Method Validation Application Protein Biomarkers

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1461ISSN 1757-6180Bioanalysis(2009) 1(8), 1461147410.4155/BIO.09.130 2009 Future Science Ltd

Review

Recent drug development is based on the mech-anism of action of the drug on specific biologicaltargets and pathways. Biomarkers reflective ofthese pathways have been linked to physiologi-

cal data to aid drug-development decisions [1].There are high expectations in the pharmaceu-tical industry that biomarker applications willdrive faster and more successful drug develop-ment [24], as exemplified in the multitudes ofbiomarker conferences and publications devotedto this relatively new application of biomarkers.Biomarkers expressed in disease-specific path-ways can provide evidence that a drug hits itstarget to exert functional changes. Studies ofthe target and proximal biomarkers can providepharmacodynamic (PD) information for expo-

sure/effect modeling. Data from downstreamdistal biomarkers can provide proof-of-biologyof the drugs effect on disease progression [5].

Protein therapeutics via target-mediatedmechanisms have been successfully devel-oped. The bioanalysis of protein therapeuticsis based on the evolving practices from smallto large molecules: the US FDA issued guid-ance for bioanalytical method validation tosupport pharmacokinetic (PK) studies with thefocus on conventional small-molecule drugs,mainly by LCMS methods [6]. Additional

White papers on ligand-binding assays(LBAs)

that are widely used to study biotherapeuticshave been published [79]. At the 3rd AmericanAssoc iation of Pharmaceutical Scientis ts(AAPS)/FDA Bioanalytical Workshop, the

validation and implementation of bioanalyticalmethods for both small- and macro-moleculeswere discussed and consensus reports were sub-sequently published in a themed issue of theAAPS journal [911].

The terminology of GLP compliance has beengenerally used in the pharmaceutical industryto indicate bioanalysis in support of PK/toxico-kinetic (TK) studies that are conducted accord-ing to guidance from the FDA and/or otherregulatory agencies [6]. It is not uncommon tosee analysts working in the PK/TK arena adopt-

ing the same guidance for biomarker methodvalidation. At the same time, since biomarkerkits approved by the FDA or other regulatoryagencies have been routinely used for diseasediagnosis, clinical chemists also participate inbiomarker analysis for drug development andperform the assays under regulations fromagencies such as the Clinical Lab ImprovementAmendments in the USA. The end-users of thedata also come from two camps: PK/PD scien-tists who are familiar with PK-type data andphysicians/clinicians who are comfortable with

the routine clinical chemistry output.

Method validation and application of protein

biomarkers: basic similarities anddifferences from biotherapeutics

Protein drug development and biomarkers share common bioanalytical technologies that are applied for different

purposes. A t-for-purpose approach should be used for biomarker assays at various stages of novel biomarker

development and their application to drug development. Biomarker quantications can be absolute or relative,

depending upon the characteristics of the standard curve, which include the reference standard, substituted matrix

and parallelism. Appropriate method-validation experiments should be carried out on sample collection, relative

accuracy and precision, range nding, parallelism, selectivity, specicity and stability in order to meet the need for

exploratory or advanced application that is specied for a study. The interaction of a biotherapeutic with the targetligand or inter-related biomarkers should be taken into consideration for method platform choice and validation.

Direct adoption of commercial diagnostic kits can produce confounding data. Therefore, kit comparison, modication

and appropriate validation experiments are often carried out to meet the specic purpose for drug development.

Multiplex assays and physicochemical methods can complement the single-analyte ligand-binding assay for protein

drugs and biomarkers.

Jean W Lee

Pharmacokinetics and Drug

Metabolism, Amgen Inc.,

One Amgen Center Drive30E-3-B, Thousand Oaks,

CA 91320, USA

Tel.: +1 805 447 9463

Fax: +1 805 499 9027

E-mail: [email protected]

BiotheRapeutics

Therapeutics derived frombiological products or processes

Ligand-Bindingassay

Analytical methods thatdetermine the analyte using thesignal resulting from the binding

reaction of the reagent andthe analyte


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DemonstrationDiscovery Characterization Qualification Surrogacy

Studies of cells, animal model

or human with tight patient control

Confirmatory with small

human population atmultiple sites

Multiple sites

Large sample size Extended populations Multiple drugs of similar mechanism

Exploratory method validation Advanced method validation (GLP similar)

Biomarker development

Drug development

NonclinicalLead optimization Pivotal clinical Post-approvalEarly clinical

Nonregulated Regulated (GLP) PK bioanalysis

Post-approval

surveillance Safety and

efficacy biomarkers

Patient stratification

Other therapeutic indications

Market differentiation

Safety biomarkers

Efficacy biomarkers Proof of Biology

Protocol design

PK/PD modeling

Dose selection

Biomarker panel

selection

Target and

candidate selection

Candidate attrition and refinement

A

B

Review|Lee

Bioanalysis(2009) 1(8)1462 future science group

The inconsistency in adaptations of regula-tions in either bioanalytical or clinical laborato-ries and a lack of regulatory guidance contribute

to the confusion regarding biomarker data qual-ity required for drug development. A positionpaper proposed that biomarker assay val idationand implementation should be fit-for-purposeto produce reliable data appropriate for theapplication [12]. Biomarker applications are verydifferent from those of diagnosis, which pro-hibit the direct adoption of clinical laboratory

practices. The intended use of biomarker datashould be considered in order to determine therigor of method validation and implementation

for the specified purpose. Biomarker analysis tosupport PDs should be similar to that for PKstudies with differences based upon the uniqueendogenous nature of the heterogeneous bio-marker [1215]. This review focuses on the simi-larities and differences of protein biomarkerassays compared with those from PK bioanalysisfor biotherapeutic development.

