Bioanalytical Challenge For reprint orders, please contact: [email protected]
Challenges in keeping pace with immunogenicity advances throughout the clinical development of a biological therapeutic Progress in the assessment of immunogenicity over the past decade has been rapid; therapeutics in late-phase clinical studies today are subject to antidrug antibody analyses that have evolved considerably since the start of their clinical development. Data from bridging and direct ELISA methods used for the analysis of antidrug antibodies generated in response to an antibody therapeutic will be presented. The case study will highlight some of the challenges faced and the learning points gained from supporting immunogenicity analysis for these therapeutics.
The demand for assays capable of detecting antidrug antibodies (ADAs) has increased significantly over the last 20 years, driven primarily by a rise in the number of biological therapeutics entering drug development. Owing to their biological origin, size and complexity, such therapeutics carry a greater risk of eliciting an immune response in those receiving the treatment, a response detected most commonly using ligand-binding assays (LBAs). The challenge has been to understand the unique requirements of developing LBAs for this application and how these differ from those of pharmacokinetic (PK) assays. The first widely recognized and comprehensive white paper on the subject was published in 2004 by Mire-Sluis et al. [1] , addressing the analytical challenges posed by ADAs. Four years later, Shankar et al. provided additional focus on appropriate experimental designs and the application of statistics [2] . Guideline documents from both the EMA [3,4] and the US FDA [5,6] have gone some way to clarifying expectations from regulatory bodies. How LBAs should be designed and optimized to achieve these expectations is less well documented in the current literature. From investigational new drug approval to the completion of Phase III clinical studies typically takes in excess of 6 years [7] . Therapeutics at late-stage clinical assessment today have been developed
10.4155/BIO.14.135 © 2014 Future Science Ltd
Chris Jones*,1 & Andrew Roberts1 LGC, Newmarket Road, Fordham, Cambridgeshire, CB7 5WW, UK *Author for correspondence: [email protected] 1
through a period that has seen the analysis of ADAs evolve significantly. This learning curve is neatly highlighted by the case study of an antibody therapeutic in clinical trials from 2008 to 2012. Initial assays for ADA assessment were developed in 2006. These were ELISA of a direct format and were isotype specific. Significant challenges in procuring positive control material for IgE and IgM solely led to the successful development of a direct assay for IgG; thus, a bridging ELISA was also developed. The direct IgG and bridging ELISAs were sequential in format, featuring wash steps between each reagent incubation step. Both utilized therapeutic antibodies immobilized to the solid phase as capture reagents. Samples or controls were incubated on the assay plate before the addition of the detection reagent. The direct IgG ELISA utilized a horseradish peroxidase (HRP)-conjugated anti-IgG specific for the Fc region for detection reagent, whereas the bridging ELISA used biotinylated therapeutic coupled with extravidin HRP. The control used for both assays was a polyclonal antispecies IgG, with both assays using a 20-fold minimum required dilution. Assays were validated for use in support of regulated studies in 2008, with a partial validation performed during assay re-establishment in 2012. Both assays were used together in support of Phase II clinical tri-
Bioanalysis (2014) 6(14), 1953–1960
part of
ISSN 1757-6180
1953
Bioanalytical Challenge Jones & Roberts
Key Terms Immunogenicity: The ability of a therapeutic to elicit an immune response. This has potential consequences for safety and efficacy. Screening cut-point: The screening cut-point enables the classification of samples as either potential ADA-positive samples or ADA-negative samples based on the statistical threshold calculated from a drug-naive population.
als. As such, two datasets were generated using the same sample populations up to a relatively late phase of clinical development. Opportunities to compare and contrast two immunogenicity datasets from the same population are rare. These data provide an insight into the relative merits of each assay format and, moreover, clearly illustrate the advances in understanding in how to optimize LBAs for ADA assessment that have been made over the past 8 years. Failures encountered The global Phase IIb study was a multicenter study comparing the safety and efficacy of two dosing regimens with 300 patients recruited across three cohorts: two dosed and one placebo. Subjects were dosed daily for 6 days and samples for analysis of ADAs were taken predose, at day 15 and at day 29. The ADA datasets from the analysis of the same samples by the two assays are markedly different and show limited correlation between the assay formats. Analysis of these samples using the bridging ELISA detected positive ADA responses in 29 patients, while the direct IgG ELISA detected positive responses in 64 patients. Twenty-five patients produced ADAs that were detected in both the bridging and the direct IgG ELISAs. Whereas the direct IgG ELISA did not detect any confirmed positive ADA responses in predose samples, the bridging ELISA detected and confirmed positive ADAs in three patients’ predose samples in whom the related subsequent postdose samples tested negative. In total, 97% of the positive samples identified by the bridging ELISA were also determined to be positive by the direct ELISA. The direct IgG ELISA detected a further 35 patients to be positive for ADAs. Retrospective analysis of the two datasets and the development and validation of the assays was undertaken to investigate why the parallel datasets were not more comparable. The investigation covered a range of factors, spanning assay format, study design, possible immunological factors and, crucially, analytical performance. Bridging versus direct immunoassay format The fundamental difference between the two assay formats is the specific spectrum of analyte detected. Given that the immunoglobulins detected by the two immunoassays are potentially different, it may be expected that different results would be obtained. The direct ELISA
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Bioanalysis (2014) 6(14)
