surrogate matrix and surrogate analyte approaches for definitive quantitation of endogenous...

ReseaRch aRticle

Development and validation of LC–MS/MS methods for the determination of drugs and metabolites in biological samples are relatively straightforward, with ample regulatory guidance and many years of experience guiding the work [1,2]. Calibration and QC samples can be pre-pared by spiking known amounts of a reference standard into an analyte-free control matrix with good selectivity. Quantitation of biomarkers by LC–MS/MS is complicated by the endogenous presence of the analytes and a lack of regulatory guidance to direct method validation practices. Often, an analyte-free biological matrix cannot be obtained and used to create reference samples. There are two main approaches for the defini-tive quantitation of endogenous compounds by LC–MS in a high-throughput setting that over-come this challenge: the use of an authentic ana-lyte as a calibrator in a surrogate matrix [3,4] or a surrogate analyte as a calibrator in a biological matrix [5–7]. In the surrogate matrix approach, a substitute to the biological matrix that is devoid of the analyte is used for creation of the calibra-tion samples. This may be a synthetic mixture (e.g., bovine serum albumin [BSA] in phosphate

buffered saline [PBS]), an analyte-depleted matrix (e.g., charcoal stripped or immuno-depleted) or a biological matrix from an alter-nate species (e.g., for protein/peptide analytes where the sequence is not fully conserved across species). For the surrogate analyte approach, a stable-isotope-labeled (SIL) analog of the target analyte is used in place of the authentic analyte. Because the SIL has an SRM transition unique from the authentic analyte, calibration standards can be prepared in the biological matrix.

Regardless of the approach taken, definitive quantitation [8] of endogenous analytes requires that the response for the calibrators used truly depicts the response for the authentic analyte in the biological matrix of interest over the calibration range.

ParallelismThe term parallelism is used to describe how well a set of calibration standards track the response of the authentic analyte in the biologi-cal matrix. For ligand-binding assays, parallelism is assessed by diluting a biological sample in the buffer used for calibration standard preparation.

Surrogate matrix and surrogate analyte approaches for definitive quantitation of endogenous biomolecules

Background: Quantitation of biomarkers by LC–MS/MS is complicated by the presence of endogenous analytes. This challenge is most commonly overcome by calibration using an authentic standard spiked into a surrogate matrix devoid of the target analyte. A second approach involves use of a stable-isotope-labeled standard as a surrogate analyte to allow calibration in the actual biological matrix. For both methods, parallelism between calibration standards and the target analyte in biological matrix must be demonstrated in order to ensure accurate quantitation. Results: In this communication, the surrogate matrix and surrogate analyte approaches are compared for the ana lysis of five amino acids in human plasma: alanine, valine, methionine, leucine and isoleucine. In addition, methodology based on standard addition is introduced, which enables a robust examination of parallelism in both surrogate analyte and surrogate matrix methods prior to formal validation. Results from additional assays are presented to introduce the standard-addition methodology and to highlight the strengths and weaknesses of each approach. Conclusion: For the ana lysis of amino acids in human plasma, comparable precision and accuracy were obtained by the surrogate matrix and surrogate analyte methods. Both assays were well within tolerances prescribed by regulatory guidance for validation of xenobiotic assays. When stable-isotope-labeled standards are readily available, the surrogate analyte approach allows for facile method development. By comparison, the surrogate matrix method requires greater up-front method development; however, this deficit is offset by the long-term advantage of simplified sample ana lysis.

Barry R Jones*1, Gary A Schultz1, James A Eckstein2 & Bradley L Ackermann2

1Advion Bioanalytical Labs, a Quintiles Company, Ithaca, NY, USA 2Eli Lilly & Company, Indianapolis, IN, USA *Author for correspondence: E-mail: [email protected]

2343ISSN 1757-6180Bioanalysis (2012) 4(19), 2343–235610.4155/BIO.12.200 © 2012 Future Science Ltd

For reprint orders, please contact [email protected]

If the observed signal decreases in accordance with the dilution factor used, the two matrices provide similar recovery and parallelism exists. The term dilutional linearity describes the same experiment conducted after spiking the bio-logical matrix with the target analyte, typically above the ULOQ (AQL) [9–11].

The ability to recover the predicted signal for a standard spiked into biological matrix is a third way to assess parallelism. This experi-ment, referred to as spike recovery, is frequently performed during the validation of both ligand-binding and MS methods. Recovery is calculated by the following formula, where bm = biological matrix and sm = surrogate matrix:

% 100covre eryspiked sm blank sm

spiked bm blank bm)=

-

-

^^

hh

Typically, spike recovery is assessed at three concentrations spanning the range of ana lysis. Although recommendations have been pub-lished for ligand-binding methods [10–12], regu-latory guidance has not been issued regarding this subject for biomarker quantitation.

The use of standard addition to assess parallelismAlthough estimating spike recovery is straight-forward, the process for identifying a surrogate matrix that behaves parallel to the true biologi-cal matrix is a significant challenge for LC–MS biomarker methods. Fortunately, because we assume a linear MS response in the biological matrix, it is possible to employ standard addition methodology to document the response for a tar-get analyte. In this article, a procedure based on standard addition to quickly and robustly assess parallelism for both the surrogate matrix and surrogate analyte methods is presented. Under this method, a series of calibration solutions is spiked into the biological matrix and used to establish the slope of the curve and provide an estimate of the endogenous concentration of the analyte in the biological matrix by extrapola-tion to the negative x-axis. For surrogate matrix methods, the same calibration solutions can be used to prepare curves in multiple candi-date matrices, allowing rapid determination of preferred composition based on a comparison of the slopes obtained. For surrogate analyte methods, standard addition is performed as a check to show that both forms of the analyte give similar slopes when spiked into the same biologi-cal control matrix pool following balance of the authentic and surrogate analyte SRM responses.

For both methods, the endogenous concentra-tion of the biological matrix extrapolated from standard addition is compared with the endog-enous concentration interpolated from the sur-rogate matrix or surrogate analyte calibration curve. Agreement between these independent determinations is used to check if a method is ready for validation (Figure 1).

