Immunogenicity dynamics and covariate effects after satralizumab administration predicted with a hidden Markov model

Immunogenicity is the propensity of a therapeutic protein to generate an immune response to itself. While reporting of antidrug antibodies (ADAs) is increasing, model-based analysis of such data is seldom performed. Model-based characterization of factors affecting the emergence and dissipation of ADAs may inform drug development and/or improve understanding in clinical practice. This analysis aimed to predict ADA dynamics, including the potential influence of individual covariates, following subcutaneous satralizumab administration. Satralizumab is a humanized IgG2 monoclonal recycling IL-6 receptor antagonist antibody approved for treating neuromyelitis optica spectrum disorder (NMOSD). Longitudinal pharmacokinetic (PK) and ADA data from 154 NMOSD patients in two pivotal Phase 3 studies (NCT02028884, NCT02073279) and PK data from one Phase 1 study (SA-001JP) in 72 healthy volunteers were available for this analysis. An existing population PK model was adapted to derive steady-state concentration without ADA for each patient. A mixed hidden Markov model (mHMM) was developed whereby three different states were identified: one absorbing Markov state for non-ADA developer, and two dynamic and inter-connected Markov states-transient ADA negative and positive. Satralizumab exposure and body mass index impacted transition probabilities and, therefore, the likelihood of developing ADAs. In conclusion, the mHMM model was able to describe the time course of ADA development and identify patterns of ADA development in NMOSD patients following treatment with satralizumab, which may allow for the formulation of strategies to reduce the emergence or limit the impact of ADA in the clinical setting.