Table 1. Comparison of pharmacokinetic and biomarker bioanalysis.

Intendedapplication

Method types Pre-analyticsample

collection

Referencestandard

Analytes Calibratormatrix

Validationsample and

QC preparation

Accuracy

PK study

PK parameters

of BA and BE

Mostly definitive

quantification

methods

Test with spiked

standard

Well

characterized

and pure

Exogenous

and well

defined

Analyte-free

biological

matrix

Spiked reference

standard into

biological matrix

Absolute

accuracy

Biomarker study

PD: safety and

efficacy

Definitive,

relative,

quasiquantitative

or qualitative

methods

Consider

pathway

conversion and

artifact from

cell activation;

diurnal effect

Many are not

well

characterized or

pure, may not

be the same

as endogenous

Endogenous,

not well

defined

Substituted

matrix

Spiked reference

standard for VS

and QC, pooled

authentic samples

for sample

controls

Mostly

relative

accuracy

BA: Bioavailability; BE: Bioequivalance; LLOQ: Lower limit of quantification; PD: Pharmacodynamic; PK: Pharmacokinetic; QC: Quality control; VS: Validation samples.

Figure 1. (A) Biomarker- and (B) drug-development processes.PD: Pharmacodynamics; PK: Pharmacokinetics.

pRoteinBiomaRkeR

Protein that is objectivelymeasured and evaluated as anindicator of normal biologicprocesses, pathogenic processesor pharmacologic response to atherapeutic intervention


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Method validation & application of protein biomarkers | Review

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Intended purposes of biomarker

bioanalysis are different

from biotherapeutics

Development of novel biomarkers follows phasesof discovery, characterization and clinical qual-

ification/validation (FiguRe 1a) analogous tothose of drug development (FiguRe1B) [5,16,17].Initially, biomarkers are discovered for explor-atory studies in cell systems, animal models orwell-controlled human studies. The data providecharacterization of the biomarker in the path-way for internal decision making. Application toadvanced studies will test the linkage to clinicaloutcome using small sets of patient populationsat multiple sites. Some biomarker results mayshow negative or uninterpretable linkage. Otherswith promising results may advance to clinical

qualification (validation) studies where extensivedata are collected from multiple sites (with largepatient numbers and extended populations) onmultiple drugs and for multiple indicationsinvolving the same pathway. Surrogacy can onlyoccur after the accumulation of a huge amountof data before the biomarker can replace theclinical outcome. The level of rigor of methodvalidation and documentation increases fromexploratory to advanced use.

The drug-development phases are depicted inFiguRe1B. The drug exposure data determinedin animal TK and PK and human PK help to

define the therapeutic window and decide theproper dose and dosing frequency. The use ofbiomarkers at various phases of drug develop-ment is depicted in the boxes below the bar.These include go/no-go decision making oncandidates, PK/PD modeling to decide dose andfrequencies, patient stratification and safety, andefficacy monitoring [3,4,1319].

The objective of method validation is todemonstrate that a particular method is reli-able for the intended application [6]. Thus, afit-for-purpose approach for method validationand sample assays is suitable for both drug and

biomarker bioanalysis. During discovery andlead optimization of drug development, fastdecision making on multiple drug candidates issupported by methods with or without minimalprestudy characterization by non-GLP methods.To support TK and PK studies, GLP methodswith rigorous method validation are usuallyrequired (FiguRe1B).

The purposes of biomarkers are more diversethan those of drug development; however,method-validation approaches can be roughlycategorized into those of exploratory or advanced

application(FiguRe1a)

.FiguRe2

depicts the basicconcept of fit-for-purpose biomarker method val-idation and how the exploratory and advancedvalidations are used during biomarker develop-ment. The rigor of method development, valida-tion and documentation for advanced applica-tion is more intense and GLP similar, except fora few distinguishing features. The basic similari-ties and major differences are listed in taBLe1and further discussed later in this article.

Pre-analytic considerations that

impact biomarker analysis

Pre-analytic considerations for biomarkers arerepresented in FiguRe3. It is necessary to selectthe potential biomarkers and the correspond-ing biological matrix, define the intended pur-pose and decide the type of method validationthat suits the application. A work plan maybe used to clarify the purpose and lay out theexperiments to be conducted [20].

Table 1. Comparison of pharmacokinetic and biomarker bioanalysis (cont.).

Selectivity Specificity Assay acceptancecriteria

Stability Reproducibility

PK study

Spike recovery test on

~six matrix lots at LLOQ

and one other level

Test against target ligand, in addition to

similar structures; measurement of free

drug preferred over total

4-6-X rule Use QC samples Incurred sample

repeats

Biomarker study

Spike sufficient amount

over basal level, test

more lots from healthy

and disease populations

Test against drug molecule(s), precursor

and downstream molecules;

measurement of total target biomarker

may be the pragmatic option over the

free form

Depends on drug effect,

disease and biological

modulation and method

performance

Use sample

controls and trend

analysis for long

term storage

Sample controls

BA: Bioavailability; BE: Bioequivalance; LLOQ: Lower limit of quantification; PD: Pharmacodynamic; PK: Pharmacokinetic; QC: Quality control; VS: Validation samples.