– by virtue of its anti-IgG (Fc) detection antibody – will detect IgG regardless of valency, potentially with greater sensitivity, as double-arm binding of the analyte is possible. The bridging ELISA – by virtue of its use of therapeutic antibody as both capture and detection reagents – will be highly specific and theoretically able to detect all multivalent isotypes of immunoglobulin with the possible exception of IgG4, as discussed below. The sampling time-point from the study is also relevant, as following initial dosing in week 1, sampling occurred at day 15 and day 29. From a generalized immunological perspective, these time-points will likely capture the humoral IgG that will follow the primary immune response that is typically characterized by low-affinity IgM. This should be measurable in both assay formats. A further distinction between the assay formats is the inability of the bridging ELISA to detect IgG4, potentially accounting for the decreased detection rate with this assay. IgG4 has been shown to undergo Fab arm exchange in vivo to form antibodies with different binding specificities in the two Fab arms [8] . This may render a proportion of the IgG4 molecules unable to bind in a bivalent fashion [8] , which is a prerequisite for the bridging assay format. However, from an immunological perspective, it is proposed that IgG4 is typically the last form of class switch observed in the temporal progression of immunoglobulin production [9] . IgG4 is hypothesized to have an anti-inflammatory blocking effect that, with IgG2, may modulate the proinflammatory action of IgG1 and is reported to be typically observed during long-term exposure to antigens, such as chronic parasitic infection or desensitization therapy by repeat exposure to low levels of antigens [9] . This type of immune response does not fit the profile that may be expected from the Phase IIb dosing regimen or the sampling time-points. Although we have not been able to formally exclude the possibility that the ADA response has a significant IgG4 component, we believe it is unlikely that IgG4 is the causal factor in the differing datasets. A factor that is relevant for both assay formats was drug tolerance. Therapeutics present in the matrix will likely interact with ADAs, reducing the availability to bind in the assay, thus inhibiting assay function. Drug tolerance was known to not be a significant issue for these assays, as the disease indication was severe, with a criterion for acceptance to the clinical study of organ dysfunction. Analysis of PK data showed swift clearance of the therapeutic and earlier studies had demonstrated a loss of therapeutic via urine. Immunoassay optimization to aid viable cut-points More pertinent is to address analytical factors and to critique relative assay performance based on
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Keeping pace with immunogenicity advances through the development of a biological therapeutic
Bioanalytical Challenge
Table 1. Overview of absorbance data from screening cut-point assessments. Batch 1
Batch 2
Batch 3
Batch 4
Batch 5
Batch 6
Minmum OD
0.0286
0.0232
0.0239
0.0274
0.0262
0.0265
Maximum OD
1.0506
0.6215
0.8839
0.9936
0.9695
1.1225
Maximum OD (biological outlier excluded)
0.1830
0.0976
0.2524
0.2389
0.1952
0.1880
Mean OD
0.0869
0.0571
0.0718
0.0797
0.0786
0.0838
Direct ELISA
Cut-point factor 1.640
Bridging ELISA Minimum OD
0.0121
0.01315
0.0115
0.0122
0.0132
0.0121
Maximum OD
0.0160
0.0163
0.0165
0.0161
0.0190
0.0170
Mean OD
0.0145
0.0148
0.0139
0.0139
0.016
0.0145
Cut-point factor 1.071 OD: Optical density.
the obtained raw data values, particularly the data observed for ADA-negative samples in the region of the screening cut-point. In practice, to determine the cutpoint, drug-naive samples were run across six batches with 51 individuals per batch analyzed across three assay plates. Outliers were removed by use of box-andwhisker plots and the data were assessed for normality using the Shapiro–Wilk test. Data for both ELISAs showed evidence of non-normality, irrespective of logtransformation, and thus were ranked and the batch cut-points set at the 95th percentile. The mean ratio of cut-point divided by the negative control pool of sera for each batch was used as the assay cut-point factor. The 95th percentile is used as a safety mechanism to ensure the assay will pick up all true ADA-positive samples, as well as a theoretical 5% of false positives. Data ranges (prior to removal of technical outliers) and the mean raw data for each batch for the direct and bridging ELISAs used in the determination of the screening cut-points during the validation performed prestudy in 2012 are shown in Table 1. Absorbance levels for the bridging ELISA are very low compared with the direct assay. The bridging ELISA has a narrow range of observed absorbance levels and the calculated cut-point factor is therefore correspondingly low. Data from the direct IgG ELISA are less constrained and show a greater variance of absorbance values, with a higher cut-point factor as a result. The extent of the difference between the data is highlighted in Figure 1A–C . Figures 1A & 1B display the raw absorbance data for the six cut-point batches for the direct and bridging ELISAs, respectively. Figure 1C shows the data from both assays on the same scale. The highest
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absorbance signal on the bridging ELISA is lower than the minimum signal observed on the direct ELISA. Cut-point data vary due to analytical and biological factors. The screening cut-point should reflect the natural (biological) variation in responses from drugnaive individuals as opposed to the day-to-day (analytical) variation in the absolute background signal. Analytical variation is controlled and modeled by running cut-point samples using defined reagents and controls on multiple occasions by multiple analysts using a balanced experimental design. Biological variation, on the other hand, is a function of the population the data are modeled on (a constant between the datasets here), the immunochemistry of the assay and the stringency imposed by the reagents and assay conditions. In a drug-naive sample, biological variation is potentially the product of pre-existing antibodies, nonspecific assay interference or, in a bridging ELISA, this is theoretically attributable to target engagement. The respective ELISA data indicate different levels of stringency between the two assays. Low absorbance data and a narrow distribution indicate that biological variation has been minimized on the bridging ELISA – the signal observed is not representative of any biological variation between samples, but rather is more likely instrument noise from the plate reader. As such, these stringent conditions are not favorable; calculating statistical cut-points from data that are representative only of instrument noise is not desirable. That an assay is well optimized to detect biological variation can be seen by how reproducible raw data are across the multiple determinations made during the cut-point setting. Data for the direct and bridg-
