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ABSTRACT Vaccine development requires innovative approaches to improve immune responses while reducing the number of immunizations. In this study, we explore the impact of controlled antigen release on immune activation and regulation using programmable infusion pumps and biodegradable biomaterials in OT‐II and wild‐type mice to understand the adaptive immune response through controlled antigen delivery in the absence of adjuvant. Ovalbumin (OVA) was delivered via an exponentially decreasing profile, mimicking clearance of infection, and an exponentially increasing profile, mimicking induction of infection. Exponentially decreasing OVA delivery through infusion pumps promoted regulatory T‐cell (Treg) activation in secondary lymphoid organs and suppressed pro‐inflammatory T‐helper type 17 (Th17) responses in blood. An exponentially increasing OVA profile enhanced central memory T‐cell (TCM) populations in submandibular blood and humoral immune responses in cardiac blood serum, demonstrating distinct immune modulation based on release kinetics. OVA was also delivered using a biodegradable PLGA‐PEG‐PLGA (PPP) depot, which provided controlled OVA release in an exponentially decreasing pattern. PPP‐OVA treatment significantly reduced the frequency of pro‐inflammatory T‐helper type 1 (Th1) cells while increasing CD25⁺FOXP3⁺Treg cells in the spleen. Moreover, to identify T‐cell populations that most accurately characterize the divergence in Treg and T‐helper response to OVA kinetics, a Sequential Feature Selection (SFS) algorithm with Machine Learning (ML) models was used. ML algorithms identified gMFI of RORγt⁺as a key feature in submandibular blood and the ratio of gMFI of FOXP3⁺to GATA3⁺as the marker that was significantly changed by treatments in inguinal lymph nodes (iLN) when infusion pumps were used to deliver OVA. In addition, ML‐based SFS identified CD25⁺FOXP3⁺regulatory T cells as the most important feature, influencing the expression of other cell types in both inguinal lymph nodes (iLN) and spleen when PPP was used to deliver OVA. This finding suggests that the exponentially decreasing profile may generate anti‐inflammatory responses. Overall, these findings suggest that controlled antigen delivery enhances immune regulation and memory T cells, providing new insights into immune responses mediated by the release kinetics. 

The immune response is a dynamic process by which the body determines whether an antigen is self or nonself. The state of this dynamic process is defined by the relative balance and population of inflammatory and regulatory actors which comprise this decision making process. The goal of immunotherapy as applied to, e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of the regulatory actors - thereby shutting down autoimmune pathways in the response. While there are several known approaches to immunotherapy, the effectiveness of the therapy will depend on how this intervention alters the evolution of this state. Unfortunately, this process is determined not only by the dynamics of the process, but the state of the system at the time of intervention - a state which is difficult if not impossible to determine prior to application of the therapy. To identify such states we consider a mouse model of RA (Collagen-Induced Arthritis (CIA)) immunotherapy; collect high dimensional data on T cell markers and populations of mice after treatment with a recently developed immunotherapy for CIA; and use feature selection algorithms in order to select a lower dimensional subset of this data which can be used to predict both the full set of T cell markers and populations, along with the efficacy of immunotherapy treatment.