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Discovery

Demonstration

Characterization

Qualification

Surrogacy

Novel biomarker

development

Advanced method validationExploratory method validation

In-study method validation

Prevalidation

Pre-analytical and analytical method

feasibility method optimization

Review|Lee


Stability of the analytes in stock solution andbiological matrix during the processes of samplecollection, storage, shipping, freezing/thawingand throughout the last assay should be evaluatedfor drug compounds and biomarkers [21]. Thesample collection stability for monoclonal anti-body drugs in serum has been well established;however, the stability of peptides often dependsupon blood collection, anticoagulants and timeof exposure to high temperatures. Therefore,plasma or serum sample collection for novel

peptide biotherapeutics and biomarkers shouldbe investigated for possible stability issues.

Pre-analytic variables have hindered datautility in proteomic biomarker discovery andvalidation [22,23]. Errors from variable specimencollection can be higher than those arising fromsample analysis itself. The conversion of precur-sors to the biomarker of interest will lead tooverestimation, while degradation will resultin underestimation of the analyte. Inhibitorsof relevant activation or proteolysis should beincluded in the collection syringe or added to

the sample promptly. Some biomarkers can onlybe quantified in plasma so as to avoid prote-olysis or platelet activation of the coagulationpathway during serum collection. Bulk serumcollected into a bag can result in lower recoverythan in serum from venipuncture used in a clin-ical study for some biomarkers [24]. The shear-ing effect through a small bore needle or theuse of high-speed centrifugation on blood cellsmay cause endothelial cell act ivation, resultingin analytical artifacts. For biological fluids ofrelatively low protein content (e.g., urine and

cerebral spinal fluid), collection tubes, transfer

pipettes and storage containers must be evalu-ated to minimize adsorption of a peptide/protein to the contact surfaces.

It is important to standardize techniques forall sample collection and handling and to keepthese consistent throughout the duration of theuse of the assay [25,26]. For example, the G-forceand revolution per minute conversion shouldbe defined for each laboratorys centrifuge inorder to avoid mistakes. The standard processesof collections from multiple sites, barcodes and

transports to the analytical laboratory shouldbe followed.

Inappropriate collection time and otheradverse conditions often lead to confoundingor uninterpretable data. If there is a diurnaleffect, it is prudent to pool samples or to collectthem at the same time of the day. The initialsurvey of healthy and patient samples providea rough idea of biological variability. The clini-cal question is the comparison of the treatmentversus placebo. Appropriate clinical (placeboand/or predose samples) and assay control

(sample control or QC) data can be assessedfor analyt ical and biological variability, to pro-duce unbiased clinical answers. However, forcancer studies, placebo or baseline samples maynot be available to provide data to parse outthe true drug effect versus the biological andassay variability.

Reference standard: the

basic yardstick

The basic requirements of reference standardshold true for both PK and biomarker assays. The

standard is required to be:

Figure 2. Concept of fit-for-purpose method validation of biomarkers at variousdevelopment phases.


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Method development and validation:

Reference standard Relative accuracy and precision

Sensitivity, selectivity and specificity Stability

Quality and sample controls

Pre-analytical

sample integrity

Define purpose of study and

biomarker measurements: Which biomarker(s) to be

included in the study? Exploratory or advanced

application?

Choose the rightbiological matrix and

collection time forbiomarker assay



n Purified and well characterized

n Representative of the analyte in the unknownsamples

n Available in a large quantity to support thedevelopment program

n Stable under the defined conditions

n Accessible to the participating laboratories

Small-molecule biomarkers are well definedand pure reference standards can be procuredin large quantities to meet these requirements.Absolute, definitive quantification methods canbe developed and validated, similar to those of PKassays [12]. Examples are the regulatory peptides(e.g., insulin), steroid hormones and metabolites.However, since most biomarkers are large pep-tides or proteins with molecular weights greaterthan 5000 Da and generally heterogeneous in

nature, reference standard characterization andprocurement can be challenging.

Biotherapeutics may also be heterogeneous.The reference standards are purified and char-acterized extensively by physicochemical andbiological methods. For example, intact molec-ular weights are determined by SDSPAGE orMALDITOFMS. The primary structure ofthe protein is assessed by LCMS peptide map-ping and Edman degradation. Higher orderstructures are defined by Fourier transfer infra-red spectroscopy, near UV circular dichroism,

fluorescence spectroscopy and surface plasmonresonance. Surface hydrophobicity is defined byaniline naphthalene sulfonate binding. Thermalstability and stressed data are obtained fromdifferential scanning calorimetry and dynamiclight scanning. Potency is defined by the specificcellular bioactivity of the drug. Specificationsare defined to assure lot-to-lot reproducibility.Storage and shipping conditions and boundariesare also specified to assure stability. Aggregationis detected by differential scanning calorimetry,size exclusion LC, SDS, capillary and isoelec-

trofocusing electrophoresis and analytical

ultracentrifugation. Storage degradation isdetected by peptide mapping and SDSPAGE.Documents of the characterization and stabil-ity of a standard, such as a certificate or recordof analysis and stability, are available to thebioanalytical laboratory.

Protein biomarker reference standards rarelymeet the requirements of drug compounds; oftenthe standard is impure, poorly characterized, notfully representative of the endogenous analyte oravailable only in limited quantities. Generally, nodocument of certification is provided from eitherinternal or commercial suppliers. It is doubtfulthat the same kind of extensive characteriza-tion for biotherapeutic reference standards willever be used for a protein biomarker unless ithas achieved qualification or surrogacy [27,28].In addition, the reference material may differsubstantially between lots and manufacturers,

which is a major problem contributing to datainconsistency [29,30]. This issue must be addressedwith a collaborative effort from pharmaceuticaland diagnostic manufacturers in the future.

