (b) Separation
of toluene-α-d and toluene-4-d. (c) Separation of naphthalene-2-d and
naphthalene. Mobile phase: 45:20:35 methanol–acetonitrile–water. Flow rate:
2 mL/min.
Figure 5: Separation of deuterated benzenes. (a) Recycle chromatograms.
(b) Chromatogram obtained after 12 cycles. Column: 6 mm × 15 cm
(× 4 = 60 cm). Mobile phase: 25:20:55 methanol–acetonitrile–water. Flow rate:
2 mL/min.
Recent Developments in HPLC and UHPLC May 201918
Tanaka et al.
naphthalene and naphthalene-2-d
in 45:20:35 methanol–acetonitrile–
water. The results suggest that such
isotopic isomers can be used to
demonstrate the high efficiency of
modern chromatographic systems
and columns. The use of optimized
conditions is suggested.
In the case of monodeuterobenzoic
acid, the H/D isotope effect was
found to be in the order, p- > m- >
o-deuterobenzoic acid (17). The isotope
effect observed with toluene-α-d was
smaller than that for toluene-4-d. This
can be explained by the steric effect
of the substituent, or a neighbouring
group, partially shielding the CH/CD
bond from intermolecular interactions.
Mechanistic Interpretations of H/D
Isotopic Selectivity: Polarizability
or lipophilicity of a stationary phase
is affected by the density of the alkyl
groups, as indicated by the larger
α(CH2), representing the behaviour of
a typical hydrophobic group, with high
coverage C18 than low coverage C18
in the same aqueous mobile phase
(18). High polarizability stationary
phases, such as PBB and PYE, provide
larger α(H/D) values for aliphatic CH/
CD along with the smaller α(CH2) than
C18 (11). These results indicate the
contribution of attractive interactions
between a solute and the stationary
phase to retention, namely the
dispersion interactions (instantaneous
dipole-induced dipole interactions)
that play a major role for H/D isotopic
separations because of the greater
polarizability of the CH than the CD
group (10,11,12).
The greater α(H/D) values in
methanol–water than acetonitrile–water
at the same α(CH2) values (Figure 2[b])
can be explained by the greater
contribution of dispersion interactions
in the former. When acetonitrile–water
and methanol–water provide similar
α(CH2), the free energy changes
associated with the transfer of one
methylene group (CH2) from the
mobile phase to the stationary phase
are similar. Acetonitrile is known to be
absorbed by the stationary phase more
than methanol (19), making the C18
stationary phase less hydrophobic and
less polarizable than the C18 phase
in methanol–water, even when the
two systems provide similar α(CH2)
values. In other words, similar α(CH2)
can be obtained when an acetonitrile–
water mobile phase is more aqueous
than methanol–water. The intrinsic
polarizability of the stationary phase
is more strongly reflected in solute
retention in methanol–water than in
acetonitrile–water, resulting in the
larger H/D isotope effects.
The amount of organic solvent
extracted into the stationary phase
from the mobile phase may also
cause the difference in the phase
ratio, influencing the retention
factors. Acetonitrile–water provides
considerably larger retention factors
for a variety of solutes than methanol–
water, when the two types of mobile
phase provide similar α(CH2) values
(20), as observed in this study
(Figure 2[b]). Hydrophobic solutes
tend to be associated with organic
solvent molecules in aqueous mobile
phases, which can also contribute to
the smaller α(H/D) in a mobile phase
containing acetonitrile, which is more
polarizable than methanol. This way,
the larger isotope effects observed in
methanol–water than in acetonitrile–
water can be rationalized, and should
be considered for H/D isotopic
separations.
Conclusions
Separations based on one H/D
substitution on aromatic hydrocarbons
were achieved by recycle
chromatography in the reversed-phase
mode, and the discrimination of
isomers with a difference in the
position of only one deuterium atom
was demonstrated using 5-μm C18
silica particles and using ternary
mobile phases, methanol–acetonitrile–
water, selected to provide the best
compromise between isotopic
selectivity and column backpressure.
References(1) N. Tanaka and E.R. Thornton, J. Am.
Chem. Soc. 98, 1617–1619 (1976).
(2) G.P. Cartoni and I. Ferretti, J. Chromatogr.
122, 287–291 (1976).
(3) S.J. van der Wal, J. Liquid Chromatogr. 8,
2003–2016 (1985).
(4) S.J. van der Wal, Chromatographia 22,
81–87 (1986).
(5) L. Lim, H. Uzu, and T. Takeuchi, J. Sep.
Sci. 27 1339–1344 (2004).
(6) K. Miyamoto, T. Hara, H. Kobayashi, H.
Morisaka, D. Tokuda, K. Horie, K. Koduki,
S. Makino, O. Nunez, C. Yang, T. Kawabe,
T. Ikegami, H. Takubo, Y. Ishihama, and
N. Tanaka, Anal. Chem. 80, 8741–8750
(2008).
(7) F. Gritti and S. Cormier, J. Chromatogr.
A 1532, 74–88 (2018). (and additional
personal communication)
(8) K. Kimata, M. Kobayashi, K. Hosoya, T.
Araki, and N. Tanaka, J. Am. Chem. Soc.
118, 759–762 (1996).
(9) K. Kimata, K. Hosoya, T. Araki, and N.
Tanaka, Anal. Chem. 69, 2610–2612
(1997).
(10) N. Tanaka and E.R. Thornton, J. Am.
Chem. Soc. 99, 7300–7307 (1977).
(11) M. Turowski, N. Yamakawa, J. Meller, K.
Kimata, T. Ikegami, K. Hosoya, N. Tanaka,
and E.R. Thornton, J. Am. Chem. Soc.
125, 13836–13849 (2003).
(12) A. Valleix, S. Carrat, C. Caussignac, E.
Léonce, and A. Tchapla, J. Chromatogr. A
1116, 109–126 (2006).
(13) E. Kanao, T. Kubo, T. Naito, T. Matsumoto,
T. Sano, M. Yan, and K. Otsuka, J. Phys.
Chem. C 122, 15026−15032 (2018).
(14) N. Tanaka, A. Yamaguchi, K. Hashizume,
M. Araki, A. Wada, and K. Kimata, J.
High Resolut. Chromatogr. 9, 683–687
(1986).
(15) N. Tanaka, K. Hosoya, K. Nomura, T.
Yoshimura, T. Ohki, R. Yamaoka, K.
Kimata, and M. Araki, Nature 341,
727–728 (1989).
(16) K. Okusa, Y. Iwasaki, I. Kuroda, S. Miwa,
M. Ohira, T. Nagai, H. Mizobe, N. Gotoh,
T. Ikegami, D.V. McCalley, and N. Tanaka,
J. Chromatogr. A 1339, 86–95 (2014),
supplementary information.
(17) W.J.S. Lockley, J. Chromatogr. A 483,
413–418 (1989).
(18) K. Kimata, K. Iwaguchi, S. Onishi, K.
Jinno, R. Eksteen, K. Hosoya, M. Araki,
and N. Tanaka, J. Chromatogr. Sci. 27,
721–728 (1989).
(19) R.M. McCormick and B.L. Karger, Anal.
Chem. 52, 2249–2257 (1980).
(20) N. Tanaka, H. Goodell, and B.L. Karger, J.
Chromatogr. 158, 233–248 (1978).
Kazuhiro Kimata has retired from
Nacalai Tesque, Inc.
Tsunehisa Hirose belongs to the
research and development division of
Nacalai Tesque, Inc.
Eisuke Kanao is a graduate student
at the Graduate School of Engineering,
Kyoto University, in Kyoto, Japan.
Takuya Kubo is an Associate Professor
at the Graduate School of Engineering,
Kyoto University.
Koji Otsuka is a Professor at the
Graduate School of Engineering, Kyoto
University.
Ken Hosoya is a Professor in
the Graduate School of Life and
Environmental Sciences, Kyoto
Prefectural University, in Kyoto, Japan.
Kohei Yoshikawa is a graduate
student in the Department of
Biotechnology, Graduate School of
Engineering, Osaka University, in
Osaka, Japan.
Eiichiro Fukusaki is a Professor in
the Department of Biotechnology,
Graduate School of Engineering, Osaka
University.
Nobuo Tanaka is an Invited Professor
in the Department of Biotechnology,
Graduate School of Engineering,
Osaka University.
19www.chromatographyonline.com
Tanaka et al.
