Impact of positive control binding properties on anti-drug antibody assay performance

ABSTRACT

Background

Monitoring anti-drug antibodies (ADAs) is a critical component of recombinant protein drug development. Positive controls in ADA assays are essential for evaluating assay quality, yet the influence of their binding properties on assay performance is not well understood.

Research design and methods

We evaluated a panel of surrogate positive controls with varying binding characteristics to investigate how their affinity and kinetic parameters impact ADA assay performance. Binding properties were measured using Bio-Layer Interferometry (BLI), and assays were evaluated for relative sensitivity and drug tolerance. This was a laboratory-based study; no human or animal subjects were involved.

Results

We observed a correlation between higher affinity (lower K_D (equilibrium dissociation constant)) and lower k_off (off-rate constant) with increased relative assay sensitivity. However, no consistent relationship was found between these binding parameters and drug tolerance. These findings suggest that binding kinetics of the positive control can significantly influence sensitivity but may not predict drug tolerance.

Conclusions

Understanding the relationship between positive control binding properties and ADA assay performance can support the selection of reagents that optimize sensitivity. Limitations include the use of surrogate positive controls, which may not fully replicate the complexity of clinical ADA responses.

Plain Language Summary

When developing protein-based drugs, it’s important to test for anti-drug antibodies (ADAs), which can interfere with how well a drug works. These tests use positive control reagents to ensure the assay is working as expected. However, it’s not fully understood how the binding characteristics of these controls affect test results. In this study, we tested a group of lab-generated positive controls with different binding properties, including how tightly they bind to the drug and how quickly they bind and fall off. Using a technique called Bio-Layer Interferometry, we measured the strength and speed of these interactions. We then evaluated how these binding properties impacted the sensitivity of the ADA test and its ability to function in the presence of the drug (drug tolerance). We found that tighter binding and slower dissociation were linked to greater test sensitivity. However, there was no clear relationship between the binding properties and drug tolerance. This suggests that while the binding strength and kinetics of the positive controls can influence test sensitivity, they may not predict how the test performs when drug is present. These findings could help improve assay design, though it’s important to note that our use of lab-made controls may not fully replicate the complexity of actual patient antibodies.

1. Introduction

Anti-drug antibody (ADA) assays are critical for understanding the immunogenicity of biologic drugs and most importantly the impact of ADAs on the drug’s pharmacokinetics (PK), efficacy, and patient safety. The US Food and Drug Administration (FDA) recently emphasized the importance of understanding the impact with a recent labeling guidance that organizes the immunogenicity information in a single location, starting with the adequacy of the assay and then highlighting the impact of ADAs on patients’ relevant outcomes [1]. A proper positive control for an ADA assay enables the assay developer to understand if the assay is able to perform adequately to detect ADAs with a clinically significant impact and is crucial for meeting regulatory recommendations [2].

The literature connecting positive control affinity and/or binding kinetics to key assay properties is unclear [3–8]. The relationship can depend on the particular assay format, platform, and diversity of the clones being considered as positive controls. We set out to understand how the binding properties of a panel of anti-idiotypic (anti-ID) monoclonal antibodies (mAb) impact the relative assay sensitivity and measured drug tolerance in two bridging ADA screening enzyme-linked immunosorbent assays (ELISAs). Understanding this relationship can both guide our choice of positive controls as well as viewing these anti-IDs as ADA surrogates, and can inform how patient ADAs will be detected in the screening ADA assay.

2. Materials and methods

2.1. Materials for semi-homogeneous ADA bridging ELISA

The two assays utilized are the Trastuzumab ADA ELISA and the Tocilizumab ADA ELISA. For the Trastuzumab ADA assay, three different human Immunoglobulin G subclass 1 (IgG1) positive controls were used. The two Trastuzumab anti-ID mAb antibodies from Bio-Rad (Hercules, CA) are part numbers HCA176 and HCA 177. The Trastuzumab anti-ID from Genentech (South San Francisco, CA) is CDR hu4D5:9991.