Many biomarker standards are obtained fromrecombinant expression in noneukaryotic cells;they may differ from the endogenous forms inimmunoreactivity and bioactivity. The recom-binant reference standard serves as a relativeyardstick of measurement, assuming that theimmunoreactivity of the endogenous form isproportional to that of the recombinant form

(parallelism) [13]. Thus, such methods providerelative quantification. If there is no refer-ence standard or proportionality between theendogenous form or the reference standard doesnot exist, the methods are quasiquantitativein nature.

Standard calibrator matrix &

selectivity: matrix effect matters

n Standard calibrator matrixThe preparation of standard calibrators ina substituted matrix is a major difference

between therapeutic and biomarker analysis.

Figure 3. Process of biomarker selection, pre-analytic decisions and method validation to supportdrug development.


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Most biomarkers are endogenous compoundswith measurable levels in the biological matrix.Standard calibrators are preferably prepared inthe intended analyte-free sample matrix [6]; how-ever, it is difficult to find analyte-free biological

matrix for biomarkers. The alternative optionis to use a substituted matrix, such as a proteinbuffer, a corresponding biological matrix fromanother species without the biomarker or todeplete the biomarker in the biological matrixby stripping with affinity adsorption or char-coal. The use of a substituted matrix would avoidthe need for continual screening and testingof numerous lots of samples to identify blankcontrols for standard preparation.

When the calibrators are prepared in a substi-tuted matrix by spiking a reference standard that

may not be in the same form as the endogenousbiomarker, two types of experiments should beperformed to demonstrate method validity:

n Comparison of spike recovery from the samplematrix and the substituted matrix to show thatthe concentrationresponse relationshipsare similar;

n Performance of parallelism tests on authenticsamples to show that the endogenous biomar-ker behaves in a similar immunochemicalmanner to the standards.

If the results fail to show similarities, the

method is considered to be quasiquantitative [20].

n Selectivity & matrix effectSelectivity is the ability of the method to deter-mine the analyte unequivocally in the presenceof components that may be expected to be pres-ent in the sample. For small peptides, extrac-tion procedures similar to those of small drugmolecules can be used to isolate, concentrate andanalyze the peptides by LCMS/MS. A stablelabeled isotope internal standard is added to thesamples to correct for recovery and ionization

variability. For protein molecules, the extrac-tion step would denature the protein and aninternal standard for LBAs would not be avail-able. Usually, a simple buffer dilution would bethe pretreatment step, the lack of an extractionprocess and internal standard dictates that LBAspecificity and selectivity are solely dependentupon the ligand-binding reagents [31]. Therefore,the selection of reagents is of utmost importancefor both biotherapeutic and biomarker LBAs.

With no process to remove matrix com-ponents, the LBA would be prone to matrix

interferences (the matrix effect). Unrelated

compounds in the matrix, such as heterophilicantibodies, rheumatoid factor and proteases,may inhibit or enhance the binding of proteinanalytes to the reagents. Often, the immuno-reactive signal would be suppressed, resulting in

decreased sensitivity and a negative bias.When carrying out method development for

biotherapeutics, standard matrix curves frommultiple individual lots are assessed for theirperformance closeness to a buffer standardcurve. Reagents and incubation conditions canbe manipulated so that the readouts from thematrix lots converge to those of the buffer curve.Dilution with a high salt buffer and/or chaotropicor chelating agent may reduce the matrix effect.The amount of dilution required to sufficientlyremove the matrix effect is referred to as the min-

imal required dilution (MRD)[7]

. Since bindingprotein types and levels are affected by the healthstatus and collection conditions, selectivity testsare conducted by spike recovery at the LLOQand at a higher level from at least six matrix lots.Thus, accurate spike recovery at the LLOQ con-firms assay sensitivity beyond the single matrixpool used for standard/QC preparations duringaccuracy and precision experiments.

For biomarkers, the matrix effect would alsobe tested by spike recovery. However, the basallevels of the individual lots are determined firstagainst the standard curve in the substituted

matrix. Then, the reference material is spikedinto each matrix lot, at a level comparable tothat of the basal concentration. The spike con-centration cannot be substantially lower thanthe baseline and the spiked volume should notexceed 5% of the individual matrix volume [20].Spike recovery is calculated after subtraction ofthe basal value and compared with the nominalspike concentration or the mean of the test lots.If most of the endogenous levels are relativelyhigh, the LLOQ of the buffer standard wouldnot be established for the biological samples. As

a result of biological variability, more than thesix lots from various populations required forbiotherapeutics should be tested for biomarkers(e.g., more than ten from each population) [29,30].

The relative concentrations of the analyte/ inter-ferent will vary with dose, subject and time point.Combination therapies may change the amountof target and binding proteins or bioavailabilityof the drug if there is a drugdrug interaction.One option would be to pool incurred samplesfrom previous studies of concomitant drugs fromaround the T

maxand trough levels and use these as

test samples for specificity and selectivity tests [31].


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n ParallelismParallelism is a dilutional linearity test of anauthentic sample. The objective is to show thatthe endogenous analyte in the unknown sample,which may be different from the standard and/

or vary with subjects, behaves similarly, regard-less of dilution by the standard matrix (or asubstituted matrix in the case of a biomarker).The experiments are performed for both bio-therapeutics and biomarkers. However, theexperimental design and results interpretationare slightly different.

For biotherapeutics, incurred samples fromseveral subjects are diluted with the standardblank matrix and analyzed. Therefore, theexperiment can only be conducted after thein-study commences. Several dilutions are

performed to dilute high concentration studysamples into the standard-curve range for quan-tification. Each result (the regressed value of thediluted sample multiplied by the dilution fac-tor) is compared with the mean of the quantifi-able results and should be within the acceptancecriteria. Although metabolites or drugdruginteractions may cause nonparallelism, failedresults may be caused by errors from multipledilutions of the high concentration samplesrather than real interferences.