20 Recent Developments in HPLC and UHPLC – MAY 2019
ADVERTISEMENT FEATURE
“Co-injection”: A Simple Solution to Improve Peak Shape Gesa J. Schad1 and Yusuke Osaka2, 1Shimadzu Europa GmbH, 2Shimadzu Corporation
Peak shape is one of the most crucial aspects in liquid chromatography
(LC), as peak distortion can lead to poor resolution and integration. A
common reason for anomalous shape, such as fronting, tailing, band
broadening, or splitting, especially of early eluting peaks, is the sample
solvent because it can interfere with the adsorption of the sample at the
column head. A different viscosity from that of the mobile phase, higher
ionic or elutropic strength, and insuffi cient mixing are the underlying
causes of peak distortion and dispersion in early eluting bands (1). It
is good practice to dissolve the sample in an eluent composition as
close as possible to the mobile phase at the time of sample injection.
However, because of necessary sample preparation, stability, and
solubility constraints, this may not always be possible.
This application describes a simple, yet effective instrument feature
that helps to avoid peak distortion at the head of the column as a result
of a misfit in sample solvent. By co-injection of an additional solvent, the
negative impact of the sample solvent can be counteracted (2).
Peak Dispersion as an Effect of Sample Solvent
Ultrahigh-pressure liquid chromatography (UHPLC) requires lower
system dispersion volume than that of conventional high performance
liquid chromatography (HPLC). Therefore, UHPLC systems commonly
include tubing with an internal diameter (i.d.) of 0.1 mm or less. In
addition, the internal diameter of columns used for UHPLC systems is
narrow, with 2.0 to 3.0 mm being the most common dimensions; peaks
are therefore more prone to be affected by extracolumn dispersion
compared to HPLC.
Smaller internal diameters result in less effective mixing of the sample
and eluent within the tubing. If the sample solvent is organic and has a
higher concentration and higher elutropic strength than that of the mobile
phase, it can interfere with the adsorption of the sample on top of the
column after injection and result in broad, misshapen peaks. A small
column internal diameter can worsen this phenomenon.
Figure 1: Co-injection settings screen for (a) application example 1 and (b) application example 2.
Co-injected reagent 15 μL
Co-injected reagent 15 μL
Injection to HPLC
Mix
Sample
Figure 2: Co-injection process.
Table 1: Analytical conditions: Application example 1
Column: 75 × 3.0 mm, 2.2-μm Shim-packTM XR-ODSII
Mobile Phase: 7:3 (v/v) water–methanol
Flow Rate: 1.0 mL/min
Column Temp.: 40 °C
Injection Volume: 1-, 2-, 5-, 10-μL
Detection: UV 272 nm
Sample: Caffeine (6:4 [v/v] methanol–water)
Co-injected Reagent: Water
“Co-injection” Sample Pretreatment Function as a Remedy
The Shimadzu i-Series Plus systems feature an automatic
pretreatment function in the autosampler known as co-injection.
This function enables aspiration of an additional solvent for
co-injection from a specifi c vial and injection of the solvent together
with the sample into the analytical column (Figure 1).
The program also allows mixing and waiting to be included in the
procedure. Figure 2 shows the resulting operational methodology as
performed by the autosampler.
Application Example 1: Co-injection of Dilution Solvent
As an example of the benefi t of the co-injection function on peak
shape, 1-μL, 2-μL, 5-μL, and 10-μL injections of caffeine standard
in 6:4 methanol–water (v/v) were performed and the resulting
chromatograms were compared to data obtained when using
a co-injection of water as described in Figures 1 and 2. Table 1
lists the analytical conditions and Figure 3 shows the number of
theoretical plates obtained for each injection.
Figure 4 shows the chromatograms obtained with and without
co-injection of dilution solvent. As can be seen from the graph
and chromatograms, peak shape worsens in the standard analysis
without co-injection as the injection volume increases as a result
of the effects of the sample solvent. A decrease in the number of
theoretical plates (N) from 84% at 2 μL to 13% at 10 μL injection
volume compared to N at 1 μL injection was observed. On the other
hand, when water was co-injected as a dilution solvent, N was
improved compared to the standard analysis, with the value being
89.9% for 5 μL and 61.3% for 10 μL.
Recent Developments in HPLC and UHPLC – MAY 2019 21
ADVERTISEMENT FEATURE
Shimadzu Europa GmbHAlbert-Hahn-Str. 6–10, D-47269 Duisburg, Germany
Tel.: +49 203 76 87 0 fax: +49 203 76 66 25
E-mail: [email protected] Website: www.shimadzu.eu
Co-injection Standard
120.0%
100.0%
80.0%
60.0%
40.0%
Th
eo
reti
cal Pla
te
20.0%
0.0%1 2 5 10
Injection Volume (μL)
175
400
300
200
100
0
0.75 1.00 1.25 1.50
750
500
250
150
125
100
Inj. volume: 1 μL Co-injected
Normal
Co-injected
Normal
Co-injected
Normal
Co-injected
Normal
Inj. volume: 5 μL
Inj. volume: 10 μLInj. volume: 2 μL
75
50
25
0 0
0.75 1.00 1.25 1.50 0.75
1,250
1,000
750
500
250
0.75 1.00 1.25 1.50
0
1.00 1.25 1.50
mAUmAU
mAU mAU
Time (min)
Time (min)
Time (min)
Time (min)
mAU
600
Co-injected
Normal2
7.0 8.0 9.0Time (min)
500
400
300
200
100
7.5 10.0
0
Time (min)
Figure 3: Theoretical plates obtained for each injection volume (with 1 μL being 100%).
Figure 4: Peak shape comparison of caffeine standard with and without co-injection of water.
Figure 5: Peak shape comparison of co-injection in ion-pair chromatography.
Application Example 2: Co-injection of an Ion-Pair Reagent
In ion-pair analysis as well, deterioration of peak shape or split peaks can
be seen because of inadequate mixing of the sample and the mobile
phase containing the ion-pair reagent. In some cases peak shape can
be improved by co-injecting a solvent containing the ion-pair reagent,
using the settings displayed in Figure 1(b). Table 2 lists the analytical
conditions and Figure 5 compares the chromatograms obtained with
and without co-injection.
As shown in the chromatograms in Figure 5, when using standard
injection, peaks 1 and 2 are split. The most likely reason is insufficient
mixing of the sample compounds and the mobile phase containing the
ion-pair reagent, resulting in partial ion-pair formation.
Co-injection of the mobile phase containing the reagent enabled
adequate mixing of the sample and the mobile phase within the needle,
resulting in good chromatographic peak shape.
Conclusion
The strong eluting power of sample solvents can lead to peak dispersion
of early eluting peaks. Co-injection of water, for example, to dilute
the detrimental effects of the strong solvent on peak shape results in
a better focus of the analytes in the stationary phase and therefore
sharper, higher peaks. Ion-pairing is often used for polar analytes, but
ineffi cient mixing of target molecules with an ion-pair reagent before
the column can result in split peaks. Co-injection with the same ion-pair
reagent signifi cantly improves peak shape. The exclusive co-injection
function offers considerable benefi ts with regards to improved data
quality without additional manual sample pretreatment.
References
(1) S. Keunchkarian, M. Reta, L. Romero, and C. Castells, Journal of Chromatography
A 1119, 20–28 (2006).
(2) Y. Osaka, Shimadzu Application News No. L522, Peak Shape Improvement
Using the Auto-Pretreatment Function (Co-injection) of i-Series Plus Integrated
Liquid Chromatograph
Table 2: Analytical conditions: Application example 2
Column:75 mm × 3.0 mm, 2.2-μm Shim-packTM
XR-ODSII
Mobile Phase:
Dissolve 3.4 g of monobasic potassium phosphate
and 1.7 g of sodium lauryl sulfate in 1000 mL of a
mixture of 1:1 water and acetonitrile
Flow Rate: 0.8 mL/min
Column Temp.: 40 °C
Injection Volume: 15 μL
Detection: UV 345 nm
Sample: Berberine chloride (water)
Co-injected Reagent: Mobile phase
Some analytical chemists often
wonder: What is the future direction
of separation science? One school
of thought holds that this field is
mature and not much remains to be
done. Spectroscopy went through
a similar phase a few decades ago,
but the introduction of digital signal
processing revolutionized the whole
field of molecular spectroscopy
and nuclear magnetic resonance
(NMR) spectroscopy. It is impossible
to imagine any modern infrared
(IR) or NMR spectrum that has not
undergone a Fourier transform or
other mathematical manipulations.
Separation scientists have been
quite hesitant to adapt mathematical
techniques to enhance peak
resolution, but perhaps we can extract
more from less, even if the physical
separation is not fully developed. The
purpose of analytical separations
(for example, chromatography,
electrophoresis) is to obtain useful
information. This can be qualitative
or quantitative in nature. Things that
enhance the speed of the process and
the accuracy of the information are
highly desirable.