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1955
Bioanalytical Challenge Jones & Roberts ing IgG ELISAs were ranked by response for each cutpoint batch, as shown in Figures 2 & 3, respectively. If biological variation is observed, samples with higher responses should rank in the same relative position in all six batches. Conversely, if biological variation is not evident, it should be apparent in the random ranking of each sample across the six batches. The difference in consistency of response is clear, with the direct IgG ELISA showing a level of reproducibility of relative ranking of the samples not observed with the data from the bridging ELISA. The bridging ELISA does not appear to be generating data based on biological variation in the samples. The ability of the dataset of the bridging assay (comprising a narrow range of very low absorbance signals) to form a parametric distribution is further compromised as the data will be skewed to the low end, where the minimum instrument signal is reached. Mire-Sluis et al. defined the cut-point as the statistically determined point that distinguishes nonspecific binding in the matrix above which a putative positive can be detected (i.e., differentiating signals that are attributable to nonspecific binding from specific binding) [1] . Highly stringent assay conditions that effectively remove nonspecific binding could impact on the functionality of the cut-point, because while a cut-point applied to such data can be mathematically correct, the cut-point is not providing an objective distinction between non-specific and specific binding and so is arguably experimentally inappropriate. A highly stringent assay will likely also affect the confirmatory assay. The confirmatory cut-point is based on the degree of competitive signal inhibition observed following immunodepletion by the addition of a drug to a sample containing ADAs. The confirmatory cutpoints for these assays were defined by analysis of signal inhibition across the same 51 in-study predose samples, with an experimental design that was comparable to the methodology for determining the screening cut-point. A parametric cut-point was defined at the 99% confidence limit for the bridging ELISA at 14.7% signal inhibition and the direct IgG ELISA at 25.2% signal inhibition. Kubiak et al. analyze the challenges of using confirmatory assays with a bridging format in detail and discuss the concept of orthogonality between the Key Terms Confirmatory assay: Samples identified as above the screening cut-point are then assayed in the presence of a predetermined level of therapeutic in order to confirm the screening result by immunodepeletion. Quasiquantitative analysis: A lack of truly representative control for antidrug antibody (ADA) responses necessitates a quasiquantitative and tiered approach to ADA analysis. Typically, this is through the screening of samples and subsequent confirmation and titrating of those identified as positives.
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Bioanalysis (2014) 6(14)
screening assay and the confirmatory assay in solutionphase immunoassays [10] . They provide statistical evidence that screening cut-point and confirmatory cutpoint data are often not orthogonal, finding a positive correlation between the two [10] . From a theoretical viewpoint on assay development, we believe our data show that if an assay is too stringent, the same challenge remains for the confirmatory assay as for the screening assay; where the raw data are signals barely above instrument noise, the impact of immunodepletion on the sample may be difficult to measure. The impact that the drug spike may have on the binding of the assay is not translated to the signal as the assay is too clean, resulting in a narrow distribution of signal inhibition data and a low confirmatory cut-point. It is likely that an inability to confirm potentially positive samples on the confirmatory assay explains the low detection rate of the bridging assay. This also accounts for the presence of false positives in the form of predose samples above the cut-point. This may also explain the higher than expected false-positive rate from the screening assay. Recognizing that immunoassay requirements for immunogenicity are unique The recognition that introducing an element of variation needs to be factored in when developing quasiquantitative assays for the detection of ADAs is the crux of the learning curve of the past years. For PK purposes, where sensitivity is an overriding consideration, highly stringent assays are developed because stringency is fundamental to sensitivity. The direct IgG and bridging ELISAs assessed here were developed when the PK paradigm very heavily influenced the assays for ADA detection. During the original assay validations for these assays in 2008, the focus was on the quantitative performance of positive control samples prepared across the dynamic range of a calibration (positive control) curve, with performance defined by accuracy and precision. Less emphasis was placed on the determination of statistical cut-points. When the assays were re-established in 2012, the updated validation allowed the recommendations made in Shankar et al. to be applied [2] . Cut-points for both screening and confirmatory assays were determined with increased statistical rigor. The reproducibility of end-point titers was assessed and sensitivity was estimated using a positive control titration series. Crucially, the wet chemistry of the assays was unchanged. The influence that the PK paradigm has played in the development of the assays is evident in the resultant study data. It is probable that the bridging ELISA has been outperformed in sample analysis by the direct IgG ELISA because the direct IgG assay, by virtue of a less specific detection reagent, has introduced an element of biological varia-
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Keeping pace with immunogenicity advances through the development of a biological therapeutic
A
Bioanalytical Challenge
0.30
Absorbance OD
0.25 0.20 0.15 0.10 0.05 0 0
1
2
3
4
5
6
4
5
6
8
10
12
Batches 1–6† B Absorbance OD
0.020 0.015 0.010 0.005 0 0
1
2
3 Batches 1–6
C
Absorbance OD
0.30 0.25 0.20 0.15 0.10 0.05 0 0
2
4
6
Columns 1–6: direct IgG ELISA batches 1–6† Columns 7–12: bridging ELISA batches 1–6 Figure 1. Graphical representation of absorbance data from screening cut-point assessments for the Direct IgG ELISA (A) and the Bridging ELISA (B) with all data plotted on the same scale axis in (C). † Single outlier removed to avoid skewed scale on y-axis.