For biomarkers, parallelism is an importantcomponent and should be performed during

prestudy validation if possible [30]. Severalindividual samples, with concentrations atthe high end of the standard curve from theinitial screening, are chosen. They are analyzedundiluted and with a dilution factor of threeto four. The ratio of the calculated results(observed concentration dilution factor)divided by the mean of the results are plot-ted against the inverse of the dilution factor.Parallelism is demonstrated if the ratio is notaffected by dilution.

When para llelism cannot be per formed

because samples of sufficiently high concen-tration are not available, dilutional linearitycan be performed in a manner similar to para l-lelism, using high-concentration spike samplesin place of the authentic samples. The failureto demonstrate parallelism may mean that themethod is only quasiquantitative [12,29]. In thiscase, longitudinal comparison within a sub-ject would become important, using the pre-dose baseline as a reference point. The clinica lstudy design may need to col lect more predosedsamples for within subject comparison to the

predose baseline.

Specicity: the uncertainty of what is

being measured

n Analyte versus structurallysimilar moleculesSpecificity is the ability of the assay to distin-

guish between the analyte and other structur-ally related components. Crossreaction withassay-binding reagents from structurally similarmolecules, such as metabolites, would lead tooverestimation. In contrast to small molecules,the catabolic species of macromolecule drugs arenot always known and are not purified for theinvestigations into their biological activity orassay interference. It is often assumed that theintact structure of a protein is required for thepharmacological action; however, this may notbe true for novel biotherapeutics.

For definitive quantitative methods for smallpeptides, the metabolites can be identified,purified and tested for pharmacological bio-activity. Purified metabolites can be tested forcrossreactivity against the standard curve. Theconcentrationsignal relationship of a LBA isnonlinear and often the magnitude of metaboliteinterference is not monodispersed over the entireassay range. The estimate of interference is notas straightforward as that of a chromatographicmethod, which uses a single percentage interfer-ence factor to cover the entire range. Usually,percentage crossreactivity is expressed as the

ratio of midpoint concentration of the bind-ing curve of the standard versus that of a givenmetabolite (ED

50). In addition, QC samples

should be spiked with the known metabolitesto confirm specificity [32].

For most protein therapeutics, it is difficult toascertain the metabolic species, due to hetero-geneity; this is even more difficult for biomark-ers. Multitudes of isoforms and truncated piecesthat can cause specificity problems may exist inthe matrix. Ligand binding coupled with MShas been used as a novel approach for identify-

ing the truncated forms and guiding methoddevelopment for LBAs [33].

For biomarkers, structurally related moleculesinclude the dosed drug, the precursor moleculesand homologs of the same family [29,30]. In con-trast to drug assays, the goal of specificity tests forbiomarkers is not to demonstrate absolute speci-ficity. Instead, the intended purpose is to provideinformation regarding what is being measuredfor proper data treatment and interpretation.

There is no specific guideline or consensusin the pharmaceutical sector on how speci-

ficity and selectivity experiments should be


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conducted in method validation. The NationalCommittee for Clinical Laboratory StandardsWorking Group defines interference a s beingfrom a known source and the matrix effectas being from an unidentified source [34].

Discussions on selectivity, specificity and freeversus bound tests for LBAs have been occur-ring at the AAPS meetings organized by theLigand Binding Assay Bioanalytical FocusGroup [12,31,35] . LCMS methods can be usedas an orthogonal technique to confirm speci-ficity for an LBA method, as well as to detectdifferences in isoforms of a biomarker in diseasepopulations [3640].

n Free or bound target biomarkerto biotherapeutics

Many protein drugs bind soluble ligand tar-gets. Depending on the binding kinetics, freeand bound forms of the biotherapeutics and thetarget ligand coexist in the biological samplesat very different levels. It is necessary to knowwhat the method should be measuring in orderto confirm assay specificity [35].

Proper PK/PD models are based upon avail-able data for free and/or bound (e.g., IgE andomalizumab), bound (e.g., VEGF and VEGF-TRAP) or total (e.g., IL-6 and canakinumab)forms [4144]. Knowledge of the free drug levelsis preferred since it reflects the species that is

biologically active in vivo. The ligand may existin a soluble form in the plasma at high (e.g.,IgE) or low (e.g., IL-6) abundance. If the drugis a monoclonal antibody against an abundantsoluble target, the ligand may cause interferencein the drug assay if the LBA reagent binds tothe same or overlapping epitope. Vice versa, thepresence of a high-concentration drug wouldinterfere with the target biomarker assay.

Depending on the mechanism of action,the driver of the PD effect can be the freebiomarker or the drugreceptor complex, for

which data would be desirable. In addition,the total concentration of the target may pro-vide information on possible compensatoryrise due to induction or membrane shedding.However, issues of protein binding for bio-markers and the relevant PD data requiredhave rarely been discussed, due to the lack ofadequate analytical tools to provide data for thethorough understanding of the physiology andbinding kinetics.

In the case of small-molecule biomarkers, anextraction method using organic solvents or a

solid phase can dissociate protein binding prior

to LCMS/MS analysis. The method wouldprovide total (free plus bound) quantificationof the small biomarker.

