Detecting and Eliminating Matrix Effectsimages2.advanstar.com/PixelMags/lcgc-asia-pacific/pdf/... · 2014. 2. 27. · Wayne State University, Detroit, Michigan, USA Fred E. Regnier

LC TROUBLESHOOTING

What is the point of the column

dead time?

PERSPECTIVES IN

MODERN HPLC

Myths in UHPLC

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March 2014

Volume 17 Number 1

www.chromatographyonline.com

For quantitative LCÐMS analysis

Detecting and Eliminating Matrix Effects

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3

Editorial P olicy:

All articles submitted to LC•GC Asia Pacific

are subject to a peer-review process in association

with the magazine’s Editorial Advisory Board.

Cover:

Original materials: nadia

Columns13 LC TROUBLESHOOTING

Column Dead Time as a Diagnostic Tool

John W. Dolan

What good is that big, ugly peak at the beginning of the

chromatogram?

16 PERSPECTIVES IN MODERN HPLC

Myths in Ultrahigh-Pressure Liquid Chromatography

Michael W. Dong

The advent of ultrahigh-pressure liquid chromatography (UHPLC)

and its successful commercialization in the last few years has

brought forth a modern high performance liquid chromatography

(HPLC) platform capable of higher speed, resolution, precision, and

sensitivity. Currently, all major HPLC manufacturers have some

type of low-dispersion UHPLC products with upper pressure limits

ranging from 15,000 to 19,000 psi (1000 to 1300 bar) on the market.

This installment describes a number of popular myths or half-truths

in UHPLC and provides data that contradict or even repudiate some

of these commonly held beliefs.

25 MS — THE PRACTICAL ART

Mass Spectrometry for Natural Products Research:

Challenges, Pitfalls, and Opportunities

Nadia B. Cech and Kate Yu

As applied to natural products research, mass spectrometry (MS) is

fraught with challenges and pitfalls. Here is an account of strategies

to conduct effective research despite these obstacles.

Departments34 Products

36 Application Notes

COVER STORY5 Strategies for the Detection and Elimination of Matrix

Effects in Quantitative LC–MS Analysis

Amitha K. Hewavitharana, Swee K. Tan, and P. Nicholas Shaw

Currently available methods for the detection of matrix effects

in liquid chromatography–mass spectrometry (LC–MS) are

tedious and complex; therefore, a simpler method is required.

Although there are no methods to completely eliminate matrix

effects, the most well-recognized

technique available to correct for

matrix effects is that of internal

standardization using stable

isotope–labelled versions of the

analytes. As this method can

prove expensive, an alternative

method of correction is likely to

be useful. In this study, a simple

method based on recovery is

assessed for the detection of matrix

effects. Two alternative methods

for the rectif cation of matrix effects

in LC–MS are also assessed:

Standard addition and the coeluting

internal standard method.

March | 2014

Volume 17 Number 1


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4 LC•GC Asia Pacific March 2014

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KEY POINTS

• LC–MS is now the predominant technique for

qualitative analysis in biological matrices.

• Matrix effects are a major challenge when analysing

biological matrices.

• An efficient strategy to detect and reduce matrix

effects is proposed.

High performance liquid chromatography (HPLC)

coupled to mass spectrometry (MS) has become the

predominant analytical method for the quantitative

determination of analytes in biological matrices because

of its high specificity, sensitivity, and throughput (1–3).

However, matrix effects have become a major concern in

quantitative liquid chromatography–mass spectrometry

(LC–MS) because they detrimentally affect the accuracy,

reproducibility, and sensitivity (3). Matrix effects occur

when compounds that are coeluted with the analyte

interfere with the ionization process in the MS detector,

thereby causing ionization suppression or enhancement

(2–7). Compounds with high mass, polarity, and basicity

are possible candidates to cause matrix effects (4–8).

However, the mechanisms involved in matrix effects

have not been fully explored. One of the proposed

theories to explain matrix effects is that the coelution of

interfering compounds, especially basic compounds, may

deprotonate and neutralize the analyte ions and, thus,

reduce the formation of protonated analyte ions (2,4).

Another theory postulates that less-volatile compounds

may affect the efficiency of droplet formation and reduce

the ability of charged droplets to convert into gas-phase

ions (2–4,8). In addition, matrix effects may also be caused

by high viscosity interfering compounds that could possibly

increase the surface tension of the charged droplets and

reduce the efficiency of droplet evaporation (2,4,6).

Several methods have been proposed for the

detection and assessment of matrix effects, including

post-extraction spike and post-column infusion methods.

The post-extraction spike method evaluates matrix effects

by comparing the signal response of an analyte in neat

mobile phase with the signal response of an equivalent

amount of the analyte in the blank matrix sample spiked

post-extraction. The difference in response determines the

extent of matrix effect (2,3,9). The major drawback of this

method is that for endogenous analytes such as metabolites

(for example, creatinine) blank matrix (urine or plasma) is

not available. The post-column infusion method assesses

matrix effects qualitatively. A constant flow of analyte is

infused into the HPLC eluent, followed by injection of the

blank sample extract. A variation in signal response of the

infused analyte caused by coeluted interfering compounds

indicates ionization suppression or enhancement (3,10).

By identifying the ionization suppression or enhancement

regions of the chromatogram, analytical methods can be

developed to eliminate matrix effects by preventing the

elution of the analyte peak in regions where matrix effects

occur. However, the process of post-column infusion is time-

consuming and requires additional hardware, and it is not

appropriate for multianalyte samples. Considering these

drawbacks of existing methods, we propose a simple, fast,

and reliable method to detect matrix effects that can be

applied to any analyte including endogenous compounds

such as creatinine and to any matrix without requiring any

additional hardware.

To obtain accurate and reliable LC–MS data, several

methods have been suggested to reduce or eliminate

Amitha K. Hewavitharana, Swee K. Tan, and P. Nicholas Shaw, School of Pharmacy, The University of Queensland,

Brisbane, Australia.

Currently available methods for the detection of matrix effects in liquid chromatography–mass spectrometry (LC–MS) are tedious and complex; therefore, a simpler method is required. Although there are no methods to completely eliminate matrix effects, the most well-recognized technique available to correct for matrix effects is that of internal standardization using stable isotope–labelled versions of the analytes. As this method can prove expensive, an alternative method of correction is likely to be useful. In this study, a simple method based on recovery is assessed for the detection of matrix effects. Two alternative methods for the rectif cation of matrix effects in LC–MS are also assessed: Standard addition and the coeluting internal standard method.

Strategies for the Detection and Elimination of Matrix Effects in Quantitative LC–MS Analysis


LC•GC Asia Paciàc March 20146

Hewavitharana et al.

matrix effects. Matrix effects can be reduced simply by

injecting small amounts of samples or by diluting samples

(11,12). However, this approach can only be feasible when

the sensitivity of the assay is very high (12). Methods to

reduce or eliminate matrix effects include optimizing sample

preparation to remove interfering compounds from the

samples (1,9,10,13), changing chromatographic parameters

to avoid coelution of analytes and interfering compounds

(4,14–17), and changing MS conditions to reduce the

occurrence of matrix effects in the ion source. However,

these methods are not without their limitations. Most of the

sample cleanup methods fail to remove impurities that are

similar to the analyte and, hence, likely to be coeluted with

the analyte (11,18). Modifying chromatographic conditions

can be time-consuming, and some of the additives used in

the mobile phase to improve separation have been found to

suppress the electrospray signal of the analytes (3,4,9,15).

Furthermore, even when the sample is devoid of coeluted

substances, trace impurities present in the mobile phase can

significantly suppress the analyte peak (19).

It is clear from the above that matrix effects in LC–MS

cannot be completely eliminated. Therefore, the only

option available is the rectification of data to eliminate

the matrix effects. Calibration techniques such as the

external-matched standards method, the echo-peak

technique, and the most commonly used approach, the

internal standard method, have been developed to correct

the data (15,18–21). However, these calibration techniques

also have their drawbacks. For example, the matrix-matching

technique requires many blank matrices and appropriate

blank matrices are not always available for the preparation

of external standards (9,11,18,23). It is also impossible to

match the matrix of the calibration standards with each of the

samples exactly, as each sample has coeluting, interfering

compounds that are thereby exposed to a different extent of

ionization suppression (18). Echo-peak does not compensate

for matrix effects completely because both standard and

analyte peaks are not eluted at the exact same retention time

(11). The stable isotope–labelled internal standards (SIL-IS)

approach is the best available option but it is expensive

and standards are not always commercially available for the

analyte of interest (4,9,23).

The standard addition method for correcting matrix effects

is widely used in spectrophotometric analysis, especially in

atomic spectroscopy (24–27). However, this method is less well

documented with other analytical techniques and currently there

is no record of its practical use in compensating matrix effects

in LC–MS. Standard addition does not require a blank matrix

and is therefore appropriate for compensating matrix effects

for any analyte including endogenous metabolites in biological

fluids (20,28,29). In this study, we investigated the possibility

of using the method of standard addition in routine LC–MS

analysis to compensate for matrix effects and to thereby obtain

improved data. We also investigated the use of a coeluting

structural analogue of the analyte as the internal standard as

an alternative to the expensive and often unavailable stable

isotope–labelled internal standard for correcting matrix effects.

Although coeluting structural analogue compounds are used

to extend the linear range of calibration curves (30), there have

been no reports of their use in compensating matrix effects in

routine LC–MS analysis. In addition, we report a simple and

effective method for the detection as well as the correction of

matrix effects in routine LC–MS. All studies were carried out

using a creatinine assay applied to human urine samples.

ExperimentalInstrumentation: Separation of compounds was achieved

with an Agilent 1100LC binary pump and Agilent 1100

autosampler (Agilent Technologies). The HPLC column used

was a 150 mm × 2.1 mm, 4-µm dp Cogent Diamond-Hydride

100A column (MicroSolv Technology). For detection and

quantification, an API 3000 tandem mass spectrometer

equipped with a turbo ion spray interface and the software

program Analyst 1.5 (Applied Biosystems) was used.

Materials: Creatinine, creatinine-d3 (2-amino-1-

[trideuteriomethyl]imidazolidin-4-one), and cimetidine (N′′-

cyano-N-methyl-N′-[2[(5-methyl-1H-imidazol-4-yl)methylthio]-

ethyl]guanidine) were purchased from Sigma. HPLC-grade

acetonitrile and deionized water from a Milli-Q water system

(Millipore) were used for mobile-phase preparation. Human

urine samples were obtained from volunteers.

Chromatographic Conditions: Separations were carried

out at an ambient temperature of approximately 25 °C. The

flow rate was 200 μL/min. The injection volume was 10 μL.

Mobile-phase A consisted of deionized water containing 0.1%

(v/v) formic acid, and mobile-phase B comprised 0.1% (w/v)

formic acid in acetonitrile.

Table 1: The percent recoveries of creatinine in five human

urine samples. C0 is the known concentration of creatinine

standard added to sample, 6 mM; C1 is the concentration of

creatinine measured in the unspiked urine sample; and C2

is the concentration of creatinine measured after spiking (to

a final spiked concentration of 6 mM). Recovery calculation

procedure is explained in the results and discussion section.

Sample C0 (mM) C2 (mM) C1 (mM) Recovery (%)

1 6.000 10.693 6.762 65.52

2 6.000 11.141 7.101 67.24

3 6.000 11.486 7.590 64.94

4 6.000 11.624 7.934 61.49

5 6.000 12.521 8.279 70.69

Mean 65.98

SD 3.36

4.00E+07

3.00E+07

2.00E+07

1.00E+07

-0.015 -0.01 0.005 0.01-0.001

Creatinine concentration (mM)

Sig

nal re

spo

nse

00.00E+00

Figure 1: Standard addition method. A three-point calibration

line is extrapolated to zero response to estimate the original

concentration of creatinine present in the urine sample.




B over the first 20 min, and then maintained at 50% B for

1 min, beforeve returning to 90% B from 21 to 24 min. The

percentage of B was maintained at 90% B for 10 min for

re-equilibration before the next sample was injected. The total

run time was 24 min.

Coeluting Internal Standard Method: An isocratic elution

method was used for elution. The mobile-phase composition

was 55% B over 10 min. The total run time was 10 min.

MS Conditions: Creatinine, creatinine-d3, and cimetidine

were detected in a positive multiple reaction monitoring

(MRM) mode in which the transitions of m/z 113.9 to 44.0,

m/z 117.0 to 47.0, and m/z 252.8 to 95.1 were monitored,

respectively. The MS and electrospray ionization parameters

were optimized and the analyzer settings were as follows:

ion spray voltage (IS) 5000 V; entrance potential (EP)

10 V; orifice/declustering potentials (DP) 26 V (creatinine),

36 V (creatinine-d3), and 36 V (cimetidine); ring/focusing

Detection of Matrix Effects and Standard Addition Method:

Gradient elution was used for analyte separation. The

mobile-phase composition was varied from 90% B to 50%

20

Cre

ati

nin

e c

on

cen

trati

on

Neg

ativ

e co

ntrol

Posit

ive

contr

ol

Stan

dard a

ddition (a

)

Stan

dard a

ddition (b

)

15

10

5

0

(a) (b)

S

N

N

NH

NH

O

NH

N

N

NH2

HN

Figure 2: Comparison of approaches using negative

control (external standard calibration), positive control

(SIL-IS), standard addition (a) with extrapolated calibration

line and standard addition (b) with equation. Detailed

explanation in text.

Figure 3: Chemical structures of (a) creatinine and

(b) cimetidine.

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0.001, 0.003, 0.006, 0.01, 0.015, and 0.02 mM in 90% (v/v)

acetonitrile. A calibration curve of the ratio of the peak area

of creatinine and the peak area of creatinine-d3 versus

creatinine concentration was then plotted to determine the

concentration of the above five aliquots of 1000-fold diluted

urine.

For the standard addition method, two levels of additions

were prepared. A 0.003 mM addition was prepared by

mixing 10 μL of 10-fold diluted filtered urine, 30 μL of 0.1 mM

creatinine, 900 μL of acetonitrile, and 60 μL of water to make

the final volume up to 1000 μL. The 0.006 mM addition was

prepared by mixing 10 μL of 10-fold diluted filtered urine,

60 μL of 0.1 mM creatinine, 900 μL of acetonitrile, and 30 μL

of water to make the final volume up to 1000 μL. The zero

addition was prepared in the same manner but water was

added in place of the 0.1 mM creatinine solution. A calibration

plot of peak area of creatinine versus the added creatinine

concentration was plotted for each sample and the original

concentration of creatinine in five samples were estimated by

extrapolating the line of best fit to zero peak area.

