Immune Potentiation of PLGA Controlled-Release Vaccines for Improved Immunological Outcomes

With the emergence of SARS-CoV-2 and the continued emergence of new infectious diseases, there is a need to improve and expand current vaccine technology. Controlled-release subunit vaccines provide several benefits over current vaccines on the market, including the use of less antigen and fewer boost doses. Previously, our group reported molecules that alter NF-κB signaling improved the vaccine's performance and improved adjuvant-related tolerability. In this report, we test how these immune potentiators will influence responses when included as part of a controlled-release poly(lactic-co-glycolic) vaccine formulation. Murine in vivo studies revealed that SN50 and honokiol improved antibody levels at early vaccine time points. Microparticles with SN50 produced strong antibody levels over a longer period compared to microparticles without SN50. The same particles also increased T-cell activity. All of the immune potentiators tested further promoted Th2 humoral responses already exhibited by the control CpG OVA microparticle formulation. Overall, under controlled-release conditions, immune potentiators enhance the existing effects of controlled-release formulations, making it a potentially beneficial additive for controlled-release vaccine formulations.

Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening

The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-β production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.

Next-Generation Adjuvants: Applying Engineering Methods to Create and Evaluate Novel Immunological Responses

Adjuvants are a critical component of vaccines. Adjuvants typically target receptors that activate innate immune signaling pathways. Historically, adjuvant development has been laborious and slow, but has begun to accelerate over the past decade. Current adjuvant development consists of screening for an activating molecule, formulating lead molecules with an antigen, and testing this combination in an animal model. There are very few adjuvants approved for use in vaccines, however, as new candidates often fail due to poor clinical efficacy, intolerable side effects, or formulation limitations. Here, we consider new approaches using tools from engineering to improve next-generation adjuvant discovery and development. These approaches will create new immunological outcomes that will be evaluated with novel diagnostic tools. Potential improved immunological outcomes include reduced vaccine reactogenicity, tunable adaptive responses, and enhanced adjuvant delivery. Evaluations of these outcomes can leverage computational approaches to interpret "big data" obtained from experimentation. Applying engineering concepts and solutions will provide alternative perspectives, further accelerating the field of adjuvant discovery.

Discovery of New States of Immunomodulation for Vaccine Adjuvants via High Throughput Screening: Expanding Innate Responses to PRRs

Stimulation of the innate immune system is crucial in both effective vaccinations and immunotherapies. This is often achieved through adjuvants, molecules that usually activate pattern recognition receptors (PRRs) and stimulate two innate immune signaling pathways: the nuclear factor kappa-light-chain-enhancer of activated B-cells pathway (NF-κB) and the interferon regulatory factors pathway (IRF). Here, we demonstrate the ability to alter and improve adjuvant activity via the addition of small molecule "immunomodulators". By modulating signaling activity instead of receptor binding, these molecules allow the customization of select innate responses. We demonstrate both inhibition and enhancement of the products of the NF-κB and IRF pathways by several orders of magnitude. Some modulators apply generally across many receptors, while others focus specifically on individual receptors. Modulators boost correlates of a protective immune responses in a commercial flu vaccine model and reduced correlates of reactogenicity in a typhoid vaccine model. These modulators have a range of applications: from adjuvanticity in prophylactics to enhancement of immunotherapy.

IgG Immune Complexes Inhibit Naïve T Cell Proliferation and Suppress Effector Function in Cytotoxic T Cells

Elevated levels of circulating immune complexes are associated with autoimmunity and with worse prognoses in cancer. Here, we examined the effects of well-defined, soluble immune complexes (ICs) on human peripheral T cells. We demonstrate that IgG-ICs inhibit the proliferation and differentiation of a subset of naïve T cells but stimulate the division of another naïve-like T cell subset. Phenotypic analysis by multi-parameter flow cytometry and RNA-Seq were used to characterize the inhibited and stimulated T cells revealing that the inhibited subset presented immature features resembling those of recent thymic emigrants and non-activated naïve T cells, whereas the stimulated subset exhibited transcriptional features indicative of a more differentiated, early memory progenitor with a naïve-like phenotype. Furthermore, we show that while IgG1-ICs do not profoundly inhibit the proliferation of memory T cells, IgG1-ICs suppress the production of granzyme-β and perforin in cytotoxic memory T cells. Our findings reveal how ICs can link humoral immunity and T cell function.

Small Molecule NF-κB Inhibitors as Immune Potentiators for Enhancement of Vaccine Adjuvants

Adjuvants are added to vaccines to enhance the immune response and provide increased protection against disease. In the last decade, hundreds of synthetic immune adjuvants have been created, but many induce undesirable levels of proinflammatory cytokines including TNF-α and IL-6. Here we present small molecule NF-κB inhibitors that can be used in combination with an immune adjuvant to both decrease markers associated with poor tolerability and improve the protective response of vaccination. Additionally, we synthesize a library of honokiol derivatives identifying several promising candidates for use in vaccine formulations.

Increased vaccine tolerability and protection via NF-κB modulation

Improving adjuvant responses is a promising pathway to develop vaccines against some pathogens (e.g., HIV or dengue). One challenge in adjuvant development is modulating the inflammatory response, which can cause excess side effects, while maintaining immune activation and protection. No approved adjuvants yet have the capability to independently modulate inflammation and protection. Here, we demonstrate a method to limit inflammation while retaining and often increasing the protective responses. To accomplish this goal, we combined a partial selective nuclear factor kappa B (NF-kB) inhibitor with several current adjuvants. The resulting vaccines reduce systemic inflammation and boost protective responses. In an influenza challenge model, we demonstrate that this approach enhances protection. This method was tested across a broad range of adjuvants and antigens. We anticipate these studies will lead to an alternative approach to vaccine formulation design that may prove broadly applicable to a wide range of adjuvants and vaccines.

Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound

Despite the severe consequences of dysfunctional swallowing, there is no simple method of monitoring swallowing outside of clinical settings. People who cannot swallow cannot eat safely, resulting in profound changes in quality of life and risk of death from aspiration pneumonia. A non-invasive swallowing detector may have widespread impact in both clinical care and research. Detection of swallowing from laryngeal sounds could become an ideal assessment tool because sounds are simple to record, quantifiable, and amenable to software analysis. The focus of this paper is to achieve high accuracy binary swallowing detection from sound recordings. A dataset with 2500 swallow sound samples and 1700 mixed laryngeal noise samples from 15 healthy adults was used to train and test three supervised machine learning algorithms. A decision tree, support vector machine (SVM), and neural network trained with the scaled conjugate gradient (SCG) method had areas under the receiver operating characteristic (ROC) curve of 0.970, 0.961, and 0.971 and average accuracies of 93.2 percent, 86.2 percent, and 93.7 percent respectively. While further work needs to be done to further optimize these algorithms and validate their efficacy, these initial results suggest machine learning strategies may be helpful to improve accuracy of swallowing detection.