For a LBA of biotherapeutics and protein bio-markers, multiple configurations present options

to measure different forms of the drug and tar-get. The data are valuable for the understand-ing of binding kinetics. For example, free andtotal drug can be measured by the appropriatechoice of binding reagent, coating density of thecapture reagent, incubation time, buffers andsample dilution. In addition, alkaline or acidicpretreatment can be used to dissociate drugligand binding and then be neutralized beforeLBA analysis for an assay that measures totalligand [45,46]. Since it may be difficult to mea-sure the free biomarker, a consistent method for

measuring the total or bound form may be apragmatic option. These techniques and applica-tions to PK/PD at different drug-developmentstages are being discussed by a work team inthe AAPS Ligand Binding Bioanalytical FocusGroup in preparation of a manuscript.

Accuracy, precision & assay range:

quantication characteristics

To ensure data quality, assay performance isevaluated during method validation with valida-tion samples (VSs) and monitored during sampleanalysis with QC samples prepared by spiking

known amounts of reference standard into thebiological matrix. VSs are used in method vali-dation to define intra- and inter-run accuracy,precision and sample stability. The prestudyvalidation accuracy and precision data of the VSdemonstrate the suitability of the standard curveassay range and performance characteristics forits intended application. QC samples are used forrun acceptance during sample analysis.

Accuracy and precision experiments andacceptance criteria for macromolecule drugswere discussed at the AAPS FDA-sponsored

workshop [9]. Briefly, results from multiple runsof VS over the entire span of a standard curvewould establi sh accuracy and precision, theLLOQ and the ULOQ. Total error is the sumof the systemic error (bias from nominal valueor percentage relative error [%RE]) and randomerror (imprecision or percentage coefficient ofvariation [%CV]). In-study run acceptance cri-teria are set based on the total-error information.At least two thirds of all QC results for a runshould be within a specific percentage (e.g., 20%for most LBAs) of the nominal values, with at

least 50% accepted for each QC level. A 4-6-X


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rule was proposed for LBA: four out of six QCs(three QC levels, each in duplicate) should bewithin x%, as determined by method validationtotal error.

For most biomarkers, since the VSs are pre-

pared by spiking the reference standard into apool of authentic matrix with unknown concen-tration, the nominal values are not known. Aninitial target mean can be determined from a fewruns and used to monitor the assay trend with-out a rigid acceptance criteria. The true value ofthe QC may be determined after multiple runs,using an approach similar to the Westgard Rule.For relative or quasiquantitative methods, thereis no accuracy assessment and, therefore, therandom error component (%CV) of the assayis more important.

n Sensitivity: low limit of assay rangeSensitivity for drug analysis is determined bythe LLOQ, which is the lowest concentrationdemonstrated to be measurable with accept-with accept-with accept-able accuracy and precision, during methodvalidation. Extrapolation beyond the LLOQ isprohibited [6].

Sensitivity is often defined by the limit ofdetection (LOD) for diagnostics kits, which isthe lowest amount of analyte in a sample thatcan be detected with 95% confidence intervalsor other stated probability [47]. For exploratory

biomarkers, variability data at the region belowthe LLOQ but measurable above the LODmay be needed. Samples from subjects fromthe intended populations are surveyed by themethod for range finding (see later). If too manysample concentrations fall below the LLOQ, themethod is not considered sensitive enough forthe intended application. In addition, a sensitivemethod is required if the drug effect is expectedto suppress the level of biomarker. In somecases, it is tolerable to have some subject samplesbelow the LLOQ and yet above the LOD. If

these data were to be used, one should be awareof the higher variability in the LODLLOQrange and interpret the data with caution. Forexample, serum C-terminal telopeptides of type1 collagen (CTx) is a bone resorption biomarker.Clinical effects of antiresorptive therapeutics,such as bisphosphonates and denosumab, onCTx is expressed as the percentage change ofpostdose concentrations over that of predose. Acommercial kit was used to monitor CTx changefor denosumab drug development. The kitLOD was 0.02 ng/ml without defined accuracy

and precision; the LLOQ of 0.049 ng/ml was

defined with accuracy of -6.6%RE, precisionof 20.1 %CV and total error of 26.7%. As themethod was for advanced application in drugdevelopment, we chose to only report data thatwere above the LLOQ and not those above the

LOD [48].

n Assay range & sample dilutionThe FDA guidance stated that concentrationsof standards should be chosen on the basis ofthe concentration range expected in a particu-lar study [6]. However, the working range of aLBA is governed mainly by the binding reac-tion with assay reagents. Most methods are verysensitive at picogram or nangram per milliliterlevel, while concentrations in a PK study formany biotherapeutics would be in the micro-micro-

gram per milliliter range. Study samples arediluted into the working range before the assay.Sample dilution can contribute significantly toassay variability within and between laboratories[P K . S -

. M

P]. Three levels of QCs (low, midand high) within the standard curve range areused to monitor accuracy and precision perfor-mance. There has been no common practice ofhow sample dilution should be monitored foreach assay; however, it is prudent to have dilu-

tion QCs in an assay run and a strategy has beenproposed to include dilution QCs in the 4-6-Xapproach [P K . S -

.

M P]. For novel biomark-For novel biomark-For novel biomark-ers, the concentration range, modulation andbiological activity of biomarker variants are notknown. They may vary with health status, time(age and season) and between individuals (gen-der, genetics and ethnicity). Biological variabil-ity should be surveyed in samples from normal

and diseased donors, especially in samples fromanticipated patient populations (e.g., 1020each) to determine if the assay range would beappropriate. The data should be compared withthe literature and the commercial kit brochure.It is not uncommon to see discordant literaturedata due to the differences in methods (e.g.,sample collection, reference standard material,reagents and assay conditions). Most of the time,patient levels are unavailable or unreliable in theresearch-grade commercial kit brochure; there-fore, the bioanalytical laboratory is responsible

for carrying out the range-finding experiments.