Advances in chromatography have
led to highly efficient separations
and we are finally beginning to grasp
the science behind high-efficiency
columns (1–3). At best, randomly
packed beds consisting of nonporous,
superficially porous, and fully porous
particles can produce reduced plate
heights h (equal to the theoretical
plate height divided by the particle
diameter, H/dp) as low as 0.5, 0.7,
and 0.9, respectively (4), whereas
in practice we are currently halfway
there. Davis and Giddings, on the
basis of statistical theory of overlap,
predicted that a multicomponent
chromatogram should be roughly
95% empty in order to provide a 90%
probability that a given analyte of
interest will appear as an isolated peak
(5). Even with modern high efficiency
separations, there are cases where
one or two critical pairs have resolution
problems, for example, deuterated
versus nondeuterated molecules,
enantiomers, or cases where there are
large number of peaks. More often, in
enantiomeric separations, the entire
separation window is empty, and yet
the enantiomers have poor resolution.
Usually there is an ambiguity
in the integration of overlapped
chromatographic peaks when using
routine drop perpendicular, skimming
methods. Thus, the development
and use of a method that suitably
separates all the components
necessary for quantitation (usually
with the aim of a baseline separation,
resolution = 1.5) commonly becomes
the bottleneck of chromatographic
analysis in research work as well as
in the pharmaceutical industry. What
if, with a click of a button, resolution
was instantaneously improved, and
there was no need to go through
the arduous process of method
development (switching stationary
phases, mobile phases)?
The primary concern is: Can
we mathematically improve
chromatographic resolution while
maintaining critical peak information
necessary for quantitation? It would
also be preferred if the protocol was
simple and straightforward. In this
article, the fundamental ideas that
govern new signal processing protocols
including deconvolution, for example,
via Fourier transformation (6,7), iterative
curve fitting and multivariate curve
resolution (8–10), power laws (11,12),
and derivatives (13) are given. These
are shown in Table 1. They fall under
three general categories: i) elimination
of extracolumn band broadening,
ii) extracting peak areas by curve
fitting, and iii) directly enhancing
resolution by reducing peak widths.
The following sections describe these
strategies with their advantages and
limitations as per the maxim of when Ph
oto
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dit: c
hri
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om
Progress in Peak ProcessingM. Farooq Wahab, Garrett Hellinghausen, and Daniel W. Armstrong, Department of Chemistry and Biochemistry, The
University of Texas at Arlington, Arlington, Texas, USA
Despite advanced separation technologies and extensive method development knowledge, peak overlap
is still commonly observed. Peak integration becomes more challenging as chromatographic resolution
decreases, especially with asymmetric peaks. Post-acquisition signal processing, well established in
optical spectroscopy and nuclear magnetic resonance (NMR), is now being used in liquid chromatography
(LC). Mathematical operations can be applied on raw chromatographic data to enhance resolution of
overlapping peaks and reduce peak widths. These techniques can maintain original area information
needed for quantitation after some modifications. This article gives a brief overview of the advantages
and limitations of recently introduced mathematical procedures such as the Fourier deconvolution of
extracolumn effects, iterative curve fitting, multivariate curve resolution, modified power law, and use of
first and second derivatives in enhancing resolution. High-throughput analyses in gas chromatography
(GC), LC, and supercritical fluid chromatography (SFC) could benefit from these simple and effective
approaches in many challenging separations applications.
Recent Developments in HPLC and UHPLC May 201922
we gain something, in turn we can
lose something else. These resolution
enhancement strategies mostly only
require ubiquitous software (such as
Microsoft Excel), single channel data,
and will surely be implemented into
chromatography data software in the
future. Once fully automated, their true
power will be most apparent in ultrafast
(< 1 min), hyperfast (< 1 s) liquid
chromatography (LC) and high peak
capacity separations.
Deconvolution of Extracolumn
Effects by Fourier
Transformation (FT)
A chromatograph that does not
contribute to band broadening has yet
to be invented. The recorded signal
from the instrument is convoluted
with broadening by the injector,
connection tubings, and the detector
design. Deconvoluting this effect
would remove these extracolumn
effects from the chromatogram.
Resolution would also increase if
the separation was compromised
by the hardware and software. FT
deconvolution was first described
in the early 1980s (7). Recent work
evaluated the band broadening
elimination by FT deconvolution on
modern ultrahigh-pressure liquid
chromatography (UHPLC) systems
and narrow-bore columns as shown
in Figure 1 (6). The protocol for
FT deconvolution is a three-step
process. First, a chromatogram
must be collected with and without
the column (Figure 1[a]). Then, both
chromatograms are converted to
the frequency domain by Fourier
transformation (Figure 1[b]). Next,
the frequency transformed data
from the chromatogram with the
column are divided by the frequency
transformed data collected without
the column. The resulting quotient is
converted back to the time domain
by inverse Fourier transform (6). This
yields a chromatogram that is free of
extracolumn band broadening effects
(Figure 1[c]). There is a shift in peak
retention time resulting from the time
needed for the injected analyte to reach
the detector without the column, that
is, the system volume effect is also
corrected. Baseline noise increases
as a result of division in the frequency
domain because division by very
(a)w/o column
w/o column
with column
with column
Sig
nal
(|F(
�)|
)Si
gn
al (m
AU
)Si
gn
al (m
AU
)
(b)
00 0.2
900
Time (min)
Time (min)
0.4 0.6
104
1200
100
0
0
0.6 0.7 0.8
5
�c
10Frequency (Hz)
column-only
column-only
Retentionshift
0.8 1.0
(c)
Figure 1: Removal of extracolumn
band broadening effects by
Fourier-transform deconvolution (6).
(a) The collection of a chromatogram
with and without the column is
shown. Then, each dataset is
converted to the frequency domain
as shown in (b). Next, they are
divided, with the result shown as
“column-only”. This is converted
back to the time domain as shown
in (c). The retention time of the
chromatographic peak has also
shifted accounting for the system
volume. (Figures in MATLAB
provided by Y. Vanderheyden).
Table 1: Overview of advanced signal processing techniques
Technique Requirements Advantages
a. Fourier transform
deconvolution
• Data with & w/o column
• Advanced software
(MATLAB)
• Remove extracolumn band
broadening
• Corrects time delay from
system volume
• Increases resolution
b. Iterative curve
fitting
• Known number of
components
• Single channel data
• Advanced software
(PeakFit, OriginPro)
• Computationally heavy
• Area extraction of partial
overlapped peaks
(quantitation subjective to
user*)
c. Multivariate
curve resolution
• Known number of
components
• Multidimensional data
• Advanced software
(MATLAB)
• Computationally heavy
• Area extraction of
completely overlapped
peaks in complex matrices
(quantitation subjective to
user*)
d. Modified power
law
• Smoothed single channel
data
• Repeat for each peak,
resolution ~ 0.8 for error ≤
~1% (proportionate peaks)
• Directly increases
resolution by reducing peak
width and tailing
• Improves S/N
• Simple software (Microsoft
Excel)
• Quick procedure
e. Even derivative
peak sharpening
• Smoothed single channel
data
• Resolution ~ 0.7 for error ≤
~1% (proportionate peaks)
• Directly increases
resolution by reducing
peak width
• Simple software (Microsoft
Excel)
• Quick procedure
* User must choose model/constraints used for this operation
23www.chromatographyonline.com
Wahab et al.
small numbers as well as oscillations
are seen. However, these can easily
be decreased by digital smoothing or
cutting off all high frequency noise (ωc).
Fourier transform deconvolution has
also been applied while working with
1-cm columns at extremely high flow
rates (14).
Peak Area Extraction by
Iterative Curve Fitting
Iterative curve fitting is a versatile
approach for extracting peak areas
from partially overlapping peaks,
especially when multiple components
are overlapping to some extent. The
chromatogram containing time and
single-channel signal is exported
into a curve-fitting software, for
example, see Table 1, which considers
the entire chromatogram as a sum
of exponentially modified peaks.
It is assumed that a single peak
represents a pure component. The
number of components (peaks) are
proposed by the user, and then the
chromatogram is fitted according to
the chosen peak model by method of
minimization of residuals. There are
several peak functions, but for LC, an
exponentially decaying tail is usually
observed. The most useful model for
these purposes has been determined
as the bidirectional exponentially
modified Gaussian (BI-EMG),
which is a Gaussian function with a
one-sided exponentially decaying
tail or front as a function of time (15).
For simple chromatograms, one
can conveniently obtain a fit with a
coefficient of determination (R2) close
to 1 (if R2 = 1, then it is a perfect fit).
This is a trial-and-error approach
where the user continues to adjust
the initial parameters of the model
iteratively improving the fit until they
find it acceptable. Caution should
be exercised that an iterative curve
fitting procedure may yield several
mathematically correct answers.