tion. The assay has therefore provided a better dataset for statistical modeling of cut-points. However, there is a contradiction when looking at the relative sensitivities calculated for each assay. The bridging ELISA was estimated to be able to detect ADAs to 100 ng/ml, below the 250–500-ng/ml level deemed clinically relevant in guidance literature by the FDA [5] . The direct IgG ELISA was apparently over tenfold less sensitive, yet in practice, it detected twice as many confirmed positive patients as the bridging ELISA. The bridging ELISA displays the hallmarks of a PK assay, and this is
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a cautionary tale in relying purely upon sensitivity data when critiquing an immunogenicity assay. Defining sensitivity as the sole criterion by which to judge the suitability of an ADA assay is not recommended, and sensitivity must be balanced with an assay that generates appropriate experimental data upon which to base statistical assessments. Conclusion & future perspective An assay developed today would address these requirements. Assays are often semihomogenous
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Bioanalytical Challenge Jones & Roberts
Figure 2. Raw data generated using the Direct IgG ELISA from the six screening cut-point determination batches, ranked for assessment of reproducibility.
in format in order to maximize drug tolerance. Reduced wash steps may maximize the detection of antibodies with fast on–off rates [1] and aid optimization in order to generate appropriate biological variation in the assay. When insufficient drug tolerance is observed, the effect of an acid dissociation step is frequently assessed. Where it remains challenging to introduce variation into a drug-naive-based confirmatory assay cut-point, it is possible to introduce a low-level spike of positive control into drug-naive samples with which to determine signal inhibition by a competing free drug. More so than with a PK
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Bioanalysis (2014) 6(14)
assay, immunogenicity assays must be monitored and revalidated frequently, especially when critical reagents are replaced or patient populations change. This maintenance is critical in allowing assays to evolve in order to remain in-line with industry and regulatory expectations. Importantly, data should be reviewed after sample analysis in order to assess the effectiveness of the cut-points, and it may on occasion be justifiable to revisit the calculation of the cutpoints if the data do not meet the expectations of the statistical modeling. However, a significant challenge is that immunogenicity analysis often occurs at
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Keeping pace with immunogenicity advances through the development of a biological therapeutic
Bioanalytical Challenge
Figure 3. Raw data generated using the Bridging ELISA from the screening cut-point determination batches, ranked for assessment of reproducibility.
the end of the study, especially if a wash-out period is employed in order to minimize drug interference. Monitoring immunogenicity assay performance requires time and can be an iterative process. If immunogenicity data are on the critical path, there can be considerable time pressure to deliver data. This needs careful management with study teams as early as possible in order to ensure expectations and data delivery are not compromised. Acknowledgements The authors would like to thank A Hawes and R Longdin for
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their assistance in the preparation of this manuscript.
Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
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Bioanalytical Challenge Jones & Roberts
Executive summary Background • The increase in biological therapeutics in drug development pipelines has driven the demand for the analysis of immunogenicity, largely delivered using ligand-binding assays. • Assays are quasiquantitative and based upon the application of statistical cut-points in order to identify samples that are positive for antidrug antibodies. • The understanding of assay requirements for immunogenicity assessment has evolved rapidly.
Challenges encountered • Data from a direct IgG ELISA and bridging ELISA developed and used over a 6-year period to support in parallel a clinical program for a biological therapeutic highlight this learning curve. • When applied in a global Phase IIb study, the direct IgG ELISA detected over twofold more positive patients than the bridging ELISA.
Appropriate assay optimization is crucial for viable statistical cut-points • Analysis of the raw data highlights the fact that the bridging ELISA has very low absorbance data with a narrow distribution, which contrasts with data from the direct IgG ELISA. • Assays developed with overly stringent conditions may negatively impact the application of statistical cut-points. • The data provide evidence that the bridging ELISA may have been less effective owing to assay optimization that would have been more appropriate for a quantitative assay design.
References
5
US Department of Health and Human Services; Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER). Guidance for industry: assay development for immunogenicity testing of therapeutic proteins (draft guidance) (2009).
6
US Department of Health and Human Services; Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER). Guidance for industry: immunogenicity assessment for therapeutic protein products (draft guidance) (2013).
7
DiMasi JA, Hansen RW, Grabowski HG. The price off innovation: new estimates of drug development costs. J. Health Econ. 22, 151–185 (2003).
8
Aalberse RC, Schuurman J. IgG4 breaking the rules. Immunology 105, 9–19 (2002).
9
Collins AM, Jackson KJ. A temporal model of human IgE and IgG antibody function. Front. Immunol. 4, 235 (2013).
10
Kubiak RJ, Zhang L, Zhang J et al. Correlation of screening and confirmatory results in tiered immunogenicity testing by solution-phase bridging assays. J. Pharm. Biomed. Anal. 74, 235–245 (2013).
Papers of special note have been highlighted as: •• of considerable interest
1
••
Informative and widely accepted industry white paper.
2
Shankar G, Devanarayn V, Amaravadi L et al. Recommendations for the validation of immunoassays used for detection of host antibodies against biotechnology products. J. Pharm. Biomed. Anal. 48, 1267–1281 (2008).
••
Informative and widely accepted industry white paper.
3
Committee for Medicinal Products for Human Use (CHMP). Guideline on immunogenicity assessment of biotechnology-derived therapeutic proteins. EMEA/CHMP/ BMWP/14327/2006 (2008).
4
1960
Mire-Sluis AR, Barrett YC, Devanarayan V et al. Recommendations for the design and optimization of immunoassays used in the detection of host antibodies against biotechnology products. J. Immunol. Meth. 289, 1–16 (2004).