A negative control experiment was also carried out by

running five samples containing no internal standard and

determining their concentration using the calibration curve

prepared in the section titled “Detection of matrix effects

using recovery” above.

Comparison of the Stable Isotope–Labelled Internal Standard

and Coeluting Internal Standard Methods: Standard

solutions for the SIL-IS method were prepared by mixing

10 μL of 0.05 mM creatinine-d3, 900 µL of acetonitrile,

appropriate volumes of 1.0, 0.1, and 0.01 mM creatinine,

and deionized water to make the final volume up to 1000 μL.

The final concentration of creatinine-d3 in all standards was

0.0005 mM and the concentrations of creatinine were 0.0001,

0.0003, 0.0005, 0.001, 0.003, and 0.006 mM in 90% (v/v)

acetonitrile.

Standard solutions for the coeluting internal standard

method were prepared by using 0.05 mM cimetidine in place

of creatinine-d3 and by following the procedure mentioned

in the previous section to obtain similar final concentrations

of creatinine and the internal standard. A calibration curve

of the ratio of the peak area of creatinine and peak area of

the internal standard versus creatinine concentration were

then plotted for each internal standard to calculate the

concentrations of creatinine in seven aliquots of 1000-fold

diluted urine. A negative control experiment was run in the

same manner as described in the previous section.

Results and DiscussionDetection of Matrix Effects: To eliminate matrix effects,

they should be assessed during early analytical method

development. In this paper, we propose a simple and

reliable method, recovery value, to estimate the extent

of matrix effects in biological matrices analysis. In the

recovery value test, the original concentration of creatinine

in each of the five urine samples was determined using

a calibration plot of the peak area of creatinine versus

creatinine concentration. Each of the urine samples was

then spiked with a known concentration of standard, 0.006

mM creatinine, and the total creatinine concentrations of the

spiked samples were calculated using the same calibration

curve. The percent recovery was determined by using the

following formula:

potentials (FP) 140 V (creatinine), 250 V (creatinine-d3), and

140 V (cimetidine); collision energy (CE) 29 V (creatinine,

creatinine-d3, and cimetidine); collision exit potential (CXP)

8 V (creatinine, creatinine-d3, and cimetidine). Curtain gas

(CUR), nebulizer gas (NEB), and the collision gas (CAD)

flows were kept at 12, 8, and 4, respectively (in arbitrary units

used in the instrument). The temperature of the ion spray was

300 °C. A dwell time of 150 ms was used for all transitions.

Resolution of both Q1 and Q3 were 1 amu.

Preparation of Urine Samples: Human urine samples were

prepared by filtering a small amount of the urine through a

0.22-μm polytetrafluoroethylene (PTFE) filter (Millipore).

1000-Fold Dilution of Urine Samples: The filtered urine

sample was diluted 10-fold by mixing 30 μL of filtered urine

and 270 μL of deionized water. This was followed by 100-fold

dilution by mixing 900 μL of acetonitrile, 10 μL of internal

standard (0.05 mM creatinine-d3 or 0.05 mM cimetidine),

10 μL of the diluted filtered urine sample, and 80 μL of

deionized water to a total volume of 1000 μL.

Preparation of Standard Solutions and Quantifcation:

Stock solutions (1.00 mg/mL) of creatinine, deuterated

creatinine, and cimetidine were prepared in deionized water

and stored at -20 °C.

Detection of Matrix Effects Using Recovery: Standard

solutions were prepared by mixing appropriate volumes

of 1.0, 0.1, and 0.01 mM creatinine, 900 μL of acetonitrile

and deionized water to a final volume of 1000 μL. The

concentrations of standards were 0.0001, 0.0003, 0.0005,

0.001, 0.003, 0.006, 0.01, 0.015, and 0.02 mM of creatinine

in 90% (v/v) acetonitrile. A calibration curve of peak area of

creatinine versus creatinine concentration was obtained.

Creatinine was added to five filtered urine samples

and then diluted 1000-fold to a final added creatinine

concentration of 0.006 mM. The creatinine concentrations of

spiked and unspiked urine were determined using the above

calibration plot to calculate the assay recovery.

Comparison of the Stable Isotope–Labelled Internal Standard

and Standard Addition Methods: Standard solutions for the

SIL-IS method were prepared by mixing 10 μL of 0.05 mM

creatinine-d3, 900 μL of acetonitrile, appropriate volumes

of 1.0, 0.1, and 0.01 mM creatinine, and deionized water to

make the final volume up to 1000 μL. The final concentration

of creatinine-d3 in all standards was 0.0005 mM and the

concentrations of creatinine were 0.0001, 0.0003, 0.0005,

Cimetidine

Creatinine

Time (min)

(a) (b)

Time (min)

Creatinine

Creatinine-d3

Figure 4: Chromatograms of (a) creatinine with coeluting

internal standard, cimetidine (0.005 mM) and (b) creatinine

with coeluted SIL-IS, creatinine-d3 (0.005 mM).




When percent recovery is closer to 100 (within normally

expected error of ±3 standard deviations [SD]), it can be

deduced that there is minimal or no matrix effect. A value

of percent recovery less than 100 indicates that ionization

suppression is present and a value greater than 100

indicates ionization enhancement.

As presented in Table 1, the mean percentage of

recoveries for the five urine samples was 65.98%. This

result shows that the matrix effects profoundly affected

the urine samples and thus degraded the accuracy of the

quantitative LC–MS analysis. A high percentage of matrix

effects was anticipated because the sample cleanup for

the urine samples involved only filtration and dilution in

this experiment and there was no mechanism in place to

compensate for the resulting matrix effects. Furthermore,

the results of subsequent experiments described later in

this paper confirm the presence of matrix effects. The use

of the recovery value to detect matrix effects is simple

and cost-effective since it requires only two LC–MS runs.

Also, it is ideal for the determination of the concentration

of analytes in biological fluids that have complex matrices

since it does not require a blank matrix and only involves the

addition of analytes into the same sample matrix. Thus, it is

certainly an easier and simpler method when compared to

other methods available such as post-column infusion and

post-extraction spike method, as discussed above. Unlike

post-column infusion, which assesses the matrix effects in

a qualitative manner, the recovery value method provides

quantitative data.

% recovery = × 100%Concentration of analyte recovered, C

2–C

1

Concentration of analyte added, C0

[1]

where C0 is the added known concentration of standard;

C2 is the concentration of analyte in final solution after spiking

with known concentration of standard; and C1 is the original

concentration of analyte in initial solution.

Cre

ati

nin

e c

on

cen

trati

on 20

25

15

10

5

0

Neg

ativ

e co

ntrol

Posit

ive

contr

ol

Coelutin

g inte

rnal

stan

dard

Figure 5: Comparison of approaches using negative control

(external standard calibration), positive control (SIL-IS), and

coeluting internal standard. The coeluting internal standard is

cimetidine. Detailed explanation in text.




(positive control), 1.42 (standard addition using extrapolative

calibration curve), and 1.083 (standard addition using

equation).

The mean concentration values of creatinine obtained

from using the standard addition method are very close

to those from the SIL-IS method and therefore it may be

considered as a potential alternative to the expensive and

less versatile SIL-IS method. Standard addition involves the

addition (spiking) of an analyte or a mixture of analytes into

the sample and measuring the analyte concentrations before

and after spiking. The spiking and subsequent analysis can

be done once or twice depending on whether the equation or

calibration curve option is used. There is no need for running

many standards for the construction of calibration curves for

each analyte. Thus, standard addition is especially suited for

multicomponent analysis and when there are few samples

to analyze. Furthermore, when the matrix is complex and the

sample cleanup procedure is lengthy and matching matrix

in calibration standards is impossible, standard addition

provides a simple and effective alternative.

Although the SIL-IS method is generally regarded as the

best option available for correcting matrix effects in LC–MS,

a coeluted internal standard also suppresses the analyte

signal and is therefore not the ideal option (9). Because the

same concentration of SIL-IS is added to all standards and

samples, in some samples the SIL-IS concentration may be

significantly higher than that of the analyte, and therefore

the extent of suppression by the IS may be significant.

When considering this, the standard addition method can

be considered as being closest to ideal since there is no

additional suppression and the matrix has not been changed

in the process of analysis. Other than the additional workload

required (which may not be the case in multicomponent

analysis) the method of standard addition can be considered

as the optimal route to providing the most accurate data.

Furthermore, it is a very practical and inexpensive option,

because SIL-IS are not available for many analytes, and

those that are available are very expensive.

Comparison of Stable Isotope–Labelled Internal Standard and Coeluting Internal Standard MethodsA SIL-IS has physicochemical properties and will undergo

ionization processes that are almost identical to those of

the analyte of interest. Therefore, a SIL-IS is eluted at the

same retention time as the analyte and experiences the

same extent of matrix effects (30,32–34). However, SIL-IS is

very expensive and not always commercially available (4).

Structural analogue internal standards that have chemical

structures, physicochemical properties, and retention

times that are sufficiently close to SIL-IS may provide a

cheaper and more readily available alternative to SIL-IS

in compensating for matrix effects. Although structural

analogues have been used in LC–MS (34) and were

demonstrated to be capable of increasing the linearity of the

method, both accuracy and precision were not improved

as compared to the SIL-IS method. This was because the

structural analogue used was not coeluted with the analyte of

interest, which resulted in variable exposures to matrix effects

in MS. A number of compounds such as hypoxanthine,

xanthine, and quinine dihydrochloride monohydrate have

been used as internal standards for creatinine analysis

(35,36). The majority of such compounds are not eluted

Comparison of Stable Isotope–Labelled Internal Standard and Standard Addition MethodsBecause of its proven accuracy and reproducibility, the SIL-IS

method was chosen as the reference (or positive control)

method and was thus compared with other methods. In

the comparison of SIL-IS and standard addition methods,

the SIL-IS method was run and analyzed using the same

chromatographic and MS conditions as that of the standard

addition method. The calibration curve of the peak area ratio

of creatinine to that of creatinine-d3 (SIL-IS) versus creatinine

concentration was linear with an R2 value of 0.989 up to a

concentration of 0.015 mM creatinine.

Although standard addition is a method that is widely

used in atomic spectrophotometric analysis, it has not been

so used in quantitative LC–MS analysis. In this study, we

investigated the possibility of using standard addition to

rectify matrix effects in LC–MS. Three runs were performed

for each sample (as described in the experimental section),

but there was no need for calibration standards; that is, each

sample has its own calibration curve with three points. A

calibration plot obtained for a typical sample in this study

is shown in Figure 1. The calibration line was extrapolated

to zero response to estimate the original concentration of

creatinine in the urine sample.

If the linear range of the calibration of analyte is already

established, and the concentration of analyte in both the

added and original samples fall within that range, an easier

alternative approach for standard addition is to do only one

addition and calculate the only unknown in the following

equation, the original analyte concentration in the sample:

[2]=[X]

i

[S]+[X]i

IX

IS+X

where [X]i is the original concentration of analyte in initial

solution, [S] is the known concentration added, IX is the

signal response from initial solution, and I(S+X) is the signal

response from the final solution after the addition of a known

concentration of standard.

Ellison and Thompson (31) highlighted that standard

addition can only be precise and feasible when the

analytical calibration curve is linear throughout the targeted

concentration range. From our earlier experiments it has

been demonstrated that the calibration curve for creatinine

was linear to a concentration of 0.015 mM, and we were

therefore able to estimate the original concentration of analyte

in the samples by using equation 2.

Figure 2 illustrates the results of the comparison of

methods: The mean concentrations of creatinine were

7.534 mM (negative control), 12.356 mM (positive control,

SIL-IS), 13.424 mM (standard addition using extrapolated

calibration curve), and 13.266 mM (standard addition

using equation). As described in the experimental section,

in the negative control experiment no method was

used to compensate for matrix effects. The accuracies

of the methods, shown as the percent error (deviation

from the mean value of reference method, SIL-IS) were

39.0% (negative control), 8.64% (standard addition using

extrapolative calibration curve), and 7.36% (standard addition

using equation). The precision values of the methods,

presented as relative standard deviations (using five

replicates in each case) were 0.612 (negative control), 0.450

ES393229_LCA0314_010.pgs 02.26.2014 00:05 ADV black



ConclusionsMatrix effects represent a major problem affecting the

accuracy of LC–MS analysis. In this study, a simple method

has been introduced for the qualitative and quantitative

detection of matrix effects. This method is based on

recovery calculations; if the responses are within a

previously established linear range the same data can be

used to quantify the analyte using the standard addition

method. This means that the detection and correction of the

matrix effects may be both achieved in a simple single-step

process.

Two alternatives to the SIL-IS method, the standard

addition method and the coeluting internal standard

method, were evaluated for their capacity to correct for

matrix effects. The standard addition method proved

to be a viable alternative and is likely to be a better

alternative in terms of accuracy and versatility. It could

also be a superior method in terms of time and labour for

multicomponent analysis using LC–MS such as pesticide

analysis. However, the coeluting internal standard method

as applied using cimetidine for the creatinine assay was

found to be less effective in correcting for the matrix effect.

A better structural analogue with similar chemical structure,

physicochemical properties, and ionization properties may

have produced better results. Because this approach was

significantly better than the negative control, it could well

be a good alternative for alleviating matrix effects if the

appropriate compound is used as the coeluting internal

standard.

sufficiently close to the retention time of creatinine and,

in addition, some of these compounds may be present in

human body fluids and are therefore unsuitable as internal

standards for urine samples.

In this study, we used cimetidine as an internal standard

(Figure 3) because it is a cheaper and more versatile alternative

to SIL-IS (37), and it is not endogenously present in human

urine. The chromatographic conditions were altered to a final

composition of 55:45 acetonitrile–water in isocratic mode to

produce coelution of creatinine and cimetidine and, hence,

a similar matrix effect. As shown in Figure 4, the two peaks

of creatinine and cimetidine overlapped to a very significant

degree using these conditions. The coeluting internal standards

method was then compared to the SIL-IS method, and the

results are shown in Figure 5. The mean concentrations of

creatinine for the negative control, SIL-IS, and coeluting internal

standard methods were 9.68 mM, 19.18 mM, and 14.7 mM,

respectively. The percent differences from the SIL-IS method

(positive control) were 49.4% (negative control) and 23.3%

(coeluting internal standard). The precision values of the

methods, expressed in terms of relative standard deviation

(using five replicates in each case) were 0.258 (negative

control), 0.561 (reference), and 0.852 (coeluting internal

standard). Although the coeluting internal standard method

using cimetidine lacks accuracy when compared with the SIL-IS

method, the results were still much improved when compared

to the method without internal standard. It is clear that the use of

compounds that are merely coeluted with the analyte of interest

is insufficient for optimal compensation of matrix effects.

