Evaluating a Multiscale Mechanistic Model of the Immune System to Predict Human Immunogenicity for a Biotherapeutic in Phase 1.

A mechanistic model of the immune response was evaluated for its ability to predict anti-drug antibody (ADA) and their impact on pharmacokinetics (PK) and pharmacodynamics (PD) for a biotherapeutic in a phase 1 clinical trial. Observed ADA incidence ranged from 33 to 67% after single doses and 27-50% after multiple doses. The model captured the single dose incidence well; however, there was overprediction after multiple dosing. The model was updated to include a T-regulatory (Treg) cell mediated tolerance, which reduced the overprediction (relative decrease in predicted incidence rate of 21.5-59.3% across multidose panels) without compromising the single dose predictions (relative decrease in predicted incidence rate of 0.6-13%). The Treg-adjusted model predicted no ADA impact on PK or PD, consistent with the observed data. A prospective phase 2 trial was simulated, including co-medication effects in the form of corticosteroid-induced immunosuppression. Predicted ADA incidences were 0-10%, depending on co-medication dosage. This work demonstrates the utility in applying an integrated, iterative modeling approach to predict ADA during different stages of clinical development.

A systematic study of the effect of low pH acid treatment on anti-drug antibodies specific for a domain antibody therapeutic: Impact on drug tolerance, assay sensitivity and post-validation method assessment of ADA in clinical serum samples.

We developed a homogeneous bridging anti-drug antibody (ADA) assay on an electro chemiluminescent immunoassay (ECLIA) platform to support the immunogenicity evaluation of a dimeric domain antibody (dAb) therapeutic in clinical studies. During method development we evaluated the impact of different types of acid at various pH levels on polyclonal and monoclonal ADA controls of differing affinities and on/off rates. The data shows for the first time that acids of different pH can have a differential effect on ADA of various affinities and this in turn impacts assay sensitivity and drug tolerance as defined by these surrogate controls. Acid treatment led to a reduction in signal of intermediate and low affinity ADA, but not high affinity or polyclonal ADA. We also found that acid pretreatment is a requisite for dissociation of drug bound high affinity ADA, but not for low affinity ADA-drug complexes. Although we were unable to identify an acid that would allow a 100% retrieval of ADA signal post-treatment, use of glycine pH3.0 enabled the detection of low, intermediate and high affinity antibodies (Abs) to various extents. Following optimization, the ADA assay method was validated for clinical sample analysis. Consistencies within various parameters of the clinical data such as dose dependent increases in ADA rates and titers were observed, indicating a reliable ADA method. Pre- and post-treatment ADA negative or positive clinical samples without detectable drug were reanalyzed in the absence of acid treatment or presence of added exogenous drug respectively to further assess the effectiveness of the final acid treatment procedure. The overall ADA results indicate that assay conditions developed and validated based on surrogate controls sufficed to provide a reliable clinical data set. The effect of low pH acid treatment on possible pre-existing ADA or soluble multimeric target in normal human serum was also evaluated, and preliminary data indicate that acid type and pH also affect drug-specific signal differentially in individual samples. The results presented here represent the most extensive analyses to date on acid treatment of a wide range of ADA affinities to explore sensitivity and drug tolerance issues. They have led to a refinement of our current best practices for ADA method development and provide a depth of data to interrogate low pH mediated immune complex dissociation.