Similarly, it is ambiguous to fit several
peaks under a single peak, which is
mathematically possible, but it will not
reflect the reality.
Once a suitable fit is determined
for the separation, a baseline must be
established to extract each underlying
peak area. In most cases, a simple
linear baseline is sufficient. However,
in gradient elution or multidimensional
separations, a nonlinear baseline
could be utilized by choosing it from
the software. The use of iterative
curve fitting to extract peak areas
from overlapping peaks is illustrated
in Figure 2. A simulated separation
of seven peaks in under a minute
is shown. There are two sets of
overlapping segments with differing
degrees of tailing and efficiencies.
Since it was simulated, their true area
of each peak was known. The exact
peak areas of peaks 1 to 7 were 4,
3, 6, 8, 5, 10, and 9 area units in the
absence of noise, respectively. Using
the BI-EMG model, this separation
was fitted with an R2 of 0.9996.
After this mathematical fitting, peak
areas can be extracted, as well as
other peak information, including
efficiency, tailing factors, peak height,
zeroth, first, second, and statistical
moments. In this case, the extracted
areas are in order of peaks 1–7, 3.99,
2.99, 5.99, 7.99, 5.01, 9.98, and
9.02, respectively, with an excellent
match of theoretical areas in the
presence of random noise. Overall,
curve-fitting procedures are powerful
for extracting peak areas when it is
clear that there is no hidden peak
under the peak of interest. Choosing a
pure Gaussian peak is only a limiting
case because real peaks often have a
“tail” or “front” better described by an
EMG function.
Model-Free Approaches for
Peak Information Extraction
Various powerful methods exist
as well as iterative curve fitting for
extracting peak information even
when the peaks overlap completely,
where an iterative curve fitting method
mentioned above will fail (16). Unlike
iterative curve fitting, these methods
require multidimensional data, that
is, various signals are acquired at the
same time. Second, these signals
must be specific to the molecule of
interest. For example, a photodiode
array generates an entire spectrum
of a given component, similarly
mass spectrometry (MS) generates
an analyte-specific signal. Third, in
order to identify multiple peaks in a
completely coeluting peak envelope,
the key requirements are that the
compounds that are coeluting must
be known and their pure spectra must
be present in the software library.
The latest example is that of the
vacuum UV (VUV) GC detector. The
mathematical technique is termed
as linear combination of weighted
reference spectra. The VUV software
can extract complete peak information
of coeluting compounds if the spectra
of coeluting compounds are known
and they are sufficiently distinct. The
observed spectrum at each data point
is treated as the sum of pure spectra
for the coeluting compounds following
equation 1:
Observed spectrum at a given data
point = f1 A1 + f2 A2 + ... [1]
A1 and A2 are the pure absorbance
spectra of each component, and f1
and f2 are corresponding scaling
factors. These scaling factors are
determined by linear regression
by minimization of residuals. The
fit coefficients f1 and f2 plotted
over the time region of a coelution
Time (s)
Iterative CurveFitting
Extract peak areas(using peak models)
7
1
3
2
4
5
6
7
1
3
2
4
5
6
7
10 20 30 40 50 60
5
3
1
-1
7
5
3
1
-10 10 20 30 40 50 600
Sig
na
l
Sig
na
l
Time (s)
(b) Fitted Chromatogram (Exponentially Modified Gaussian Model)
(a) Raw Chromatogram
Figure 2: Iterative curve fitting of seven simulated peaks with different peak
heights, areas, and shape. (a) The raw chromatogram obtained from the
simulation are shown. After fitting, using a bidirectional exponentially modified
Gaussian model and a linear baseline, each peak area can be extracted.
Customization can be made to the constraints, which improves the fit and
allows the user to fit any peak shape.
Recent Developments in HPLC and UHPLC May 201924
Wahab et al.
event represent chromatographic
signals for each of the coeluting
compounds. Measured VUV
absorbance spectra can be converted
into chromatographic signals using
spectral filters (16).
Multivariate curve
resolution-alternating least squares
(MCR-ALS) is another tool that can
estimate underlying elution and
spectral profiles for a chromatogram
even in the case of complete
overlap of peaks (Rs = 0). The main
requirement from a chromatographic
point of view is to collect data from
multiple channels like the case of VUV.
The availability of photodiode array
detectors in high performance liquid
chromatography (HPLC) systems
has made this procedure convenient
because it allows the construction of
a multidimensional data matrix. The
goal of MCR-ALS is to decompose
the observed data matrix (D) of
a chromatogram into elution (C)
and pure spectral profiles (ST) that
optimally fit the data matrix as shown
in equation 2. E is the experimental
error in the estimated convergence:
Data matrix (D) = Elution profile (C) *
Spectral profile (ST) + Error (E) [2]
MCR-ALS requires an initial
estimation of pure spectral profiles
(ST). Perhaps the fastest way to
get the initial estimate is if the
components are known and a pure
spectrum is available for each
component. If the components and
their pure spectral profiles are not
available, then the most common
way is to estimate the concentration
profiles using evolving factor analysis
(EFA) (17) or simple-to-use interactive
self modelling mixture analysis
(SIMPLISMA). The details on EFA
can be found in the seminal work
by Maeder (17), and in examples in
previous LCGC reviews on MCR-ALS
(18) for peak purity analysis. Since
MCR-ALS and the linear combination
of weighted reference spectra
approach used in VUV requires an
initial estimate of concentration or
spectral profile, enantiomers might
be more difficult to differentiate,
especially if there is no separation
because their UV–vis absorbance and
their MS spectra would be identical.
Similarly, universal response detectors
cannot be used with MCR-ALS,
which essentially eliminates all data
from flame ionization detectors
(FID), thermal conductivity detectors
(TCD), barrier discharge ionization
detectors (BID), conductivity
detectors, and refractive index (RI)
detectors. However, MCR-ALS is not
limited to UV–vis or MS. In addition,
this procedure is subjective to the
user because the constraints can
be inappropriately chosen and lead
to unrealistic peak shapes. Most
MCR methods use non-negativity
and unimodality, but other various
constraints, such as closure,
trilinearity, selectivity, and other
shape constraints, make MCR the
most sophisticated technique among
all described herein. When multiple
peaks are determined under a similar
curve, computation is more difficult
and can increase post-processing
time. Some commercial spectroscopy
software has already implemented
MCR-ALS but, to our knowledge, most
chromatography data software has not
except for one (19).
Direct Resolution Enhancement
by Power Law
Unlike MCR-ALS, the power law
approach is a single-channel method
and it can be applied on any detectors
not amenable to MCR-ALS. The power
law directly increases chromatographic
resolution (Rs) of overlapping peaks
to baseline separation (Rs = 1.5) so
they are easier and more accurately
visualized and integrated (11,12). The
fundamental principle of a recently
proposed power law is that raising
a given output signal to a power, n,
(where n is an integer > 1) increases
the signal magnitude if it is > 1 or
decreases the signal magnitude
if it is < 1 (11). The power law (or
power transform) reduces tailing,
noise, maintains retention time,
and increases resolution between
overlapping chromatographic peaks.
Already, a simpler version of power
law is integrated in some software
(20), where collected chromatographic
signal data can be raised to a power
(max of n = 3) and then integrated
(a) Original Data
(b) Segment #1 (n=21) (c) Segment #2 (n=18)
Segment #1 Segment #2
Noise9.5
0.9
0.5
-0.10.55 0.70 0.80 0.90
2 41.1
0.5
-0.1
7.5
5.5
3.5
1.5
-0.50.00 1.00
1
4-5 0.1
-0.2
0.9
-0.1
2 4
0.9
-0.1
2-3
6
7
2.00 3.00 4.00
Sig
na
l (m
AU
)Sig
na
l (m
AU
)
Sig
na
l (m
AU
)
Time (min)
Time (min)Time (min)
Figure 3: Directly increasing resolution of two overlapping pairs by modified
power law. (a) The original separation data of hormones (in order of elution)
is shown: 17α-ethynylestadiol, estrone, estriol, estradiol, androstadienone
(androsta-4,16-dien-3-one), progesterone, and testosterone. See reference 4 for
chromatographic information. (b) and (c) show each overlapping pair baseline
separated of each segment; segment 1 with a power (n) of 21 and segment
2 with a power (n) of 18. The area of peaks 2 and 4 can be recovered using
equation 3. Adapted with permission from reference 20.
25www.chromatographyonline.com
Wahab et al.
normally. However the simple law is
not suitable for quantitation because
the relative area of exponentially
enhanced peaks has changed after
the mathematical operations relative
to the original peaks (12). As a result,
a modified power law approach was
introduced in 2019, which maintained
peak area integrity and offered all the
benefits of a simple power law (11).
