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Therapeutic antibodies can elicit unwanted immune responses in a subset of patients, which leads to the production of anti-drug antibodies (ADA). Some of these ADAs have been reported to effect the pharmacokinetics, efficacy and/or safety of the therapeutic antibodies. The sequence diversity of antibodies are generated by VDJ recombination and mutagenesis. While the antibody generation process can create a large candidate pool for identifying high-affinity antibodies, it also could produce sequences that are foreign to the human immune system. However, it is not clear how VDJ recombination and mutagenesis impact the clinical ADA rate of therapeutic antibodies. In this study, we identified a positive correlation between the clinical ADA rate and the number of introduced mutations in the antibody sequences. We also found that the use of rare V alleles in human-origin antibody therapeutics is associated with higher risk of immunogenicity. The results suggest that antibody engineering projects should start with frameworks that contain commonly used V alleles and prioritize antibody candidates with low number of mutations to reduce the risk of immunogenicity.

Protein-based therapeutics may elicit undesired immune responses in a subset of patients, leading to the production of anti-drug antibodies (ADA). In some cases, ADAs have been reported to affect the pharmacokinetics, efficacy and/or safety of the drug. Accurate prediction of the ADA response can help drug developers identify the immunogenicity risk of the drug candidates, thereby allowing them to make the necessary modifications to mitigate the immunogenicity. In this study, we leveraged the rich clinical study data collected by Roche/Genentech to identify factors that impact drug immunogenicity. We focused on conventional monoclonal antibodies, but have included a variety of additional drug modalities in the analysis. We show that the clinical ADA incidences are associated with the mechanism of action of the drugs, the mechanism of action of comedications, the routes of drug administration and the diseases of the patient cohort. By combining these clinical factors with the in silico epitope prediction, we improved the prediction accuracy of drug immunogenicity in clinical trials (AUC of cross validation improved from 0.72 to 0.93). Graphical Abstract

The availability of highly sensitive immunoassays enables the detection of antidrug antibody (ADA) responses of various concentrations and affinities. The analysis of the impact of antibody status on drug pharmacokinetics (PK) is confounded by the presence of low-affinity or low-concentration antibody responses within the dataset. In a phase 2 clinical trial, a large proportion of subjects (45%) developed ADA following weekly dosing with AMG 317, a fully human monoclonal antibody therapeutic. The antibody responses displayed a wide range of relative concentrations (30 ng/mL to >13 μg/mL) and peaked at various times during the study. To evaluate the impact of immunogenicity on PK, AMG 317 concentration data were analyzed following stratification by dose group, time point, antibody status (positive or negative), and antibody level (relative concentration). With dose group as a stratifying variable, a moderate reduction in AMG 317 levels (<50%) was observed in antibody-positive subjects when compared to antibody-negative subjects, but the difference was not statistically significant in all dose groups. The most significant reduction in AMG 317 levels was revealed when antibody data was stratified by both time point and antibody level. In general, high ADA concentrations (>500 ng/mL) and later time points (week 12) were associated with significantly (up to 97%) lower trough AMG 317 concentrations. The use of quasi-quantitative antibody data and appropriate statistical methods was critical for the most comprehensive evaluation of the impact of immunogenicity on PK.

Antibodies can be generated against a therapeutic protein upon administration to human subjects. When the therapeutic protein closely mimics one of the subject's endogenous proteins, those antibodies might bind to the endogenous protein in addition to the therapeutic protein. This scenario results when tolerance to the endogenous protein is broken. The consequences of breaking tolerance include an autoimmune response where antibodies are generated against the endogenous protein. These autoantibodies could have significant clinical relevance depending on several factors, including the redundancy of action of the endogenous protein as well as the concentration, binding affinity, and neutralizing potential of the antibodies. The consequences of a therapeutic-protein-induced autoimmune reaction can be challenging to manage as the stimulus for further perpetuation of the immune response can shift from the therapeutic protein to the endogenous protein. The potential for inducing an autoimmune response is one of the reasons that the immune response to a therapeutic protein should be monitored if it persists through the end of the study.

Romiplostim is an Fc-peptide fusion protein that activates intracellular transcriptional pathways via the thrombopoietin (TPO) receptor leading to increased platelet production. Romiplostim has been engineered to have no amino acid sequence homology to endogenous TPO. Recombinant protein therapeutics can be at a risk of development of an antibody response that can impact efficacy and safety. Hence, a strategy to detect potential antibody formation to the drug and to related endogenous molecules can be useful. The immunogenicity assessment strategy involved both the detection and characterization of binding and neutralizing antibodies. The method for detection was based on a surface plasmon resonance biosensor platform using the Biacore 3000. Samples that tested positive for binding antibodies in the Biacore immunoassay were then tested in a neutralization assay. Serum samples from 225 subjects with immune thrombocytopenic purpura (ITP) dosed with romiplostim and 45 ITP subjects dosed with placebo were tested for romiplostim and TPO antibodies. Prior to romiplostim treatment, 17 subjects (7%) tested romiplostim antibody positive and 12 subjects (5%) tested TPO antibody positive for pre-existing binding antibodies. After romiplostim exposure, 11% of the subjects exhibited binding antibodies against romiplostim and 5% of the subjects with ITP showed binding antibodies against TPO. The antibodies against romiplostim did not cross-react with TPO and vice versa. No cases of anti-TPO neutralizing antibodies were detected in romiplostim-treated subjects. The incidence of anti-romiplostim neutralizing antibodies to romiplostim was 0.4% (one subject); this subject tested negative at the time of follow-up 4 months later. No impact on platelet profiles were apparent in subjects that had antibodies to romiplostim to date. In summary, administration of romiplostim in ITP subjects resulted in the development of a binding antibody response against romiplostim and TPO ligand. One subject developed a neutralizing antibody response to romiplostim that impacted the platelet counts of this subject. No neutralizing antibodies to endogenous TPO were observed.

