As a result, the current model simulations reasonably agree with many accepted immunological phenomena. responses under various initial parameter conditions, the model suggests hypotheses for future experimental investigation and contributes to the mechanistic understanding of immunogenicity. With future experimental validation, this model may potentially provide a platform to generate and test hypotheses about immunogenicity risk assessment and ultimately aid in immunogenicity prediction. With the rapid expansion of therapeutic proteins into an important class of medicines, the issue of unwanted immunogenicity has stimulated much research effort and regulatory attention. The consequences of immunogenicity, in particular the induction of antidrug antibodies (ADA), have the potential to become a serious issue during drug development, due to their impact on drug pharmacokinetics (PK), efficacy, and/or safety.1 Immunogenicity involves complex biological mechanisms, which could span multiple system scales, from subcellular processing and cellular interaction, to organ and whole-body functions. Although various techniques have been developed VE-822 to assess the immunogenicity risk of therapeutic proteins,2,3,4,5,6,7 success in predicting immunogenicity is still not prevalent, due to the involvement of complicated mechanisms and large numbers of impacting factors. Mathematical modeling may serve as a complementary approach to help understand immunogenicity, since it can quantitatively recapitulate, and especially integrate, complicated mechanisms. Mathematical models for the immune system mainly involve two categories of modeling techniques, differential equations (DEs) and agent-based models. DEs have a long history in modeling the immune system. For example, Bell8 developed a mathematical model for B-cell clonal selection and antibody production as early as 1970. Recently, the adaptive immune response to influenza A computer virus contamination was modeled.9 Conversely, agent-based models are a more recent approach and model each entity (e.g., an immune cell) as an agent, which adapts its behaviors over time (e.g., movement and differentiation) based on rules that have stochastic components. Some recent examples include ImmunoGrid, an integrated large-scale agent-based model environment to simulate the human immune system,10,11 C-ImmSim, an agent-based simulator that combines computational immunology with bioinformatics,12,13 and the Basic Immune VE-822 Simulator.14,15 One limitation for agent-based models is that they tend to require larger number of parameters than their DE counterparts, so it is often difficult for sufficient experimental data to be acquired to inform the model.16 Given the comparatively long experience with DE models, we developed our model using DEs, to minimize the number of required parameters. An added benefit of a DE model is usually that it can be easily integrated with downstream applications more traditional in drug discovery and development, such as PK/PD modeling. The objective of this work was to establish a multiscale, mechanistic model that can capture the key underlying mechanisms for immunogenicity against antigenic therapeutic proteins. To focus on the essential model components, while having the potential for modular growth, this model considers the antigen-presenting cells, CD4+ T helper cells, and B cells as the major immune cells. Since dendritic cells (DCs) are the most efficient antigen-presenting cells,17 they were chosen to represent all antigen-presenting cells in the model. DC activation could be driven by maturation/danger signals that are either indicators of pathogen presence, e.g., endotoxin18 or by tissue damage upon drug administration. Due to the complexity of this process and the unavailability of many parameters, DC activation was simplified and modeled as being directly driven VE-822 by endotoxin, particularly, lipopolysaccharide, which is usually widely used in immunology studies to activate DCs19 and is known to be present in many therapeutic protein dose forms.20 Once the DCs become activated, they uptake and process the therapeutic protein, in this context the antigen (Ag), and present the T-epitope from the Ag for subsequent T-cell activation. These processes are collectively called antigen presentation, a critical step for efficient activation of the adaptive immune system, which ultimately evokes ADA production and immune Flt3 memory. Efficient antigen presentation eventually leads to the activation, proliferation, and differentiation of T and B cells, as well as the secretion of ADA that change the disposition of Ag. Although B-cell activation can be T cell dependent or impartial,21 the current model focuses on the first, because it leads to more robust antibody response with affinity maturation and isotype switching and is associated with more impactful clinical observations, such as high and persistent antibody titer.21 Our model was developed.
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