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In addition, the expected drug effect on thebiomarker concentration should be consideredfor the assay range. This is often a challengeduring the exploratory phase, as the extent ofdrug modulation is not known. The starting

assay range should aim to cover different levelsof healthy and disease populations and also theanticipated changes from a desirable drug effect.

The ancillary purpose of the range-findingexperiment is to find authentic samples of lowand high concentrations to be pooled for use assample controls (SCs). The SC concentrationswould be determined during method val ida-tion and monitored during in-study runs. SCsare useful for stability trending and detectingperformance bias due to reagent lot changes [48].

n

Data regression: curve tting &data assessmentStandard LBA curves are usually nonlinear withnonconstant error (heteroscedastic). Most LBAdata can be appropriately fitted to four- and five-parameter logistic models with weighting fac-tors. Sufficient nonzero standards (six to eight)are required to define the regression functionparameters, with additional anchor points out-side the range to help define the asymptotes [9,49].It is recommended to use the residuals of back-fitted standard values, instead of the correlationcoefficient R2, to evaluate goodness-of-fit. The

precision profile of the VS data during accuracyand precision experiments confirms the appro-priateness of the regression model. The accu-racy and precision data for definitive and rela-tive quantification methods are used to assessthe systematic and random error components fortotal error. These error components are furthermonitored with QCs during in-study.

n Acceptance criteriaThe 4-6-X rules are commonly used in PK appli-cations as acceptance criteria for each in-study

run [7,10,11]. The value of X is usually 15% forLCMS methods. For LBAs, many bioanalyti-cal laboratories use a fixed value of 20%, whileothers use a statistical approach to determine Xbased on the accuracy and precision performancedata from method validation [79].

No guidance or consensus has been given foracceptance of biomarker assays. One major pur-pose of biomarker application is to distinguishdrug effect (dosed vs placebo and/or baseline)and disease progression (healthy vs disease). Thegap between healthy and disease, and the desir-

able drug effect, should be considered for method

suitability and in determining acceptance cri-teria. For example, the change in IL-6 is muchgreater for sepsis than for asthma. A more sensi-tive method and stringent acceptance criteria willbe required in drug development for the latter

indication. During the exploratory phase, accep-tance criteria may be set according to the initialmethod performance. After pilot studies, biologi-cal modulation and assay variability data can beused to refine the initial acceptance criteria.

Stability

Stability of peptides and proteins in the stocksolution and the intended biological matrixshould be demonstrated [6]. The analyte mayundergo biological (e.g., proteolysis) and chem-ical (e.g., oxidation leading to aggregation)

changes. Adsorption to the container-vessel wallsor tubing will result in low recovery. Essentially,stability should be evaluated during sample col-lection and handling, after long-term (frozen atthe intended storage temperature) and short-term (bench-top, room temperature) storage andafter going through freezethaw cycles and theanalytical process.

Stability tests of a biotherapeutic analytein biological matrix are conducted on the VS,spiked with reference standards at low and highconcentrations. For biomarkers, since the SCreflects the authentic samples, it is preferable to

use SC over VS for stability tests. In addition,the same SC set can be monitored during in-study runs to produce long-term stability datafor trend analysis [48].

Reproducibility demonstrated by sample

control data

The screening and selectivity tests for biomarkersare more rigorous than those for biotherapeutics,with more lots of matrix from normal and targetdisease populations. In addition, pooled SCs areused to monitor assay reproducibility, reflecting

the authentic samples. For example, SC pools athigh and low levels are aliquoted and their levelsdetermined during method validation experi-ments and pilot studies from approximately 30runs. An acceptance criterion of mean 2 stan-dard deviation can be used. The SCs are thenused as QCs in all in-study runs, as well as partof the conformance samples for interlaboratoryperformance. The SC data can be a commonthread to compare precision and relative accuracyamong multiple studies by different analyticallaboratories. In addition, SC data can be used to

detect reagent lot variability [48].


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Application of commercial kits

Commercial kits for diagnostic use have beencommonly adopted for drug development sincethey are readily available. The varieties of kitsrange from the well-established FDA-approved

(or FDA-cleared) kits to less-proven for researchuse only or for investigational use only kits. Asthe purposes of drug development are differentfrom that of diagnosis, it is not recommendedto directly adopt a kit method for drug develop-ment without method validation [30]. The valida-tion experiments should evaluate the referencematerial and standard matrix, determine perfor-mance characteristics, patient range and drugmodulation and set up SCs.

The standard calibrators can be a major contrib-utor to confounding data in research kit applica-

tion. If there are multiple commercial kit sources,it is prudent to assay the same set of authenticsamples using various kits for comparison. It is notsurprising to find that the results are totally differ-ent from one kit to another because the calibrators(yardsticks) are of different forms. In addition, thecalibrators from one supplier can be different withtime, due to changes in purification processes andrecalibration. If a bulk standard material in suf-ficient quantity can be acquired from one supplier,standard calibrators should be prepared in-house,in the appropriate matrix, to assure calibrator con-sistency throughout an advanced application, such

as in the example of serum CTx [48]. The bulkstandard material also allows the preparation ofsufficient levels of calibrators with anchor pointsfor appropriate curve fitting with weighting, aswell as spiked QCs for accuracy and precisionexperiments to define the assay range.

The assay range should be evaluated againstthe population range and the desirable drugeffect. When the biomarker levels are extremelylow, as is the case with the free soluble recep-tor activator of NFkB ligand, many literatureresults using a research kit reported concentra-

tions below the LLOQ. Therefore, most of thepopulation baseline values would actually beassay noise [50].