The aforementioned methodology has been applied for several definitive biomarker assays in our laboratory including a f ive-amino acid method in human plasma using LC–APCI/MS/MS. This assay was used to perform a direct comparison of surrogate matrix and surrogate analyte methods. Results from this comparison are presented along with additional examples that allow conclusions to be made regarding the relative strengths and weaknesses of surrogate matrix and surrogate analyte methods for LC–MS/MS biomarker quantitation.

Experimental � Reagents

Reference standards of 1-methylhistamine dichloride (telemethylhistamine [TMH] 99.5%; 0.6319 salt factor), 1-methyl-4-imidazoleacetic acid hydrochloride (telemethylimidazoleacetic acid [TMIAA] 100%; 0.800 salt factor) and N -́methyl-d

3-histamine dichloride ([2H

3] TMH

99%, 0.6374 salt factor; internal standard) were obtained from Sigma Aldrich (St Louis, MO, USA). 1-methylhistamine-4-imidazoleacetic acid-d

3 ([2H

3] TMIAA) was provided by Eli

Lilly and Company (Indianapolis, IN, USA) and used as the internal standard for TMIAA.

Reference standards of arachidonoyl etha-nolamide (anandamide [AEA] supplied as 1 mg/ml solution in ethanol), arachidonoyl ethanolamide-d

4 (AEA-d

4, ([2H

4]) supplied as

1 mg/ml solution in ethanol; internal standard) and arachidonoyl ethanolamide-d

8 (AEA-d

8,

([2H8]) supplied as 1 mg/ml solution in methyl

acetate; surrogate analyte) were purchased from Cayman Chemical Company (Ann Arbor, MI, USA). Reference standards of 2-arachidonoyl glycerol (2-AG supplied as 10 mg/ml solution in acetonitrile), 2-AG-d

5 ([2H

5] 2-AG supplied as

0.5 mg/ml solution in ethanol; internal standard) and 2-AG-d

8 ([2H

8] 2-AG supplied as 100 µg/ml

solution in ethanol; surrogate analyte) were also purchased from Cayman Chemical Company.

SIL reference standards of [13C15N] l-ala-nine (98.2%; surrogate analyte), [2H

8]

l-valine (98.5%; surrogate analyte), [13C6 15N]

Key Terms

Surrogate matrix: Substitute matrix used to prepare calibration standards that is devoid of the target analyte and suitably depicts the behavior of the biological sample matrix.

Surrogate analyte: Substitute reference compound that is used to prepare calibration standards in biological matrix for the purpose of quantifying the endogenous analyte of interest.

Parallelism: Ability for a set of calibration standards to track the response of the authentic analyte in the biological matrix.

Dilutional linearity: Method used to demonstrate parallelism in which a sample in biological matrix is spiked above the ULOQ and diluted with various amounts of surrogate matrix. A decrease in response consistent with the applied dilution suggests parallelism between the two matrices.

Spike recovery: Method for estimating method recovery for a target analyte in a biological matrix. This method, which is often used as a surrogate for accuracy, reflects both extraction recovery and ionization suppression.

ReseaRch aRticle | Jones, Schultz, Eckstein & Ackermann

Bioanalysis (2012) 4(19)2344 future science group

l-isoleucine (97.9%; surrogate analyte), [13C 2H

3] l-methionine (99.0%; surrogate ana-

lyte), [13C6 15N] l-leucine (96.0%; surrogate

analyte), [13C3 15N] l-alanine (95.0%; inter-

nal standard) and [2H10

] l-leucine (99.3%; internal standard) were purchased from Sigma Aldrich. Authentic analyte reference standards of l-alanine, l-valine, l-isoleucine, l-methionine and l-leucine were also purchased from Sigma Aldrich. [13C

3 15N] l-alanine was used as the

internal standard for l-alanine and [13C 15N] l-Alanine, and [2H

10] l-Leucine was used as the

internal standard for the remaining surrogate and authentic analytes.

HPLC-grade methanol, acetonitrile, ethyl acetate and water were obtained from Honeywell Burdick and Jackson (Phoenix, AZ, USA). Hexane was purchased from EMD Chemicals (Gibbstown, NJ, USA). Heptaf luorobutyric acid modifier was purchased from Fluka (Sigma Aldrich Chemie GmbH, Buchs, Germany). Surrogate matrix (0.2 or 4% BSA in PBS) was prepared using BSA and PBS, pH 7.4, from Sigma Aldrich. Control human plasma (K

2EDTA) was purchased from Bioreclamation,

Inc. (Hicksville, NY, USA).

� Extraction proceduresTMH & TMIAATMH and TMIAA were isolated in human plasma using a protein precipitation extraction procedure. A 100-µl aliquot of sample was com-bined with 50 µl of an aqueous solution contain-ing the internal standards (10 ng/ml [2H

3] TMH

and 40 ng/ml [2H3] TMIAA). Internal standard

solution was added to all samples except for dou-ble blanks, to which 50 µl of HPLC-grade water was added. To this mixture 400 µl of acetoni-trile was added and subsequently vortexed and centrifuged. The supernatant was evaporated to dryness under a steady stream of nitrogen at 45°C. The dried extracts were resuspended with 100 µl of mobile phase A. The calibration refer-ence standards were prepared in surrogate matrix from 0.03 to 30 ng/ml for TMH and from 0.1 to 30 ng/ml for TMIAA.