Five different human IgG1 positive controls were used for the Tocilizumab ADA assay. The human IgG1 clones are manufactured by Bio-Rad, the clones used for the Tocilizumab ADA assay are part numbers HCA253, HCA254, HCA255, HCA256, and HCA 257 respectively.

2.2. Procedure for semi-homogeneous Biotin-Digoxigenin (DIG) Trastuzumab ADA screening assay and semi-homogeneous Biotin-DIG Tocilizumab ADA screening assay

The two semi-homogeneous Trastuzumab and Tocilizumab Biotin-DIG ADA assays followed the same protocol using their respective therapeutic conjugated reagent materials and controls. The assay was carried out over two days. On day one, the samples and positive controls were diluted in assay diluent (1X PBS, 0.5% BSA, 0.05% P20, 0.05% ProClin 300, pH 7.4) to a minimum required dilution (MRD) of 1:20. Following the 1:20 dilution of samples and controls, a master mix solution was prepared by diluting the respective assay therapeutic conjugated material (labeled with either DIG or Biotin) to 4 micrograms per milliliter (µg/ml) each, in assay diluent. The diluted samples and or controls were mixed 1:1 with the master mix solution and incubated at room temperature, overnight (16–22 hours), with agitation, in a sealed 96-well round-bottom polypropylene plate (Corning, Corning, NY). On day two, 100 µL of the solution was transferred from the polypropylene plate to a high binding capacity streptavidin microtiter plate (Roche, Basel, Switzerland), which was washed three times and incubated at room temperature for two hours, with agitation. After the two-hour incubation, the plate was washed again three times and 100 µL of Anti-Digoxigenin-horseradish peroxidase (HRP) (Roche, Basel, Switzerland) at 50 mU/mL was added and incubated at room temperature for one hour, with agitation. The plate was then washed three times after the one-hour incubation period and 100 µL of Tetramethylbenzidine (TMB) substrate (KPL Laboratories, Gaithersburg, MD) was added and placed on the plate shaker. The reaction was stopped between ten to fifteen minutes using 100 µL of 1 M (molar) phosphoric acid. The absorbance from the plates was read at 450 nanometer (nm), with a reference at 650 nm using a SpectraMax plate reader (Molecular Devices Corporation, Sunnyvale, CA). A negative control of pooled human serum (n = 8 replicates) from healthy donors was included on each plate when the assays were performed.

2.3. Conjugations of therapeutics: Trastuzumab and Tocilizumab

2.3.1. Biotinylation of therapeutics (Biotin-Trastuzumab, Biotin-Tocilizumab)

Trastuzumab and Tocilizumab were both labeled with Biotin using EZ-Link Sulfo-NHS-LC-Biotin, No-Weigh (Thermo Scientific, Waltham, MA). The protein was labeled according to the manufacturer’s protocol and a 10:1 molar challenge ratio of Biotin to Trastuzumab and Biotin to Tocilizumab was used. Biotin incorporation was evaluated using liquid chromatography-mass spectrometry (LC-MS) (data not shown).

2.3.2. DIG labeling of therapeutics (DIG-Trastuzumab, DIG-Tocilizumab)

Trastuzumab and Tocilizumab were both labeled with DIG using 3-Amino-3-Deoxydigoxigenin Hemisuccinamide, Succinimidyl Ester (Invitrogen, Waltham, MA). Labeling was carried out according to the protocol set by the manufacturer. Both of the therapeutic conjugations were carried out in PBS at a pH of 7.8, along with a 10:1 molar challenge ratio of DIG to Trastuzumab or Tocilizumab. DIG incorporation was evaluated using liquid chromatography-mass spectrometry (LC-MS) (data not shown).