Committee for Medicinal Products for Human Use (CHMP). Guideline on similar biological products containing monoclonal antibodies – non-clinical and clinical issues EMA/CHMP/BMWP/403543/2010 (2012).
Bioanalysis (2014) 6(14)
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Challenges in keeping pace with immunogenicity advances throughout the clinical development of a biological therapeutic Progress in the assessment of immunogenicity over the past decade has been rapid; therapeutics in late-phase clinical studies today are subject to antidrug antibody analyses that have evolved considerably since the start of their clinical development. Data from bridging and direct ELISA methods used for the analysis of antidrug antibodies generated in response to an antibody therapeutic will be presented. The case study will highlight some of the challenges faced and the learning points gained from supporting immunogenicity analysis for these therapeutics.
The demand for assays capable of detecting antidrug antibodies (ADAs) has increased significantly over the last 20 years, driven primarily by a rise in the number of biological therapeutics entering drug development. Owing to their biological origin, size and complexity, such therapeutics carry a greater risk of eliciting an immune response in those receiving the treatment, a response detected most commonly using ligand-binding assays (LBAs). The challenge has been to understand the unique requirements of developing LBAs for this application and how these differ from those of pharmacokinetic (PK) assays. The first widely recognized and comprehensive white paper on the subject was published in 2004 by Mire-Sluis et al. [1] , addressing the analytical challenges posed by ADAs. Four years later, Shankar et al. provided additional focus on appropriate experimental designs and the application of statistics [2] . Guideline documents from both the EMA [3,4] and the US FDA [5,6] have gone some way to clarifying expectations from regulatory bodies. How LBAs should be designed and optimized to achieve these expectations is less well documented in the current literature. From investigational new drug approval to the completion of Phase III clinical studies typically takes in excess of 6 years [7] . Therapeutics at late-stage clinical assessment today have been developed
10.4155/BIO.14.135 © 2014 Future Science Ltd
Chris Jones*,1 & Andrew Roberts1 LGC, Newmarket Road, Fordham, Cambridgeshire, CB7 5WW, UK *Author for correspondence: [email protected] 1
through a period that has seen the analysis of ADAs evolve significantly. This learning curve is neatly highlighted by the case study of an antibody therapeutic in clinical trials from 2008 to 2012. Initial assays for ADA assessment were developed in 2006. These were ELISA of a direct format and were isotype specific. Significant challenges in procuring positive control material for IgE and IgM solely led to the successful development of a direct assay for IgG; thus, a bridging ELISA was also developed. The direct IgG and bridging ELISAs were sequential in format, featuring wash steps between each reagent incubation step. Both utilized therapeutic antibodies immobilized to the solid phase as capture reagents. Samples or controls were incubated on the assay plate before the addition of the detection reagent. The direct IgG ELISA utilized a horseradish peroxidase (HRP)-conjugated anti-IgG specific for the Fc region for detection reagent, whereas the bridging ELISA used biotinylated therapeutic coupled with extravidin HRP. The control used for both assays was a polyclonal antispecies IgG, with both assays using a 20-fold minimum required dilution. Assays were validated for use in support of regulated studies in 2008, with a partial validation performed during assay re-establishment in 2012. Both assays were used together in support of Phase II clinical tri-
Bioanalysis (2014) 6(14), 1953–1960
part of
ISSN 1757-6180
1953
Bioanalytical Challenge Jones & Roberts
Key Terms Immunogenicity: The ability of a therapeutic to elicit an immune response. This has potential consequences for safety and efficacy. Screening cut-point: The screening cut-point enables the classification of samples as either potential ADA-positive samples or ADA-negative samples based on the statistical threshold calculated from a drug-naive population.
als. As such, two datasets were generated using the same sample populations up to a relatively late phase of clinical development. Opportunities to compare and contrast two immunogenicity datasets from the same population are rare. These data provide an insight into the relative merits of each assay format and, moreover, clearly illustrate the advances in understanding in how to optimize LBAs for ADA assessment that have been made over the past 8 years. Failures encountered The global Phase IIb study was a multicenter study comparing the safety and efficacy of two dosing regimens with 300 patients recruited across three cohorts: two dosed and one placebo. Subjects were dosed daily for 6 days and samples for analysis of ADAs were taken predose, at day 15 and at day 29. The ADA datasets from the analysis of the same samples by the two assays are markedly different and show limited correlation between the assay formats. Analysis of these samples using the bridging ELISA detected positive ADA responses in 29 patients, while the direct IgG ELISA detected positive responses in 64 patients. Twenty-five patients produced ADAs that were detected in both the bridging and the direct IgG ELISAs. Whereas the direct IgG ELISA did not detect any confirmed positive ADA responses in predose samples, the bridging ELISA detected and confirmed positive ADAs in three patients’ predose samples in whom the related subsequent postdose samples tested negative. In total, 97% of the positive samples identified by the bridging ELISA were also determined to be positive by the direct ELISA. The direct IgG ELISA detected a further 35 patients to be positive for ADAs. Retrospective analysis of the two datasets and the development and validation of the assays was undertaken to investigate why the parallel datasets were not more comparable. The investigation covered a range of factors, spanning assay format, study design, possible immunological factors and, crucially, analytical performance. Bridging versus direct immunoassay format The fundamental difference between the two assay formats is the specific spectrum of analyte detected. Given that the immunoglobulins detected by the two immunoassays are potentially different, it may be expected that different results would be obtained. The direct ELISA
1954
Bioanalysis (2014) 6(14)