AEA & 2-AGAEA and 2-AG were isolated in human plasma using a solid-supported liquid–liquid extrac-tion. A 300-µl aliquot of sample was acidified with 20 µl of 1.5% formic acid. Next, 50 µl of a 25 ng/ml [2H

4] AEA and 250 ng/ml [2H

5]

2-AG solution in 50/50 acetonitrile/water was added to all samples except double blanks. The

mixture was loaded onto an Isolute® 400 mg supported liquid extraction-positive extraction block (Biotage, NC, USA). The extracts were eluted from the block with 1600 µl of 10/90 hex-ane/ethyl acetate and then evaporated to dryness under a steady stream of nitrogen at 45°C. The dried extracts were resuspended with 100 µl of 70/30 methanol/water. The calibration reference standards were prepared in biological matrix from 0.05 to 25 ng/ml for AEA and from 0.5 to 250 ng/ml for 2-AG. [2H

4] AEA, with four

deuterium atoms substituted on the ethanol-amide, was used as the internal standard and [2H

8] AEA, with eight deuterium atoms substi-

tuted on the fatty acid chain, was used as the sur-rogate analyte for AEA. The same scheme was used for 2-AG. The [2H

8] AEA

and [2H

8] 2-AG

compounds were incompletely labeled with deu-terium and contained a measurable amount of unlabeled analyte, and thus could not be used as internal standards.

l-alanine, l-valine, l-isoleucine, l-methionine & l-leucineAmino acids were isolated in human plasma and surrogate matrix using a protein precipitation extraction procedure. First, 50 µl of an aqueous solution containing 20 µg/ml of [2H

10] l-leucine

MS

pea

k ar

ea r

atio

Mean interpolatedconcentration

Negative x-interceptconcentration

-0.3 -0.1 0.1 0.3 0.5

0.00

0.05

0.10

0.15

Analyte concentration (arbitrary units)

Biological matrixor authentic analyteSurrogate matrix or surrogate analyte

Figure 1. Conceptual illustration of the use of standard addition to assess parallelism with surrogate analyte and surrogate matrix MS methods. The negative x-intercept endogenous concentration is extrapolated from the calibration curve prepared with authentic analyte in biological matrix. The interpolated endogenous analyte concentration is derived from the mean MS peak area ratio from six replicate injections of biological matrix read from the surrogate matrix or surrogate analyte curve.

Surrogate matrix & surrogate analyte approaches | ReseaRch aRticle

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and 20 µg/ml [13C3 15N] l-alanine was added to

each 20 µl aliquot of sample, except for double blanks to which 50 µl of HPLC-grade water was added. After addition of 800 µl acetoni-trile, the mixture was vortexed, centrifuged and 50 µl of supernatant was evaporated to dryness under a steady stream of nitrogen at 45°C. The dried extracts were resuspended with 200 µl of mobile phase A. Surrogate and authentic analyte calibration reference standards were prepared in either biological or surrogate matrix from 1 to 100 µg/ml.

� LC For the TMH/TMIAA and AEA/2-AG assays, the chromatographic system consisted of two Shimadzu LC-20AD pumps, a Shimadzu CBM-20A Controller and a Shimadzu SIL-20AC autosampler (Shimadzu Scientific Instruments, Columbia, MD, USA). The chromatographic system for the amino acid assay consisted of two Shimadzu LC-20AD pumps, a Shimadzu SCL10A Controller and a LEAP CTC PAL autosampler.

For the TMH/TMIAA assay, mobile phase A was an aqueous solution containing 0.5% (v/v) heptafluorobutyric acid. Mobile phase B was methanol/water/formic acid (95/5/1 v/v/v). A 2 × 100 mm, 3.5 µm particle size Waters XBridge™ XDB Phenyl column (Waters, Milford, MA, USA) was used with a linear gradient and flow rate of 600 µl/min.

For the AEA/2-AG assay, mobile phase A was methanol/water/formic acid (50/950/1 v/v/v) and mobile phase B was methanol/water/formic acid (950/50/1 v/v/v). A 2 × 100 mm, 3 µm par-ticle size YMC-Pack ODS-AQ column (YMC, Allentown, PA, USA) was used with a linear gradient and flow rate of 750 µl/min.

For the amino acid-panel assay, mobile phase A was an aqueous solution containing 0.3% heptafluorobutyric acid and mobile phase B was acetonitrile containing 0.3% heptafluo-robutyric acid. A 2.1 × 50 mm, 5 µm particle size Waters Atlantis T3 column was used with a linear gradient and flow rate of 600 µl/min. The LC parameters were optimized for baseline separation of leucine and isolecucine. Because these two molecules have the same SRM transi-tion, chromatographic separation was necessary for independent quantitation.

� MSAn API 4000 LC–MS/MS triple quadrupole mass spectrometer equipped with a TurboIonSpray™

ionization source (AB Sciex, Concord, Ontario, Canada) was used for MS/MS ana lysis, and was performed in the SRM mode.

For the TMH/TMIAA assay, mass transitions m/z 141.0→95.1 for TMIAA, 144.0→71.1 for TMIAA-d

3 internal standard, 126.1→109.1 for

TMH and 129.0→112.1 for TMH-d3 internal

standard were used. Analyses were performed in positive ion mode with electrospray ionization.

For the AEA/2-AG assay, mass transitions m/z 348.3→62.0 for AEA, 352.3→66.0 for [2H

4] AEA internal standard, 356.3→63.0 for

[2H8] AEA surrogate analyte, 379.3→287.2 for

2-AG, 384.3 → 287.2 for [2H5] 2-AG internal

standard and 387.3→294.2 for [2H8] 2-AG sur-

rogate analyte were used. The analyses were done in positive ion mode with APCI.

For the amino acid assay, mass transitions m/z 90.1→44.1 for l-alanine, 92.1→46.1 for [13C 15N] l-alanine surrogate analyte, 94.1→47.1 for [13C

3 15N] l-alanine internal standard,

150.1 → 104.1 for l-methionine, 154.1→108.1 for [13C 2H

3] l-methionine surrogate analyte,

118.1→72.1 for l-valine, 126.1→80.1 for [2H8]

l-valine surrogate analyte, 132.1→86.1 for l-leu-cine and l-isoleucine, 139.1→92.1 for [13C

6 15N]

l-leucine and [13C6 15N] l-isoleucine surrogate

analytes and 142.1→96.1 for [2H10

] l-leucine internal standard were used. The analyses were performed in positive ion mode with APCI.

Peak areas were integrated on a Windows XP platform by the MDS Sciex program Analyst®, version 1.4.2. Peak area informa-tion is imported to the Watson Laboratory Information Management System v 7.3.0.01 (Thermo Scientific, Waltham, MA, USA) for processing. With a surrogate analyte method, the peak area information from the surrogate ana-lyte transition is used for the surrogate standard curves and any surrogate analyte QC or blank. For biological matrix samples, QCs and blanks, the authentic analyte transition information is imported to Watson.