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Thomas Karnes, J. Chromatogr. B 875, 333 (2008).

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Amitha K. Hewavitharana, Swee K. Tan, and P. Nicholas

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Queensland in Brisbane, Australia. Direct correspondence to:

[email protected]

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LC TROUBLESHOOTING

Often considered a necessary evil,

the first peak in a chromatogram

can be a useful diagnostic tool for

troubleshooting liquid chromatographic

(LC) separations. Most people I

encounter refer to this as the column

dead time peak, abbreviated t0.

However, it has a wide variety of other

names: Junk peak, garbage peak,

solvent front, or hold-up time, with

tM as the most common alternative

abbreviation. This represents the time

it takes something to go through the

LC column that does not interact with

the column. A corresponding dead

volume (or hold-up volume), VM, is

the volume of mobile phase inside the

column. This volume comprises both

the volume of mobile phase between

the packing particles (the interstitial

volume) and the volume within the

particles (the pore volume). We’ll see

that t0 can be a useful diagnostic tool

to identify potential problems with an

LC method.

Measuring t0If we want to use the column dead time

as a tool, we need to be able to identify

it. Most LC detectors will generate a

peak at t0, the most obvious exception

being the mass spectrometric

detector (liquid chromatography–mass

spectrometry [LC–MS]). Therefore,

the chromatogram usually has a peak

similar to the first baseline disturbance

in Figure 1. If the sample is very clean

and has minimal unretained material, a

small baseline disturbance as shown

in Figure 1(a) may appear. More

commonly, there is sufficient unretained

material to generate a large, off-scale

peak (Figure 1[b]). Although there are

more exact measurement techniques

for t0, such as injection of D2O, most

of us just use the retention time of the

peak. I prefer to pick a measurement

that is easy to reproduce, because

most of the time an estimate of t0 is

sufficient. For Figure 1(a), this is the

point the disturbance crosses the

baseline, noted by the arrow. Because

a large unretained peak usually is off

scale so that the top of the peak may

be inconvenient to locate, I usually pick

the point where the peak rises from

the baseline (arrow in Figure 1[b]). Of

course the retention time reported by

the data system is another convenient

measurement of the dead time.

To confirm a measured value of

t0 or to determine it if there is no

corresponding disturbance in the

baseline, as with LC–MS, we can

estimate the column dead volume, VM,

and convert it to t0. If you are using

a 4.6-mm i.d. column, VM can be

estimated as follows:

VM ≈ 0.01L [1]

where VM is in millilitres and L is in

millimetres. Thus, for a 150 mm ×

4.6 mm column, VM = 0.01 × 150 mm

= 1.5 mL. For columns of other internal

diameters, you can use

VM ≈ 0.5Ldc2/1000 [2]

where dc is the column internal

diameter in millimetres. For a 50 mm

× 2.1 mm column, VM ≈ 0.5 ×

50 × 2.12/1000 = 0.11 mL. Either

of these estimates is good to within

approximately ±10% for columns

packed with totally porous particles.

These estimates are based on the

assumption that VM represents ~65%

of the volume of an empty column and

that about half of this volume is inside

the particles and half is between the

particles.

The column dead time is simply the

column volume divided by the flow rate,

F (in millilitres per minute):

t0 = VM/F [3]

Using t0 as a Diagnostic ToolI regularly use t0 to help diagnose

problems submitted to me by readers.

Here are some of the ways this can be

useful.

Verify the Unretained Peak: I find

that it is useful to check to be sure

that the presumed t0 peak is in the

right place. For this, simply calculate

t0 using equation 1 or 2 and 3, then

compare this to the observed peak

in the chromatogram. For example,

if the chromatogram of Figure 1(b)

was obtained with a 150 mm ×

4.6 mm column operated at 2 mL/

min, t0 ≈ 1.5 mL/2 mL/min = 0.75 min.

This agrees with the observed peak

at approximately the same retention

time. If the calculated and measured

values of t0 differ by more than ~20%,

it is advisable to try to figure out why.

Several possibilities are discussed

below.

t0 Larger Than Expected: If t0 is

larger than expected, the most likely

cause is a flow-related problem. For

isocratic methods, the retention time

of retained peaks should change by

the same proportion as t0 when the

flow rate is changed. If, for example,

the above case had an observed t0

of 1.0 min, the retained peaks should

increase by 1.0/0.75 = 1.33-fold. If this

is confirmed, check for flow-related

problems. Larger than expected values

of t0 indicate a drop in the flow rate.

Always check the most obvious case

first — is the flow rate set properly? It

should also be obvious that historical

retention data should be consulted

Column Dead Time as a Diagnostic ToolJohn W. Dolan, Walnut Creek, LC Resources, California, USA.

What good is that big, ugly peak at the beginning of the chromatogram?



LC TROUBLESHOOTING

to be sure an abnormality in t0 really exists.

Assuming that the flow rate is set correctly, the most likely causes of a flow rate problem are leaks, air bubbles, and problems with the check valves or pump seals. A secondary symptom may be low pressure, depending on how far off t0 is. If leaks are not obvious, I would open the pump purge valve and run 5 mL or so of solvent to waste from each flow channel in use. Thus, if you are using a two-pump high-pressure mixing system, purge both pumps; if it is a low-pressure mixing system, purge

each solvent line. This should remove any bubbles from the system. If the problem persists, carefully check each fitting in the pressurized flow stream for leaks. Sometimes the fine point of a twisted laboratory wipe or facial tissue can be used to probe the fittings for possible leaks. If leaks in the flow stream are not found, a pump problem is most likely.

If you are using acetonitrile as one of the solvents and the pump does not have active check valves, it is possible for the inlet check valves to stick. This is from the formation of polymers on the surface of the check-valve seat, and usually can be corrected by sonicating the check valves for a few minutes in methanol. A more detailed discussion of this can be found in an earlier “LC Troubleshooting” column (1). The outlet check valves can also leak if they become contaminated. If you have a pump with outlet check valves, sonicating them may help. If you choose to sonicate the check valves, be careful that you know how they are assembled, in case they come apart in the process. Worn pump seals can also leak, resulting in a lower than expected flow rate. Check the maintenance log for the pump. If the seals haven’t been replaced in the past year, I would suggest replacing them. If the seals are newer, you can inspect the pump more closely for possible signs of leaking. Most pumps have a hole or drain tube below the pump head behind the check valves, where any leakage from the seals will exit. Look for signs of leakage, such as visible liquid or white deposits of buffer residue. Replace the pump seals if there is any question of its integrity.t0 Smaller Than Expected: If the observed t0 peak comes out

earlier than expected, one of two possibilities exists. The easier to address is a mistake in the flow rate setting so that the flow rate is too high. Although I suppose it is possible, I have never heard of a pump or controller software failure that resulted in excessive flow rates, so operator error is the most likely source of a flow-related problem.

With the most common forms of LC (reversed phase, normal phase, ion exchange, ion pairing, and so forth), t0 should be the first disturbance in the chromatogram. If something is eluted earlier than the expected retention of t0, the compounds may be excluded from the pores of the packing material. The exception to this is in size-exclusion chromatography, where everything should come out before t0. If a molecule is restricted from entering the packing pores, it only has access to the volume of column between the particles (the interstitial volume), which I mentioned at the beginning was approximately half of the total solvent volume, or 30–35% of the column volume if the total dead volume is ~65%. I’ve seen the interstitial volume quoted as 40% of the column volume (2), but in either case, a significant portion of the dead volume is inside the particles. If sample molecules are restricted from entering the pores of the packing, they will be eluted before t0. Such peaks may be confused with the true t0 peak, because we are used to assigning the first peak in the chromatogram as t0.

Two common causes of restricted access to the pores exist. The most obvious is that the molecules are too large to enter the pores. A rule of thumb is that the pore diameter should be 3–4 times the hydrodynamic radius of the molecule. Most analytical columns have pores in the 8–12 nm (80–120 Å) range, which will accommodate molecules of up to ~10,000 Da. Above this size, for example proteins, larger-pore columns, with 30–40 nm pores, are used. Thus, if your sample is a pharmaceutical product, although the analyte of interest may be <1000 Da, the formulation may contain polymers or other excipients in excess of 10,000 Da that may be excluded from the pores. In some cases, dimers or other aggregations of sample molecules can result in a material that is stable enough to

1 2Time (min)

(a) (b)

3 4 2Time (min)

4

0 5 10

Base

Base

(a) (b)

Neutral Neutral

Acid

Acid

0 5 10

Figure 1: Examples of t0 peaks (arrows). (a) Chromatogram with little unretained material; (b) large peak normally observed at t0.

Figure 2: Illustration of exclusion of a charged molecule from packing pores. (a) No ion pairing reagent present, charged base is poorly retained, acid is retained; (b) with ion pairing reagent, charge on particle surface attracts base, increasing its retention, but repels the acid, excluding it from the pores. See text for details. Adapted from reference 3.



LC TROUBLESHOOTING

chromatograph, but too large to enter the pores. Any of these

large molecules may be eluted before the column dead

volume.

A second cause of restricted access to the pores is that

chemical repulsion between a sample molecule and the pore

may exist. An example of this is illustrated in Figure 2 (3). In

Figure 2(a), a sample of adrenaline (base), benzyl alcohol

(neutral), and naphthalene sulphonate (acid) is separated

on a C18 column with a methanol–buffer mobile phase at

pH 6. Adrenaline has a pKa of 8.55, so it will be fully ionized

under these conditions, and naphthalene sulphonate has a

pKa of <1, so it will also be ionized. Adrenaline is unretained

because in its charged form it is very polar and not retained.

On the other hand, naphthalene sulphate has sufficient

nonpolar nature that it is well retained, even though it is

ionized. With the addition of 14 mM octane sulphate as an

ion-pairing reagent, the results of Figure 2(b) are obtained.

Here, the ion-pairing reagent is assumed to be immobilized

on the surface of the C18 stationary phase, creating an in

situ ion-exchange surface with a negative charge that is

used to retain the positively charged adrenaline. However,

although the pH of the mobile phase was not changed, you

can see that the naphthalene sulphonate is now unretained.

This is because the pores now contain a net negative charge

that repels the negatively charged naphthalene sulphonate.

You can imagine a similar situation where a pore with a

net charge would repel a sample molecule of opposite

charge, resulting in exclusion from the particles and elution

before the column dead time. This phenomenon, called

ion exclusion, is occasionally observed in ion-exchange

chromatography. Any chemical change in the pore surface

that repels sample molecules will have the same result.

Using t0 to Check for “Good” ChromatographyAnother use I make of t0 is to check for the quality of the

separation, especially when readers submit problem

chromatograms for me to diagnose. For isocratic separations

(those with a constant mobile phase composition), the

retention factor, k, is calculated as:

k = (tR – t0)/t0 [4]

where tR is the retention time of the peak of interest. The

retention factor is a measure of the distribution of the

sample between the stationary phase and the mobile

phase. As an indicator of chromatographic quality, I like to

see 1 < k < 20, or better 2 < k < 10, as has been discussed

in past “LC Troubleshooting” columns (for example,

reference 4). If k < 1 is observed, the peak is likely to have

poor retention-time reproducibility and more likely than

strongly retained compounds to have interferences from the

tail of the t0 peak. The retention factor can be estimated by

using t0 as the unit of measure for retention and measuring

retention beginning at the observed value of t0. For the

chromatogram of Figure 1(b), this is done by dividing the

baseline up in units of t0 instead of minutes. The first peak

is eluted a little over 1 t0 unit past t0, so it has a k value of a

little more than 1. Similarly, the second and third peaks have

k values of a bit more than 2 and 3, respectively. Although

equation 4 does not apply to gradient elution, the general

principle of keeping the first peak away from t0 to avoid

interferences still holds. From this standpoint, I like to see

the first peak of interest in a gradient come off the column at

least 1 t0 unit past t0.

ConclusionsAlthough at first glance, the solvent peak at the beginning of

the chromatogram has no value, it can be a useful tool to help

diagnose problems with the chromatogram. We can compare

column dead time estimates with the observed retention time

of the t0 peak and get an idea of what might be going wrong.

When the first peak comes out after the expected retention

time, the problem is usually flow-related and most commonly

caused by a leak. Peaks that are eluted before the expected

retention t0 time are most likely excluded from the pores of

the column, because they are too large or are repelled from

the pores. So we can see that nothing (t0) really is a useful

diagnostic tool.

References(1) J.W. Dolan, LCGC North Am. 26(6), 532–538 (2008).

(2) U.D. Neue, HPLC Columns (Wiley-VCH New York, USA, 1997), p. 53.

(3) J.H. Knox and R.A. Hartwick, J. Chromatogr. 204, 3–21 (1981).

(4) J.W. Dolan, LCGC North Am. 25(7), 704–709 (2007).

John W. Dolan is vice president of LC Resources, Walnut Creek,

California, USA. He is also a member of LC•GC Asia Pacific’s

editorial advisory board. Direct correspondence about this

column should go to “LC Troubleshooting” LC•GC Asia Pacific,

4A Bridgegate Pavilion, Chester Business Park, Wrexham Road,

Chester, CH4 9QH, UK, or email the editor-in-chief, Alasdair

Matheson, at [email protected]

Contact your local distributoror visit www.ace-hplc.com

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LC•GC Asia Pacià c March 201416

PersPectives in Modern HPLC

For five decades since the 1960s, the

pressure limits of high performance

liquid chromatography (HPLc)

systems remained stagnant at

6000 psi (400 bar). this pressure

limit was appropriate for the

column packings available at the

time, which continuously trended

towards smaller particle sizes (that

is, from 30, 10, 5, to 3 μm). there

appeared to be no concerted efforts

to increase the system pressure

ratings during that period with

the exception of an exploratory

study published in 1969 (1). the

“breakthrough” in ultrahigh-pressure

liquid chromatography (UHPLc)

came in 1997 with proof-of-concept

research by James Jorgenson (2) and

follow-on studies by Milton Lee (3).

these early studies demonstrated

spectacular performance (column

efficiency, N = 200,000 plates) at

very high pressures (>60,000 psi)

in research systems using capillary

columns. However, the impact of their

discoveries for typical practitioners

and for routine applications were

only possible after the debut of

commercial UHPLc equipment with

reliable autosamplers and gradient

capabilities

in 2004, Waters corporation

introduced the first UHPLc system —

the Acquity UPLc (Ultra-Performance

Lc) system with an upper pressure

limit of 15,000 psi (1000 bar) together

with Acquity UPLc columns (1.0 and

2.1 mm i.d.) packed with sub-2-μm

hybrid particles (4–8). Although

this pressure rating was modest

in comparison to that achieved

using the early research systems,

the new UHPLc system generated

considerable excitement and

established higher performance

benchmarks and expectations. these

early systems enjoyed immediate

acceptance in research applications

despite some initial concerns over

injection precision and other issues in

quality control (Qc) applications (7,9).

other manufacturers quickly followed

with their own UHPLc systems. By

2010, the transformation from HPLc to

UHPLc was essentially complete with

UHPLc product offerings available

from most major vendors. today, all

UHPLc systems have reduced system

dispersion and dwell volumes as well

as improved precision and sensitivity

(10).

the fundamentals, benefits,

potential issues, and best practices of

UHPLc are well documented (5–7,

9–15). some of the key benefits are as

follows:

• Faster analysis with good (or

acceptable) resolution — the

primary incentive for new users

in high-throughput screening,

liquid chromatography–mass

spectrometry (Lc–Ms), routine

testing, and method development.