– by virtue of its anti-IgG (Fc) detection antibody – will detect IgG regardless of valency, potentially with greater sensitivity, as double-arm binding of the analyte is possible. The bridging ELISA – by virtue of its use of therapeutic antibody as both capture and detection reagents – will be highly specific and theoretically able to detect all multivalent isotypes of immunoglobulin with the possible exception of IgG4, as discussed below. The sampling time-point from the study is also relevant, as following initial dosing in week 1, sampling occurred at day 15 and day 29. From a generalized immunological perspective, these time-points will likely capture the humoral IgG that will follow the primary immune response that is typically characterized by low-affinity IgM. This should be measurable in both assay formats. A further distinction between the assay formats is the inability of the bridging ELISA to detect IgG4, potentially accounting for the decreased detection rate with this assay. IgG4 has been shown to undergo Fab arm exchange in vivo to form antibodies with different binding specificities in the two Fab arms [8] . This may render a proportion of the IgG4 molecules unable to bind in a bivalent fashion [8] , which is a prerequisite for the bridging assay format. However, from an immunological perspective, it is proposed that IgG4 is typically the last form of class switch observed in the temporal progression of immunoglobulin production [9] . IgG4 is hypothesized to have an anti-inflammatory blocking effect that, with IgG2, may modulate the proinflammatory action of IgG1 and is reported to be typically observed during long-term exposure to antigens, such as chronic parasitic infection or desensitization therapy by repeat exposure to low levels of antigens [9] . This type of immune response does not fit the profile that may be expected from the Phase IIb dosing regimen or the sampling time-points. Although we have not been able to formally exclude the possibility that the ADA response has a significant IgG4 component, we believe it is unlikely that IgG4 is the causal factor in the differing datasets. A factor that is relevant for both assay formats was drug tolerance. Therapeutics present in the matrix will likely interact with ADAs, reducing the availability to bind in the assay, thus inhibiting assay function. Drug tolerance was known to not be a significant issue for these assays, as the disease indication was severe, with a criterion for acceptance to the clinical study of organ dysfunction. Analysis of PK data showed swift clearance of the therapeutic and earlier studies had demonstrated a loss of therapeutic via urine. Immunoassay optimization to aid viable cut-points More pertinent is to address analytical factors and to critique relative assay performance based on
future science group
Keeping pace with immunogenicity advances through the development of a biological therapeutic
Bioanalytical Challenge
Table 1. Overview of absorbance data from screening cut-point assessments. Batch 1
Batch 2
Batch 3
Batch 4
Batch 5
Batch 6
Minmum OD
0.0286
0.0232
0.0239
0.0274
0.0262
0.0265
Maximum OD
1.0506
0.6215
0.8839
0.9936
0.9695
1.1225
Maximum OD (biological outlier excluded)
0.1830
0.0976
0.2524
0.2389
0.1952
0.1880
Mean OD
0.0869
0.0571
0.0718
0.0797
0.0786
0.0838
Direct ELISA
Cut-point factor 1.640
Bridging ELISA Minimum OD
0.0121
0.01315
0.0115
0.0122
0.0132
0.0121
Maximum OD
0.0160
0.0163
0.0165
0.0161
0.0190
0.0170
Mean OD
0.0145
0.0148
0.0139
0.0139
0.016
0.0145
Cut-point factor 1.071 OD: Optical density.
the obtained raw data values, particularly the data observed for ADA-negative samples in the region of the screening cut-point. In practice, to determine the cutpoint, drug-naive samples were run across six batches with 51 individuals per batch analyzed across three assay plates. Outliers were removed by use of box-andwhisker plots and the data were assessed for normality using the Shapiro–Wilk test. Data for both ELISAs showed evidence of non-normality, irrespective of logtransformation, and thus were ranked and the batch cut-points set at the 95th percentile. The mean ratio of cut-point divided by the negative control pool of sera for each batch was used as the assay cut-point factor. The 95th percentile is used as a safety mechanism to ensure the assay will pick up all true ADA-positive samples, as well as a theoretical 5% of false positives. Data ranges (prior to removal of technical outliers) and the mean raw data for each batch for the direct and bridging ELISAs used in the determination of the screening cut-points during the validation performed prestudy in 2012 are shown in Table 1. Absorbance levels for the bridging ELISA are very low compared with the direct assay. The bridging ELISA has a narrow range of observed absorbance levels and the calculated cut-point factor is therefore correspondingly low. Data from the direct IgG ELISA are less constrained and show a greater variance of absorbance values, with a higher cut-point factor as a result. The extent of the difference between the data is highlighted in Figure 1A–C . Figures 1A & 1B display the raw absorbance data for the six cut-point batches for the direct and bridging ELISAs, respectively. Figure 1C shows the data from both assays on the same scale. The highest
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absorbance signal on the bridging ELISA is lower than the minimum signal observed on the direct ELISA. Cut-point data vary due to analytical and biological factors. The screening cut-point should reflect the natural (biological) variation in responses from drugnaive individuals as opposed to the day-to-day (analytical) variation in the absolute background signal. Analytical variation is controlled and modeled by running cut-point samples using defined reagents and controls on multiple occasions by multiple analysts using a balanced experimental design. Biological variation, on the other hand, is a function of the population the data are modeled on (a constant between the datasets here), the immunochemistry of the assay and the stringency imposed by the reagents and assay conditions. In a drug-naive sample, biological variation is potentially the product of pre-existing antibodies, nonspecific assay interference or, in a bridging ELISA, this is theoretically attributable to target engagement. The respective ELISA data indicate different levels of stringency between the two assays. Low absorbance data and a narrow distribution indicate that biological variation has been minimized on the bridging ELISA – the signal observed is not representative of any biological variation between samples, but rather is more likely instrument noise from the plate reader. As such, these stringent conditions are not favorable; calculating statistical cut-points from data that are representative only of instrument noise is not desirable. That an assay is well optimized to detect biological variation can be seen by how reproducible raw data are across the multiple determinations made during the cut-point setting. Data for the direct and bridg-