All calculated concentrations were based on the peak area ratio of each analyte to the respective internal standard. Concentrations were determined by inverse prediction from the calibration curves using the validated regression model for the assay.

� Response balanceFor the surrogate analyte methods (AEA/2-AG and amino acid assays) the SRM responses of the surrogate analytes were balanced with the responses of the corresponding authentic



analytes prior to ana lysis. This was done by injecting a neat solution containing all surrogate and authentic analytes at equal nominal concen-trations onto the LC–MS/MS system. Solutions at nominal concentrations were prepared by cor- nominal concentrations were prepared by cor-nominal concentrations were prepared by cor-recting for analyte purity as defined by the cer-tificates of ana lysis issued by the vendor. The mean peak area from the chromatograms of each surrogate analyte was compared with the mean peak area of the corresponding authentic ana-lyte. If the percent difference was greater than 5%, the more responsive transition was detuned, typically using collision energy or declustering potential. The response balance was repeated until the percent difference between each surro-gate analyte and corresponding authentic analyte was less than 5%.

� Parallelism assessmentIn our laboratory, parallelism is evaluated in a prevalidation experiment using duplicate authen-tic analyte/biological matrix calibration curves, duplicate authentic analyte/surrogate matrix curves, six replicates of the unspiked biological matrix, dilutions of the biological matrix with surrogate matrix and biological matrix spiked with analyte at concentrations spanning the range of ana lysis. If a dilution QC will be vali-dated, this sample is also evaluated in the par-allelism experiment, prepared above range and diluted with surrogate matrix to a concentra-tion above the mid-curve level (assayed n = 6). Theoretical concentrations of the biological matrix QCs are defined by the nominal over-spike concentration plus the endogenous analyte concentration. For the parallelism experiment, the true endogenous analyte concentration is calculated from the negative x-axis intercept of the standard addition curve. The precision and accuracy acceptance criteria applied to the bio-logical matrix QCs in the parallelism is typically 15%, but may depend on the needs of the assay and the intended use of the generated data.

Once parallelism is demonstrated by all QC samples meeting predefined acceptance criteria, formal assay validation may begin with the LC–MS conditions used in the parallelism experi-ment. In validation, the true endogenous ana-lyte concentration is determined from the mean calculated concentration of ≥6 replicates of the biological matrix interpolated from the surro-gate matrix curve. Having demonstrated paral-lelism, it is unnecessary to assay the standard addition curve throughout the three precision and accuracy experiments of the validation.

Results � Application of surrogate matrix method

TMH and TMIAA were quantified in human plasma using a surrogate matrix approach. Calibration samples and blanks were prepared in 0.2% BSA in 10 mM PBS. QC samples were prepared in K

2EDTA human plasma spiked with

TMH at 0.12, 1.5, 2.5 and 12.5 ng/ml and with TMIAA at 1.2, 15, 25 and 125 ng/ml. The abil-ity to dilute the endogenous analyte with sur-rogate matrix and yield accurate back-calculated concentrations was demonstrated by endogenous dilution samples – human plasma diluted 1.5-times, 2.5-times and five-times with surrogate matrix to and analyte concentration within three- to five-times the LLOQ. The ability to dilute high analyte concentrations to within the calibrated range was demonstrated with a five-times dilution QC using the surrogate matrix. The precision and accuracy of these QC samples are tabulated in Table 1. The theoretical TMH and TMIAA concentrations were defined as the nominal analyte spike concentration plus the endogenous analyte concentration determined from the negative x-intercept of the biological matrix calibration curve. Parallelism was indi-cated by accurate and precise quantitation of these biological matrix QC samples, which can be seen graphically in Figure 2 for TMH. This assessment was done prior to and outside of the formal validation, and the precision and accuracy values presented are not validation results.

The negative x-intercept for TMH was 0.340 ng/ml, which agreed well with the inter-polated value of 0.362 ng/ml. The calculated TMH concentrations of the endogenous dilu-tion samples and spiked plasma samples were all within 10% of the theoretical concentrations. Six replicates of each QC sample were assayed. The calculated percentage RSD (%RSD) of each QC level was within 5%.

These results provided evidence that 0.2% BSA in PBS is a qualified surrogate matrix for determination of TMH in human plasma. Having demonstrated parallelism, the assay was ready for validation under the specific LC–MS/MS conditions used for parallelism assessment. It has been our experience that par-allelism should be re-assessed whenever changes are made to the extraction, LC or MS condi-tions. An illustration of this point is shown by the data in Table 2, which documents the influence of collision energy on the ana lysis of TMIAA. When optimized for maximum SRM response, the collision energy for the TMIAA

Key Term

Response balance: Process of adjusting the LC–MS response of either the surrogate or authentic analyte such that they are equal to each other within specified tolerance.



m/z 144.0→71.1 transition was 35 eV. As shown, this setting resulted in poor parallelism. When the collision energy was reduced to 17 eV, accept-able parallelism was achieved. Close examina-tion of blank biological matrix chromatograms under both conditions revealed a closely eluting interference peak observed when the collision energy was set to 35 eV. A reduction to collision energy = 17 eV minimized this peak while still preserving sufficient sensitivity.

� Application of surrogate analyte methodFor the surrogate analyte approach, the LC–MS response of the neat surrogate analyte (typically

a SIL analog of the authentic analyte) must be shown to be equivalent to the LC–SRM–MS response of the neat authentic analyte at equal concentration. Otherwise, a response factor must be experimentally determined and bal-anced by adjustment of reference concentrations or by MS/MS detuning.

A surrogate analyte approach was used to quantify AEA and 2-AG in K

2EDTA human

plasma. Response factors between the authentic analytes and surrogate analytes were determined by six replicate injections of neat solutions con-taining equal nominal concentrations of each. Factors were calculated by dividing the peak areas of authentic analyte by surrogate analyte. The response factor was 14.3 for 2-AG and 6.2 for AEA. These large response factors were due to inefficient deuterium labeling evident in the MS spectra of each analyte. The response factor for AEA was further affected by gas-phase deute-rium scrambling in the fragmentation process. This resulted in a distribution of peaks in the product ion spectrum of AEA despite selection of a single isotope from the molecular ion as the precursor (Figure 3).