For research-grade commercial kits, QCs orauthentic sample controls may not be available. Itis the analysts responsibility to set up these con-trols to characterize assay accuracy and precisionand to monitor assay performance. For example,method validation using commercial kits forexploratory and advanced biomarker applica-tions have been reported for tartrate-resistantacid phosphatase (TRACP 5b) and serum CTx

for bone resorption, respectively [24,48].

The same basic principle of fit-for-purposemethod validation must be applied for theadoption of commercial kits for PK bioana-lysis. Again, the responsibility resides with thebioanalytical laboratory to establish the assay

characteristics and determine run-acceptancecriteria. Moreover, kit comparison and rigorousspecificity tests should be conducted [32].

Multiplex assays

Definitive quantitative methods using LCMSare capable of multi-analyte assays using thespecific mass-to-charge ratios of each analyte ofinterest. For peptide analytes, precursor peptides(e.g., the prodrug or endogenous propeptide)and their potential metabolites can be quanti-fied simultaneously [38,51]. The data handling

of multiple analytes would be similar to that ofconventional small molecules and metabolites.Multiplex LBA platforms and applications have

been developed for biomarkers. Multiple analyteprofiling can be bead-based (e.g., Luminex) orplanar (e.g., MesoScale Discovery) formats.Multiplexing saves time and requires less sam-ple volume. A panel of potential biomarkers istested during early phase to find those that indi-cate drug effect. The disproportionate variablebiological ranges of the biomarker analytes andnonlinearity of the assays should be considered inmethod design and assay development [52]. After

the selection of the few relevant biomarkers, thedecision can be made to use either several single-analyte methods or a multiplex of fewer analytesfor robust assays in later phases.

Future perspective

The position paper on fit-for-purpose biomarkerassay validation briefly discussed the differ-ences between biomarker applications and drugbioanalysis [12]. Protein drug development andbiomarkers share common bioanalytical tech-nologies but are being applied for different pur-

poses. This review explains in detail the basicsimilarities and differences between assays forbiomarkers and assays for biotherapeutics tosupport PK/PD studies.

The intended applications of bioanalysis oftherapeutics are usually well defined in eachstudy protocol. Method validation and bioana-lysis are performed in a GLP-compliant labo-ratory, with clear guidance from regulatoryagencies and consensus recommendations fromposition publications. The recent discussions onbiotherapeutics, in relation to their correspond-

ing biomarkers, are total and free analyte assays

Fit-FoR-puRpose

methodvaLidationProcess of dening study intentand establishing withexperimental data whether theassay performancecharacteristics are reliable forthe intended application


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Executive summary

Intended purposes of biomarker & biotherapeutic bioanalysis

n The purposes are well defined for pharmacokinetic (PK) bioanalysis in a study.

n The intended applications of biomarkers are more diverse and may not be well-defined.

n A fit-for-purpose approach for biomarker method validation and analysis is needed; the rigor of method validation and assay

documentation depend upon exploratory or advanced application.

Pre-analytic considerations

n Pre-analytic considerations include the choice of biomarkers and corresponding biological matrix, the intended application and method

validation plan based on the need for the specified exploratory or advanced application.

Reference standards

n The reference standards of PK assays and definitive biomarkers are well defined.

n The reference standards are not the same as, but represent, the endogenous analytes in relative quantitative methods.Standard calibrator matrix & selectivity

n Biomarker standard curves often use a substituted matrix devoid of the analyte.

n More extensive matrix tests are required for biomarkers compared with biotherapeutics.

n Parallelism of authentic samples diluted with standard matrix are required for a relative method.

Specificity

n Method should be specific for analyte versus structurally similar molecules (including precursors and pathway metabolites).

n Specify if free, bound or total target biomarker or biotherapeutic will be measured by the method.

Accuracy, precision & assay range

n Accuracy and precision validation data are used to characterize method performance.

n Sensitivity: limit of detection may be used in addition to the LLOQ with caution.

n Assay range is extended by sample dilution.n Nonlinear curve fitting is used for data regression. It is necessary to assess data variability from various sources.

n Acceptance criteria: 4-6-X rule for biotherapeutic PK; flexible for biomarkers, depend upon the pathological modulation and drug effect,

in addition to method performance.

Stability

n Sample controls are used to reflect authentic samples.

Reproducibility demonstrated by incurred sample or sample control data

n There is no requirement for incurred sample with thorough matrix tests and sample control tracking.

Application of commercial kits

n No direct adoption; users are responsible for appropriate validation.

Multiplex assays

n Saves sample volume and time at the early stages.

[35,46] . The information is important for theunderstanding of drugtarget interactions andperforming robust PK/PD modeling [4144].

The intended applications of biomarkersare more diverse than those of biotherapeu-

tics. There are applications at various stagesof novel biomarker development (FiguRe1a),which are intertwined with drug develop-ment [29]. The rough category of exploratoryor advanced application is a wide spectrumthat demands flexibility in method validationrigor. Appropriate experiments should be car-ried out on sample collection, relative accuracy,precision, range finding, parallelism, selectivity,specificity and stability [20]. Biomarker bioana-lysis and method validation are continuous

processes for accumulating knowledge throughthe development of inter-related biomarkers andunderstanding proteinprotein interactionsof biomarkers and biotherapeutics of similarmechanisms. Multiplex assays and other physi-

cochemical methods are evolving to enhancethis knowledge.

The development of companion diagnosticswil l open up collaborative opportunities forthe pharmaceutical and diagnostic sectors foreffective development and applications of novelbiomarkers in drug development and prognosis.

Acknowledgements

The author thanks Michael Hall for critical review of

the manuscript.


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