The modified power law relies on
this fundamental characteristic by
normalizing the peak of interest’s
maximum to a value of 1 (and the rest of
the chromatogram accordingly) before
raising the chromatographic signal
to a power that provides the desired
resolution. The chromatographic data
can be exported to Excel and the
peak area quantitated with an external
method either in Excel or by numerical
integration. It is desirable to smooth
the raw data and correct the baseline
if a drifting baseline resulting from a
gradient method has emerged. Each
peak in a critical pair is first normalized
to unit height followed by raising the
chosen peak signal to a desired power.
It is recommended to have Rs ≥ 0.8.
The area recovery is described below
in equation 3.
To visualize this method, an example
from a recent article is shown in
Figure 3, where two critical overlapping
pairs are present and identified as
segment 1 and segment 2 (20). Noise
is high, and all chromatographic peaks
are tailing, making integration difficult
(Figure 3[a]). After applying powers
in each segment (Figures 3[b] and
3[c]), peak widths are reduced, and
signal-to-noise (S/N) is significantly
enhanced. After raising these segments
to powers, it is much easier to integrate,
and the original peak area can be
back-calculated using equation 3
where n = the power used to get
baseline resolution:
Original Area = Height (original peak) *
Area (normalized powered peak) *√n
[3]
Questions that remain are: How is
the correct power chosen, and how
much error is there?
Originally, each pair had different
magnitudes of overlap (more or less
resolution) so different powers were
needed to get a baseline resolution
(Rs = 1.5). Choosing what power
(a) Original Peak
0.55
0.45 0.45
0.35
0.25
0.15
0.05
-0.05
0.68 a bD
D D D
DD
D
DD
c
c cc
a a
ab
bb
0.58
0.48
0.38
0.28
0.18
0.08
-0.020 1000 2000
Time (s)
3000 4000 5000
0.35
0.25
0.15
0.05
-0.050 10 0 10 1000 2000 3000
Time (s) Time (s)
Sig
nal
Ab
sorb
an
ce
Sig
nal
Sig
nal
Time (s)
0.550.5
0.4
0.3
0.2
0.1
0
-0.1
0.6
(d) Recycling LC of Deuterated Benzenes
(b) Sharpened Peak (c) Sharpened Section IV
Figure 4: Sharpening peaks with even derivatives. (a) shows a simulated
Gaussian peak (in blue). (b) shows the effect of sharpening the simulated peak
(in blue) by reducing the peak width (in red). This is done by subtracting the
second and adding the fourth derivatives with their appropriate multipliers. The
area of the peak is conserved. (c) and (d) show the separation of an isotope
mixture containing (a) benzene, (b) 1,3,5-benzene-d3, and (c) benzene-d6.
See reference 13 for chromatographic information. The separation takes up to
1.5 h to get baseline resolution needed for quantitation. However, using even
derivative peak sharpening (c), section IV (in black) can be baseline resolved (in
red) increasing throughput by ~1 h. Adapted with permission from reference 13.
Original data
(a) Extra-column FT deconvolution
(b) lterative curve fitting
(c) Simple power law (y-axis scaled by 1/1000)
(d) Derivative peak sharpening
0 0.5
Time (min)
Sig
nal
1
Figure 5: Overview of each signal
processing technique. Original data
simulated of six components partially
separated in a under a minute. (a)
Fourier-Transform Deconvolution:
Dead volume of an Agilent 1200
HPLC was determined at 3 mL/min
and used to remove the extracolumn
band broadening. (b) Iterative Curve
Fitting: The chromatogram was fitted
using a bidirectional exponentially
modified Gaussian model providing
the extracted areas of each peak
under the curve. (c) Simple power
law: The data were raised to
power = 3 then scaled down to fit
in the same signal window as other
methods. The modified power law
could be used to quantitate the
individual peak areas one at a time.
(d) Derivative peak sharpening:
Adding the first and subtracting the
second derivatives with constants
K1 and K2 of 0.0051 and 0.000005,
respectively.
Recent Developments in HPLC and UHPLC May 201926
Wahab et al.
of n to use is somewhat arbitrary,
that is, two overlapping peaks might
be baseline separated by using
a power of 3, but if a power of 10
was used one would still get higher
resolution. Where do we stop? Since
the chosen n is limitless (limit towards
infinity), very large powers could
be chosen. However, if such large
powers are needed to get Rs = 1.5,
then error might be very large. To
determine the constraints of this
method, errors have been reported
according to changing resolution
when quantitating proportionate as
well as disproportionate overlapping
chromatographic peaks (11,20). Peak
area quantification was accurate
within 1% error when Rs was > 0.8
for two overlapping proportionate
peaks (50:50 area ratio) (20).
With overlapping peaks of area in
proportion of 1:99, error was much
higher at similar resolution (20).
Depending on the case, some
method development might be
necessary to obtain a resolution
around 0.8 before applying power
transformation.
Direct Resolution Enhancement
by Even Derivative Peak
Sharpening
Using even derivatives to enhance
chromatographic resolution is another
example of directly increasing the
resolution of chromatographic peaks
post-data acquisition. The fundamental
property of sharpening peaks is that
for a symmetric peak function, the
area under a derivative is zero (13).
Real chromatographic peaks are
rarely symmetric, but the area under a
derivative for a tailing or fronting peak
is negligible (on the order of 10-11 units,
that is, signal•time). Therefore, if we
add or subtract even time-derivatives
of peaks from the raw chromatographic
data, the peak areas should not
change. The result is a sharper peak,
which increases the chromatographic
resolution between adjacent peaks. It
is important to smooth the data so the
noise is minimal before subtraction or
addition. The idea can be expressed
mathematically, as shown in equation 4:
Sharp Peak = Signal – K2 (second
derivative) + K4 (fourth derivative) [4]
K2 and K4 are constant multipliers
with consistent units to make the
derivatives dimensionless. The user
can empirically tune these values
until the desired peak widths are
obtained. Small dips are commonly
observed at the front and back of the
chromatographic envelope, but do
not change the peak area or interfere
with integration if properly included
in integration (13). An Excel template
was created to automate this process,
such that a chromatogram could be
exported and then resolved (13).
To visualize this technique, a
simulated Gaussian peak with an area
of 1 is shown in Figure 4(a). The result
of subtracting the second derivative
and adding the fourth derivative (each
with an appropriate multiplier) is shown
in Figure 4(b). The sixth derivative was
also added, but its effect is negligible.
The peak width is reduced and the
peak height increased while the area
remains = 1. Thus, the even derivative
method is a peak-shaping protocol to
make the peaks narrow. This method
can operate on all components of a
chromatogram simultaneously, unlike
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Wahab et al.
the modified power law where each
peak has to be treated individually (20).
In Figure 4(c), a twin-column recycling
HPLC chromatogram separating
d3- and d6-benzenes from ordinary
benzene is shown (13). In recycling
HPLC, the analytes are continuously
injected and detected, that is, they are
recycled in the chromatograph until
the desired resolution is obtained. For
this separation, it takes about 1.5 h
to separate deuterated benzenes
completely (Figure 4[d]). Instead of
waiting 1.5 h for baseline resolution, a
faster approach would be to determine
each peak area by equation 3.
Figure 4(c) shows the peak sharpening
of the fourth recycled chromatogram
(segment IV from Figure 4[d]). From
this point onwards, accurate peak
area estimation (< ~1% error) can be
obtained even before the physical
separation is complete. Error for peak
area determination of two overlapping
proportionate peaks was determined
to be within 1% if the chromatographic
resolution was > 0.7 (13).
A Quick Comparison of Peak
Resolution Methods
Figure 5 provides a quick overview
of the four methods discussed above
when multidimensional data are not
available or when not applicable. These
techniques can be applied on any
single-channel data in any mode of
chromatography (GC, LC, or SFC) and
in capillary electrophoresis with any
detector. The original data (Figure 5)
consists of six overlapping peaks
with noise. The instrumental band
broadening can be removed by FT
deconvolution. As is evident, Figure 5(a)
increases the resolution by removing the
tailing caused by the instrument itself.
The iterative curve fitting procedure
can resolve the six peaks baseline
with accurate areas as exponentially
modified peaks (Figure 5[b]). MCR-ALS
provides similar results to iterative
curve fitting; however, it requires
multidimensional data and does not
need a peak model. In order to easily
visualize all the six peaks, one can apply
a positive integer power by raising the
signal to power 3 (12) on Figure 5(a).
Finally, the first and second derivative
sharpening method (13) can be applied
on Figure 5(a) to make the peaks
baseline for convenient integration.
Further studies are underway to improve
these resolution enhancing procedures.