To balance these responses, an artificial ‘purity’ was ascribed to the surrogate analyte based on the response factor data. The surrogate analyte stock solutions were reprepared using this compensa-tory purity value, which brought the response factor closer to unity. The response balance pro-cedure was repeated and fine-tuned if necessary by detuning the more responsive transition using collision energy or declustering potential.

After balancing responses, parallelism between the surrogate and authentic analytes

0.3

0.2

0.1

00

0.6

0.5

-0.5 0.5

Norminal concentration (ng/ml)

Standard additionbiological matrixSurrogate matrix 0.2% BSA in phosphate buffered saline

-x = b/m

Pea

k ar

ea r

atio

1.0 1.5 2.0 3.02.5

0.4

Figure 2. Parallelism assessment of telemethylhistamine in human plasma and 0.2% bovine serum albumin in phosphate buffered saline.

Table 1. Precision and accuracy of human plasma QC samples for the telemethylhistamine parallelism prevalidation experiment†.

Biological matrix diluted with surrogate matrix

Plasma pool

Biological matrix spiked with telemethylhistamine

Plasma pool(five-times)

Plasma pool(2.5-times)


Low plasma pool

Mid plasma pool

High plasma pool

Plasma pool five-times dilution QC

Mean calculation (ng/ml)

0.330 0.336 0.334 0.362 0.459 1.952 2.827 13.612

Theoretical (ng/ml)

0.340 0.340 0.340 0.340 0.460 1.840 2.840 12.840

n 6 6 6 6 6 6 6 6

%RSD 3.0 1.4 3.1 1.5 3.4 1.4 0.7 1.5

%RE -3.0 -1.4 -1.8 6.5 -0.3 6.1 -0.5 6.0†Calculated concentrations were determined by interpolation from a surrogate matrix calibration curve. Theoretical endogenous concentration was determined from the negative x-intercept of the standard addition curve. All dilutions were performed using surrogate matrix. %RE: Percentage relative error; %RSD: Percentage relative standard deviation.



should be demonstrated as described above for the surrogate matrix approach. Validation should only begin after a successful demonstra-tion of parallelism. In validation and beyond, it is recommended that response balance be checked prior to each noncontiguous analyti-cal batch and adjusted to be within prescribed tolerance.

It is noted that the issue of response balance has not been fully addressed in previous papers involving the surrogate analyte method. In a pre-vious report by Jian et al., no correction was used for AEA since the surrogate analyte ([2H

4] AEA)

gave a response within 10% of authentic AEA [13]. As noted in Figure 2, our experience with [2H

4] and [2H

8] AEA was considerably differ-

ent and revealed unacceptably low purity for both reagents. In our experience, [2H

8] AEA

as internal standard can not be used owing to excessive contamination by authentic AEA. The deuterium scrambling observed in the product ion spectrum of [2H

8] AEA effectively lowered

the purity further making response balance even more critical. Collectively, this example under-scores the importance of response balancing to avoid potentially large errors when similar situa-tions are encountered using the surrogate analyte approach.

� Direct comparison of surrogate analyte and surrogate matrix approaches: ana lysis of l-alanine, l-valine, l-isoleucine, l-methionine & l-leucineSurrogate analyte curves, surrogate matrix curves, standard addition curves, endogenous dilution samples and biological matrix QC sam-ples were prepared and assayed in three sepa-rate analytical batches. The percentage relative error (%RE) of the calculated concentration for

authentic analyte QC samples in the biological matrix was used to assess precision and accuracy.

l-alanine, l-valine, l-isoleucine, l-methionine and l-leucine in human plasma were assayed in the same analytical batch using both surrogate analyte and surrogate matrix approaches. The surrogate analyte response factors were deter-mined for each analyte. Several issues may affect the response balance between surrogate and authentic analytes, including kinetic iso-tope effects [14], inefficient labeling, inaccura-cies of purity designation, gas-phase deuterium exchange and elution time shift of deuterated analytes, among others. Given all of the poten-tial variables, response factors must be deter-mined and balanced empirically. Prior to each acquisition, the response factors between sur-rogate and authentic analytes were brought to unity (± 5%) by detuning the more responsive transition using collision energy. The response balance was checked following each batch as well. Table 3 illustrates the results of a response balance procedure for a single run. Although the post-batch response factors were within 5%, they changed over the course of the run, under-scoring the need for prebatch response balance for every noncontiguous run.

Each analyte showed good parallelism using both surrogate analyte and surrogate matrix approaches. This is demonstrated graphi-cally in Figure 4 using l-valine as an example. A measure of parallelism is to compare the extrapolated endo genous analyte concentra-tion obtained from standard addition to the interpolated value obtained from the surrogate analyte or surrogate matrix curve (depending on the application). This comparison is shown in Table 4 and using the mean values from the three batches. There was generally good

Table 2. Parallelism results for telemethyimidazoleacetic acid with collision energy = 35 and 17 eV†.

Biological matrix diluted with surrogate matrix

Plasma pool

Biological matrix spiked with telemethyimidazoleacetic acid

Plasma pool(five-times)



LowPlasma pool

Mid plasma pool

High plasma pool

Plasma pool five-times dilution QC

Collision energy = 35 eV

%RE -10.6 -8.7 -10.0 -4.3 -10.9 -6.8 -11.8 -6.7

%RSD 1.5 1.1 1.4 2.5 2.4 1.4 2.1 1.4

Collision energy = 17 eV

%RE -1.4 -3.7 -0.5 1.9 -2.7 -2.8 -1.9 -2.2

%RSD 1.7 2.8 1.9 1.6 1.0 2.6 2.9 1.8†The method is ready for validation but care must be taken to control the collision energy setting for telemethyimidazoleacetic acid.%RE: Percentage relative error; %RSD: Percentage relative standard deviation.



