A Novel Lack-of-Fit Assessment as a System Suitability Test for Potency Assays.

Bioassay data analysis is used to determine the potency of protein therapeutics. To properly determine potency, the experimental data need to be fitted to a model that adequately describes the observed dose-response relationship. Typical models include 4-parameter logistic curve fits, 5-parameter logistic curve fits or parallel line analysis. Lack-of-fit assessment can be used as a measure of potency assay system suitability to ensure appropriate closeness of the chosen model fit to the experimental data. We present a novel lack-of-fit approach that overcomes the shortcomings of previously described lack-of-fit tests, such as the conventional analysis of variance (ANOVA) F-test and the lack-of-fit sum of squares test. Simulation studies and examples are used to assess the performance of the new lack-of-fit test. The results show that the described lack-of-fit approach can effectively reject poorly fitted data while retaining well-fitted data, and has advantages in potency assay applications where instrument-to-instrument variability in absolute readout is expected.LAY ABSTRACT: Potency assays are analytical procedures used for characterization as well as release and stability analysis in drug development and for approved products. Dose-response data generated from a drug sample and a well-characterized reference standard are evaluated to determine the potency of the drug sample relative to the reference standard. In order to obtain a potency determination, dose-response data need to be fitted to a proper model that adequately describes the observed dose-response relationship. There are different options described to assess the goodness-of-fit of the data. One approach is the goodness-of-fit assessment based on F-test. This approach compares the lack-of-fit error (representing the discrepancy between observed data and fitted curve) to the pure error (representing the random noise between replicate measurement) to determine if the observed lack-of-fit error can be attributed to random noise. A limitation of goodness of fit assessments via F-test lies in its propensity to penalize precise data (small lack-of-fit error can be considered significantly high if the assay has exceptionally low pure error) and accept undesirable noisy data (large undesirable lack-of-fit error can be considered insignificant due to large pure error). An alternative approach based on lack-of-fit sum of squares is only applicable to certain types of assays where the magnitude of measurements is consistent across different instruments given that the lack-of-fit sum of squares will increase when the magnitude of the assay signal measurements increase, even if the relative magnitude of assay data versus fitted curve remains the same. We introduce here a novel approach that overcomes the limitations of F-test and sum of squares-based approaches. This new approach will effectively reject poor data and retain good data, and it is independent of differences in absolute readout across instruments.

Increased rheumatoid factor interference observed during immunogenicity assessment of an Fc-engineered therapeutic antibody.

Protein therapeutics may elicit an anti-therapeutic antibody (ATA) response in patients. This response depends on a number of factors including patient population, disease state, route of delivery or characteristics specific to the product. Therapeutics for immunological indications often target relatively young and healthy patients with hyperactive immune systems who have periodic flares and remissions. The hyperactive immune system of these patients can add several levels of bioanalytical complexity due to the presence of cross reactive molecules such as autoantibodies. In addition, the long-term chronic dosing regimen often necessary in this patient population can increase their risks of immunogenicity against the therapeutic and lead to safety concerns. Therefore, development of a sensitive and drug-tolerant ATA method is important. Bridging ATA assays are usually very sensitive and drug-tolerant methods for immunogenicity assessment; however these methods are particularly vulnerable to any factor that is able to bridge the conjugated therapeutics used as reagents and can generate false positive signal. Although there are many potential interfering factors in serum, rheumatoid factors (RFs), autoantibodies associated with rheumatoid arthritis (RA), are of particular concern in this type of assay. MTRX1011A is a non-depleting anti-CD4 monoclonal antibody therapeutic that was clinically tested in RA patients. This paper will discuss the bioanalytical challenges encountered during development of a clinical ATA assay for MTRX1011A. These challenges highlight interference due to patient disease state, in this case presence of RF in RA patients, as well as specific molecule-related interference caused by an engineered mutation in the Fc region of MTRX1011A designed to enhance its binding to the neonatal Fc receptor (FcRn). We will discuss the characterization work used to identify the cross-reactive epitope and our strategy to overcome this interference during development of an effective ATA assay to support clinical evaluation of MTRX1011A.