Conclusions
Resolution enhancement strategies
seem to be the next step in improving
chromatographic separations,
not only to determine peak areas
of overlapping peaks, but also to
deconvolute system effects, reduce
noise, and fix asymmetry. These
strategies aim to increase throughput
and offer cost-effective solutions
compared to traditional method
development. Their automation will
surely make them extremely useful to
the chromatography community and
hence this intelligent peak processing
is the future of chromatography. In
general, the techniques described in
this review either remove extracolumn
band broadening (Fourier transform
deconvolution), extract peak area from
under a curve (iterative curve fitting and
multivariate curve resolution), or directly
enhance chromatographic resolution
(modified power law and even derivative
peak sharpening). There are benefits
and limitations of each technique, one
might be more favourable than another
for a specific application, and the users
have to apply their own judgement
on the choice of resolution enhancing
methods.
Acknowledgements
The authors thank Yoachim
Vanderheyden and Ken Broeckhoven
for providing MATLAB figures for
FT deconvolution (Figure 1). We
also thank Prof. Thomas O’Haver for
collaboration.
References(1) S. Bruns, E.G. Franklin, J.P. Grinias,
J.M. Godinho, J.W. Jorgenson, and U.
Tallarek, Journal of Chromatography A
1318, 189–197 (2013).
(2) A.E. Reising, S. Schlabach, V. Baranau,
D. Stoeckel, and U. Tallarek, Journal of
Chromatography A 1513, 172–182 (2017).
(3) M.F. Wahab, D.C. Patel, R.M.
Wimalasinghe, and D.W. Armstrong,
Analytical Chemistry 89, 8177–8191
(2017).
(4) F. Gritti and M.F. Wahab, LCGC Europe
31, 90–101 (2018).
(5) J.M. Davis and J.C. Giddings, Analytical
Chemistry 55, 418–424 (1983).
(6) Y. Vanderheyden, K. Broeckhoven, and
G. Desmet, Journal of Chromatography A
1465, 126–142 (2016).
(7) N.A. Wright, D.C. Villalanti, and M.F.
Burke, Analytical Chemistry 54,
1735–1738 (1982).
(8) H. Parastar and R. Tauler, Analytical
Chemistry 86, 286–297 (2014).
(9) S.N. Chesler and S.P. Cram, Analytical
Chemistry 45, 1354–1359 (1973).
(10) A. De Juan and R. Tauler, Critical
Reviews in Analytical Chemistry 36,
163–176 (2006).
(11) M.F. Wahab, F. Gritti, T.C. O’Haver, G.
Hellinghausen, and D.W. Armstrong,
Chromatographia 82, 211–220 (2019).
(12) P.K. Dasgupta, Y. Chen, C.A. Serrano,
G. Guiochon, H. Liu, J.N. Fairchild, and
R.A. Shalliker, Analytical Chemistry 82,
10143–10150 (2010).
(13) M.F. Wahab, T.C. O’Haver, F. Gritti, G.
Hellinghausen, and D.W. Armstrong,
Talanta 192, 492–499 (2019).
(14) D.C. Patel, M.F. Wahab, T.C. O’Haver,
and D.W. Armstrong, Analytical
Chemistry 90, 3349–3356 (2018).
(15) S. Misra, M.F. Wahab, D.C. Patel, and
D.W. Armstrong, Journal of Separation
Science 42, 1644–1657 (2019).
(16) J. Schenk, J.X. Mao, J. Smuts, P. Walsh,
P. Kroll, and K.A. Schug, Analytica
chimica acta 945, 1–8 (2016).
(17) M. Maeder, Analytical Chemistry 59,
527–530 (1987).
(18) D.W. Cook, S.C. Rutan, C. Venkatramani,
and D.R. Stoll, LCGC North America 36,
248–255 (2018).
(19) https://www.shimadzu.com/an/
literature/hplc/jpl217011.html
(Accessed 24 April 2019)
(20) G. Hellinghausen, M.F. Wahab, and D.W.
Armstrong, Journal of Chromatography A
1574, 1–8 (2018).
M. Farooq Wahab is a Research
Engineering Scientist-V at the
University of Texas at Arlington.
His research interests include
fundamentals of separation science,
SFC, HILIC, and developing signal
processing methods for resolution
enhancement. He received a
Young Investigator Award from the
Chinese American Chromatography
Association at Pittcon 2019. He
carried out postdoctoral research
with Professor Armstrong after
completing his Ph.D. at the University
of Alberta.
Garrett Hellinghausen is a PhD
student at the University of Texas at
Arlington. He has developed chiral
separation methodologies using
newly synthesized chiral stationary
phases under the direction of
Professor Armstrong. Recently, he has
investigated new signal processing
techniques with a focus on their
application in fast chromatography.
Daniel W. Armstrong is the Welch
Distinguished Professor of Chemistry
at the University of Texas at Arlington.
Professor Armstrong has received over
30 national and international research
and teaching awards. His research
interests involve chiral recognition,
macrocycle chemistry, synthesis
and use of ionic liquids, separation
science, mass spectrometry, and
peak processing. He had over 700
publications including 35 patents.
Recent Developments in HPLC and UHPLC May 201928
Wahab et al.
Monoclonal antibodies (mAbs)
are a frequently studied group of
therapeutic proteins. mAbs are
inherently heterogeneous and their
post-translational modifications—
such as glycosylation, oxidation,
deamidation, or fragmentation,
and, particularly, their charge
heterogeneity—need to be
characterized because charge
variants can be responsible
for the efficacy and toxicity of
the product (1,2). The two most
commonly used methods for
charge variant assessment are
capillary electrophoresis (CE) and
ion-exchange chromatography (IEX)
(3–6).
Cation exchange chromatography
(CEX) is currently considered to
be the “gold standard” method
for the separation of main isoform,
acidic, and basic variants of mAbs
(7–9). In CEX separations, analytes
can be eluted by applying either
pH-gradient (10–12), salt-gradient
(9,13,14,15), or salt-mediated
pH-gradient modes (16). Because
of the nonvolatile nature of the
salts (NaCl, KCl) and buffers (MES,
phosphate) commonly used in the
CEX mobile phase, this analytical
strategy is not compatible with mass
spectrometry (MS). To avoid this
issue, Alvarez et al. applied a series
of trap cartridges for desalting the
CEX effluent before entering the MS
system (17). Another possibility was
to replace common salts and buffer
components with MS-compatible
buffers. Some research groups
recently reported that ammonium
acetate and ammonium carbonate
(or bicarbonate) buffer systems
are particularly promising and can
provide appropriate chromatographic
retention and peak shape,
together with suitable MS signals
(18,19,20,21). Those buffer systems
have been applied for various
mAbs possessing a wide range of
isoelectric points (pI). Elution of
the mAb products was achieved by
increasing the ammonium acetate
concentration at constant pH (22,23),
or by applying a pH gradient by
adding ammonium hydroxide and
methanol into eluent B (24). The
most promising application was
recently presented by Yan et al. who
performed a simultaneous ionic
strength and pH gradient to develop
a generic method, which can be
applied for various mAbs (19). They
did this with a gradient wherein the
total ionic strength increased from
40 to 150 mM, while the pH changed
from 5.6 to 7.4.
The purpose of this article is to
understand the impact of pH and
ionic strength gradients on the
retention of mAbs when working with
ammonium acetate and ammonium
carbonate buffer systems.
Materials and Methods
FDA- and EMA-approved mAbs
were obtained as European Union
pharmaceutical-grade drug products
from their respective manufacturers.
Ammonium acetate, acetic acid,
and ammonium carbonate were
purchased from Sigma-Aldrich. High
performance liquid chromatography
(HPLC)-grade water was obtained
from Fisher Scientific.
Ammonium acetate and ammonium
carbonate-based buffer systems
were systematically tested for mAb
separations. The weaker eluent
was a mixture of 10-mM ammonium
acetate and 10-mM acetic acid
(pH~5.2), which maintained
a low ionic strength, while the
stronger eluent was ammonium
acetate and ammonium carbonate
mixed in different ratios and
concentrations—10 mM, 25 mM,
50 mM, and 100 mM of both
acetate and carbonate. Sixteen
different combinations of buffer
composition were applied, and
Optimization of MS-Compatible Mobile Phases for IEX Separation of Monoclonal Antibodies Evelin Farsang1, Amarande Murisier2, Krisztián Horváth1, Olivier Colas3, Alain Beck3, Davy Guillarme2, and
Szabolcs Fekete2, 1Department of Analytical Chemistry, University of Pannonia, Veszprém, Hungary, 2School of
Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland, 3Center of Immunology
Pierre Fabre, Saint-Julien-en-Genevois, France
Characterization of monoclonal antibodies (mAbs) and related products requires the identification of
chromatographic peaks with mass spectrometry (MS). However, the conventional salt- and pH-gradient
elution techniques used in ion-exchange chromatography (IEX) are inherently incompatible with MS.