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5

0

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35

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342m/z

m/z

m/z

348 [M + H]+

352 [M + H]+

352 [M + H]+

63

Rel

ativ

e io

n a

bu

nd

ance

(%

)R

elat

ive

ion

ab

un

dan

ce (

%)

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344 346 348 350 352 354 356 358 360

340 342 344 346 348 350 352 354 356 358 360

340 342 344 346 348 350 352 354 356 358 360

NH

CH3

OHO

NH

CH3

O

HO DD

DD

HO

D CH3

NH

D D O

D D

DDD

62

N+

HO

H

HH

N+

HO D

D

DD

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H

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66

Figure 3. Structure and MS and MS/MS spectra of anandamide, [2H4] anandamide and [2H8] anandamide. (A & B) Anandamide (endogenous analyte), (C & D) [2H

4] anandamide (internal standard), (E & F) [2H

8] anandamide (surrogate

analyte). (A, C & E) Q1 scan, (B, D & F) product ion scan. (E) The inefficient deuterium labeling is evident in the Q1 MS spectra of [2H8]

anandamide. (F) Evidence of gas-phase deuterium scrambling is shown in the MS/MS spectra of [2H8] anandamide by the distribution

of product ions arising from the precursor ion at m/z 356.3.



agreement between the negative x-intercept values and those calculated from the surrogate curves, with slightly better agreement overall for the surrogate matrix approach. For the par-allelism experiment, these negative x-intercept values were used as the theoretical endogenous analyte concentration for calculation of %RE at each QC level. Good QC statistics, %RE typically within 15% at each level, qualify the suitability of the surrogate matrix or surro-gate analyte(s) under the specific extraction/LC–MS conditions employed. For purposes of this comparison, the standard addition curves were assayed in each of the three validation batches to obtain inter-assay mean endo genous analyte concentrations via extrapolation to negative x-intercept.

The statistical performance of each method was evaluated using human plasma QC samples assayed six-times in each of the three batches. Depending on the target concentration, QC samples were prepared either by dilution of endogenous analyte with surrogate matrix or by analyte over-spiking. For the surrogate matrix approach, the endogenous analyte is typically diluted to within three- to five-times the LLOQ to demonstrate quantitation in the first quartile of the calibration curve. Because the endogenous concentration varies per analyte, three different dilution factors were used (two-, five- and ten-times). To calculate %RE of these QC samples, the mean (n = 18) interpolated endogenous ana-lyte concentration was used as the theoretical analyte concentration, plus the nominal spiked analyte concentration for the over-spiked QC samples. The precision and accuracy of the QC samples measured using the surrogate analyte regression are shown in Table 5 (overall %RE ranged from -1.1 to 2.7%, %RSD ranged from 6.1 to 8.6%). The precision and accuracy of the QC samples obtained from the surrogate matrix regression are shown in Table 6 (overall %RE

ranged from -5.0 to 3.2%, %RSD ranged from 4.0 to 9.9%). The results are within acceptance criteria typical of a xenobiotic assay validation for both approaches [12].

DiscussionA key concept introduced in this article is the application of standard addition for parallelism assessment prior to formal method validation. It has been found that when the concentration of an unspiked biological matrix pool derived from standard addition agrees with the concentration obtained by direct interpolation from the stan-dard curve, parallelism exists and the assay is ready for validation. As a general rule of thumb, it is preferable that the two values agree within 15%. It is important to note that this conven-tion was adopted simply to be consistent with standard tolerances associated with bioanalytical

L-valine, standard additionL-valine, surrogate matrix[2H8] L-valine, surrogate analyte

-40 -20 20 40 60 800

0.2

0.4

0.6

0.8

1.0

1.2

100 1200

Negative x-intercept = ‘true concentration’

Spike concentration (µg/ml)

Pea

k ar

ea r

atio

Figure 4. Surrogate analyte and surrogate matrix parallelism for l-valine. All curves were fit to a 1/x2-weighted linear regression.

Table 3. Response balance for precision and accuracy batch number 2.

Analytes at 0.25 µg/ml Equimolar solution mean observed percentage difference (n = 6)

Difference after adjustment of collision energy prior to batch (%; n = 6)

Difference after batch (%)

l-alanine versus [13C1 15N] l-alanine 0.8 1.2 4.0

l-methionine versus [13C1 2H

3]

l-methionine-29.4 2.3 -1.1

l-valine versus [2H8] l-valine -6.5 -2.9 -0.7

l-leucine versus [13C6 15N] l-leucine -21.0 -2.8 -2.7

l-isoleucine verus [13C6 15N]

l-isoleucine-17.9 -3.9 -1.6



assays. It was not derived mathematically and is not applied as criterion for assay acceptance. The strength of this approach lies in its simplic-ity and because it requires convergence of two independent measures.

Recently, an alternative approach was used by McNeill and co-workers in their publica-tion of a validated surrogate analyte method for asparagine in human plasma [14]. These authors performed a statistical comparison of

Table 4. Comparison of endogenous analyte concentration determined by extrapolation to negative x-intercept from the standard addition curve (true concentration), the interpolated value by surrogate matrix and the interpolated value by surrogate analyte.

Endogenous analyte Negative x-intercept from standard addition

Surrogate analyte Surrogate matrix

Extrapolated mean† (µg/ml) Interpolated mean† (µg/ml)

%RE Interpolated mean† (µg/ml)

%RE

l-alanine 25.9 25.0 -3.5 25.1 -3.1

l-valine 24.3 26.6 9.5 24.1 -0.8

l-isoleucine 10.7 10.5 -1.9 9.9 -7.5

l-methionine 3.2 3.6 12.5 3.4 6.2

l-leucine 20.1 21.9 9.0 19.2 -4.5†Three batches; n = 18.%RE: Percentage relative error.

Table 5. Precision and accuracy results of the biological matrix QCs for the five-amino acid method via surrogate analyte approach.