• superiority in high-resolution

separations of complex samples —

peak capacities of 600–1000 are

now possible in a reasonable time

(<60 min under gradient conditions).

this capability is transformative

in life science research and

the analysis of complex

pharmaceuticals, filling an unmet

need for Qc applications (13–15).

• other benefits of UHPLc versus

conventional HPLc include

substantial solvent savings (5- to

15-fold), increased mass sensitivity

in Uv detection (3–10-fold), and

improved precision for both

retention times (2- to 3-fold) and

peak areas (<0.1% rsd).

in the last few years, UHPLc has

evolved from a scientific curiosity

for early adopters in research and

high-throughput screening into a

modern standard HPLc platform.

As the saga of UHPLc unfolded, a

number of myths or half-truths have

emerged. the goal of this column

instalment is to describe some of the

more interesting myths and provide

evidence to delineate or repudiate

these widely held misconceptions.

the myths:

• You don’t need an expensive

UHPLc system — high-temperature

Lc or core–shell columns will get

you there.

• viscous heating is a “huge” issue

for sub-2-μm particle columns.

• A 2.1-mm i.d., sub-2-μm column is

the best choice for UHPLc.

• Gold-plated fittings with double

ferrules are needed in UHPLc.

• A binary high-pressure mixing

pump is a “must”.

• UHPLc provides substantially

higher Uv sensitivity than

conventional HPLc.

• Method transfer between UHPLc

and HPLc is very easy (“a piece of

Myths in Ultrahigh-Pressure Liquid chromatography

The advent of ultrahigh-pressure liquid chromatography (UHPLC) and its successful commercialization in the last few years has brought forth a modern high performance liquid chromatography (HPLC) platform capable of higher speed, resolution, precision, and sensitivity. Currently, all major HPLC manufacturers offer some type of low-dispersion UHPLC products with upper pressure limits ranging from 15,000 to 19,000 psi (1000 to 1300 bar). This instalment describes a number of popular myths or half-truths in UHPLC and provides data that contradict or even repudiate some of these commonly held beliefs.

Michael W. Dong, Genentech, south san Fransisco, california, UsA.




technology. With overwhelming

increases in efficiency over fully

porous material demonstrated in initial

studies (+40% versus 1.7-μm particles

and >200% versus 3.0-μm fully

porous particles), its impact can be

transformative in modern HPLc.

today, the objections to UHPLc

versus high-temperature Lc or

core-shell columns by skeptics are

waning as UHPLc is becoming a

mainstream platform.

Viscous heating is a “huge” issue

for sub-2-μm particle columns:

A frictional heating phenomenon is

observed when the mobile phase

is pumped at a relatively high flow

rate and operating pressure through

columns packed with very small

particles. the heat generated is

cumulative, giving rise to longitudinal

thermal gradients along the

length of the column. the heat is

simultaneously dissipated through the

column wall, resulting in radial thermal

gradients and parabolic flow profiles

that cause band broadening. this is

a popular research topic with dozens

of papers already published (19–21).

capital investment. Along the same

line are comments that superficially

porous sub-3-μm (core–shell) material

is a more cost-effective alternative to

expensive UHPLc systems.

nowadays, most practitioners may

realize that these arguments are not

valid because UHPLc can be used in

combination with these approaches

(including two-dimensional [2d]

Lc) with superior results, as they

are options rather than alternatives

(13,14). the use of high-temperature

Lc above 60–70 °c is not viable for

thermally labile pharmaceuticals

and compounds (16,17). core–

shell (also known as fused core,

solid core, or superficially porous)

material is becoming the dominant

contender to totally porous material

for all applications (18). However, the

notion that core–shell columns will

lessen the need for UHPLc is less

compelling with recent introductions of

1.3- and 1.6-μm core–shell particles

that deliver ~400,000 plates/m (18).

i believe the availability of sub-2-μm

core–shell material represents an

exciting advancement in column

cake”) and method revalidation is

not needed.

• Lower-dispersion UHPLc systems

are better.

Dispelling Some Popular Myths in UHPLCYou don’t need UHPLC —

high-temperature LC or core–shell

columns will get you there: in

April of 2010, i was invited to a local

meeting to give a presentation on

UHPLc. the format turned out to

be a debate between two opposing

viewpoints on UHPLc versus

high-temperature Lc. i remembered

being surprised by some comments

that UHPLc was a marketing hype

invented by the vendors to extract

more money from the user. At

first, i thought that this conspiracy

theory was a joke but it turned

out to be serious. i also recalled

hearing this line of argument about

high-temperature Lc around 2006,

mostly from vendors in the “have not”

camps that indicated high column

temperatures will allow one to use

small-particle columns without a major

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for columns packed with very small

particles (<1.5 μm), operation at very

high pressures (>800 bar), or with

a forced air oven and if there are

critical pairs sensitive to temperature

changes.

A 2.1-mm i.d., sub-2-μm column

is the best choice for UHPLC: the

most popular UHPLc column format

consists of 2.1-mm i.d. columns

packed with sub-2-μm particles;

these columns are particularly

well-matched to the first commercial

systems in 2004 (6). since then, there

has been a trend towards UHPLc

systems to accommodate existing

HPLc methods with larger-diameter

columns and a higher flow rate range

(>2 mL/min), a bigger column oven

(>150 mm), larger injection loops

(>20 μL), and larger internal diameter

connection tubing. these UHPLc

systems would have higher system

dispersion, a trade-off for better

flexibility and compatibility to existing

HPLc methods.

Figure 1 compares the isocratic

performance of three 50-mm long

columns of various diameters (2.1,

3.0, and 4.6 mm) packed with 1.8-μm

c18 particles with appropriate flow

adjustments. note that the 4.6-mm i.d

column displays significantly higher

column efficiencies (N = 12,860

versus 8170 for the 2.1-mm i.d.

column). this observation is in line

with the notion that the detrimental

“wall effect” is more pronounced

for narrower columns (4,8), as it

is exceedingly difficult to pack

narrow-bore columns with high

reduced plate heights. Figure 1 also

shows the effect of system dispersion

or extracolumn band-broadening

as lower column efficiencies (N)

are observed for early peaks (for

example, the first peak, which is

toluene). note that the extracolumn

effect is more severe for 2.1-mm i.d.

columns because of the smaller peak

volumes (6,8).

For most users, a strong case can

be made for 3-mm i.d. columns,

particularly in Qc applications. these

columns generally have higher

column efficiencies in comparison

to their 2.1-mm i.d. counterparts and

support practical flow rates of

0.6–1.5 mL/min. their use may

provide easier transitions for HPLc

users familiar with 4.6-mm i.d.

columns (15).

where HPLc method conditions are

transferred to UHPLc, this effect can

be partially mitigated by deliberately

setting the UHPLc methods to

a lower column temperature (for

example, 5 °c). Another viable

solution to reduce the effect of

longitudinal heating is to introduce

intermediate active cooling by

connecting shorter columns together

to form a longer column (22). this

longitudinal heating effect may

cause issues when transferring

methods, particularly across UHPLc

platforms from different vendors

and with varying types of column

ovens.

For most users, it is important

to acknowledge the existence of

viscous heating, however, it may not

be a serious practical issue except

these complex effects are dependent

on the type of column oven, particle

size, column length and diameter,

thermal conductivity of the mobile

phase, and flow rate.

it turns out that radial thermal

gradients are indeed problematic

when the column wall temperature

is controlled under isothermal

conditions (that is, in a water bath

or to some extent in a forced air

column oven). For still-air column

ovens, the longitudinal heating effect

is more serious and can increase

the temperatures at the end of the

column by 10 °c to 20 °c (19,20).

Although this does not cause band

broadening, it raises the average

temperature of the column, causing

lower retention and potential

selectivity changes. in situations

Figure 1: comparative chromatograms of efficiency performance of three 50-mm

long, 1.8-µm dp c18 columns of various inner diameters: (a) 2.1 mm, (b) 3.0 mm,

and (c) 4.6 mm. observed UsP column efficiencies, N, are labelled for peaks 1,

3, and 5. An Agilent 1290 UHPLc system was used in this evaluation. column:

Agilent Zorbax eclipse Plus c18 (50 mm, 1.8 μm); mobile phase: 70% methanol

in 0.1% formic acid in water; flow rate: (a) 0.5 mL/min at 35 °c, (b) 1.0 mL/min at

35 °c, and (c) 2.0 mL/min at 35 °c; detection: 250 nm at 80 points/s; pressure: (a)

570 bar, (b) 460 bar, (c) 520 bar; sample: 1.0 μL of test mix containing (in order of

peak appearance) toluene, ethylbenzene, propylbenzene, tert-butylbenzene, and

anthracene.




tend to be vendor-specific and are

dictated by pump designs (piston

volume, availability of variable

stroke volume) and the mixer type.

Quaternary low-pressure mixing

pumps have larger dwell volumes

(because mobile phases are selected

by a proportionating valve at low

pressure and pumped by a single

pump with mixing occurring inside

the pump). they are particularly

useful for method development. they

also have substantially lower price

tags because only a single pump is

needed to form gradient or for mobile

phase blending. Quaternary UHPLc

pumps are now available from all

major manufacturers and many have

dwell volumes less than 0.5 mL, which

are acceptable for most analyses by

UHPLc.

UHPLC provides substantially

higher UV sensitivity than

HPLC: reports on higher Uv

detection sensitivity with UHPLc

versus conventional HPLc can be

misleading. UHPLc using small

internal diameter columns have

often been reported to have much

higher sensitivity (peak heights).

this is because peak volumes are

proportional to column void volumes,

so a smaller column will produce a

much higher peak height for the same

sample amount injected. However,

when the sample amount is scaled to

the column volumes, both HPLc and

UHPLc should yield similar sensitivity,

provided detector noise and

flow-cell pathlengths are equivalent.

this is borne out by comparative

chromatograms shown in Figure 2

of a sample analyzed on a HPLc

system and an UHPLc system using

identical columns with a conventional

HPLc method. detailed analysis

shows the signal-to-noise ratio (s/n)

and gradient shifts to be comparable

on both systems. the operating

pressures were 160 and 200 bar,

respectively, a reflection of the smaller

internal diameter connection tubing of

the UHPLc system. retention times on

the UHPLc system were 0.8 to 1.4 min

lower because of its smaller dwell

volumes. More discussion on how

to mitigate this issue during method

transfers can be found in the next

section. note that an early eluted peak

(for example, M235) has ~30% higher

sensitivity (19 mAU in UHPLc versus

14 mAU in HPLc). this is a result of

fittings remain quite expensive. As

a result of these current offerings,

gold-plated nuts and double metallic

ferrules are no longer requirements for

UHPLc.

A binary high-pressure mixing

pump is a “must”: Low dwell volume

is advantageous to reduce gradient

delay time for fast gradients (6–8).

Binary high-pressure mixing pumps

have inherently low dwell volumes

(because different mobile phases are

pumped by two different pumps and

mixing is external to the pumps). they

are preferred for high-throughput

screening and Lc–Ms applications.

it was quickly found that some

mixing volumes, provided by external

mixers, are needed for efficient

solvent blending to reduce baseline

perturbations in Uv detection (7,11).

the optimum mixing volumes required

for high-sensitivity Uv detection

Gold-plated àttings with double

ferrules are needed in UHPLC:

reliable fittings for UHPLc column

connections and leak-free operation

at high pressures were found to

be problematic during the early

days of UHPLc. Gold-plated nuts

(to prevent seizing of the threads)

and double ferrules were used in

first-generation UHPLc fittings. they

have fixed insertion depths and

are not universally compatible with

columns from different manufacturers.

currently, many choices are available,

including reusable fittings for

finger-tight or wrench-tight operation

that can be resealed many times with

a pressure rating up to 20,000 psi.

some examples found to be

convenient and reliable are opti-Lok

from optimize technologies, vHP-320

from idex-Upchurch, and viper from

thermo Fisher/dionex, though these

Figure 2: comparative chromatograms of a retention marker solution for a

multi-chiral drug spiked with expected impurities on (a) an HPLc (Agilent 1200

with a quaternary pump) and (b) an UHPLc system (Agilent 1290 with a binary

pump). column: 150 mm × 4.6 mm, 3.0-μm dp Ace-3-c18; mobile-phase A:

20 mM ammonium formate, pH 3.7; mobile-phase B: acetonitrile with 0.5% formic

acid; gradient: 5–15% B in 5 min, 15–40% in 25 min, 40–90%B in 3 min, total run

time = 42 min; flow rate: 1.0 mL/min at 30 °c; detection: 280 nm; sample: 10 μL of

a retention time marker test mix containing the drug substance at 0.5 mg/mL spiked

with expected impurities. note that the noise and gradient shift were found to be

comparable for the two chromatograms. the operating pressure was found to be

at 160 and 200 bar, respectively. the retention times for the sample components for

the UHPLc system were found to be 0.8 to 1.4 min lower than those from the HPLc

system because of the lower system dwell volume (0.3 mL versus 1.0 mL).




the lower dwell volume and system

dispersion of the UHPLc system.

in UHPLc, innovative flow

cell designs with total internal

reflectance allow the construction of

smaller-volume flow cells (0.5–2 μL)

with the same 10-mm pathlength

as a standard HPLc flow cell

(8–10 μL). this design concept

has also led to the development

of extended-pathlength flow cells

(such as 25–60 mm) to enhance

detector sensitivity (6,23). Figure 3

shows comparative chromatograms

of the same sample injected on

an UHPLc system with a standard

(10-mm) flow cell (Figure 3[a]) and

on the same UHPLc system with

an extended-pathlength flow cell

(60-mm) (Figure 3[b]). note that

while the noise was found to be

similar (AstM noise of 25 μAU), the

signals (peak heights) were six times

higher on the extended flow cell,

as was expected. However, these

extended-pathlength flow cells may

be less useful for impurity analysis

using area percent calculations

because detector signal saturation

can easily occur on the main peak

(8). they also have higher dispersion

and are therefore more compatible

with larger internal diameter

columns. nevertheless, they can be

advantageous for impurities testing

based on external standardization,

determination of trace genotoxic

impurities (24), and cleaning

verification applications of highly

potent compounds (25).