Ammonium acetate- and ammonium carbonate-based mobile phase systems have been recently applied in
IEX-MS, but the influence of the eluent composition on peak shape and retention has not been discussed
nor studied systematically until now. The aim of the present study was to understand the impact of ionic
strength, buffer capacity, and pH-response on the retention behaviour and peak shape of mAb species.
Ph
oto
Cre
dit: c
hri
s/s
toc
k.a
do
be.c
om
29www.chromatographyonline.com
the retention times of three mAbs
(bevacizumab, daratumumab, and
rituximab) were measured to study
the impact of pH and ionic strength
gradients on retention properties.
After establishing generic conditions,
seven intact mAbs (eculizumab,
panitumumab, reslizumab,
pembrolizumab, atezolizumab,
adalimumab, and rituximab
embracing a pI range of 6.1 to 9.4)
were injected and eluted applying an
8-min-long linear gradient. Finally,
two gradient separations were further
optimized to analyze the mixtures
of panitumumab and cetuximab,
and nivolumab and ipilimumab.
All mAbs were diluted in water at
1 mg/mL, except for the ipilimumab
and nivolumab combination, for
reasons discussed in the results and
discussion section.
The separations were performed
either on an Agilent 1290 Infinity
ultrahigh-performance liquid
chromatographic system or on a
Waters Acquity UHPLC H-Class Bio
system. A 50 × 2 mm, 5-μm ProPac
Elite weak cation-exchanger column
(Thermo Fisher Scientific) was
employed. Fluorescence detection
was performed at 280 nm/360 nm
excitation/emission wavelength.
Buffer capacity and pH response
were calculated for every buffer
composition considering the mass
and charge balance for the acid
and the basic agents through
an algorithm written in Python
programming language (version
3.7, Anaconda Python Distribution,
Numpy package). Retention
modelling and method optimization
were performed by DryLab 4
chromatographic modelling software
(Molnár-Institute).
Results and Discussion
Finding the Optimal Volatile
Buffer Composition to Perform
mAb Separations: Diverse results
were presented in some recent
papers regarding the pH, ionic
strength, buffer capacity, and
conductivity responses of some
volatile mobile phase systems—
including ammonium acetate, acetic
acid, ammonium bicarbonate,
and ammonium hydroxide—when
developing a gradient program. In
addition, some published recipes
suffer from the lack of either suitable
buffering capacity or appropriate
ionic strength. Therefore, some
in silico calculations were first
performed to predict the buffer
capacity and pH response of
ammonium acetate, acetic acid,
and ammonium carbonate systems.
These components were found to
be highly promising on the basis of
initial screening experiments. Mobile
phase A consisted of a mixture of
ammonium acetate and acetic acid,
while mobile phase B consisted of
ammonium acetate and ammonium
Recent Developments in HPLC and UHPLC May 201930
Fekete et al.
Figure 1: pH (green curve) and buffer capacity (grey curve) responses of the
mixture of 10-mM ammonium acetate and 10-mM acetic acid (A eluent), and
50-mM ammonium acetate and 50-mM ammonium carbonate (eluent B) as a
function of mobile phase composition (% eluent B).
7.5
7.0
6.5
6.0
5.5
5.0
0 20 40 60
%B
80 1000
2
4
8
10
12
6pH
β,
mM
Figure 2: Contour plot of apparent retention factor of bevacizumab as a
function of ammonium carbonate and ammonium acetate concentration in
mobile phase B. Generic 10-min-long linear gradient (0–100%B) was run at
0.3 mL/min using a 50 × 2 mm cation exchanger column. Mobile phase A
consisted of 10-mM ammonium acetate and 10-mM acetic acid.
100
90
80
70
60
50
40
30
20
1020 40 60
Concentration of Ammonium Acetate (mM)
Co
nce
ntr
ati
on
of
Am
mo
niu
m C
arb
on
ate
(m
M)
80 100
12
14
16
18
20
22
24
carbonate. Their individual
concentrations varied between
10 mM and 100 mM. Simultaneously,
three intact mAbs were injected
and analyzed in 16 different buffer
composition combinations. Retention,
peak shape, and selectivity
were studied, and the following
conclusions could be drawn from
the experimental observations and
in silico calculations. First, to have
sufficient retention to elute the mAbs
and appropriate peak shape the total
ionic strength of the mobile phase B
should be higher than 70 mM at the
elution of the most retained peaks.
Second, the buffer capacity should
be maintained above 6 mM. Under
these conditions, most mAbs will
elute in a pH range between 5.5
and 7.5. In addition, the ionic
strength should be kept as low
as possible to obtain suitable MS
sensitivity.
Further to these results, a
salt-mediated pH gradient was
also performed to gain an in-peak
focusing effect as a result of the
salt gradient, and to extend the
possibilities of changing selectivity
by simultaneously performing a pH
gradient. In this combined mode,
the elution is based on both salt
displacement and on changing the
charge state of the mAbs.
All of these needs can be fulfilled
by using 10-mM ammonium acetate
and 10-mM acetic acid as mobile
phase A, and 50-mM ammonium
acetate and 50-mM ammonium
carbonate as mobile phase B.
Figure 1 shows the pH response
and buffer capacity of such a buffer
system as a function of %B eluent.
The impact of ionic strength
on retention was studied under
various conditions. Figure 2 shows
a contour plot of bevacizumab’s
apparent retention factor (kapp) as a
function of ammonium carbonate and
ammonium acetate concentrations
in mobile phase B. As a side note,
the gradient delay volume and
system residence time were taken
into account when determining
the composition at elution. As
expected, the ammonium carbonate
concentration had a more significant
effect on retention than ammonium
acetate. This is logical, since the
carbonate salt contains twice as
much ammonium ion, which is a
31www.chromatographyonline.com
Fekete et al.
Figure 3: Cation exchanger chromatographic profiles of bevacizumab at
different mobile phase B compositions. Generic 10-min-long linear gradient
(0–100%B) was run at 0.3 mL/min using a 50 × 2 mm cation exchanger
column. Mobile phase A consisted of 10 mM ammonium acetate and 10 mM
acetic acid.
B”:50 mM ammonium-acetate +
50 mM ammonium-acetate +
50 mM ammonium-acetate +
100 mM ammonium-carbonate
50 mM ammonium-carbonate
10 mM ammonium-carbonate
”
B”:”
B”:”B”:
0 2 4 6 8 10 12
Retention Time (min)
”
Figure 4: CEX chromatograms of intact mAbs (platform method). Mobile phase
A: 10-mM ammonium acetate and 10-mM acetic acid, mobile phase B: 50-mM
ammonium acetate and 50-mM ammonium carbonate, gradient: 10–70%B
in 8 min, flow rate: 0.3 mL/min, column: 50 × 2 mm weak cation exchanger.
Peaks: (1) eculizumab, (2) panitumumab, (3) reslizumab, (4) pembrolizumab,
(5) atezolizumab, (6) adalimumab, and (7) rituximab.
p/ = 9.4
7
6
5
4
3
2
1
p/ = 8.9
p/ = 8.6
p/ = 7.6
p/ = 7.1
p/ = 6.8
p/ = 6.1
0 1 2 3 4 5 6 7 8
Retention Time (min)
counter ion, compared to the acetate
salt. As an example, a mixture of
10-mM ammonium acetate and
100-mM ammonium carbonate
resulted in kapp = 12.6, while the
opposite combination of 100-mM
ammonium acetate and 10-mM
ammonium carbonate provided an
almost two times higher retention of
kapp = 24.1. In addition, ammonium
carbonate is required to ensure
the sufficiently high mobile phase
pH. On the other hand, ammonium
acetate is also required to provide a
nearly linear pH response and high
enough buffer capacity.
Figure 3 illustrates the impact
of salt concentration (ammonium
carbonate) on selectivity. It
seems that not only retention but
also selectivity is affected by
ammonium carbonate concentration.
Interestingly, neither the highest
(100 mM) nor the lowest (10 mM) salt
concentration provided the highest
selectivity, but the best selectivity
was achieved at 50 mM. By applying
50-mM ammonium carbonate, two
basic variants (small peaks after the
main peak) and some acidic variants
(partially resolved pre-peaks) could
be separated. At low concentration
of ammonium carbonate, only one
sharp peak was observed. It is
likely that the steepness of the pH
response during the gradient at a
fixed gradient composition steepness
is the lowest when ammonium
carbonate is at 50 mM at elution.