2.4. Octet binding affinity and kinetics assay procedures

The binding kinetics were measured using Octet RED 384 system (Sartorius, Göttingen, Germany). The temperature of the instrument was kept at 30℃ and the streptavidin coated biosensors (Sartorius, Göttingen, Germany) were prehydrated for 10 minutes in the same assay diluent used for the ADA assays. The positive controls, biotinylated drug material, or buffer were diluted to the desired concentrations and 200 µL was transferred to a 96-well microtiter F-bottom plate (Greiner, Kremsmünster, Austria) part number 655,209. For the loading of the biotinylated drug material onto the biosensors, 200 nanograms per milliliter (ng/mL) of biotinylated material was added to one column, while the other column was used as a negative control receiving only buffer. Once the plate was loaded onto the instrument, the steps went as followed; first, the biosensors were equilibrated for 60 seconds at 1000 RPM (revolutions per minute) (same RPMs for all steps), second, the biosensors were then transferred to the loading columns for 600 seconds in either biotinylated material or assay diluent, third, the tips were then added to new columns with assay diluent to get a baseline measurement for 600 seconds, fourth, the biosensors were transferred to the columns with the positive controls at varying concentrations for 600 seconds, the fifth and final step was a dissociation step were the biosensors were added back to the columns that were used during the baseline step for 3600 seconds. Once the assay was complete, the data was automatically generated by the Octet User Software (version 12.0.1.8). Data were fit to a monovalent or bivalent model using the Octet software to determine on-rate constants (k_on) and off-rate constants (k_off); these were used to determine affinity/equilibrium dissociation constant (K_D = k_off/k_on).

2.5. Ethical considerations and patient consent

This study did not involve the use of patient-identifiable information or direct patient involvement. Normal human serum samples (n = 30) were purchased from BioIVT, a commercial vendor that collects human biospecimens under IRB-approved protocols, with informed consent obtained from all donors. All samples were fully de-identified prior to distribution. As such, specific ethics committee approval was not required for this work, and patient consent to participate or to publish identifiable information was not applicable. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.

No patient samples or identifiable patient information were used in the conduct of this research. As such, patient consent to participate or consent to publish identifiable information was not required. This study did not involve human subjects or their personal data, and therefore, no ethics committee approval for consent waivers was necessary.

3. Results

In an ADA bridging ELISA, the labeled drug is incubated overnight with the sample or anti-ID allowing complexes to form between the ADA or anti-ID and the two labeled drug molecules (Supplemental (Sup) Figure (Fig) 1). The formation of these three-molecule complexes, with a sufficiently long incubation, would be K_D (equilibrium dissociation constant) driven. Following the overnight incubation, the mixture is added to the coated plate, washed, and detection reagents are added. The dissolution of complexes during the washing and other incubation steps would be off-rate driven.

We initially used panels of commercially available anti-ID mAbs for two mAb therapeutics (Trastuzumab and Tocilizumab). The format utilized in both of these assays was that of a bridging ADA ELISA. These anti-ID antibodies had a wide range of K_D values and on- and off-rates (Figure 1) to their respective drug counterparts. The anti-ID K_D values, determined by Octet – a method used to measure the binding kinetics and affinity of different protein interactions using Bio-Layer Interferometry (BLI) – range from 4 picomolar (pM) to 8.3 nanomolar (nM) (Sup Table S1 and Sup Fig S2). All 8 of the anti-IDs tested had affinities greater than 1 pM, well within the range of affinity-matured antibodies from patients [9–11]. While the anti-IDs used in this study do not fully represent ADAs in patient samples since they are monoclonal IgG1 antibodies, these anti-IDs were selected to represent the range of affinities observed in patient ADAs [10].

Figure 1.

Panel of positive controls for two ADA assays (Trastuzumab in orange and Tocilizumab in purple). The positive controls have a wide range of k_on (y-axis), k_off (x-axis), and equilibrium affinities (diagonals). k_on: on-rate constant (1/Ms): second-order reaction constant for association (units: per molar per second); k_off: off-rate constant (1/s): first-order reaction constant for dissociation (units: per second); mAb: monoclonal antibody; pM: picomolar (unit of concentration); nM: nanomolar (unit of concentration). Shapes and colors indicate different antibodies and assay types, as shown in the figure key.

We tested a titration series for each anti-ID in the bridging ADA ELISAs for Trastuzumab (Figure 2(a)) and Tocilizumab (Figure 2(b)). Thirty normal human serum (NHS) samples were used to establish cutpoints for each assay (data not shown); we then calculated the relative assay sensitivity for each of the anti-IDs (Sup Table S1). Relative assay sensitivity graphed versus equilibrium binding affinity, K_D, (Figure 2(c)) or off-rate constant (k_off, where a lower k_off corresponds to a slower dissociation) (Figure 2(d)) demonstrated a strong correlation for each of these parameters, with Spearman values of 0.83 or 0.92, respectively.

