Biochemical and biophysical research communications · 2026
Cytokine expression mediated immunogenic response of therapeutic IgG aggregates obtained by forced degradative stress factors and the role of a deep neural network in its prediction.
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Dash Rozaleen, Sreenivasan Shravan, Chowdhury Anandi, Jesubalan Naveen G, Chirmule Narendra, Sauna Zuben E, Rathore Anurag S
Аннотация
PURPOSE: Biotherapeutic products can aggregate during different stages of the product life cycle. Activation of immune cells due to aggregates of mAb is an important issue. Cytokines are significant biomarkers and potent mediators of several biological processes.
METHODS: Samples of two different mAbs were subjected to different stress to generate aggregates and were characterized by various techniques. Pro and anti-inflammatory cytokines expressed in PBMC due to aggregates were determined.
RESULTS: Samples containing aggregates of both mAbs resulted in >2-fold increase in cytokine levels when compared to unstressed samples. Thermal and stirring stressed mAb samples resulted in the highest levels of cytokine release (>10-fold). Cytokine levels, in general, increased with time, turbidity, protein loss and Z-average diameter. However, cytokine levels were not directly dependent on HMW species but instead showed a direct correlation with the sub-visible and higher aggregates. Further, using Machine Learning (ML) we evaluated diverse characteristics associated with aggregation for two monoclonal antibody (mAb) products (rituximab and infliximab) in the context of immunogenicity. A novel, deep neural network regression model was developed to predict cytokine release considering both mAb aggregation attributes and upstream immune mediators such as IgM and anti-CCP, reflecting interactions within a broader immune network. The normalized concentrations of cytokines in aggregated and monomeric mAb-treated samples were investigated, revealing that the proposed digital framework, based on a branching neural network, effectively forecasts normalized cytokine concentrations under the specified conditions.
CONCLUSIONS: The study demonstrates immunogenicity elicited by aggregates of mAb and how modelling using ML can predict the immunogenicity risk of mAb aggregates.
METHODS: Samples of two different mAbs were subjected to different stress to generate aggregates and were characterized by various techniques. Pro and anti-inflammatory cytokines expressed in PBMC due to aggregates were determined.
RESULTS: Samples containing aggregates of both mAbs resulted in >2-fold increase in cytokine levels when compared to unstressed samples. Thermal and stirring stressed mAb samples resulted in the highest levels of cytokine release (>10-fold). Cytokine levels, in general, increased with time, turbidity, protein loss and Z-average diameter. However, cytokine levels were not directly dependent on HMW species but instead showed a direct correlation with the sub-visible and higher aggregates. Further, using Machine Learning (ML) we evaluated diverse characteristics associated with aggregation for two monoclonal antibody (mAb) products (rituximab and infliximab) in the context of immunogenicity. A novel, deep neural network regression model was developed to predict cytokine release considering both mAb aggregation attributes and upstream immune mediators such as IgM and anti-CCP, reflecting interactions within a broader immune network. The normalized concentrations of cytokines in aggregated and monomeric mAb-treated samples were investigated, revealing that the proposed digital framework, based on a branching neural network, effectively forecasts normalized cytokine concentrations under the specified conditions.
CONCLUSIONS: The study demonstrates immunogenicity elicited by aggregates of mAb and how modelling using ML can predict the immunogenicity risk of mAb aggregates.