Method transfer between UHPLC

and HPLC is “a piece of cake”

and method revalidation is

unnecessary: Method transfer is

the formal process of demonstrating

that a validated method, developed

or validated in one laboratory, can

be properly executed by another

laboratory operating under a good

manufacturing practice (GMP)

environment. this ensures that

accurate, quality data can be

generated in the latter (21). Formal

method transfer between two different

HPLc systems is typically not

needed unless they are deemed “not

equivalent.” there are three scenarios

for “method transfers” between HPLc

and UHPLc: same HPLc method on

different types of equipment (HPLc

versus UHPLc); newly developed

UHPLc methods “back transfer”

to HPLc conditions; and existing

(legacy) HPLc methods to UHPLc

methods.

Same HPLC methods on different

types of equipment (HPLC and

UHPLC, the simplest case): For

laboratories having both HPLc and

UHPLc equipment, it would be ideal

if equivalent results using the same

HPLc method could be obtained

on both types of equipment. As

demonstrated in the previous section,

results are fairly equivalent with the

exception of retention time shifts

because of the smaller dwell volumes

of a typical UHPLc (~0.3 mL for

UHPLc versus ~1.0 mL for HPLc).

this can be remedied by several

means: increasing the dwell volume

of UHPLc system by using a larger

external mixer (probably not very

practical); building an initial isocratic

segment into the HPLc method

and allowing the user to adjust the

duration of this segment in the method

(generally preferred); or using optional

simulation software available on some

chromatography data systems to

simulate the performance of various

equipment by automatic method

adjustments (26,27).

“Back transferring” or “translating”

UHPLC methods to HPLC method

conditions (in method development

labs): Many laboratories prefer to use

UHPLc for rapid method development

including column and mobile phase

screening and method optimization

(5–7,21) and then “back transfer”

the optimized UHPLc methods to

HPLc conditions using longer column

with larger particles via geometrical

scaling. the approach is typically

used to support global manufacturing

operations since UHPLc may not be

available universally. case studies

for method transfer processes are

available elsewhere (7,21,28,29).

Method transfer from HPLC to UHPLC

(for GMP operation): the primary

driver to purchase UHPLc equipment

is the ability to perform faster analysis

with “good” resolution. A 2–3-fold or

greater reduction in analysis time,

while maintaining similar resolution,

is readily achievable using UHPLc.

For instance, a 15-min method using

a 150 mm × 4.6 mm column packed

with 5-μm particles can theoretically

be performed on a 50 mm × 2.1 mm

column packed with 1.7-μm particles

with equivalent column efficiency

in 5 min (10). even faster analysis

(1.5 min or a ninefold increase) is

possible if optimum flow rates are

used (optimum linear velocity is

inversely proportional to particle size).

A geometrical scaling approach is

typically used to accomplish such

transfers (29).

some ground rules for method

scaling between HPLc and UHPLc:

column length is scaled to particle

size keeping the column length to

particle size ratio the same; flow

rate is scaled to cross-sectional

area of the column (also inversely

proportional to the particle size if

optimum flow can be used); gradient

time is scaled to column length; and

flow rate and injection volume are

scaled to column void volume. one

important requirement is that the new

UHPLc column used must contain

identical bonded phase materials to

eliminate any selectivity differences.

Also, mobile phases used should be

identical (type of buffer, strength,

pH, and organic modifier). details

on this geometrical scaling are

available from the Pharmacopeial

Forum (29) and calculator programs

are available at various vendors’

websites (Waters, Agilent, and

thermo Fisher/dionex) and other

sources (30).

For validated HPLc methods, there

were numerous discussions on what

constitutes a method adjustment

versus a method change, and at what

point a method revalidation is needed

(21,31). the current consensus

appears to be that a partial method

validation (including specificity,

intermediate precision, linearity, and

robustness) should be considered as

well as a demonstration of method

equivalency between the two methods

(7,21,28,31). this process may be

straightforward for simple assays

but can be challenging for complex

samples (15,21), particularly for Qc

methods for commercial products.

Lower-dispersion UHPLC systems

are better — some pros and cons:

this statement may not be a myth

because lower system dispersion is

always considered to be better (6,8).

Low system dispersion systems are

desirable because they allow the use

of smaller columns without efficiency

loss. However, there are also some

important caveats and trade-offs.

Lower dispersion is achieved by a

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and UHPLc systems (6,32). it should

be noted that UHPLc systems have

substantially lower dispersion than

convention HPLc systems and larger

sample loop or flow cell, switching

valve, and connection tubing all

contribute to system dispersion or

bandwidth.

it is useful to realize that system

dispersion before the column

(injector, loop, switching valve)

is generally less important since

most high-resolution analyses

are conducted under gradient

conditions (since sample bands are

refocused at the top of the column).

Post-column dispersion (tubing from

column to detector and detector

flow cell or mass spectrometer

source) is more critical because

it will broaden separated bands.

nevertheless, post-column tubing

and detector Uv flow cells can easily

be changed in some cases. note that

a low-dispersion kit to reduce system

bandwidth is often available from

some vendors (such as Agilent) (33).

Also, the use of small sample loops

(<20 μL), column ovens (<200 mm),

and connection tubing (<0.003 in.

i.d. which generates substantial back

pressure at flow rates greater than

1 mL/min) in some low-dispersion

systems, can be less compatible

with legacy HPLc methods. so,

lower system dispersion is a good

thing for demanding applications for

maximum performance — but may

lead to some sacrifice in system

convenience and flexibility for routine

analysis with diverse methods.

Summary and Conclusionsthis instalment addresses eight

popular myths in UHPLc and provides

evidence and references to delineate

and repudiate some of these beliefs.

Here is a summary of the conclusions:

• UHPLc is complementary to

high-temperature Lc and core–

shell columns and can be used by

itself or in combination with these

approaches.

• viscous heating is not a “huge”

practical issue for sub-2-μm particle

columns using still-air ovens under

“normal” operating conditions.

• A 2.1-mm i.d., sub-2-μm column

is a common column for UHPLc;

however, a strong case can be

made for 3-mm i.d. columns,

particularly for Qc applications.

a compilation of comparative

system dispersion measurements

(5σ bandspread or instrumental

bandwidth) of a number of HPLc

reduction of the volume of sample

fluidic path (that is, sample loop,

switching valve, connection tubing,

and flow cell). table 1 shows

Figure 3: comparative chromatograms of a retention marker solution for a

multi-chiral drug spiked with expected impurities on (a) an UHPLc system with a

standard 10-mm Uv flow cell (Agilent 1290 with a binary pump) and (b) the same

UHPLc system with an extended-pathlength flow cell (60 mm). HPLc method

conditions are identical to those in Figure 2 except that the injection volume is

2 μL. An AstM noise of 24 μAU was found for both chromatograms. the limits of

quantitation (LoQ) were found to be 0.05% and 0.01% for (a) and (b), respectively, at

2 μL injection. note that an LoQ of 0.002% was found for (b) using a 10-μL injection

though the main peak would saturate the detector signal.

Table 1: comparative data on system dispersion (5σ band spread) of various HPLc and

UHPLc systems. data courtesy of Waters corporation.

System Band Spread (µL) (5σ)

shimadzu UFLc 41

Agilent 1200 28

shimadzu nexera (with microbore flow cell) 26

Agilent 1290 (configured for dual column) 23

thermo Accela 21

Agilent 1290 (configured for single column) 20

dionex Ultimate 3000 17

Waters Acquity UPLc H-class with column manager 12

Waters Acquity UPLc H-class with column heater 9

Waters Acquity UPLc (with 1-µL loop) 8

Waters Acquity UPLc i-class Fnt (flow through needle) 7.5

Waters Acquity UPLc i-class FL (fixed loop) 5.5

Ftn = flow through needle, FL = fixed loop (1 μL). note that a low-dispersion kit to reduce

system bandwidth is often available from some vendors (for example, Agilent) (33).


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(20) F. Gritti and G. Guiochon, Anal. Chem.

80, 5009–5020 (2008).

(21) B. debrus, e. rozet, P. Hubert, J.-L.

veuthey, s. rudaz, and d Guillarme in

UHPLC in Life Sciences, d. Guillarme,

J.-L. veuthey, and r.M. smith, eds.

(royal society of chemistry Publishing,

cambridge, United Kingdom, 2012), pp.

67–98.

(22) K. Broeckhovn, J. Billen, M. verstraeten,

K. choikhet, M. dittmann, G. rozing,

and G. desmet, J. Chromatogr. A 1217,

2022–2031 (2010).

(23) A. Gratzfeld-Huesgen, Agilent

technologies, 2012, 5991-0115en.

(24) A. teasdale, ed., Genotoxic Impurities:

Strategies for Identification and Control

(Wiley, Hoboken, new Jersey, UsA,

2011).

(25) M.W. dong, e.X. Zhao, d.t. Yazzie, c.c.

Gu, and J.d. Pellett, Amer. Pharm. Rev.

15(6), 10–17 (2012).

(26) M. dittmann, K. choikhet, P. stemer,

and K. Witt, “Method transfer Between

UHPLc and HPLc: issues and

solutions,” presented at Pittcon 2011,

Atlanta, Georgia, UsA, 2011.

(27) Agilent 1290 infinity Lc with intelligent

system emulation technology, Agilent

technologies, 20135990-8670en,

2013.

(28) G. vanhoenacker, F. david, P. sandra,

B. Glatz, and e. naegele, Agilent

Applications notes, 5990-3981 en,

2009.

(29) U.d. neue, d. Mccabe, v. ramesh, H.

Pappa, and J. deMuth, Pharmacopeial

Forum 35(6), 1622–1626, 2009.

(30) d. Guillarme, http://www.unige.ch/sci-

ences/pharm/fanal/lcap/telechargement-

en.htm.

(31) M. swartz and i. Krull, LCGC North Am.

24(8), 480–490 (2006).

(32) s. Fekete, i. Kohler, s. rudaz, and d.

Guillarme, J. Pharm. Biomed. Anal.,

in press, http://dx.doi.org/10.1016/j.

jpba.2013.03.012.

(33) J.J. stankovich, F. Gritti, P.G. stevenson,

and G. Guichon, J. Sep. Sci. 36(17),

2709–2717 (2013).

Michael W. Dong is a senior scientist

in small Molecule drug discovery at

Genentech in south san Francisco,

california, UsA. He is responsible

for new technologies, automation

and supporting late-stage research

projects in small molecule analytical

chemistry and Qc of small molecule

pharmaceutical sciences. He holds a

Phd in analytical chemistry from the

city University of new York, UsA, and

a certificate in Biotechnology from U.c.

santa cruz, UsA. He has conducted

numerous courses on HPLc/UHPLc,

pharmaceutical analysis, HPLc method

development, drug development

process and drug quality fundamentals.

He is the author of Modern HPLC for

Practicing Scientists and a co-editor of

Handbook of Pharmaceutical Analysis

by HPLC. He is a member of the

editorial advisory board of LCGC North

America.

data collected here stemmed from

equipment and columns available at

the time of evaluation and may not

be representative of those currently

available. the opinions expressed

in this article are solely those of the

author and bear no reflection on

those of LCGC Asia Pacific or other

organizations.

References(1) B.A. Bidlingmeyer, r.P. Hooker, c.H.

Lochmuller, and L.B. rogers, J. Sep.

Sci. 4(6), 439–446 (1969).

(2) J.e. Macnair, K.c. Lewis, and J.W.

Jorgenson, Anal. Chem. 69, 983–989

(1997).

(3) n. Wu, J.A. Lippert, and M.L. Lee, J.

Chromatogr. A 911, 1–12 (2001).

(4) U.d. neue, M. Kele, B. Bunner, A.

Kromidas, t. dourdeville, J.r. Mazzeo,

e.s. Grumbach, s. serpa, t.e. Wheat,

P. Hong, and M. Gilar, in Advances in

Chromatogr., s. Fanali, P.r. Haddad,

c. Poole, P. schoenmakers, and d.K.

Lloyd, eds. (elsevier/crc Press, Boca

raton, Florida, UsA, 2009), pp. 99–143.

(5) d. Guillarme, J.-L. veuthey, and r.M

smith (ed), UHPLC in Life Sciences


cambridge, United Kingdom, 2012).

(6) K.J. Fountain and P.c. iraneta, in

UHPLC in Life Sciences, d. Guillarme,

J.-L. veuthey, and r.M smith, eds.


cambridge, United Kingdom, 2012), pp.

283–311.

(7) M.W. dong, LCGC North Am. 25(7),

656–666 (2007).

(8) M.W. dong, Modern HPLC for Practicing

Scientists (Wiley, Hoboken, new Jersey,

UsA, 2006).

(9) n. Wu and A.M. clausen, J. Sep. Sci.

30, 1167–1182 (2007).

(10) d. Guillarme and M.W. dong, Amer.

Pharm. Rev. 16(4), 36–43 (2013).

(11) M.W. dong, in Chromatography: A

Science of Discovery, r.L. Wixom and

c.W. Gehrke, eds. (Wiley, Hoboken, new

Jersey, UsA, 2010), pp. 328–332.

(12) d.t.t. nguyen, d. Guillarme, s. rudaz,

and J.L. veuthey, J. Sep. Sci. 29,

1836–1848 (2006).

(13) d. Guillarme, J. ruta, s. rudaz, and

J.-L. veuthey, Anal. Bioanal. Chem. 397,

1069–1082 (2010).

(14) d. Guillarme, e. Grata, G. Glauser, J.-L.

Wolfender, J.-L. veuthey, and s. rudaz,

J. Chromatogr. A 1216, 3232–3243

(2009).

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rangelova, J. richards, d. Prudhomme,

and n.P. chetwyn, J. Pharm. Biomed.

Anal. submitted.

(16) J. ruta, d. Guillarme, s. rudaz, and

J.L. veuthey, J. Sep. Sci. 33, 2465–2477

(2010).

(17) s. Heinisch, in UHPLC in Life Sciences,

d. Guillarme, J.-L. veuthey, and r.M.

smith, eds. (royal society of chemistry

Publishing, cambridge, United Kingdom,

2012), pp. 102–128.

