Recommendations for the validation of flow cytometric testing during drug development: II assays

Journal of Immunological Methods 363 (2011) 120–134

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Journal of Immunological Methods j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j i m

Research paper

Recommendations for the validation of flow cytometric testing during drug development: II assays Denise M. O'Hara a, Yuanxin Xu b, Zhiyan Liang c, Manjula P. Reddy d, Dianna Y. Wu e, Virginia Litwin f,⁎ a

Pharmacokinetics, Dynamics and Metabolism, Pfizer (formerly Wyeth), 35 Cambridge Park Drive, Cambridge, MA 02140, USA Clinical Laboratory Science, Genzyme Corporation, One Mountain Road, Framingham, MA 01701, USA c Translation Sciences Department, MedImmune LLC, One MedImmune Way, Gaithersburg, MD 20878, USA d Oncology Biomarkers, Centocor Research & Development, Inc, 145 King of Prussia Road, Radnor, PA 19087-4517, USA e Discovery Medicine & Clinical Pharmacology R&D, Bristol-Myers Squibb Company, 311 Pennington Rocky-Hill Road, Pennington NJ 08534, USA f Laboratory Science, Covance Central Laboratory Services Inc, 8211 SciCor Drive Indianapolis, IN 46214, USA b

a r t i c l e

i n f o

Article history: Received 16 April 2010 Received in revised form 12 September 2010 Accepted 27 September 2010 Available online 11 October 2010 Keywords: Flow cytometry Validation Biomarker assays Pharmacokinetics assays Pharmacodynamic assays Immunogenicity assays

a b s t r a c t Flow cytometry-based assays serve as valuable tools for various aspects of the drug development process ranging from target discovery and characterization to evaluation of responses in a clinical setting. The integrity of the samples and the appropriate selection and characterization of the reagents used in these assays are in themselves challenging. These concerns taken together with the flow-based technology makes the validation of flow cytometry assays a challenging effort. Therefore, apart from summarizing the role of flow cytometry technology in various stages of drug development, this manuscript focuses on recommendations for the validation of methods applying flow cytometry. Information is also provided on the relevant validation parameters for different types of flow cytometry assays to guide the users of this platform. Together, the recommendations and the information on regulatory guidelines provided in this manuscript represent the consensus of all the authors and can assist the flow cytometry user in implementing the appropriate method validation strategies. © 2010 Elsevier B.V. All rights reserved.

Abbreviations: AAPS, American Association of Pharmaceutical Scientists; ADA, anti-drug antibody;7-AAD, 7-aminoactinomycin;CLIA, Clinical Laboratory Improvement Amendments;CLSI, Clinical and Laboratory Standards Institute;CMS, Centers for Medicare & Medicaid Services;CV, Coefficient of variation;DOE, Design of Experiment;GCP, Good Clinical Practice;GLP, Good Laboratory Practice;GMP, Good Manufacturing Practice;GMFI, geometric mean fluorescent intensity;ICH, International Conference on Harmonization; ISAGE, International Society of Hematotherapy and Graft Engineering;ISR, incurred sample reanalysis;LBA, ligand binding assay;LBAFG, Ligand Binding Assay Bioanalytical Focus Group;MAb, monoclonal antibody;MFI, mean fluorescence intensity;MESF, molecules of equivalent soluble fluorochrome;MOA, mechanisms-of-action;MS, mass spectrometry;Nab, neutralizing antibody;NK, Natural Killer Cell;QC, quality control;PBMC, peripheral blood mononuclear cells;PD, pharmacodynamic;PI, propidium iodide;PK, pharmacokinetics;RBC, red blood cell;SD, Standard Deviation;SDI, Standard Deviation Index;TBNK, T, B and Natural Killer Cells;TK, Toxicokinetics. ⁎ Corresponding author. Tel.: + 1 317 273 7611. E-mail addresses: Denise.O'Hara@pfizer.com (D.M. O'Hara), [email protected] (Y. Xu), [email protected] (Z. Liang), [email protected] (M.P. Reddy), [email protected] (D.Y. Wu), [email protected] (V. Litwin). 0022-1759/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jim.2010.09.036

1. Introduction Flow cytometry assays are used at all stages of the drug development process (Table 1). This powerful technology allows for specific measurement of cellular components on the cell surface and within intracellular compartments. It is also amenable to the measurement of soluble analyte(s) such as cytokines, drug compound, or anti-drug antibodies (ADA) in serum or plasma samples. The advantage of using flow cytometry methods is the ability to use multiparameter analysis allowing simultaneous detection of several functional characteristics of a cell. In fact, the validity of drug screening using several single parameter assays to demonstrate an effect has recently been questioned (Nolan, 2007). Only when several parameters are simultaneously measured at the single cell level can insights into sub cellular network interactions be gained.

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Table 1 Application of the regulatory requirements for flow cytometry assays during different stages of drug development. Stage of drug development

Regulations

Scope of applicable regulations

Flow cytometry method support

Validation

Drug discovery/target validation characterization/ compound selection

Not regulated

NA

• Well characterized and reproducible assays are critical though validation is not required

Non-clinical testing/toxicology

GLP GMP

• Toxicology study protocol follows GLP regulations • The laboratory operates under GLP regulations

In vitro samples collected for: • Target identification • Candidate selection • Mechanism-of-action • Proof-of-principle potential biomarker • Potency for candidate ranking Ex vivo samples collected for: • Toxicity • PK • Immunogenicity • PD biomarker • Potency for drug release

Clinical testing

GCP GLP GMP

• Clinical study protocol follows GCP regulations • Safety testing operates under CLIA regulations • PK and immunogenicity testing follows GLP regulations • Biomarker testing follows GLP-like processes

Ex vivo samples collected for • Drug safety and toxicity evaluation in clinical trials • Potency for drug release • PK • Immunogenicity • PD biomarkers