Figure 2.

Relative assay sensitivity depends on the positive control. Dilution series of 3 positive control antibodies in the Trastuzumab ADA ELISA (in order of slower to faster dissociation) (a) and a series of 5 positive control antibodies in the Tocilizumab ADA ELISA (b). Relationship between relative assay sensitivity vs positive control equilibrium affinity K_D for both ADA ELISAs (c) or k_off (d) on a log-log scale. The graphs are shown on a log-log scale due to the wide range in both values and the Spearman correlation was calculated due to the non-linear relationship between assay sensitivity and either K_D or k_off. ELISA: enzyme-linked immunosorbent assays; ADA: anti-drug antibody; mAb: monoclonal antibody; K_D: equilibrium dissociation constant (units: pM); k_off: off-rate constant (1/s): first-order reaction constant for dissociation (units: per second); ng/mL: nanograms per milliliter; µg/mL: micrograms per milliliter; pM: picomolar. Shapes and colors indicate different antibodies and assay types, as shown in the figure key.

We next evaluated drug tolerance as the second key characteristic of a clinical ADA assay. We tested titration series of each anti-ID in the presence of three different levels of drug. Three example graphs are shown from the Tocilizumab ADA assay (Figures 3(a–c)), and the drug tolerance was calculated at 100 ng/mL anti-ID concentration. The Tocilizumab anti-IDs mAbs A, B, and C were selected to represent the balance between relative assay sensitivity and drug interference illustrated in Figure 3(d). Visually, MAb A showed the most drug interference, meaning the largest right shift with drug present in the sample but also had a relatively high sensitivity and signal (OD of ~ 3.5) at 100 ng/mL (Figure 3(a)). The cartoon (Figure 3(d)) shows that MAb A with high sensitivity but also high drug interference results in a calculated drug tolerance of 116 µg/mL. MAb B also has high drug interference and moderate sensitivity and signal (OD of ~ 2) at 100 ng/mL anti-ID, resulting in the lowest calculated drug tolerance of 12 µg/mL (Figure 3(b)). Lastly, MAb C has the least interference and a moderate sensitivity and signal (OD of ~ 2) at 100 ng/mL, resulting in the highest measured drug tolerance of >200 µg/mL (Figure 3(c)). Measured drug tolerance appears to be a balance between the relative assay sensitivity and drug interference for a particular anti-ID (Figure 3(d)).

Figure 3.

Drug tolerance is a combination of sensitivity and drug interference. Three drug tolerance experiments for the Tocilizumab anti-idiotype (anti-ID) antibodies mAb A (a), mAb B (b), and mAb C (c) tested with no or increasing concentrations of unlabeled drug. (d) cartoon illustrates that the drug interferes by binding to one or two arms of the anti-ID and disrupting the signal generating complex. The calculated drug tolerance depends on the amount of starting complex at 100 ng/mL positive control (sensitivity) and the amount of drug interference (the % of complexes that are disrupted and no longer generate a signal). ADA: anti-drug antibody; mAb: monoclonal antibody; µg/mL: micrograms per milliliter; w/: with. Shapes and colors for dilution series of the mAbs indicate different drug concentrations tested as shown in the figure key. Green boxes represent the signal generating complex. Red boxes represent signal interference.

A graph of calculated drug tolerance versus anti-ID affinity (Figure 4(a)) and k_off (Figure 4(b)) demonstrates a complicated relationship between the drug tolerance and binding properties, unlike the strong correlation k_off and affinity have with relative assay sensitivity. Figure 4 shows that the antibodies’ calculated drug tolerance varies irrespective of their affinities or off-rates, indicating no clear trend with drug tolerance and binding kinetics.

Figure 4.