(18) s. Fekete, e. oláh, and J. Fekete, J.

Chromatogr. A 1228, 57–71 (2012).

(19) L. nováková, J.-L. veuthey, and d.

Guillarme, J. Chromatogr. A 1218,

7971–7981 (2011).

• there are many excellent fittings

that can be used and resealed

many times with pressures up

to 20,000 psi. Gold-plated nuts

and double ferrules are not a

requirement.

• A binary high-pressure

mixing pump is preferred for

high-throughput separations,

though most major vendors also

offer quaternary low-pressure

mixing pumps with marginal

increases in dwell volumes.

• UHPLc systems do not provide

substantially higher sensitivity

in Uv detection with standard

10-mm long Uv flow cells.

However, 2–6-fold increases

are possible with the use of

extended-pathlength flow cells (25

to 60 mm long).

• Method transfer (translation)

between UHPLc and HPLc can be

challenging for complex methods.

A partial method revalidation is

a typical regulatory expectation

(or requirement) including

a demonstration of method

equivalency.

• Lower-dispersion UHPLc systems

are indeed better but expect some

sacrifice in flexibility with respect

to injection volumes, compatibility

to longer columns, and higher flow

rates required by routine analysis

with legacy HPLc methods.

Acknowledgementsthe author is grateful to sam Yang,

christine Gu, Mohammad Al-sayeh,

and eileen Zhao of Genentech;

Jim Jorgenson of the University of

north carolina; davy Guillarme and

szabolcs Fekete of the University

of Geneva; raphael ornaf of vertex

Pharmaceuticals; Ken Broeckhoven

of vrije Universiteit Brussel; tom

Waeghe of MacMod; John dolan

from Lc resources; Bill Barber from

Agilent technologies; Pam iraneta

and eric Grumbach of Waters; Joe

dicesare and Wilhad reuter of

Perkinelmer; and ross Woods of

the University of texas at Arlington.

it should be recognized that the

design of modern UHPLc equipment

constitutes many trade-offs between

system bandwidth, sensitivity, and

compatibility to conventional HPLc

methods, dwell volume, mixing

efficiency (Uv sensitivity), and

cost, flexibility, and reliability. the

ES392754_LCA0314_024.pgs 02.25.2014 21:16 ADV blackyellowmagenta


MS – THE PRACTICAL ART

Among natural products chemists

there is a joke that goes like this:

Nuclear magnetic resonance (NMR)

spectroscopy is like your mother; she

knows what is good for you and tells

you what you need to hear. Mass

spectrometry (MS) is like your lover,

willing to say whatever you want to

hear whether it is true or not.

This joke reflects a common

attitude in natural products research;

NMR serves as a primary tool,

whereas MS is relegated to the task

of providing the molecular formulas

of pure compounds. Yet over the

past several decades, we have

witnessed astonishing growth in MS.

Electrospray ionization (ESI) has

enabled the analysis of biological

molecules previously deemed

intractable, and instruments that

offer astounding mass accuracy

are becoming routinely available.

Nonetheless, as applied to natural

products research, MS is still fraught

with challenges and pitfalls. What

follows is an account of the strategies

that my laboratory (and those of

colleagues and collaborators)

espouse to conduct effective

research despite these obstacles. In

this column, I have tried to paint an

honest picture of what we actually do

(and don’t do) in the laboratory rather

than what is theoretically possible.

As such, this account reflects my

own perspective and opinions. By no

means do I present it as the only (or

last) word on the topic.

MS for Structure ElucidationNatural products research is primarily

concerned with identifying useful

compounds from natural sources,

including plants, fungi, bacteria, and

marine organisms. These organisms

share the common characteristic of

being complex mixtures of thousands

of structurally diverse molecules

present at varying abundance (1).

(Note that reference 1 describes

a typical plant extract mixture in

which there are estimated to be 920

compounds theoretically detectable

by a given liquid chromatography

with ultraviolet absorbance

detection [LC–UV] method. These

represent only a subset of the

compounds present in the mixture.)

Natural products chemists seek

to unravel this complexity and

distill it to the key active elements

that contribute to a desired effect.

Thus, we isolate anti-inflammatory

compounds from marine sponges,

insecticidal compounds from fungi,

or antimicrobial compounds from

traditional plant-derived medicines.

Like many scientists educated in

the 1990s, I can thank Sean Connery

— or, more precisely, his character

in the film “Medicine Man” — for

my introduction to natural products

research. Those acquainted with

this classic may recall a scene

in which a cancer-curing natural

product mixture is injected into a

portable gas chromatography–mass

spectrometry (GC–MS) system,

which, having been transported

by canoe, miraculously operates

in the midst of the jungle on

generator-provided power. Within

seconds, the structure, including

stereochemistry, of a new molecule

responsible for the biological

activity of this mixture appears on a

blinking LED screen. Fantastic as it

seems even by 2013 standards, this

scenario could represent the holy

grail of natural products research.

Such an instrument — portable and

able to elucidate the structure of

components in a mixture without the

need for user intervention or pure

standards — would revolutionize our

field, not to mention all of chemistry

and biology. Regrettably, analytical

equipment of such awesome

Mass Spectrometry for Natural Products Research: Challenges, Pitfalls, and OpportunitiesNadja B. Cech1 and Kate Yu2, 1University of North Carolina Greensboro, North Carolina, USA, 2Waters Corporation, Milford,

Massachusetts, USA.

A common attitude in natural products research is that nuclear magnetic resonance (NMR) spectroscopy serves as a primary tool, whereas mass spectrometry (MS) is relegated to the task of providing the molecular formulas of pure compounds. Yet over the past several decades, we have witnessed astonishing growth in MS. Electrospray ionization has enabled the analysis of biological molecules previously deemed intractable, and instruments that offer astounding mass accuracy are becoming routinely available. Nonetheless, as applied to natural products research, MS is still fraught with challenges and pitfalls. Here is an account of strategies to conduct effective research despite these obstacles.




How GC–MS Is Applied for Unknown IdentiàcationGC–MS instruments are arguably

the best mass spectrometers

for identifying unknown small

molecules. Their effectiveness stems

from compatibility with electron

ionization (EI), which generates

highly consistent and searchable

fragmentation spectra. By searching

an unknown against spectral

libraries, one can generate candidate

structures in seconds. For example,

the National Institute of Standards

and Technology (NIST) standards

database contains over 200,000 EI

spectra. Despite the availability of

such excellent databases, however,

the limitations of EI hinder our ability

to identify unknown compounds. EI’s

harshness often makes detecting

the molecular ion difficult, and the

ability to identify compounds is

limited by information previously

entered into the database. Also, EI

spectra often do not enable isomers

to be distinguished. Therefore,

identifications made on the basis of

mass spectral databases are always

tentative and must be confirmed

with an orthogonal technique like

NMR. Even so, we can gain much

by using MS to obtain a tentative

structure before beginning the

isolation process. The MS result often

facilitates a decision about whether

isolation is worthwhile (on the basis of

a compound’s novelty) and simplifies

isolation and structure elucidation.

How Liquid Chromatography–Mass Spectrometry Is Applied for Unknown IdentiàcationMost biologically interesting

molecules, including the majority

of natural products, are nonvolatile

and, therefore, unsuitable for direct

analysis by GC–MS. Even more

problematic, most biologically

relevant matrices (including many

natural product extracts) are also

nonvolatile. ESI-MS can ionize

nonvolatile species, enabling direct

coupling between LC and MS. Its

application for this purpose has

become so commonplace that the

term LC–MS typically implies the

use of ESI. Before the advent of

electrospray, analysts routinely spent

much time and effort extracting and

derivatizing biological samples to

render them suitable for GC–MS

products research, where identifying

compounds is the most critical

goal, scientists expert in isolation

and NMR structure elucidation

dominate. Unfortunately, this reliance

on NMR has encouraged a bias

toward structurally interesting and

easily isolable compounds. Such

compounds often become the

major focus, even at the expense

of those that are more biologically

interesting. Also, as we shall later

see, combination effects (synergy)

may be overlooked in the isolation

process. Finally, to achieve pure

samples for NMR analysis requires

an intense isolation effort, one

often wasted when applied to

known compounds, particularly

commercially available ones. MS

can enable identification without

isolation, so it offers the potential to

resolve many of these issues. Still,

limitations loom, and they must be

addressed before we can consider

MS an optimal technique for

structure elucidation.

capability does not currently

exist. The tool that comes closest,

however, is the mass spectrometer.

Advantages and Disadvantages of MS as a Tool for Solving StructuresMolecular mass is an intrinsic

quality of any analyte undergoing

measurement that, by means of

MS, we can easily calculate a

priori. Despite this faculty, we mass

spectrometrists are not particularly

good at de novo structure elucidation

for unknown small molecules.

MS data provide an excellent

complement to NMR data for solving

unknown structures and are useful

for rapidly searching a database

to determine whether a compound

was previously identified. Those

benefits notwithstanding, NMR is

the far more effective technique

for solving unknown structures,

provided sufficient quantities of

purified material are available. It is

hardly surprising, then, that in natural

100(a)

(b)

4

1 23

56

7

7

8

NL:

1.08 x 109

Rela

tive a

bu

nd

an

ceA

bso

rban

ce (

mA

U)

90

80

70

60

50

40

30

20

10

1600

1400

1200

1000

800

600

400

200

0

0 2 4 6 8

0 2 4

4

6 8 10 12 14

Time (min)

Time (min)

0

Figure 1: Comparison of (a) LC–MS and (b) LC–UV chromatograms for the same

botanical extract. The peaks in the chromatogram represent a series of alkylamides

extracted from the plant Spilanthes acmella (11). The chromatogram in (a) is a base

peak chromatogram normalized to a total ion count of 1.08 × 109. Only two of the

eight compounds detectable by LC–MS can be detected with LC–UV.




barrier today in the field of natural

products research. This barrier could

be surmounted if a comprehensive

and searchable database of MS–MS

spectra analogous to that available

for GC–MS data existed. Of the

databases that seek to fill this need,

none are perfect for natural products

applications. Currently, the best

databases for searching natural

product molecular formulas are

the Dictionary of Natural Products,

AntiMarin (University of Canterbury,

New Zealand), and SciFinder.

None of these are open-access or

completely comprehensive, nor do

they provide searchable MS–MS

spectra. Open-access, searchable

databases for small molecules do

exist — for example MassBank

(MassBank Project, Keio University,

Tsuruoka City, Yamagata, Japan)

and ChemSpider (Royal Society

of Chemistry). None, however,

are tailored specifically to natural

products. Admittedly, generating a

comprehensive database of natural

product MS–MS spectra would be

a far-from-trivial accomplishment.

Doing so would require empirical

measurements made from standards

of all molecules of interest.

Furthermore, such a database, if

compiled, would also need to address

issues of limited reproducibility

among different instruments. Only

through the combined efforts of many

researchers could such a natural

product database be compiled.

Moreover, those researchers would

need access to diverse natural

product standards. Finally, they would

require expertise in analyzing data

presented in numerous software

formats and in collecting and

interpreting those data on multiple MS

platforms.

It is tempting to propose that

the requirement of empirically

measured MS–MS spectra could

be circumvented by generating

predicted spectra from known

molecular structures. Indeed, excellent

databases of predicted fragment

spectra are available for peptides,

and the field of proteomics would not

exist in its current, advanced form

without these databases. Useful,

predicted MS–MS spectra for peptides

can be generated because when

these compounds are subjected to

collisionally induced dissociation, they

analysis. Nowadays, this process is

commonly skipped in favor of LC–MS,

for which sample preparation is much

simpler.

The approach for identifying

unknowns using LC–MS is

fundamentally different than that

employed for GC–MS. ESI yields

molecular or pseudomolecular ions

without appreciable fragmentation

for many analytes. Thus, coupling

ESI to an instrument with accurate-

mass capabilities (such as an

Orbitrap [Thermo Scientific] or

quadrupole time-of-flight [QTOF]

mass spectrometer) enables rapid

determination of the molecular formula

of interest. A caveat is that in-source

fragmentation (for example, the loss

of water) and adduct formation (for

example, with sodium or acetate

ions) may complicate the process of

identifying the molecular ion. Isotope

ratios inherent in the MS data can help

confirm correct formula assignment,

and various software packages for

deconvoluting MS and LC–MS data,

such as IntelliXtract (ACD/Labs),

ExactFinder (Thermo Scientific),

and Apex (Sierra Analytics), can

identify likely clusters and fragments

according to characteristic mass

differences. It is also possible (and

highly advisable) to manually inspect

mass-spectral data for characteristic

mass differences among peaks.

For example, formation of a sodium

cluster will result in a 22 Da mass

difference between the mass-to-

charge ratio (m/z) of the [M + H]+ ion

and the [M + Na]+ ion, and neutral

loss of water will result in a difference

of 18 Da. Assignments of molecular

formulas based on MS data alone,

however, should always be viewed

with caution. A common mistake is

to search structural databases using

the molecular formula of a fragment

or cluster ion, find nothing, and then

incorrectly conclude that the structure

was not previously reported.

Assuming that formulas are

correctly assigned, they can be

searched against those of known

compounds as a first stage in

identification. Useful though this

approach is, it doesn’t solve the de

novo structure-elucidation quandary.

Many molecules share the same

molecular formula and, consequently,

are indistinguishable by accurate

mass and isotope patterns alone.

For example, literally hundreds of

isobaric (same mass) flavonoids

have been identified from plants,

and searching a flavonoid molecular

formula in the Dictionary of Natural

Products (CRC CHEMnetBASE

database) or SciFinder (CAS, a

division of the American Chemical

Society) can yield countless hits.

The structure of the true analyte

may or may not be included among

these, depending on whether it was

previously reported and uploaded to

the relevant database. To assign the

correct structure from among isobaric

candidates, we need orthogonal

information. NMR data are best for

this purpose, assuming the availability

of sufficient purified analyte. Where

an analyte has not been purified,

applying tandem MS (MS–MS)

measurements is the next-best tool

for structure confirmation. The term

“MS–MS” refers to a two-stage MS

experiment whereby an ion of interest

(the precursor ion) is selected in the

first stage and then fragmented. The

masses of the fragments are then

measured, constituting the second

stage. Multiple methods of generating

fragment ions for MS–MS experiments

exist. Among the most common is

collisionally induced dissociation

(CID), wherein the precursor ion

collides with gas molecules and thus

fragments. When the fragments are

formed from high-energy collisions,

the technique is called higher-energy

collisional dissociation (HCD).