Compared to other methodologies commonly used in drug development, such as plate based ligand binding assays (LBA) and mass spectrometry, flow cytometric methods can be more challenging to validate. These challenges are related to the combination of cells, reagents, lack of cellular reference material and complex instrumentation. Numerous publications have discussed “fit-for-purpose” method validation for methodologies including biomarkers (Lee et al., 2005, 2006), pharmacokinetics (PK) (DeSilva et al., 2003) ADA and neutralizing antibody assays (NAb) (Mires-Sluis et al., 2004; Gupta et al., 2007; Shankar et al., 2008) but none of these important white papers specifically discussed method validation using flow cytometry. This manuscript presents a discussion of the use of flow cytometry within the aggressive timelines of current-day drug discovery and development process and recommendations for method validation in accordance with the various regulatory requirements. 2. Flow cytometry in drug development The life cycle of a drug begins with the identification of a therapeutic need and potential drug target(s). Experimental compounds identified in early screening undergo extensive evaluation resulting in the selection of a smaller number of lead compounds. After non-clinical toxicology assessment, final candidates progress to early and late stage clinical testing to establish safety and efficacy. Successful compounds are then launched and marketed. The types of flow cytometric assays in use during these different stages of drug development and the information they provide are presented in the following sections. 2.1. Drug discovery and target validation During drug discovery, molecular targets are identified which interfere with disease processes, followed by the establishment of reproducible proof-of-principle (POP) and screening assays.

• Methods supporting TK, PK, and immunogenicity the validation process and documentation aligns with GLP regulations • Biomarker method validation follows a fit-for-purpose approach, documentation follows GLP • Potency release assays follow GMP guidelines • Most methods used for safety testing in clinical trials are FDA approved methods and require only limited validation per CLIA • Potency release assays follow GMP guidelines • PK and Immunogenicity testing align with GLP regulations • PD biomarker methods follow the fit-for-purpose approach

The current focus in drug discovery and development is to develop new drugs more quickly and more cost effectively. High content screening methods employed early in the process have helped to facilitate this goal. Flow cytometry provides the ability to develop high throughput methods for screening compounds directly against intended target molecules and detecting unexpected cross-reactivities at the cellular level. The multiparameter approach can be exploited in selective screening in order to generate a broader understanding of complex interrelated mechanisms-of-action (MOA) exhibited by specific compounds and as a result improve the screening and selection of compounds (Raventos-Suarez and Long, 2010). For oncology compounds, drug discovery teams utilize flow cytometry methods for analysis of cell cycle (Traganos et al., 2001), proliferation (Portevin et al., 2009), apoptosis (Darzynkiewicz et al., 1992; Zamai et al., 1993), and cell signaling (Krutzik et al., 2004), whereas identification of immunotherapeutic compounds utilizes immune activation assays, measuring activation marker expression and/or cytokine induction. Increasingly, established and engineered (transfectants, siRNA gene knock-downs, etc.) cell lines are used as models to support drug discovery in relation to target validation. Engineered cell lines, most commonly Chinese hamster ovary (CHO) although other cell lines may also be used and are selected based on fit-for-purpose. These cell lines are engineered to stably express a recombinant target and can be used to detect, for example, a (chimera, humanized or human) MAb drug that binds to the cellular target antigen. Such cell lines can also be used to measure drug potency and PK via drug binding, or to detect neutralizing antibodies that inhibit drug binding to the cells. Examples of using flow cytometry methods to provide insight into the MOA of the drug are complement mediated cytotoxicity and antibody dependent cell cytotoxicity, that lead to target cell depletion. Thus during the drug discovery phase, in vitro cultured cells are the most common sample type for flow cytometry studies.

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2.2. Characterization and compound selection Experimental compounds must be extensively evaluated to understand MOA and potential toxicity prior to embarking on the costly process of moving a compound forward on the drug development pathway. Flow cytometry has been of particular value in defining the specific interactions that experimental compounds may have on cells expressing the drug target. A wide variety of applications which are utilized with in vitro studies to determine mechanisms of toxicity include measurements of oxidative stress (Bus et al., 1976), calcium flux (June, 2001), apoptosis, cell cycle and cell signaling assays. The added-value of multiparameter analysis is evident here as well. Mechanisms and consequences of toxicity can be defined more precisely and efficiently with methods which measure multiple physiological endpoints. Flow cytometry is used to characterize the binding properties and receptor occupancy of biologics (biotherapeutic antibodies, proteins and peptides) specific for extracellular antigens. Orthogonal methods (such as surface plasma resonance) may be used to assess binding between drug and target, but immobilized proteins may have altered conformation resulting in changes in binding characteristics. Flow cytometric methods on the other hand, allow for the characterization of drug binding to cell surface targets with the native conformation intact and thus will generate more physiologically relevant results. Flow cytometric methods can also be used to assess drug potency for ranking candidates and selection of the lead candidate. Depending on the drug MOA, such methods can be based on binding, inhibition of binding, cell cytotoxicity (cell depletion), cell signaling (such as intracellular protein phosphorylation), proliferative response, and induction of production of soluble or intracellular proteins.

2.3. Toxicology Potency release assays of the drug material used in toxicology studies may utilize flow cytometry and follows GMP guidelines. Non-clinical toxicological evaluation primarily involves in vivo/ex vivo studies evaluating the safety of lead compounds. During toxicological assessment, flow cytometry has proven to be valuable by providing increased precision and decreased variability which has resulted in cost reduction in the form of time, reagents and animal costs. Toxicity biomarkers, pharmacodynamic (PD) biomarkers, and preliminary dosing ranges for clinical studies are often defined at this stage (McFarland and Harkins, 2010). Flow cytometric analysis has been used to assess druginduced vascular damage (cell death and circulating endothelial cell assays) (Kerns et al., 2005). The simultaneous measurement of cellular activation changes, or levels of protein expression on defined cell subsets allows for an extensive characterization of toxicity affects, hematopoiesis or immuno-modulation. For both human and animal studies, the flow cytometric bone marrow differential analysis has many advantages over the manual cytologic technique (Saad et al., 2000; Fuentes-Arderiu and Mestrev, 2009). In addition to defining toxicity, target modulation and functional assays

may also be performed to understand the MOA during toxicological evaluation of the drug. For non-clinical toxicology studies virtually any sample type might be utilized. For ex vivo studies, bone marrow, spleen, lymph node, and peripheral blood are the most common.