On the contrary, the highest pH
response steepness was observed
with 10-mM ammonium carbonate,
and indeed bevacizumab eluted
in a single-focused peak. In this
latter case, because large solutes
approach an “on/off”-like retention,
the selectivity was quenched by
the steep pH gradient. If the mobile
phase composition needs to be
further optimized, for example
when the proposed mixture of
50-mM ammonium acetate and
50-mM ammonium carbonate does
not provide suitable retention nor
selectivity, then the first choice
would be to change the ammonium
carbonate concentration in mobile
phase B.
Generic MS-Compatible IEX
Conditions as Platform Method
for mAbs: Our proposed mobile
phase was also tested for several
intact mAbs as candidates for a
generic mobile phase of a platform
method. The gradient was run from
10 to 70%B, corresponding to a pH
range of 5.5 ≤ pH ≤ 7.3. Various
mAbs covering a wide range of pI
(6.1 ≤ pI ≤ 9.4) could be eluted with
appropriate retention, peak shape,
and selectivity (Figure 4). mAbs with
pI > 7.3 could also be eluted as a
result of the high ionic strength of
mobile phase B (100 mM in total).
The elution order of the mAbs
followed their pI, thanks to the nearly
linear pH response of the gradient.
Therefore, the relative isoelectric
points of eluting proteins can be
estimated on the basis of their
relative retention. Both acidic and
basic variants could be separated
from the main peaks for most mAbs.
The suggested MS-compatible
salt-mediated pH-gradient method
can therefore be considered as
a multiproduct charge sensitive
separation method. For most of the
compounds, the separation quality
was very similar to a pure salt
gradient, and slightly better than a
pure pH gradient method.
Optimized MS-Compatible IEX
Conditions for mAb Combinations:
Recent Developments in HPLC and UHPLC May 201932
Fekete et al.
Figure 5: Applications of the suggested MS-compatible mobile phase for mAb
combinations: (a) fast separation of nivolumab (peak 1) and ipilimumab (peak
2) and (b) a high-resolution separation of panitumumab (peak 3) and cetuximab
(peak 4). See the experimental conditions in “Optimized MS-Compatible IEX
Conditions for mAb Combinations”.
1
2
3
0
0 2 4 6 8 10 12 14 16
1 2 3 4
(a)
(b)
5 6
4
Retention Time (min)
Retention Time (min)
The combination of different mAbs in
one single product could improve the
therapeutic efficacy. Two promising
combinations are: i) ipilimumab and
nivolumab, which have a synergic
effect when CTLA and PD1 are
targeted simultaneously for the
same cancer; and ii) cetuximab
and panitumumab, which target the
nonoverlapping EGFR epitopes (25).
Those combinations were
mimicked by mixing 1.2 mg/
mL ipilimumab and 0.4 mg/
mL nivolumab, and 1 mg/mL
panitumumab and 1 mg/mL
cetuximab. Then, the generic
conditions were further optimized
to perform either a fast or a
high-resolution separation.
Working with salt-mediated pH
gradients can significantly extend
the design space for method
development, when compared to a
pure salt or pH gradient. The two
most important method variables in
salt-mediated pH-gradient CEX are
the gradient steepness (proportional
to gradient time, tG) and the salt
concentrations in the B eluent (16).
Here, the salt concentration was
fixed at 20 mM in A and 100 mM in B,
while varying tG. Experiments were
performed at three tG levels (6 min,
10 min, and 18 min, at 0.3-mL/
min on a 50 × 2 mm column) and
retention models as well as resolution
maps were built. The separations
were finally optimized based on
resolution maps. As an extra note,
selectivity can be further increased
by changing the salt concentration in
mobile phase B, if required.
For the nivolumab and ipilimumab
combination, a fast separation was
proposed by running a gradient from
32 to 52%B in 6 min, at 0.45 mL/
min (Figure 5[a]). For the other
combination of panitumumab and
cetuximab, a longer separation was
suggested because both mAbs
possess numerous charge variants.
In this case, a gradient from 10 to
75%B in 18 min, at 0.3 mL/min,
enabled the separation of acidic
and basic variants of both mAbs
(Figure 5[b]).
Conclusion
This article demonstrated it is
possible to establish some generic
IEX conditions for charge variants
analysis of mAbs, including only
volatile components in the mobile
phase (MS-compatible). The best
performance was achieved using
10-mM ammonium acetate and
10-mM acetic acid as mobile phase
A, and 50-mM ammonium acetate
and 50-mM ammonium carbonate
as mobile phase B. These generic
conditions were successfully applied
for a wide range of mAbs with pI
ranging from 6.1 to 9.4, and for
mixtures of mAb products. In an
upcoming study, the developed
conditions will be tested with MS
detection.
Acknowledgements
Davy Guillarme thanks the Swiss
National Science Foundation for
support through a fellowship to
Szabolcs Fekete. Jean-Luc Veuthey
from the University of Geneva is also
acknowledged for useful comments
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Fekete et al.
and discussions. Evelin Farsang and
Krisztián Horváth acknowledge the
financial support of the Hungarian
National Research, Development and
Innovation Office (NKFIH FK128350).
References(1) Y. Du, A. Walsh, R. Ehrick, W. Xu, K.
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Evelin Farsang is a PhD student
at the University of Pannonia, in
Veszprém, Hungary. She graduated
in environmental engineering in 2012
and in chemistry in 2016. Her primary
research focus is the study of retention
behaviour of biological macromolecules
in ultrahigh-performance liquid
chromatographic systems.
Amarande Murisier is a PhD
student at the University of Geneva,
in Switzerland. Her Ph.D. thesis
focuses on novel chromatographic
and electrophoretic techniques for the
analysis of therapeutic proteins in the
group of J.L. Veuthey and D. Guillarme.
Krisztian Horvath is an associate
professor at the University of Pannonia,
and member of the board of the
Hungarian Society for Separation
Sciences and the Analytical Division
of Hungarian Chemical Society.
He graduated with a degree in
environmental engineering in 2002
and obtained his Ph.D. in chemistry in
2007 from the University of Pannonia.
His research interests include the
study of retention behaviour of small
and large molecules in HPLC, and
method development and optimization
in one- and two-dimensional liquid
chromatography.
Olivier Colas is a technician in
analytical chemistry at the Centre
d’Immunologie Pierre Fabre in France.
He is focused on mass spectrometry
for the characterization of mAbs,
antibody–drug conjugates (ADCs),
immunocytokines, Fc-fusion proteins
and peptides (top down, middle up
and down, bottom up, denaturing and
native). He also runs chromatography
and electrophoresis methods such
as CEX, hydrophobic interaction
chromatography (HIC), reversed-phase
LC, capillary isoelectric focusing
(cIEF), and CE-sodium dodecyl
sulfate (SDS), and has co-authored 22
publications.
Alain Beck is Senior Director,
Biologics CMC and Developability,
Pierre Fabre Laboratories in France.
He is also associated editor of mAbs.
He has contributed to the R&D of
immuno-oncology mAbs, clinical
stages mAbs and ADCs in oncology,
and peptides and vaccines in infectious
diseases. He has published +210
papers and reports (h-index: 45; +8150
citations) and has been involved in
more than 260 scientific meetings as
chairperson, invited plenary or keynote
speaker, panellist, moderator, advisor,
and organizer as well as meetings and
workshops with regulatory agencies.
Davy Guillarme obtained his Ph.D.
degree in analytical chemistry from
the University of Lyon, in France, in
2004. He has worked at the University
of Geneva in Switzerland for 15 years
as a senior lecturer. He has authored
more than 240 journal articles related to
pharmaceutical analysis. His expertise
includes HPLC, ultrahigh-pressure
liquid chromatography (UHPLC),
hydrophilic interaction liquid
chromatography (HILIC), LC–MS,
supercritical fluid chromatography
(SFC), SFC–MS, and analysis of
proteins and mAbs. He is an editor of
Journal of Chromatography B, and an
editorial advisory board member of
several journals including Analytical
Chemistry, Journal of Chromatography
A, Journal of Separation Science,
and LCGC Europe. He is the
recipient of the LCGC Emerging
Leader in Chromatography Award in
chromatography in 2013 and he won
the Silver Jubilee Medal in 2018.
Szabolcs Fekete holds a Ph.D.
degree in analytical chemistry from the
Technical University of Budapest, in
Hungary. He worked at the Chemical
Works of Gedeon Richter Plc at the
analytical R&D department for 10
years. Since 2011, he has worked at
the University of Geneva in Switzerland
in the Analytical Pharmaceutical
Chemistry group. He has contributed
~130 journal articles and authored
book chapters and edited handbooks.
His main interests include liquid
chromatography (reversed phase,
IEX, size-exclusion chromatography
[SEC], HIC, SFC, and HILIC), column
technology, mass transfer processes,
method development, pharmaceutical,
and protein analysis.
Recent Developments in HPLC and UHPLC May 201934
Fekete et al.
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