Many mass analyzers enable the

generation of MS–MS data, including

triple-quadrupoles, ion-trap, and

QTOF instruments. However, the

relative abundance and presence

or absence of particular fragments

can vary greatly by instrument.

Even for a single instrument, such

variation can arise from changing

parameter settings. Thus, it is

currently common practice to use

MS–MS spectra primarily as a tool to

compare standards with unknowns

under the same conditions on

the same instrument. In this way,

structural assignments can be made

by matching retention time and

MS–MS spectra. Unfortunately, the

availability of the required standards

is extremely limited, which restricts

the applicability of this approach.

Difficulty in assigning structure

based on LC–MS data is a critical

ES392802_LCADI0314_027.pgs 02.25.2014 21:19 ADV blackmagentacyan



fragment consistently. Unfortunately,

the structural diversity of natural

product secondary metabolites

makes developing rules to predict

their MS–MS spectra difficult. Current

rule-based software packages for

this purpose (for example, Mass

Frontier [Thermo Scientific], ACD/

MS Fragmenter [ACD/Labs], and

MassFragment [Waters]) generate

hundreds of predicted fragments

with no indication as to which will be

observed experimentally. Thus, these

tools are truly useful only for assigning

observed fragment structures. A

comprehensive empirical database

of high-resolution mass spectra and

MS–MS spectra for diverse structural

classes of natural products might

enable improvements in the currently

available fragmentation predictors.

Selectivity in LC–MS Analysis of Natural ProductsIt is the selectivity of MS — its

tendency to generate radically

different responses for analytes with

different structures — that led to

its playful characterization among

natural products chemists as the

misleading mistress. Selectivity in

MS analyses is primarily dictated

by the type of ionization source

used. No truly universal ionization

technique exists for LC–MS. ESI,

the most commonly used ionization

technique, is selective for analytes

that contain an inherent positive

charge or that can be charged by

protonation, deprotonation, or adduct

formation. Among charged analytes,

an additional layer of selectivity is

introduced by the analyte’s nonpolar

character, with nonpolar, chargeable

analytes yielding the highest

response (2). Although many natural

products are ionizable by ESI, they

can vary widely in responsiveness.

Several common contaminants, such

as polymers and surfactants, yield

high signals and can suppress the

response of other analytes of interest.

LC–MS analysts must, therefore,

resist the tempting assumption that

the largest peaks in the total ion

current (TIC) chromatogram represent

the most important — or even the

most abundant — compounds in the

sample.

Despite issues of selectivity, it

is possible to compare the relative

abundance of the same compound

in different samples on the basis of

peak area in LC–MS chromatograms.

In many applications of natural

products chemistry — for example,

the evaluation of purity — being able

to compare the relative abundance

of different classes of compounds in

a single sample or multiple samples

would be desirable. Differences in

ionization efficiency prohibit such

comparisons on the basis of data

obtained by electrospray ionization.

Instead, an orthogonal detection

technique, where response is more

closely correlated with analyte

abundance, must be employed.

Two popular techniques for this

purpose are evaporative light-

scattering detection (ELSD) and

charged-aerosol detection (CAD).

These detection methods do not

replace LC–MS. They are far

less sensitive and do not provide

structural information. Nevertheless,

comparison of ELSD or CAD

chromatograms with those obtained

with LC–MS can help discern which

peaks correspond to the most

abundant compounds in a sample.

Although most LC–MS separations

employ ESI for ionization, other

ionization techniques are sometimes

used to provide complementary

information or detect small molecules

not ionizable with ESI. The most

common of these are atmospheric

pressure photoionization (APPI)

(3) and atmospheric pressure

chemical ionization (APCI). APCI

is harsher than ESI and may ionize

polar compounds that lack acidic

or basic functional groups. APPI

can be applied for the analysis of

some nonpolar species that are

not amenable to ESI analysis, and

studies suggest that this technique

is somewhat more universal than ESI

for small drug-like molecules (4,5).

On the other hand, APPI and APCI

source development has been less

a focus of instrument companies

than ESI sources, which are typically

Extract 1

F1 F2 F3 F4 F5 F6 F7 F8

F1 F2 F3 F4 F5 F6 F7 F8

Extract 2

Extract 3

Synergy testing

Synergy testing

Structure elucidation

Pure compound (synergist)

Extract with synergist

Separation (chromatography)

LC–MS

LC–MS

Figure 2: Schematic of synergy-directed fractionation. A series of extracts is

profiled using LC–MS to identify known compounds (if possible) and is subjected

to synergy assays. Extracts that demonstrate synergistic effects are subjected to

separation, and the fractions are then tested again to identify which fractions contain

synergists. This process is repeated iteratively until a pure compound is isolated in

sufficient quantity for structure elucidation.




better designed. APPI also suffers

from practical limitations, including

the need to introduce dopant and the

expense of replacing source bulbs.

APCI can often achieve better linear

dynamic ranges than those possible

with ESI for some analytes, but APCI

typically yields slightly higher limits

of detection (6). Practically, the

need to switch sources and collect

copious quantities of data often

limits the ability to operate using

multiple ionization modes. For many

applications, collection of data in

the positive and negative ionization

modes using LC–MS with ESI is

sufficient. However, the possibility of

missing active compounds because

of the selectivity of this technique

should always be considered. Finally,

even with access to APCI, ESI, and

APPI, ionization is impossible for

some natural product molecules. A

need remains for better methods to

ionize small molecules that prove

intractable using existing techniques.

MS for Quantitative Analysis of Mixture ComponentsNatural products chemistry often

involves quantitative analysis.

Such analysis is necessary to

verify the solubility or extractability

of natural product molecules in

different solvents, compare levels

of bioactive compounds in different

raw materials, or measure toxins

or contaminants. MS is particularly

useful for such applications. It

can detect compounds present

in minute abundance (sub-parts-

per-billion levels) and resolve

molecules on the basis of their

m/z values. Indeed, because of

their exceptional sensitivity and

selectivity, triple-quadrupole mass

spectrometers operating in the

selected reaction monitoring (SRM)

mode have long been considered

the gold standard for the quantitative

analysis of trace mixture components.

Recently developed high-resolution

MS platforms, such as the LTQ

Orbitrap and Q Exactive systems

(both from Thermo Scientific), enable

quantitation based on accurate-mass

measurements of the molecular

ion. Such quantitation now rivals

the selectivity of triple-quadrupole

instruments (7,8).

Absolute quantitation with MS

requires pure standards. Although

some standards can be obtained

commercially (for example, from

SigmaAldrich or ChromaDex), in many

cases the compounds of interest

are unavailable. Options are to rely

on relative, rather than absolute,

quantitation or to synthesize or isolate

the standard of interest. Obtaining

natural product standards that are

sufficiently pure to accomplish

absolute quantitation is a major

challenge, one that applies even with

commercial standards. It is common

practice to report the purity of these

standards according to their response

in LC–UV chromatograms. Yet this

approach detects only contaminants

with UV chromophores. Quantitative

NMR can circumvent this problem

(9) but is not yet widely adopted as

a method to evaluate purity. Thus,

the accuracy of percent purity

claims for commercial standards is

questionable.

It is common practice when

performing quantitative analysis

via MS to distinguish coeluted

compounds by plotting selected ion

chromatograms. Here the phrase

“selected ion chromatogram” refers

to a plot of ion current versus time

for the m/z value of the compound

of interest. Such selected ion

chromatograms can be used to

plot calibration curves even for

compounds with identical retention

times. Chemists trained to perform

quantitative analysis using LC–UV

are driven absolutely mad by this

practice. They invariably cite the

concern of ion suppression (otherwise

referred to as matrix interference)

— that one ion may influence the

ionization of another, thereby skewing

the quantitative results.

If the goal of a quantitative analysis

is relative quantitation, and if the

matrix is consistent among samples,

ion suppression is not a major

concern. However, matrix suppression

can seriously undermine the

accuracy of measurements seeking

to determine absolute concentration.

The chromatographer’s solution to this

problem is to separate all components

of the mixture with baseline resolution

and quantify by LC–UV. However, for

most biologically relevant complex

mixtures, including complex natural

product extracts, the physical

limitations on chromatographic

resolution (10) make doing so

impossible in a single stage of

separation. In fact, what may in LC–

UV appear to be baseline separation

is merely baseline resolution of all

components detectable by the UV

detector, a fact revealed when LC–UV

and LC–MS data are compared for

the same sample (Figure 1).

When mixture components cannot

be fully resolved chromatographically

from others with similar absorbance,

or when they are present below the

limit of quantitation with a UV detector,

quantitation by LC–MS is often a valid

alternative to quantitation by LC–UV.

For such analyses, the extent of

matrix interference must be evaluated

by comparing the signal (peak area)

for standards in solvent versus

matrix. This approach is necessary

because matrix interference can be

caused by compounds that the mass

spectrometer cannot detect, such as

salts that wash off the column early in

the separation process. Thus, matrix

effects can occur even when the

mass spectrum appears to contain

only one compound. A number of

strategies can be used to address

matrix effects, including standard

addition and matrix matching. A

recent report by Kruve and Leito

(12) nicely demonstrates strengths

and limitations of these and other

approaches.

However tempting (particularly

for analytical chemists) is the

quest for absolute quantitation,

it may not always be necessary.

When standards are not available,

it is possible to compare relative

peak areas for a particular

sample component to draw useful

conclusions. When making such

comparisons, it is important to be

aware of biases that systematic

fluctuations in instrument response

may introduce. A common source

of such fluctuations is fouling of ion

optics over time, which may cause

the signal intensity to decrease slowly

during the analysis. Including control

standards at the beginning, middle

and end of a run can help identify this

problem. Changes in ion transmission

that result from tuning or cleaning can

also cause the absolute instrument

response to shift (consistently higher

or consistently lower) between runs.

For this reason, collecting all data

for a relative quantitation experiment

in a single analysis is advisable. If




doing so is impractical, data from the

same sample or standard analyzed

(with replicates) in each run should

be compared to ensure that the

response did not drift (beyond the

tolerance of random errors) between

analyses. If such drift does occur,

adjusting for it is extremely difficult.

Theoretically, comparing the peak

area of each analyte to that of an

internal standard can correct for

differences in instrument response

between runs. Practically, however,

this approach rarely seems to work,

perhaps because signal drift does

not occur consistently across the

chromatogram.

The Holy Grail of Natural Products Chemistry: Correlating Biological Activity with Chemical CompositionThe truly interesting questions in

natural products chemistry occur

at the intersection of chemistry

and biology. These questions seek

information about how natural

products can be used to treat

diseases, eliminate pests, produce

greener technologies, or generally

benefit the planet or humankind.

To address such questions, it is not

enough to simply determine what

is in a sample. Rather, we want

to answer the question “what are

the bioactive components of this

sample?” The following paragraphs

describe some of the traditional

methods for addressing this question,

their limitations, and potential areas

in which MS can help resolve those

limitations. (Here I should note that

biologists would pose a different

question as the Holy Grail of

natural-products research: What is

the mechanism of action for bioactive

natural products? MS certainly

can contribute something toward

investigating mechanistic questions

as well, but that topic extends beyond

the scope of this column instalment.)

Strengths and Limitations of

Bioactivity-Guided Fractionation

for Active Compound Identiàcation:

Natural products researchers

typically rely on bioactivity-guided

fractionation to identify the active

compounds in a mixture. To apply this

technique, a series of natural product

extracts (from plants, fungi, bacteria,

marine organisms, or other natural

sources) are screened against a

desired biological assay (cytotoxicity,

antimicrobial, insecticidal, and so

forth) After an extract is identified as

a good lead — one that evidences

the desired biological activity — it

is separated, usually by liquid–

liquid partitioning or column

chromatography. The resultant

fractions are then tested using the

same biological assay, and the active

fractions undergo further purification.

This process is repeated iteratively

until compounds of sufficient purity

for structure elucidation and more

in-depth evaluation of activity are

isolated.

It is highly undesirable, wasting

both time and resources, for the

bioactivity-guided fractionation

process to result in known

compounds with known activity.

Natural products chemists therefore

employ various “dereplication”

strategies to prevent the reisolation

of known compounds. The ideal

dereplication strategy would rapidly

identify all known compounds

in a mixture without any prior

purification. Both MS (13) and NMR

(14) strategies have been pursued

toward this goal. Dereplication by

MS is currently seriously limited

by the aforementioned lack of

searchable databases of LC–MS

data. To circumvent this problem,

many laboratories construct in-house

databases to enable the dereplication

of compounds commonly

encountered in their samples of

interest. For example, my collaborator

Nicholas Oberlies at the University of

North Carolina Greensboro developed

a 170-compound library with MS,

MS–MS, UV, and retention-time data

that can identify fungal secondary

metabolites previously isolated

by his laboratory (15). Using this

database, it is possible to rule out

~50% of the new fungal samples the

Oberlies laboratory extracts because

of the dominance of compounds

his laboratory has already isolated.

Similar in-house databases in

commercial and academic research

laboratories around the world hold

a treasure trove of information that

if combined into a single natural

products database, would be a

formidable tool indeed.

A major challenge associated

with bioactivity-guided fractionation

is the inherent assumption that

the bioactivity of a mixture can be

distilled to a single compound (or

series of compounds). Certainly,

historical precedent for such an

assumption exists. Key examples

include taxol, from yew bark, and

camptothecin, from the Chinese

tree Camptotheca acuminata (16).

Both were isolated by Wall and

Wani (17)using bioactivity-guided

fractionation, and both became

highly effective cancer drugs when

used in isolation (that is, without any

other components of the mixture). In

H3CO

H3CO

O

O

O

O

4

1: R = CH3, R' = CH

3

3: R = H, R' = CH3

2: R = CH3, R' = H

N+

R'

R

OH

OH

OCH3

Figure 3: Synergy-directed fractionation of the medicinal plant goldenseal

(Hydrastis canadensis) yielded the flavonoids sideroxylin (1), 8-desmethyl-

sideroxylin (2), and 6-desmethyl-sideroxylin (3). These compounds enhance the

antimicrobial activity of the alkaloid berberine (4) via efflux inhibition.


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many cases, however, particularly

those involving botanical extracts,

all or part of the activity is lost in the

isolation process. Moreover, this loss

may not always be readily apparent.

Because mixture components are

purified and tested for activity at

increasingly higher concentrations

with each successive stage of

isolation, a perceived improvement in

activity upon purification may actually

be attributable to an increase in

concentration. To determine whether

a particular isolated compound

represents the entire activity of a

mixture, it is useful at the end of

a bioactivity-guided fractionation

experiment to compare the activity

of the pure component to that of

the original mixture at identical

concentrations. Such comparisons

require the concentration of the

bioactive compound to be determined

in the original mixture. Fortunately,

the bioactivity-guided fractionation

process itself produces the pure

standard needed to measure this

concentration.