2.4. Clinical testing 2.4.1. Safety and pharmacodynamic biomarkers In some clinical trials, such as with HIV therapeutics or with lymphocyte depleting compounds (such as Rituxan), enumeration (relative percentage and absolute counts) of the major lymphocytes subsets (T cells, CD3+CD4+ T cells, CD3+CD8+ T cells, B cells, and NK cells) by flow cytometry are primary study objectives. Other compounds which cause red blood cell (RBC), platelets, lymphocytes, monocytes, and/or granulocytes depletion and/or suppression also use measurements obtained by flow cytometry as primary study objectives. Multiparameter flow cytometry allows for advanced immunophenotyping beyond the basic lymphocyte immunophenotyping assay. Subsets of T and B cells reflecting naïve and memory populations, plasma cells and plasma blasts, cellular activation status, dendritic cell monitoring, and regulatory T cells are some of the advanced phenotyping flow cytometry methods utilized in PD biomarker assessment. In addition, endothelia cells, circulating metastatic tumor cells and CD34 stem cells are evaluated as PD biomarkers with increasing frequency. Functional flow cytometric assays that measure cellular responses include: HLA specific tetramers for T cell responses; intracellular cytokine production; intracellular signaling; and proliferative responses. Functional assays are also included as efficacy biomarkers for cancer vaccine potency evaluation. For compounds which target cell depletion, functional assays are used as safety biomarkers. Functional assays can also be included in immunogenicity assessments. Due to challenges in sample processing which often requires ex vivo stimulation and incubations in CO2 incubators the incorporation of functional assays is not generally widespread. For safety and PD biomarkers, peripheral blood is the most common sample type, less often; bone marrow, tumor samples, lymph nodes and tissue biopsies can also be used as a sample source. Whole blood is usually considered the first choice sample matrix for flow cytometric clinical biomarker assays for the ease of collection. In some instances, especially for functional assays, peripheral blood mononuclear cells (PBMC) are isolated from whole blood prior to analysis which offers an advantage of freezing the cells for batch analyses. Exclusion of dead cells can be performed to minimize background signal, especially in cell function analysis. Viability dyes such as 7-aminoactinomycin (7-AAD) or propidium iodide (PI) are commonly used. These dyes can be used with fresh cells prior to sample acquisition; if RBC lysing solution and/or fixative will be used in the staining, viability dyes can be added (pulsed) prior to RBC lysis/WBC fixation and free dye washed off, or all cells are expected to be stained positive with 7-AAD or PI.

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2.4.2. Immunogenicity assays Protein and peptide-based compounds generally referred to as biotherapeutics, or biologics, have the potential to be immunogenic. Thus, development of assays to detect ADA and NAb are an important aspect in clinical evaluation. The presence of NAb in serum or plasma samples can be detected using cell based assays and flow cytometric detection (O'Hara and Theobald, 2010, Fig. 1). Briefly, the ability of a

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NAb to inhibit a cell binding or signaling event with a fluorescent read-out is assessed. In this quasi-quantitative or qualitative method (Table 2), samples may be reported as an antibody titer or scored positive or negative based on an established assay cut-point from healthy subjects or drug naïve patients. Serum or plasma samples are used for immunogenicity testing. 2.4.3. Receptor occupancy and pharmacokinetics For biologics specific for extracellular antigens, flow cytometry presents an ideal platform to measure the level of receptor engagement on the cell surface, or receptor occupancy. These assays can be difficult to develop especially when the target antigen is expressed at low levels, and rely on the availability of competing and non-competing monoclonal antibodies (MAb) to the target antigen (Latek et al., 2009; Brahmer et al., 2010). The information generated (total available receptor [pre-dose], percent bound and unbound receptor) when correlated to efficacy can be valuable in establishing appropriate dosing regimes. When receptor occupancy is assessed by direct measurement of unbound receptor and total receptor at each sample collection time point, it is important to have both competing and non-competing MAbs conjugated with fluorochromes at 1/1 ratio. Alternatively, receptor occupancy could be assessed by measuring unbound receptor at pre-dose (as total available receptor) and post-dose time points. Such an approach is based on the assumption that the total receptor expression level does not change significantly before and after drug treatment. Therefore, the total receptor expression levels may be monitored at both pre-dose and post-dose time points. Indirect immunofluorescence assays can be used for PK measurements of biotherapeutics. Briefly, compound present in the serum can be bound to cells or beads expressing the target antigen and detected with a fluorescent-labeled

Table 2 Categories of bioanalytical methods. a Assay category

Definition

Quantitative

Uses calibration standard to determine the absolute quantitative values for unknown samples. The reference material is well defined and fully representative of the endogenous analyte. Example: pharmacokinetic assays Uses a calibration standard to estimate the absolute quantitative values for unknown samples. The reference material is not fully representative of the endogenous analyte. Example: cytokine ligand binding assays Does not use calibration standard, but has a continuous response. Numeric data is reported. Example: immunogenicity assays, phenotypic and functional biomarker assays, receptor occupancy assays Lacks proportionality to the amount of analyte. Categorical data is reported. Example: immunogenicity assays, immunohistochemical assays

Relative quantitative

Quasi-quantitative Fig. 1. Labeled biotherapeutic binding to a relevant cell line and titration of a NAb positive control. Labeled drug dose response curve shown as overlaid histograms (A) or plotted as a dose curve (B). Titration of the NAb positive control with a fixed amount of drug-fluorochrome (C). (Reproduce from O’Hara, D.M., Theobald, V., 2010. Immunogenicity Testing Using Flow Cytometry, in: Litwin, V., Marder, P. (Eds.), Flow Cytometry in Drug Discovery and Development. Wiley-Blackwell, John Wiley & Sons, Inc., New Jersey, pp. 205–223.). Reprinted with permission of John Wiley & Sons, Inc.