Spike concentration in plasma

Unspiked plasma pool 3 µg/ml 50 µg/ml 80 µg/ml

l-alanine

Overall theoretical 25.0 28.0 75.0 105.3

Overall mean 25.0 27.4 73.1 101.5

Overall %RSD 6.4 4.3 7.9 7.1

Overall %RE N/A -2.1 -2.5 -3.6

l-valine


Overall mean 26.6 29.0 74.9 104.1

Overall %RSD 9.7 7.7 8.2 7.7

Overall %RE N/A -2.2 -2.3 -2.4

l-isoleucine


Overall mean 10.5 13.3 58.6 87.0

Overall %RSD 7.2 5.3 6.9 6.9

Overall %RE N/A -1.8 -3.9 -3.9

l-methionine


Overall mean 3.6 6.6 55.4 85.9

Overall %RSD 7.4 4.6 6.0 5.6

Overall %RE N/A 0.4 3.6 2.9

l-leucine


Overall mean 21.9 24.7 70.4 99.3

Overall %RSD 8.4 8.6 6.1 7.3

Overall %RE N/A -1.1 -2.2 -2.7%RE: Percentage relative error; %RSD: Percentage relative standard deviation; N/A: Not applicable.



calibration curve slopes for authentic versus SIL versions of asparagine as the method for sur-rogate analyte selection. Despite potential error introduction through extrapolation, we favor standard addition owing to ease of application. It should be pointed out that while calibra-tion slopes are indeed considered during par-allelism assessment, a statistical ana lysis is not performed.

The statistical performance of the surrogate matrix and surrogate analyte validations for the five-amino acid assay revealed little advantage of one approach over the other when care is taken to ensure parallelism by the methods described. Because definitive methods can be achieved using either approach, the choice of which method to pursue is often based on logistical concerns.

A disadvantage of the surrogate analyte method is the limited availability and compara-tively high cost of SIL analogs. A further compli-cation is that surrogate analyte methods ideally

require two differently labeled SIL analogs; one for the internal standard and one for the surro-gate analyte. In many cases multiple SIL analogs are simply not available.

Some SIL analogs when compared with authentic analytes show a large response factor, as was the case for AEA and 2-AG. This issue complicates assay management, as an empiri-cal purity must be applied to the surrogate analyte reference compound, essentially adding more surrogate analyte to compensate the large response factor. Response balance by analyte detuning is impractical in cases involving large response factors, because significant signal for the authentic analyte is discarded. Even when large response factors are compensated by an empirical purity, the response balance may still need to be fine-tuned using MS conditions prior to each batch in order to ensure accuracy. Balancing responses is a time-consuming task that adds a potentially iterative step to batch setup for the operator.

Table 6. Precision and accuracy results of the biological matrix QCs for the five-amino acid method via surrogate matrix approach.

Spike concentration in plasma

Tenfold dilution

Fivefold dilution

Twofold dilution

Unspiked plasma pool

3 µg/ml 50 µg/ml 80 µg/ml

l-alanine

Overall theoretical 25.1 25.1 25.1 25.1 28.1 75.1 105.3

Overall mean 24.5 25.9 25.1 25.1 27.6 73.4 101.9

Overall %RSD 5.7 3.4 3.4 5.5 4.4 6.3 5.5

Overall %RE -2.6 3.2 -0.2 N/A -2.0 -2.3 -3.2

l-valine


Overall mean 24.3 25.8 23.7 24.1 26.3 68.0 94.7

Overall %RSD 6.6 5.8 5.7 6.7 5.6 4.9 4.1

Overall %RE 0.6 7.1 -1.8 N/A -3.1 -8.3 -9.3

l-isoleucine


Overall mean 9.5 10.2 9.7 9.9 12.5 56.1 83.5

Overall %RSD 10.1 3.4 5.6 7.5 4.9 6.8 7.0

Overall %RE -4.4 3.1 -1.5 N/A -2.8 -6.2 -7.2

l-methionine


Overall mean 3.3 3.4 3.3 3.4 6.2 52.2 80.9

Overall %RSD 18.6 9.5 4.3 8.1 4.1 6.3 6.0

Overall %RE -4.0 1.2 -3.4 N/A -2.5 -2.2 -3.0

l-leucine


Overall mean 18.4 19.8 19.0 19.2 22.2 67.3 94.4

Overall %RSD 9.9 4.8 4.0 7.9 4.7 5.2 6.8

Overall %RE -4.4 3.2 -0.8 N/A 0.1 -2.7 -5.0%RE: Percentage relative error; %RSD: Percentage relative standard deviation; N/A: Not applicable.



An advantage of the surrogate analyte method is that parallelism is generally assured after the response balance is performed. In contrast, achieving parallelism by the surrogate matrix approach is a more challenging method-develop-ment endeavor. The choice of surrogate matrix, the purity of sample extracts, mobile-phase com-position, LC and MS settings all can have a large effect on parallelism. Moreover, the difficulty of achieving parallelism with a surrogate matrix assay increases with the number of analytes mea-sured by the assay. While a surrogate analyte assay may become more challenging from an assay management perspective as the number of analytes increase, parallelism is largely a matter of adherence to the response balance procedure.

An advantage of the surrogate matrix method is the ability to dilute samples with analyte con-centrations AQL using an analyte-free dilu-ent. Dilutional linearity is demonstrated in validation using both endogenous levels and biological matrix spiked with an AQL analyte concentration and diluted to within range with surrogate matrix. This can also be a tool to achieve acceptable surrogate matrix parallelism in difficult assays by truncating the calibration curve, provided precise and accurate dilutional linearity of an AQL sample can be demonstrated. A surrogate analyte method uses no authentic analyte-free matrix, so diluting AQL samples is complicated by the endogenous presence of the analyte in the diluent.