The development of a maturing T-cell-mediated immune response was characterized in Parkinson's disease subjects receiving recombinant human glial-derived neurotrophic factor (r-metHuGDNF) via continuous bilateral intraputaminal infusion. Eighteen of 34 subjects tested positive for anti-r-metHuGDNF-binding antibodies. Four subjects developed neutralizing activity, three of which demonstrated classic immunoglobulin class switching from IgM to IgG. An increase of anti-r-metHuGDNF IgG-binding antibodies correlated with the development of neutralizing activity. All serum samples from two subjects with neutralizing activity were characterized for IgG subclasses. These data revealed an initial anti-r-metHuGDNF IgG population where IgG1 >> IgG2 >> IgG4, and IgG3 concentrations were negligible. However, continued antigenic stimulation resulted in concentration changes where IgG4 > IgG1> IgG2, indicating a mature immune response. In addition, using in silico techniques, two immunodominant MHC class II T-cell epitopes were predicted for the native GDNF sequence. These data demonstrate development of a mature T-cell-mediated immune response in these subjects.

The immunogenicity immunoassay validation process ensures development of a robust, reproducible method. However, no matter how well developed, validated, and maintained a method is, in the course of running a large number of samples over time, it is not uncommon to see bad reagents, poorly calibrated equipment, personnel errors, or other unknown and unpredictable factors that have an impact in the performance of the method and quality of the sample results. The immunogenicity immunoassay thus needs to be closely monitored with an internal statistical quality control process overtime to ensure a consistent and reliable output. The statistical process control has been widely applied to monitor manufacturing processes and in clinical laboratories. Its application to immunogenicity immunoassays is relatively novel. Limited guidance is available to implement the process to monitor semiquantitative immunogenicity immunoassay performance. Here, we have performed a suitability evaluation for process control charts with actual laboratory data from three immunogenicity immunoassay methods each utilizing a different technology platform. Additionally, a panel of prepared samples designed to assess long-term method performance were periodically evaluated for over a year. Finally, we make recommendations for an internal quality control process based on the results of these evaluations.

We designed a three-step statistical approach to transfer bioanalytical assays (ELISA and Biacore) which evaluates the (1) average equivalence between the two labs (2) concordance in individual sample results between the two labs, and (3) long-term stability of assay performance. Each experimental design evaluated the contribution of four critical variables to the overall variability. Two lots of each variable were examined in a controlled experiment. The variables tested for ELISA were analyst, plate washer, biotinylated-therapeutic protein, and streptavidin–horseradish peroxidase; and for Biacore were analyst, instrument, chip lot, and conjugation chemistry reagent lots. Equivalence in the mean signal to noise (S/N) or mean relative units (RU) between the two labs was established through statistical evaluation of the assay performance characteristics across multiple assay variables. Concordance between the two labs in the individual sample results was subsequently verified both quantitatively and qualitatively. The long-term maintenance of assay stability was monitored by performance testing of a predefined set of samples which were prepared in sufficient quantities to last several years. The process of method validation for biomarker testing in clinical trials is to analyze the variability of the assay performance. However, different factors contribute to this variability and need to be evaluated when the method is transferred to another site/lab. Lack of understanding the critical variables can potentially result in unexpected problems and delays. The three-step statistical approach of assay transfer provides a robust process for transferring complex biological assays.

SUMMARY Objective: To develop a validated BIACORE immunoassay for the detection and characterization of serum antibodies with specificity for erythropoietic molecules (e.g. darbepoetin alfa). Methods: New Zealand White rabbits (n = 8) were immunized by an intramuscular injection of darbepoetin alfa/adjuvant at 0,4,6, and 8 weeks. Serum was collected for 6 weeks after final injection and pooled for affinity purification. Antibody immunoassay measurements were performed using a BIACORE 3000 with darbepoetin alfa immobilized to the biosensor surface. Human serum samples were spiked with the affinity-purified rabbit antibody to develop and validate the BIACORE immunoassay. Results: The assay was shown to be stable through 180 sample/regeneration cycles and had a threshold of 45.8 response units. The validated limit of detection was 0.40|ig/ml in 100% human serum. The method was robust, with variability not exceeding a 20% coefficient of variation, well within acceptable limits for typical immunoassays. Conclusion: All protein-based therapeutics have a potential for immunogenicity, and antibodies raised against these molecules may have important clinical sequelae. The biotechnology and pharmaceutical industries are challenged to address this potential by developing robust analytical platforms to detect and characterize antibodies directed against therapeutic proteins in clinical specimens. Traditionally, radioimmune precipitation assays and/or enzyme-linked immunoassays (ELISAs) are used for primary detection of host immune response; however, the BIACORE platform may be better suited for this purpose in many instances. This platform represents a robust tool that should be considered for the detection and characterization of antibodies directed against protein-based therapeutics.