Qualitative

a

Adapted from Lee et al. (2005).

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Fig. 2. Dose dependent binding of Drug X to human PBMC. Dose dependent binding of a polyclonal antibody Drug X to human PBMC is shown in histograms (MFImedian for FITC vs. cell number). Drug X concentration (ug/mL) and the corresponding MFI-median value is shown for each histogram. (Reproduce from Xu, Y., Richards, S.M., 2010. Pharmacokinetics by Flow Cytometry -Recommendations for Development and Validation of Flow Cytometric Method for Pharmacokinetic Studies, in: Litwin, V., Marder, P. (Eds.), Flow Cytometry in Drug Discovery and Development. Wiley-Blackwell, John Wiley & Sons, Inc., New Jersey, pp. 225–240.). Reprinted with permission of John Wiley & Sons, Inc.

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Fig. 3. Examples of gating. Clearly distinct cell populations are shown in (A) and not easily distinguished cell populations in (B and C). Setting the gating or boundaries for lineage specific markers is much less challenging (A) than for those markers where marker positive and negative populations are less well defined (B and C).

125

126

Table 3 Validation of FDA approved methods. Validation specimens

Number of replicates

Number of analytical runs

Acceptance criteria

Comments

Accuracy

• 3 different levels (if possible) • Manufacturer's QC or CAP Proficiency testing samples are appropriate as target values are available

3

3–5

• Within manufacturer's claims or CAP Peer Group specifications SDI of ≤2.0

Precision

• Accuracy according to the standard definitions (closeness of the result compared to the true value of the analyte) is not possible with flow cytometric methods. • For most FDA-cleared methods, CAP Proficiency testing surveys are available. Accuracy of a method can be verified by acceptable performance in proficiency testing surveys, • For major manufacturer's TBNK method are less than 10% CV within run or b 15% CV between run

3 3–5 • Within manufacturer's claims • 3 different levels (if possible) • Convenient to use the same specimens as used in accuracy assessment • This objective may not be applicable for standard FDA approved lymphocyte immuno phenotyping methods as the manufacturers do not define reporting ranges. Furthermore, it is difficult to obtain validation specimens with extreme levels of each cell subset. • The use of commercially available control material with varying levels of cell populations can be used to establish the precision over a range of values. Unfortunately, control materials are only available with different ranges of T cells or CD34 stem cells. No controls are available with varying levels of B and NK cells. 20–40 1–2 1–3 • Linear regression with scatter plots, acceptance • CLSI Guideline “Method Comparison and Bias criteria: Estimation Using Patient Samples”; • Slope=1.00 ±0.10 Approved Guideline EP9-A2 (Krouwer et al., 2002) • Coefficient of correlation r2 greater or equal than 0.95 • CLSI Guideline “Defining, Establishing, and Verifying • 20 for verification 1 1 or more • If verifying the manufacturer's ranges, the Reference Intervals in the Clinical Laboratory”; • 120 or more for establishing ranges values must be within the manufacturer's Approved Guideline C28-A3 (Horowitz et al., 2008) ranges. • Ideally for global clinical trials, reference ranges should be established in each major geographic location as ethnic variations may exist. This information can then be used to create a global reference range. At the very least, the manufacturer's ranges should be verified in each major geographic location to confirm that the ranges are appropriated for the local population.

Reportable range

Patient correlation

Reference intervals

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Parameter

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secondary antibody. A calibration curve is used to quantify the concentration of the biotherapeutic in the sample. Unlike plate based LBA that typically detect total protein drug, flow cytometric based methods that are used for PK measurements may also assess the active drug by measuring a functional read-out such as binding to cell surface target, cytotoxicity, signaling, etc. Active drug is, in general, better correlated with drug efficacy and safety parameters (Xu and Richards, 2010, Fig. 2). Receptor occupancy assays require whole blood samples, whereas; PK assays require serum or plasma samples. In addition, the PK methods require the use of antigen-conjugated beads or well characterized cell lines expressing the antigen. Characterization of the cell line is crucial and can include defining the cell line used within given cell passage numbers and generation of master and working cell banks to minimize assay variability. If changes in cell line culturing procedures are warranted, then comparability of results from each method needs to be established.

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scenarios (Overton, 1988; Roederer, 2001). Establishing a clearly defined, consistent, and standardized gating strategy is important to minimize the variability in data. The absolute cell count (cells/μL blood) of a given cell population can be calculated from the percent positive cell by two methods. Using the single platform method, the absolute cell count is determined by comparing cellular events to quantification bead events included in the analysis tube. Several manufacturers offer FDA-cleared kits which include quantification beads. Using the dual platform method, the absolute count can be derived from the absolute lymphocyte count obtained from standard hematology analyzers. The single platform method is believed to be more precise but the dual platform method is more cost effective. Incorporating absolute counts in addition to percent positive reported results may be advantageous when monitoring of changes in a given subset (Gebo et al., 2004). For example, a decrease in percent positive T cells could be due to a decrease of T cells or increase in non-T cells (B and NK); however using absolute cell count information, these two possibilities can be resolved.