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Bioanalytical Challenge Jones & Roberts ing IgG ELISAs were ranked by response for each cutpoint batch, as shown in Figures 2 & 3, respectively. If biological variation is observed, samples with higher responses should rank in the same relative position in all six batches. Conversely, if biological variation is not evident, it should be apparent in the random ranking of each sample across the six batches. The difference in consistency of response is clear, with the direct IgG ELISA showing a level of reproducibility of relative ranking of the samples not observed with the data from the bridging ELISA. The bridging ELISA does not appear to be generating data based on biological variation in the samples. The ability of the dataset of the bridging assay (comprising a narrow range of very low absorbance signals) to form a parametric distribution is further compromised as the data will be skewed to the low end, where the minimum instrument signal is reached. Mire-Sluis et al. defined the cut-point as the statistically determined point that distinguishes nonspecific binding in the matrix above which a putative positive can be detected (i.e., differentiating signals that are attributable to nonspecific binding from specific binding) [1] . Highly stringent assay conditions that effectively remove nonspecific binding could impact on the functionality of the cut-point, because while a cut-point applied to such data can be mathematically correct, the cut-point is not providing an objective distinction between non-specific and specific binding and so is arguably experimentally inappropriate. A highly stringent assay will likely also affect the confirmatory assay. The confirmatory cut-point is based on the degree of competitive signal inhibition observed following immunodepletion by the addition of a drug to a sample containing ADAs. The confirmatory cutpoints for these assays were defined by analysis of signal inhibition across the same 51 in-study predose samples, with an experimental design that was comparable to the methodology for determining the screening cut-point. A parametric cut-point was defined at the 99% confidence limit for the bridging ELISA at 14.7% signal inhibition and the direct IgG ELISA at 25.2% signal inhibition. Kubiak et al. analyze the challenges of using confirmatory assays with a bridging format in detail and discuss the concept of orthogonality between the Key Terms Confirmatory assay: Samples identified as above the screening cut-point are then assayed in the presence of a predetermined level of therapeutic in order to confirm the screening result by immunodepeletion. Quasiquantitative analysis: A lack of truly representative control for antidrug antibody (ADA) responses necessitates a quasiquantitative and tiered approach to ADA analysis. Typically, this is through the screening of samples and subsequent confirmation and titrating of those identified as positives.
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screening assay and the confirmatory assay in solutionphase immunoassays [10] . They provide statistical evidence that screening cut-point and confirmatory cutpoint data are often not orthogonal, finding a positive correlation between the two [10] . From a theoretical viewpoint on assay development, we believe our data show that if an assay is too stringent, the same challenge remains for the confirmatory assay as for the screening assay; where the raw data are signals barely above instrument noise, the impact of immunodepletion on the sample may be difficult to measure. The impact that the drug spike may have on the binding of the assay is not translated to the signal as the assay is too clean, resulting in a narrow distribution of signal inhibition data and a low confirmatory cut-point. It is likely that an inability to confirm potentially positive samples on the confirmatory assay explains the low detection rate of the bridging assay. This also accounts for the presence of false positives in the form of predose samples above the cut-point. This may also explain the higher than expected false-positive rate from the screening assay. Recognizing that immunoassay requirements for immunogenicity are unique The recognition that introducing an element of variation needs to be factored in when developing quasiquantitative assays for the detection of ADAs is the crux of the learning curve of the past years. For PK purposes, where sensitivity is an overriding consideration, highly stringent assays are developed because stringency is fundamental to sensitivity. The direct IgG and bridging ELISAs assessed here were developed when the PK paradigm very heavily influenced the assays for ADA detection. During the original assay validations for these assays in 2008, the focus was on the quantitative performance of positive control samples prepared across the dynamic range of a calibration (positive control) curve, with performance defined by accuracy and precision. Less emphasis was placed on the determination of statistical cut-points. When the assays were re-established in 2012, the updated validation allowed the recommendations made in Shankar et al. to be applied [2] . Cut-points for both screening and confirmatory assays were determined with increased statistical rigor. The reproducibility of end-point titers was assessed and sensitivity was estimated using a positive control titration series. Crucially, the wet chemistry of the assays was unchanged. The influence that the PK paradigm has played in the development of the assays is evident in the resultant study data. It is probable that the bridging ELISA has been outperformed in sample analysis by the direct IgG ELISA because the direct IgG assay, by virtue of a less specific detection reagent, has introduced an element of biological varia-
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Keeping pace with immunogenicity advances through the development of a biological therapeutic
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Columns 1–6: direct IgG ELISA batches 1–6† Columns 7–12: bridging ELISA batches 1–6 Figure 1. Graphical representation of absorbance data from screening cut-point assessments for the Direct IgG ELISA (A) and the Bridging ELISA (B) with all data plotted on the same scale axis in (C). † Single outlier removed to avoid skewed scale on y-axis.