If, as is often the case, the mixture

displays better activity than the

pure compounds, the explanation is

typically that some sort of synergy

is involved in the activity of the

mixture. Here synergy is defined

as a scenario in which the whole is

greater than the sum of its parts. The

underlying mechanisms for synergy in

complex mixtures, which have been

reviewed elsewhere, may include

several compounds interacting

at the same receptor, multiple

compounds targeting different

biological receptors, or situations

where the solubility of one compound

is improved by the presence of

another. This last case is surprisingly

common with in vitro assays of natural

products. Many natural products

chemists have encountered the

maddening experience of beginning

with a perfectly soluble (and

bioactive) mixture, and after multiple

painstaking steps of isolation ending

up with a pure compound that is

about as soluble (and bioactive) as

brick dust.

Recombining fractions from an

active extract is sometimes proposed

as a way to identify synergists. It is

not practical, however, to test the

vast number of fractions generated

by a bioassay-guided fractionation

experiment in combination. For

example, if such an experiment

generates 10 fractions in the first

stage of separation, those fractions,

taken two at a time, can be

combined in 45 different ways: n!/

(k!(n – 2)!, where n = 10 and k = 2.

To accomplish combination assays

of these fractions over a relevant

concentration range would require

an estimated 9000 assays. If at

least three stages of fractionation

are needed, the number of assays

required increases exponentially to

more than 1,000,000. Even this large

figure ignores the possibility that more

than two fractions may be required to

achieve synergy.

Why Isolate at All?: Given the

limitations of bioassay-guided

fractionation, it might seem advisable

to perform all biological assays

on mixtures, without isolation.

Unfortunately, correlating the

presence of components in a

complex mixture with its biological

activity is difficult. A too-common

scenario in the botanical medicine

literature involves testing a mixture

for some biological effect, analyzing

it for the presence of known “marker”

compounds, and then surmising

(hoping?) that these compounds

produce that effect. The choice of

marker compounds is often made

for practical reasons, such as the

availability of standards or the

ease with which they are detected.

Testing for the presence of these

marker compounds is prudent, to

authenticate the source material,

but does not of itself establish a

link between activity and chemical

composition. Sufficient statistical

power to sort the components

responsible for activity requires the

number of measurements of biological

activity to be at least as great as the

number of compounds present, which

is impossible if activity is tested for

a single mixture. Sufficient statistical

power theoretically can be achieved

if multiple mixtures containing a

range of concentrations of bioactive

molecules are tested. Carrying out

such testing, however, requires some

fractionation, so we find ourselves

back where we started.

Synergy-Guided Fractionation to

Address the “Kobayashi Maru” of

Natural Products: The preceding

section describes what appears to

be a no-win situation, a “Kobayashi

Maru” of natural products. (For the

enlightenment of those who are

not acquainted with Star Trek, the

“Kobayashi Maru” is a test in the

fictional Star Trek universe for which

there is no solution.) To investigate the

activity of mixtures makes identifying

the biologically active components

difficult, but isolating the components

from the mixture often results in loss

of activity because of the inability to

account for synergy. Our laboratory

has worked to develop strategies

to address this dilemma. Currently,

we are conducting several studies

for which the goal is to identify the

array of compounds responsible

for biological activity of botanical

medicines. To accomplish this goal,

we developed a modification of

the bioactivity-guided fractionation

approach that we refer to as

“synergy-guided fractionation” (18)

(Figure 2). This approach is similar

to bioactivity-guided fractionation,

except that fractions are tested in

a synergy assay where they are

combined with the original crude

extract or some isolated component

thereof. The synergy-guided

fractionation approach solves

the dilemma of generating an

exponentially increasing number of

combinations. It also ensures that all

compounds of interest are present

in any given biological assay. In

analytical chemistry terms, such

an experiment is essentially one of

standard addition, but it is performed

with a biological rather than a

chemical endpoint.

Our first case study of the

synergy-directed fractionation

approach involved applying it to

identify bioactive flavonoids from

the botanical medicine goldenseal

(Hydrastis canadensis) (Figure 3)

(18). These flavonoids were shown

to significantly enhance the

antimicrobial activity of the known

alkaloid berberine, also a constituent

of H. canadensis. We demonstrated

that the ability of the flavonoids to

act as efflux pump inhibitors caused

this enhancement. Importantly,

the flavonoids were inactive as

antimicrobial agents alone, and

a traditional bioactivity-guided

fractionation approach would,

therefore, have missed them. The

synergy assays that led to the




isolation of flavonoids from H.

canadensis involved combining

extract fractions with purified

berberine. We are currently engaged

in more detailed studies to identify

additional synergists by testing

in combination with the crude H.

canadensis extract.

A Role for MS in Bioactivity-

Guided and Synergy-Guided

Fractionation?: Bioassay-guided

fractionation and synergy-directed

fractionation are time-consuming

processes inherently biased

toward isolable compounds. It

would be very desirable to develop

approaches to rapidly obtain a

more comprehensive understanding

of the relationship between the

composition and biological activity

of natural product mixtures. Toward

this end, we have used MS to track

compounds of interest throughout

the isolation process by their

measured m/z values and retention

times. This practice has proven

useful for verifying whether activity

corresponds with compounds already

reported as constituents of botanical

interest (so far, generally, it has not).

In addition, we have attempted,

albeit with limited success, to

use untargeted metabolomics to

determine which compounds are

unique to active fractions and to

then focus our isolation efforts on

those compounds. Theoretically, an

advantage of this approach is that

mass-guided fractionation (isolation

focused on a particular ion) is far

more efficient than bioassay-guided

fractionation (isolation based on a

particular biological activity). Yet

a number of practical challenges

prevent this approach from being

entirely effective. First, for complex

botanical extracts, a large number

of fractions are needed to resolve

mixture components sufficiently to

distinguish bioactive from inactive

compounds. It is difficult to identify a

biological assay that is inexpensive,

robust, and efficient enough for this

purpose. Second, fractionation may

again cause synergistic effects to be

overlooked. Finally, the possibility

remains that the most biologically

interesting molecules in a given

set of extract fractions are either

undetectable, because of the

selectivity of the mass spectrometer,

or masked by other mixture

components. The undetectable

nature of such molecules renders

fractionation based only on mass

somewhat unnerving.

For all these reasons, we have

yet to significantly improve the

bioactivity-directed fractionation

process by involving MS (beyond

the initial dereplication step).

Nonetheless, we expect that, with

improvements in methodology, this

will eventually become possible.

These improvements may include

the development of better databases

for natural product identification,

improved software methods for

correlating biological and MS data,

and more creative and robust

biological assays.

The FutureCurrently, many natural products

chemists still ignore MS in the

isolation process and instead

employ NMR data to facilitate

isolation of as many unique (and

structurally interesting) compounds as

possible. Given all of the challenges

and limitations addressed here,

this rejection of MS is perhaps

unsurprising. It is clear that the

mass spectrometer portrayed in

“Medicine Man,” one that can

simultaneously solve natural product

structures and identify those that

are biologically active, does not

yet exist. The development of such

a mythical instrument will demand

continued advances in the ion source

universality, instrument sensitivity,

dynamic range, resolving power,

and — most importantly — software

and database capabilities. Such

accomplishments will require

sustained collaborative efforts

involving (but not limited to) analytical

chemists, natural products chemists,

instrument and software developers,

and biologists. Nonetheless, as

I gauge how far our field has

progressed in recent decades, I am

convinced that these advances are

indeed possible, and that the future

of MS as a tool for natural products

research is unquestionably bright.

References(1) C.G. Enke and L.J. Nagels, Anal. Chem.

83(7), 2539–2546 (2011).

(2) N.B. Cech and C.G. Enke, Rev. Mass

Spectrom. 20(6), 362–287 (2002).

(3) D.B. Robb, T.R. Covey, and A.P. Bruins,

Anal. Chem. 72(15), 3653–3659 (2000).

(4) Y. Cai, D. Kingery, O. McConnell, and

A.C. Bach, Rapid. Commun. Mass

Spectrom. 19(12), 1717–1724 (2005).

(5) D.B. Robb and M.W. Blades, Anal.

Chim. Acta 627(1), 34–49 (2008).

(6) L.C. Herrera, J.S. Grossert, and L.

Ramaley, J. Am. Soc. Mass Spectrom.

19(12), 1926–1941 (2008).

(7) A. Kaufmann, P. Butcher, K. Maden,

S. Walker, and M. Widmer, Anal. Chim.

Acta 673(1), 60–72 (2010).

(8) A. Kaufmann, Anal. Bioanal. Chem.

403(5), 1233–1249 (2012).

(9) M. Weber, C. Hellriegel, A. Ruck, R.

Sauermoser, and J. Wuthrich, Accred.

Qual. Assur. 18, 91–98 (2013).

(10) J.M. Davis and J.C. Giddings, Anal.

Chem. 55(3), 418–424 (1983).

(11) S.S. Bae, B.M. Ehrmann, K.A. Ettefagh

and N.B. Cech, Phytochem. Anal. 21(5),

438–443 (2010).

(12) A. Kruve and I. Leito, Anal. Methods 5,

3035–3044 (2013).

(13) K.F. Nielsen and J. Smedsgaard, J.

Chrom. A 1002(1-2), 111–136 (2003).

(14) G. Lang et al., J. Nat. Prod. 71(9),

1595–1599 (2008).

(15) T. El-Elimat et al., J. Nat. Prod. in press

(2013).

(16) W.-L. Lee, J.-Y. Shiau and L.-F. Shyur,

Adv. Bot. Res. 62, 133–178 (2012).

(17) M.E. Wall and M.C. Wani, J.

Ethnopharmacol. 51(1-3), 239–254

(1996).

(18) H.A. Junio et al., J. Nat. Prod. 74(7),

1621–1629 (2011).

Nadja B. Cech, PhD, earned her BS

degree in chemistry from Southern

Oregon University in 1997, and her

PhD in Analytical Chemistry from the

University of New Mexico in 2001.

Her PhD training is in the area of

mass spectrometry, and for the last

14 years she has worked to apply

this expertise to solve challenging

problems in natural products

research. As a faculty member at

the University of North Carolina

Greensboro, Dr. Cech supervises a

research group of 12 students and

postdoctoral research associates. She

is the recipient of the 2011 Journal of

Natural Products Jack L. Beal Award,

for a paper detailing approaches to

study synergy in botanical medicines.

Dr. Cech is funded by the National

Institutes of Health on several projects

that involve the identification of

botanical products effective against

inflammation or infection.

“MS — The Practical Art” Editor

Kate Yu joined Waters in Milford,

Massachusetts, USA, in 1998.

She has a wealth of experience

in applying LC–MS technologies

to various application fields such

as metabolite identification,

metabolomics, quantitative

bioanalysis, natural products, and

environmental applications.


LC•GC Asia Pacifi c March 201434

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Sigma Aldrich, St Gallen, Switzerland.


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CHROMACADEMYCOMPLIMENTS OF AGILENT TECHNOLOGIES

36814030268_2934340.pgs 02.26.2014 20:02 ADVANSTAR_PDF/X-1a blackyellowmagentacyan

36 LC•GC Asia Pacifi c March 2014

ADVERTISEMENT FEATURE

There has been a signif cant resurgence in the development of

antibody-drug conjugates (ADC) as target-directed therapeutic

agents for cancer treatment. Among the factors critical to effective

ADC design is the Drug Antibody Ratio (DAR). The DAR describes

the degree of drug addition which directly impacts both potency and

potential toxicity of the therapeutic, and can have signif cant effects

on properties such as stability and aggregation. Determination of

DAR is, therefore, of critical importance in the development of novel

ADC therapeutics.

DAR is typically assessed by mass spectrometry (MALDI-TOF or

ESI-MS) or UV spectroscopy. Calculations based on UV absorption

are often complicated by similarities in extinction coeff cients of

the antibody and small molecule. Mass spectrometry, though a

powerful tool for Mw determination, depends on uniform ionization

and recovery between compounds — which is not always the case

for ADCs.

Here we present a method for DAR determination based on SEC–

MALS in conjunction with UV absorption and differential refractive

index detection. Figure 1 shows UV traces for two model ADCs;

molecular weights of the entire ADC complexes are determined

directly from light scattering data.

Component analysis is automated within the ASTRA 6 software

package by using the differential refractive index increments (dn/dc)

and extinction coeff cients, which are empirically determined for each

species or mined from the literature, to calculate the molar mass of

the entire complex as well as for each component of the complex.

In this example an antibody has been alkylated with a compound

having a nominal molecular weight of 1250 Da (Figure 2). Molar

masses of the antibody fractions are similar, which indicates

that the overall differences between the two formulations ref ect

distinct average DARs which are consistent with values obtained

by orthogonal techniques. Note that the molar mass traces for the

conjugated moiety represent the total amount of attached pendant

groups; the horizontal trends indicate that modif cation is uniform

throughout the population eluting in that peak.

Antibody Drug Conjugate (ADC) Analysis with SEC–MALS Wyatt Technology Corporation

Wyatt Technology Corporation6300 Hollister A venue, Santa Barbara, California 93117, USA

Tel: +1 (805) 681 9009 fax: +1 (805) 681 0123

Website: www.wyatt.com

Antibody-Drug Conjugate Analysis

(■) Mw of complex

(+) Mw of antibody

(x) Mw of conjugated drug

1.0x105

1.0x104

9.0 9.5 10.0 10.5 11.0 11.5 12.0Time (min)

Complex Antibody Drug

DAR

ADC1

ADC2

167.8 (±1.2%)

163.7 (±1.2%)

155.2 (±1.8%)

155.6 (±1.2%)

12.6

8.1 6.5

10.1

Mw (kDa)

Mo

lar

Ma

ss (

g/m

ol)

ADC1

ADC2

2.0x105

Molar mass vs. time

167.8 kDa

ADC1

ADC2

163.7 kDa1.8x105

1.6x105

1.4x105

1.2x105

Mo

lar

Ma

ss (

g/m

ol)

1.0x105

8.0x104

9.0 9.5 10.0 10.5Time (min)

11.0 11.5 12.0

Figure 2: Molar masses for the antibody and total appended drug are calculated in the ASTRA software package based on prior knowledge of each component’s extinction coeff cent and dn/dc, allowing determination of DAR based on a nominal Mw of 1250 Da for an individual drug.

Figure 1: Molar masses for two distinct ADC formulations are determined using SEC–MALS analysis.



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