3. Data analysis and reportable results 3.2. Antigen expression and ligand binding levels The strength of the flow cytometer technology lies not only in the ability to simultaneously measure multiple parameters but in the flexibility to report the parameters in different ways. The appropriate data output depends on the biology of the system being investigated, the analytical or scientific question being asked, and the intended use of the results. In flow cytometry, numeric results expressed in terms of a characteristic of the sample are generated. Understanding which category of bioanalytical assay is applicable for a given method is essential in designing and implementing method development and ultimately validation strategies. Four categories of bioanalytical methods have been defined (Lee et al., 2005) and are listed in Table 2. 3.1. Percent positive cells and absolute cell counts Populations and subpopulations of cells can be identified by the expression of one or more antigens; results are then expressed as the percentage of the parent population expressing the antigen of interest. Most commonly, data are reported as percent positive cells which is established by setting a gate around a given cell population versus another (Fig. 3). Setting the boundary between well defined cell populations is straightforward when the cellular marker is quite distinct, such as with lineage specific markers (Fig. 3A). If cell populations are not well separated, selection of the initial gate is more challenging (Fig. 3B and C). Internal negative cell populations can be used to establish the positive:negative boundary marker for antigen expression on the target cell population. However, this approach can be problematic if the internal negative cell population differs too much from the target cell population (i.e. autofluorescence). In other cases, gate selection is even more complex. Various factors, such as the antigen expression patterns (distinct, contiguous, or heterogeneous), and cellular locations (extracellular, intracellular, or nuclear) influence the decision of how to establish the positive:negative cell boundary. Additionally positive and negative controls, fluorescence-minusone controls, and isotype controls are appropriate in different

In some cases, the level of antigen expression is the desired data output rather than the percentage of cells expressing the antigen of interest. Changes in antigen expression levels can indicate the activation or developmental status of a given cell population. PK analysis of compounds which monitor binding of the drug to target molecules and receptor occupancy assays also rely on monitoring quantitative levels of fluorescence. Signal from the flow cytometer can be reported as mean fluorescence intensity (MFI), geometric mean fluorescent intensity (GMFI), or median fluorescence (Shapiro, 2003). The, appropriate output depends on the system being evaluated. For example in PK analysis, for cells with homogenous expression levels of the target molecule (either cell surface or intracellular), the use of MFI or median fluorescence intensity is acceptable, however; when cells show heterogeneous expression, use of median fluorescence intensity is preferred. Given that fluorescence intensity signal can be variable depending on the instrument, instrument settings, analyst, and lab, it is recommended that the fluorescence intensity signal output be quantified using fluorescence calibration/quantitation beads (Wang L. et al., 2008). These fluorescent beads that contain measured numbers of fluorescent molecules can be purchased and analyzed in each run along with test samples. MFI of the beads generated from the assay can be plotted with known molecules of equivalent soluble fluorochrome (MESF) units to generate a calibration curve. The MESF of a test sample can then be determined by interpolation. 3.3. Immunogenicity assay read-out The read-out from ADA and NAb assays are qualitative, or at best quasi-quantitative, for reasons that are well described in the literature (Baltrukonis et al., 2006; Gupta et al., 2007; Shankar et al., 2008). The output signal from the flow cytometer reported in MFI or GMFI are used to calculate percent inhibition of binding or percent of maximum signal. An assay cut-point is established using individual healthy

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appropriate to subtract the background. Given that the precision from the calculated percent inhibition is not an accurate reflection of the assay precision and can be falsely high or low in value, the calculated percent signal or instrument read-out of MFI or GMFI are preferred. Use of percent signal as the reported output minimizes the variability from raw signal analysis and allows comparison of different samples against a positive control(s). Regardless of read-out, an assay cut-point needs to be assigned and is based on the study population of drug-naïve or healthy individuals so that samples can be scored.

naïve or disease state sera (when available), at the 95% confidence interval or mean ±1.645 SD and may be confirmed at a later date with patient data. The presence of NAb can also be determined as a qualitative, i.e. positive or negative, based on the MFI or GMFI signal and related to the assay cut-point. Alternatively a quasi-quantitative result can be obtained by calculating the percent inhibition as [1 − (sample signal ÷ max signal)] × 100; in certain cases it is appropriate to subtract the background. Another approach to obtain a quasiquantitative result is to calculate percent of maximum signal as (sample signal ÷maximum signal)×100; in certain cases it is Table 4 Validation of phenotypic biomarker assays (for research use only). Parameter

Number of specimens

Number of Number of Acceptance replicates analytical criteria runs

Accuracy

NA

NA

NA

Specificity

NA

NA

NA

Intra-assay precision

3–6

3–6

1 or more

Inter-assay precision

3–6

3–6

3–6

6

1 or more

3 Fluorescence-minus-one 5 samples are valuable in establishing the LOD

1 or more

Lower limit of 3–6 quantitation (LLOQ) Limit of detection (LOD)

Reference intervals

Stability

Comments

• Accuracy according to the standard definitions (closeness of the result compared to the true value of the analyte) is not possible with flow cytometric methods, especially when the method is novel. • The phenotype of a given cell subset must be justified or recent published data sought • Gating strategies must be verified to establish the cell subset of interest is included. • A major challenge for the more esoteric biomarker methods remains the lack of proficiency testing programs. NA • During assay development the specificity of the reagents and antibodies must be verified. In some cases, the manufacturer's claims may be acceptable. • Assess reagent specificity for detection of relevant cell populations and lack of detection of irrelevant cell population in development An acceptance criterion of within • When detecting rare events, higher imprecision may be observed. If the biomarker is valuable 25% CV (30% at the LLOQ) has then greater imprecision may be acceptable. been recommended for nonImprecision may be decreased by further assay clinical ligand binding assays. In development, including changing the MAb most cases this is achievable for clone(s), fluorochrome label or increasing the flow cytometric methods. number of events acquired An acceptance criterion of within • Sample stability must be assessed when using fresh and unfixed samples 25% CV (30% at the LLOQ) has • Preserved specimens, or QCs, may be used with been recommended for noncaution as they may not adequately mimic actual clinical ligand binding assays. specimens ≤35% CV • Greater imprecision requires more analysis of replicates and samples • For rare event detection or when imprecision • The LOD calculated as the is high, it is important to establish the signal mean of all data points plus above background. in addition to LLOQ 3 SD determination • In the first (exploratory) applications of a novel biomarker method, reference ranges are not necessarily required as the utility of the biomarker has not been established. Later in the biomarker lifecycle, if the biomarker method was found to be of use, it would be appropriate to establish reference ranges in order to determine if the biomarker itself had predictive value. NA

Stability is not addressed in this paper

NA, not applicable. Note that biomarker method validation may be performed on samples from apparently healthy volunteers if samples from diseased populations are not available, whereas; validation of the biomarker requires a comparison of healthy and diseased populations.