tion. The assay has therefore provided a better dataset for statistical modeling of cut-points. However, there is a contradiction when looking at the relative sensitivities calculated for each assay. The bridging ELISA was estimated to be able to detect ADAs to 100 ng/ml, below the 250–500-ng/ml level deemed clinically relevant in guidance literature by the FDA [5] . The direct IgG ELISA was apparently over tenfold less sensitive, yet in practice, it detected twice as many confirmed positive patients as the bridging ELISA. The bridging ELISA displays the hallmarks of a PK assay, and this is
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a cautionary tale in relying purely upon sensitivity data when critiquing an immunogenicity assay. Defining sensitivity as the sole criterion by which to judge the suitability of an ADA assay is not recommended, and sensitivity must be balanced with an assay that generates appropriate experimental data upon which to base statistical assessments. Conclusion & future perspective An assay developed today would address these requirements. Assays are often semihomogenous
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Bioanalytical Challenge Jones & Roberts
Figure 2. Raw data generated using the Direct IgG ELISA from the six screening cut-point determination batches, ranked for assessment of reproducibility.
in format in order to maximize drug tolerance. Reduced wash steps may maximize the detection of antibodies with fast on–off rates [1] and aid optimization in order to generate appropriate biological variation in the assay. When insufficient drug tolerance is observed, the effect of an acid dissociation step is frequently assessed. Where it remains challenging to introduce variation into a drug-naive-based confirmatory assay cut-point, it is possible to introduce a low-level spike of positive control into drug-naive samples with which to determine signal inhibition by a competing free drug. More so than with a PK
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assay, immunogenicity assays must be monitored and revalidated frequently, especially when critical reagents are replaced or patient populations change. This maintenance is critical in allowing assays to evolve in order to remain in-line with industry and regulatory expectations. Importantly, data should be reviewed after sample analysis in order to assess the effectiveness of the cut-points, and it may on occasion be justifiable to revisit the calculation of the cutpoints if the data do not meet the expectations of the statistical modeling. However, a significant challenge is that immunogenicity analysis often occurs at
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Keeping pace with immunogenicity advances through the development of a biological therapeutic
Bioanalytical Challenge
Figure 3. Raw data generated using the Bridging ELISA from the screening cut-point determination batches, ranked for assessment of reproducibility.
the end of the study, especially if a wash-out period is employed in order to minimize drug interference. Monitoring immunogenicity assay performance requires time and can be an iterative process. If immunogenicity data are on the critical path, there can be considerable time pressure to deliver data. This needs careful management with study teams as early as possible in order to ensure expectations and data delivery are not compromised. Acknowledgements The authors would like to thank A Hawes and R Longdin for
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their assistance in the preparation of this manuscript.
Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
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Bioanalytical Challenge Jones & Roberts
Executive summary Background • The increase in biological therapeutics in drug development pipelines has driven the demand for the analysis of immunogenicity, largely delivered using ligand-binding assays. • Assays are quasiquantitative and based upon the application of statistical cut-points in order to identify samples that are positive for antidrug antibodies. • The understanding of assay requirements for immunogenicity assessment has evolved rapidly.
Challenges encountered • Data from a direct IgG ELISA and bridging ELISA developed and used over a 6-year period to support in parallel a clinical program for a biological therapeutic highlight this learning curve. • When applied in a global Phase IIb study, the direct IgG ELISA detected over twofold more positive patients than the bridging ELISA.
Appropriate assay optimization is crucial for viable statistical cut-points • Analysis of the raw data highlights the fact that the bridging ELISA has very low absorbance data with a narrow distribution, which contrasts with data from the direct IgG ELISA. • Assays developed with overly stringent conditions may negatively impact the application of statistical cut-points. • The data provide evidence that the bridging ELISA may have been less effective owing to assay optimization that would have been more appropriate for a quantitative assay design.
References
5
US Department of Health and Human Services; Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER). Guidance for industry: assay development for immunogenicity testing of therapeutic proteins (draft guidance) (2009).
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US Department of Health and Human Services; Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER). Guidance for industry: immunogenicity assessment for therapeutic protein products (draft guidance) (2013).
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DiMasi JA, Hansen RW, Grabowski HG. The price off innovation: new estimates of drug development costs. J. Health Econ. 22, 151–185 (2003).
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Aalberse RC, Schuurman J. IgG4 breaking the rules. Immunology 105, 9–19 (2002).
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Collins AM, Jackson KJ. A temporal model of human IgE and IgG antibody function. Front. Immunol. 4, 235 (2013).
10
Kubiak RJ, Zhang L, Zhang J et al. Correlation of screening and confirmatory results in tiered immunogenicity testing by solution-phase bridging assays. J. Pharm. Biomed. Anal. 74, 235–245 (2013).
Papers of special note have been highlighted as: •• of considerable interest
1
••
Informative and widely accepted industry white paper.
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Shankar G, Devanarayn V, Amaravadi L et al. Recommendations for the validation of immunoassays used for detection of host antibodies against biotechnology products. J. Pharm. Biomed. Anal. 48, 1267–1281 (2008).
••
Informative and widely accepted industry white paper.
3
Committee for Medicinal Products for Human Use (CHMP). Guideline on immunogenicity assessment of biotechnology-derived therapeutic proteins. EMEA/CHMP/ BMWP/14327/2006 (2008).
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Mire-Sluis AR, Barrett YC, Devanarayan V et al. Recommendations for the design and optimization of immunoassays used in the detection of host antibodies against biotechnology products. J. Immunol. Meth. 289, 1–16 (2004).
Committee for Medicinal Products for Human Use (CHMP). Guideline on similar biological products containing monoclonal antibodies – non-clinical and clinical issues EMA/CHMP/BMWP/403543/2010 (2012).
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