More complicated relationship between drug tolerance and positive control binding properties. Graph of calculated drug tolerance (at 100 ng/mL positive control) versus positive control affinity (A) and k_off (B) demonstrates no clear relationship, unlike relative assay sensitivity. mAb: monoclonal antibody; K_D: equilibrium dissociation constant; µg/mL: micrograms per milliliter; pM: picomolar; k_off: off-rate constant (1/s): first-order reaction constant for dissociation (units: per second). Shapes and colors indicate different antibodies and assay types, as shown in the figure key.

4. Discussion

Our results provide clear evidence that assay sensitivity directly correlates with the affinity and the k_off of the ADA, in contradiction with previously published studies [7]. There are some notable distinctions between our study and that earlier study, including two important methodology-related differences: Egging et al. performed acid dissociation pretreatment of samples, and carried out the assay with no intermediate washing steps, both of which may have contributed to the contradictory findings.

Current health authorities’ recommendation of 100 ng/mL relative assay sensitivity [3] is shown by the dotted line (Figure 2(c,d)), and six out of eight of the anti-IDs match or exceed that recommendation. In general, these anti-IDs have affinities in the 4 pM to 2 nM range. An assay sensitivity of 100 ng/mL is therefore physiologically possible for an affinity-matured patient antibody [9–11].

Assay developers need to demonstrate that the ADA assay is able to detect ADAs in patient samples in the presence of trough-levels of drug. Previously we have shown that the initial overnight incubation step with the labeled drug improves the drug tolerance of a bridging ELISA [12].

The selection of positive controls for a clinical ADA assay is critical in demonstrating the adequacy of the assay, as the first step in being able to accurately describe clinically significant ADAs. Clinically significant ADAs could happen at relatively low ADA concentrations, or even with low-affinity ADAs, making the sensitivity of the assay important [13]. Additionally, clinically significant ADAs can occur at high doses, making the ADA assay drug tolerance an important attribute to detect ADAs in samples with higher drug concentrations.

5. Conclusions

We tested a panel of anti-ID mAb positive controls in two bridging ELISAs to understand the relationship between binding properties and relative assay sensitivity, as well as drug tolerance. The relative assay sensitivity correlated with the equilibrium affinity and off-rate for these panels of anti-IDs. The relationship between drug tolerance is more complicated than a simple correlation with the binding property. An understanding of how antibodies with various affinities behave in the clinical ADA assay can inform the drug developer on the adequacy of the assay to evaluate clinically significant ADA.

Funding Statement

This paper was not funded.

Article highlights

• Monitoring ADAs is essential for evaluating immunogenicity during biologic drug development.

• Positive controls are a critical component of ADA assays, but the impact of their binding properties on assay performance is not well understood.

• This study analyzed surrogate positive controls with a range of binding affinities and kinetic profiles.

• Higher affinity (lower K_D) and slower dissociation (lower k_off) were associated with increased ADA assay sensitivity.

• No consistent relationship was observed between binding characteristics and drug tolerance.

• These findings support the rational selection of positive controls to optimize ADA assay performance.

Disclosure statement

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.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Yuan Song, upon reasonable request.

Ethical declaration

This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The work did not involve the use of identifiable human subjects, patient data, or personally identifiable information, and therefore did not require ethical approval from an institutional review board. Normal human serum samples (n = 30) were obtained commercially from BioIVT, a vendor that collects biospecimens under Institutional Review Board (IRB)-approved protocols with informed consent and anonymization of donor information. As no patient-identifiable data or information was used, patient consent to participate or to publish was not applicable.

Author contributions

Trinidad Arceo: Methodology, Investigation, Data Curation, Formal Analysis, Visualization, Writing – Original Draft, and Writing – Review & Editing.

Ben Andrews: Methodology, Resources, Writing – Review & Editing.

Jennifer Getz: Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Supervision, Validation, Visualization, Writing – Original Draft.

Sara Haile: Visualization, Writing – Original Draft, Writing – Review & Editing.

Mauricio Maia: Conceptualization, Supervision, Writing-Review & Editing of all draft versions.

Yuan Song: Conceptualization, Project administration, Supervision, Writing – Review & Editing.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17576180.2025.2518045