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Table 5 Validation of functional biomarker assays (for research use only). Parameter

Number of specimens

Number of replicates

Number of analytical runs

Acceptance criteria

Comments

Accuracy

NA

NA

NA

NA

Specificity

NA

NA

NA

NA

Intra-assay precision

3–6 healthy donors, or use surrogate positive cells

3–6

Intra-assay: 1 run

Inter-assay precision

3–6 healthy donors, or use surrogate positive cells

3–6

3–6

An acceptance criterion of within 25% CV (30% at the LLOQ) recommended for non-clinical LBA and is achievable for flow cytometric methods An acceptance criterion of within 25% CV (30% at the LLOQ) recommended

• See comment in Table 4 • If cell banks are available, establishment of a reference value at high and low levels can be done based on data (at least 20 data values) collected in multiple runs over multiple days by at least 2 analysts • Assess reagent specificity for detection of relevant cell populations and lack of detection of irrelevant cell populations in development • Assess assay specificity for detection of target compound in a dose dependent manner (such as cytokines), lack of detection without the target, and lack of detection to the irrelevant/closely related compound None

Sensitivity

• 3 samples with target cells or target analyte at low levels Stability is not addressed in this paper ~ 20–50 healthy donors

3

2 or more

The lowest level that meets accuracy and precision criteria

1

1 or more

NA

Stability Normal distribution

• Cell and fresh blood stability needs to be taken into consideration when inter-assay precision study is designed. • If fresh blood is used, inter-day run should be completed within 48 h • If accuracy is not assessed, precision criteria should be used to define assay sensitivity

• Establish normal range within selected and relevant population • Patient baseline samples (treatment naïve) should be examined if samples available (~n = 20)

Note that biomarker method validation may be performed on samples from apparently healthy volunteers if samples from diseased populations are not available, whereas; validation of the biomarker requires a comparison of healthy and diseased populations.

4. Method validation The development of multiparametric flow cytometry assays is a complex task requiring detailed understanding of flow cytometers, fluorochromes, spectral overlap, cell lineage markers, intracellular processes, and data analysis. Assay development of flow cytometry assays is beyond the scope of this document and has been addressed elsewhere (McLaughlin et al., 2008a,b; Robinson, 2009). In addition, for use of flow cytometric assays in drug development, issues regarding interference from, or to, the compound must also be addressed during the assay development phase. In some cases it may also be necessary to compare healthy and diseased populations during the assay development phase. Assay development can also take advantage of testing multiple parameters together that have the potential to influence one another. Design of Experiment (DOE) may be used to assess these factors and their influence on assay readout, assay variability and signal/background ratios (Anderson and Anderson, 1993; Anderson and Kraber, 1999; Anderson and Whitcomb, 2000). Once an assay has been developed, it must be validated. Depending on which stage of the compound life-cycle the assay

is used, different regulations apply which influence the extent of method validation and its use (Table 1). In some cases, a method will progress along with the compound during the drug development pathway. When this occurs, validation will be an iterative process, requiring re-validation as the assay is used for different objectives and under different regulatory requirements (Lee et al., 2006). The three key elements of an assay validation are (i) establish protocol, or validation plan, (say it); (ii) the assessment (do it); (iii) and documentation (prove it). Approaches to analytical method validations which satisfy the various regulatory requirements are discussed later. Specific details regarding validation (number of replicates, acceptance criteria, etc.) are provided in Tables 3–7. Note that a fit-for-purpose approach was applied when generating these recommendations which in general represent the minimum requirements. In some cases for example, when events are rare, variability is high, or more stringent objectives are required of the method, additional evaluation and a statistical analysis plan may be warranted. Successful method validation requires that all aspects of the assay system and its application be considered when designing a method validation protocol. In some cases, it is advisable to

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Table 6 Validation of immunogenicity assays. Parameter

Number of samples

Number of replicates

Number of analytical runs

Acceptance criteria

Comments

Matrix interference

10

2

1

Recover within 80–120% of spiked PC

Normal signal distribution

50

2

1

• Minimum required dilution (MRD) to recover spiked PC • Individual healthy and disease state samples (if available) prior to the study otherwise pretreatment baseline samples may be used • ≥2 different analysts • If healthy and disease samples are comparable, assign cut-point. Or may need to assign disease specific cut point (s). Assay cut point can be confirmed and/or re-established with pretreatment baseline sample data • 2 different analysts cell based assays the imprecision maybe higher than other non-cell based LBA • 2 runs over 2 days when using whole blood dues to concerns of stability • Lowest concentration of NAb PC that can be detected with acceptable precision. • This assay parameter is not required though maybe assessed with the PC • Range of spiked drug mimics study samples

Cut-point determination ≥50 for clinical, 2 ≥15 for non-clinical

≥6 over 3 days

Intra-assay precision

(PC and NC) 2–5

2–6

1 or more

≤15–30%

Inter-assay precision

(PC and NC) 2–5

2–5

4 (per analyst)

≤20–30%

Sensitivity

≥5

≥2

≥6

Dilutional linearity

NA

NA

NA

NA

Drug interference

Multiple drug concentrations

≥2

1

Prozone effect

1

3

1

Robustness

User defined

User defined ≥2

Lowest drug concentration where PC becomes negative is considered drug tolerance limit NA • This assay parameter is not required though maybe assessed using the PC All acceptance criteria met • 2 different analysts, if available different instruments, incubation times and PC and NC ±20% difference

PC, positive control, NC, negative control.