The endogenous presence of the analyte in the biological control matrix poses challenges for the selection of validation samples. For a typi-cal xenobiotic assay, reference analyte is spiked into biological control matrix at the LLOQ, at three- to five-times the LLOQ, mid-range, upper quartile and at other places in the curve range (e.g., C

max or geometric mean), plus the dilution

QC if one is to be validated. If the biomarker assay were to be validated using exclusively biological matrix QC samples, the endogenous analyte concentration may make it difficult to have a validation QC at three- to five-times the LLOQ. Certainly an LLOQ QC will not be pos-sible for a non-zero endogenous level. In order to have a QC to cover the lower quartile for a surrogate analyte method, the low QC should be prepared using the surrogate analyte. For a sur-rogate matrix assay, the biological matrix should be diluted with surrogate matrix to a concentra-tion within three- to five-times the LLOQ. If accurate dilution of endogenous analyte has been demonstrated in the parallelism experiment, a

low QC composed of the authentic analyte in the surrogate matrix may be considered. The LLOQ QC should be prepared in surrogate matrix or prepared using the surrogate analyte, depending on the approach, to demonstrate precision and accuracy at the LLOQ.

For validation, the same biological control matrix should be used throughout precision and accuracy experiments. However, variability of the endogenous analyte concentration should be taken into account when choosing the concen-trations of the over-spiked QCs. For subsequent sample ana lysis or validation batches that use a different lot of matrix, the total concentrations of the QC samples may change. The total ana-lyte concentration of the upper quartile QCs may be AQL or the diluted endogenous at the typical dilution factor may be below quantita-tion limit. It is important to be flexible with the design of the biomarker validation samples, keeping the variability of the endogenous analyte concentration into account.

Another QC that is employed in our labora-tories is the endogenous QC. This is a pool of biological control matrix that is assayed (n = 10) in each of the three precision and accuracy experiments during validation of the method. The theoretical concentration of the endo genous analyte in the pool is the grand mean (n = 30) calculated concentration. An inter-assay preci-sion acceptance criterion is applied to the endog-enous QC qualification, typically %RSD ≤20% in our laboratory, in order to ensure reproduc-ible quantitation between batches. The pool is divided into several aliquots, stored frozen under the conditions predicted for sample ana lysis and assayed in replicates of six within each subse-quent sample ana lysis batch as a running check on assay performance throughout a study. As an alternative reference source of target analyte, the endo genous QC can also be a useful diagnostic tool to help understand unexpected results in sample ana lysis runs.

ConclusionIn this article procedures are outlined that pro-vide an approach to definitive LC–MS/MS validation of endogenous biomolecules using either the surrogate analyte or surrogate matrix method. For the ana lysis of a set of five amino acids in human plasma, comparable precision and accuracy were obtained by both methods and both assays were well within validation tol-erances prescribed by existing regulatory guid-ance for xenobiotic assays. The method selected



Executive summary

Background

� The presence of target analyte in the biological control matrix presents a significant complication for LC–MS/MS biomarker methods compared with xenobiotic bioana lysis.

� Historically, two methods have been used to address this issue: surrogate matrix and surrogate analyte. In both cases, parallelism between the response for calibration standards and authentic analyte in the biological matrix must be demonstrated to ensure accuracy.

Experimental

� Methodology using standard addition is introduced, allowing robust parallelism assessment for both approaches and rapid matrix evaluation for surrogate matrix methods.

Results

� Analysis of histamine metabolites in human plasma was used as an illustration of the surrogate matrix method. Quantitation of the endocannabinoids anandamide and arachidonoyl glycerol, also in human plasma, was used to introduce surrogate analyte approach.

� Both approaches were applied to the LC–MS/MS ana lysis of five amino acids in plasma: alanine, valine, methionine, leucine and isoleucine. Comparable precision and accuracy were obtained by both methods.

Discussion

� Surrogate analyte methods offer simplified method development, but require careful attention to the response balance between stable-isotope-labeled and native forms of the analyte.

� Surrogate matrix methods require greater up-front method development, but provide streamlined sample ana lysis depending on the nature of the surrogate matrix.

Conclusion

� The examples reported represent biomarker validations associated with regulatory submissions related to pharmaceutical development. The need to adapt the procedures presented to accommodate the notion of fit-for-purpose validation is freely acknowledged.

depends on a number of logistical factors dis-cussed including the availability of SIL forms of the target analyte.

It has been our experience that rigorous up-front examination of parallelism leads to fewer issues during formal validation. Having said this, we realize that significant effort is required to adhere to the practices prescribed in this article. In the spirit of fit for purpose [8], we acknowl-edge that these practices are not always practi-cal and scaled back approaches will be needed depending on the nature and purpose of a given biomarker assay. Indeed, establishing parallelism for multianalyte assays represents a significant challenge even when relaxed acceptance criteria are applied.

Any review of historic biomarker literature reveals several examples where the failure to show parallelism between calibration standards and biological samples calls into question the accuracy of results. It is our goal that the meth-ods presented herein will aid in the ongoing dialogue aimed at achieving more consistent practices on how biomarker concentrations are determined by LC–MS/MS.

Finally, it is noted that the procedures pre-sented for surrogate matrix and surrogate analyte methods can also be applied to large molecules, most notably protein quantitation involving a surrogate peptide. Although this

specific application was not covered, we antici-pate an increase in surrogate analyte methods owing to the facile production of multiple SIL peptides.

Future perspective With increased attention being given to the use of biomarkers in pharmaceutical development, we believe that regulatory agencies will place higher expectations on adherence to best prac-tices. Unfortunately, due to the varied nature of this work, and the recognized need to apply fit-for-purpose practices, achieving consensus on how to demonstrate accuracy in bio marker measurements will not be straightforward. Nevertheless, meaningful progress will be made in the next 5-year period. During this same period, surrogate analyte methods will continue to find increased use for both small and large molecules. Increased utilization of surrogate analyte and surrogate matrix methods will also call attention to the need to define best practices for both approaches.

AcknowledgementsThe authors would like to thank S Lowes, JE Buckholz, K McCardle, KM Bearup, L Shan, J Zhang, D Strong, S Swift-Spencer and M Burdett (Advion Bioanalytical Labs, a Quintiles Company), and J Leohr, B Lutzke, J Cox and P Milligan (Eli Lilly and Company).



Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manu-script. This includes employment, consultancies, honoraria, stock ownership or options, expert t estimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research The authors state that they have obtained appropri-ate insti tutional review board approval or have fol-lowed the princi ples outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investi gations involv-ing human subjects, informed consent has been obtained from the participants involved.

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