consult a biostatistician regarding the validation plan and acceptance criteria. When adhering to the fit-for-purpose approach it is critical that the assay validation is appropriate to the intended use. 4.1. Drug discovery through compound selection Although there are no specific regulatory guidelines for method validation at the very early stages of drug development, valid and high quality assays are essential in order to generate credible data for use in downstream decision making. A major challenge in drug discovery is to develop flexible, high throughput screening assays using state-of-theart technologies within a narrow timeline. Extensive validation is not feasible or perhaps necessary but the assays must be credible, reliable and must adhere to the most current best practices.

keeping. It is important to note that the regulatory requirement for implementing flow cytometric assays may vary depending on the method applications. Those methods that support safety, PK and immunogenicity assessment must be conducted according to GLP regulations. A “fit-for purpose” approach is generally acceptable for exploratory biomarker analysis. Details regarding method validation of non-clinical PK assays provided in the 2001 publications “Guidelines for Industry, Bioanalytical Method Validation” were primarily directed towards mass spectrometric (MS) and immunoassay procedures (FDA, 2001). The fundamental validation parameters addressed in this document were accuracy, precision, selectivity, sensitivity, reproducibility, and stability. Validation should follow the protocols or standard operating procedures and be documented appropriately for reconstructability. Recommendations for validating a novel phenotypic flow cytometric based biomarker method are provide in Table 4.

4.2. Toxicology 4.3. Clinical testing Non-clinical toxicology studies are conducted in a laboratory operating under the Good Laboratory Practice (GLP) Regulations (FDA 84; FDA 87) which are intended for nonclinical trials in the U.S. using animals, prior to clinical research in humans (Table 1). These regulations describe the quality system in which the laboratory must operate and encompass all aspects of the laboratory: personnel, facilities, equipment, operations, test and control articles, study protocols and record

Good Clinical Practice (GCP) is an ethical and scientific quality standard provided by the International Conference on Harmonization (ICH) for designing, conducting, recording and reporting trials that involve the participation of human subjects (GCP, ICH, 1995, 1996, 1997a, 2002, 2009). GCP focuses on how a clinical trial should be conducted, patients consented, confidentiality maintained, and samples collected

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131

Clinical and Laboratory Standards Institute (CLSI) Guidelines (Gratama et al., 2007) and other publications (Owens et al., 2000) describe procedures for implementing all aspects of a quality system in clinical flow cytometry laboratory operations in order to comply with CLIA regulations. Several flow cytometric methods (procedures, reagents, instrument setup and automated software) have been approved by the FDA for “In Vitro Diagnostic Use”. These include the standard lymphocyte immunophenotyping assays, stem cell enumeration in peripheral blood using the International Society of Hematotherapy and Graft Engineering (ISAGE) (Sutherland et al., 1996, 2009) protocols, and reticulocyte detection (Arkin et al., 2004). When using an FDA approved assay, the validation objective is to verify that the performance of the method meets the manufacturer's specifications for accuracy, precision, reportable ranges and verify that the manufacturer's reference intervals are appropriate for the laboratory's patient population (Table 3). The CLIS guideline EP-A15 (Carey et al., 2006; Krouwer et al., 2006) provides recommendations for experimental design to verify method performance but does not provide specific details regarding acceptance criteria. For many analytes evaluated in a clinical laboratory, medically based analytical performance standards have been established (Koch

and managed but does not address laboratory management or analytical method validation. 4.3.1. Safety testing The Centers for Medicare & Medicaid Services (CMS) regulates all laboratory testing (except research) performed on humans in the U.S. through the Clinical Laboratory Improvement Amendments (CLIA) (CDC, CLIA 88). The objective of the CLIA program is to ensure quality laboratory testing for approved, marketed drugs. Although technically, clinical trial samples are considered clinical research samples and do not fall under CMS regulations, for the most part, the analysis of clinical trial samples intended for drug safety assessment, diagnostic testing to meet enrollment criteria, and disease monitoring for efficacy assessment, is conducted in CLIA certified laboratories. The CLIA regulations describe the quality system in which the laboratory must operate and encompass all aspects of the laboratory personnel, continuing education, facilities, equipment, operations, sample management, quality assurance, results reporting, quality control monitoring and proficiency testing. Details regarding method validation for FDA-cleared and non-FDA-cleared methods are described in the CLIA regulations (CLIA, 1988 subpart K). The

Table 7 Validation of pharmacokinetic assays. Parameter

Number of samples

Number of Number of Acceptance replicates analytical runs criteria

Comments

Accuracy

≥5 (LLOQ, LQC, MQC, HQC, ULOQ)

≥2

≥6

± 20% RE (± 25% RE at the LLOQ)

Precision

≥5 (LLOQ, LQC, MQC, HQC, ULOQ)

≥2

≥6

≤20% CV (±25% CV at the LLOQ) for both intra-assay and inter-assay precision. The sum of accuracy and precision ≤30%

Specificity

NA

NA

NA

NA

Selectivity

≥10 at LLOQ

≥2

≥1

Standard calibrators

≥6 non-zero points

≥2

≥6

± 20% RE in ≥ 80% of the matrices evaluated ± 20% RE (± 25% RE at the LLOQ) for intra-curve. ≤15% CV & RE (± 20% CV & RE at the LLOQ) for inter-curve

• Expression of target protein on the cell surface needs to be monitored, acceptable ranges may be defined to meet assay performance requirements. • Use of well characterized and stable cell lines are preferred to achieve the recommended acceptance criteria for accuracy and precision. • Using primary cells with heterogeneous target expression can be challenging. • Reagents are assessed for specificity for the intended use of the assay None

Range of quantification Stability

≥2 (LLOQ, ULOQ)

≥2

≥6

≥2 (HQC, LQC)

≥2

≥1

≥2 ≥4 (above and in the upper, middle and lower parts of the standard curve) Incurred sample Minimum of 20 samples ≥2 reanalysis or 5% of total study samples

≥1

Dilutional linearity

NA, not applicable. LLOQ, lower limit of quantification. LQC, low quality control. MQC, mid-quality control. HQC, high quality control. ULOQ, upper limit of quantification. RE, relative error. CV, Coefficient of variation.

≥1

The same as accuracy and precision criteria The same as accuracy and precision criteria ± 20% RE, ≤20% CV

± 30% difference for ≥2/3 of samples

• The optimal regression model for calibration curve fitting needs to be evaluated during the assay development phase. Generally, a 4/5-parameter logistic function with or without weighting is used None None None

None

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and Peters, 1999), however; there are no universally accepted standards for precision, accuracy and inter-laboratory comparability for standard lymphocyte immunophenotyping assays (Gratama et al., 2007). Thus for flow cytometric methods, the acceptance criteria must be to meet the performance criteria for precision and accuracy established by the manufacturer (Table 3). The manufacturers do not define a reporting range for flow cytometric methods, thus this objective may not be applicable, moreover; it is difficult to obtain validation samples with extreme levels of each cell subset. Participation in a CAP proficiency testing program and/or a sample exchange with another laboratory performing the same method are suitable approaches to demonstrating acceptable assay performance over a broad reportable range. When a laboratory is testing samples for a clinical trial, often samples are received from many different geographic locations so the concept of establishing reference ranges for the laboratory's patient population translates to establishing global reference ranges. For flow cytometric methods, the laboratory can verify the manufacturer's ranges as described in CLSI guidelines (Horowitz et al., 2008) or compile global ranges from scientific publications in which reference ranges were established in various worldwide locations provided that the same methodology was used (McCoy and Overton, 1994). 4.3.2. PD and exploratory biomarker assays The analysis of clinical trial samples intended as PD biomarkers and exploratory biomarkers occurs in a variety of laboratory settings (CLIA certified, GLP compliant, and research laboratories) (Cunliffe et al., 2009). Even though CLIA regulations are not applicable, the validation specifications for nonFDA-cleared, or in-house developed methods, are an appropriate approach (CLIA, 1988). Per CLIA, the validation objective is to establish the performance specifications for accuracy, precision, sensitivity, specificity, reportable ranges, reference intervals, and any other performance characteristics required to test performance. Recommendations for the validation of phenotyping methods are provided in Table 4 and for functional methods in Table 5. The extent of the validation depends upon the intended use of the assay and the data. Therefore, fit-for-purpose analytical method validation approach is acceptable for biomarker analysis (Lee et al., 2006). The recommendations described in CLSI guidelines (Tholen et al., 2002; Krouwer et al., 2006) and previously published acceptance criteria (Xu et al., 2008) provide useful direction for this objective. Note that the validation approach describe in Tables 4 and 5 are not appropriate for assays used in patient diagnosis and treatment, even if the clinical testing is conducted in a CLIA laboratory. A major challenge for conducting biomarker testing in CLIA laboratories remains the lack of proficiency testing programs for the more esoteric methods. 4.3.3. Immunogenicity assays Immunogenicity testing, including screening, confirmatory and NAb assays, should adhere to the ICH (1997b), EMEA/CHMP (2007) and FDA (2009) guidelines and be conducted under GLP regulations. The validation parameters used in immunogenicity assays have already been described and the use of flow cytometry method should be implemented based on these recommendations (Mires-Sluis et al., 2004; Gupta et al., 2007; Shankar et al., 2008; Koren et al., 2008; Wang J. et al., 2008).

However, the assay acceptance criteria specific to flow cytometry needs to take into consideration the growth characteristic, target expression levels, and binding properties of the cells or beads (Ferbas et al., 2007) in combination with the instrument settings. Due to the use of cells and potential drift of the instrument signal output, the assay acceptance criteria may be broader than those established for other non-flow cytometry-based LBA methods. Understanding these limitations, especially when committing to the use of flow cytometric assays assists in the implementation of assay validation and for longitudinal use of instruments for the intended purpose. Assay validation parameters for immunogenicity assays follow the referenced guidelines and are summarized in Table 6. 4.3.4. PK and receptor occupancy Assay parameters that need to be characterized during a non-flow cytometry-based method validation to support PK are well described in the literature (DeSilva et al., 2003; Smolec et al., 2005; Viswanathan et al., 2007) and EMEA/CHMP 2009 guidelines. Most of these parameters are also applicable and recommended for flow cytometry-based assays intended to support PK. One of the critical factors for implementing a good PK assay is the selection of target cells with high, homogenous, and stable antigen (binding partner of the drug) expression and high affinity binding to drug. Transformed cell lines or engineered Chinese hamster ovary (CHO) cells are preferred. Primary cells may also be used, but the donor-to-donor variability needs to be assessed. Recommended validation parameters for PK assays follow previously published guidelines (DeSilva et al., 2003; Smolec et al., 2005; Viswanathan et al., 2007) and acceptance criteria (Table 7) (Xu and Richards, 2010). Incurred sample reanalysis (ISR) has recently become a regulatory requirement for validating PK methods (Rocci et al., 2007; Fast et al., 2009). If the sample reproducibility is unsatisfactory, a formal investigation is mandated to determine whether the current method is suitable. Whether this approach is applicable to other bioanalytical methods is still under debate. The challenge for flow cytometric assays is that stored sample stability might limit the ability to do ISR. Receptor occupancy assays are quasi-quantitative (Table 2) and validation should follow the fit-for-purpose approach proposed for biomarkers methods (Table 4). The critical components of a receptor occupancy assays include the target cell population, the biologic compound, and a non-competing antibody. The non-competing antibody must be exhaustively qualified and antibodies that can cause receptor internalization within the time frame of the assay should be excluded. The ratio of the MFI between the free receptor and total receptor is reported as the receptor occupancy. The selection of target cells is obvious when the MOA of a drug is well defined and the target cell population is not scarce. If the target receptor is present on the surface of multiple cell types and the MOA of a drug is not specific to one cell type, it is appropriate to select the cell population with the highest receptor density. 5. Summary To date there has been little consensus about the appropriate guidelines for flow cytometry method validation as used in drug discovery and development. More discussion is clearly needed. The method validation processes described in this paper are

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