Optimizing a Multi-Component Intranasal Entamoeba Histolytica Vaccine Formulation Using a Design of Experiments Strategy.

Amebiasis is a neglected tropical disease caused by Entamoeba histolytica. Although the disease burden varies geographically, amebiasis is estimated to account for some 55,000 deaths and millions of infections globally per year. Children and travelers are among the groups with the greatest risk of infection. There are currently no licensed vaccines for prevention of amebiasis, although key immune correlates for protection have been proposed from observational studies in humans. We previously described the development of a liposomal adjuvant formulation containing two synthetic TLR ligands (GLA and 3M-052) that enhanced antigen-specific fecal IgA, serum IgG2a, a mixed IFNγ and IL-17A cytokine profile from splenocytes, and protective efficacy following intranasal administration with the LecA antigen. By applying a statistical design of experiments (DOE) and desirability function approach, we now describe the optimization of the dose of each vaccine formulation component (LecA, GLA, 3M-052, and liposome) as well as the excipient composition (acyl chain length and saturation; PEGylated lipid:phospholipid ratio; and presence of antioxidant, tonicity, or viscosity agents) to maximize desired immunogenicity characteristics while maintaining physicochemical stability. This DOE/desirability index approach led to the identification of a lead candidate composition that demonstrated immune response durability and protective efficacy in the mouse model, as well as an assessment of the impact of each active vaccine formulation component on protection. Thus, we demonstrate that both GLA and 3M-052 are required for statistically significant protective efficacy. We also show that immunogenicity and efficacy results differ in female vs male mice, and the differences appear to be at least partly associated with adjuvant formulation composition.

Immune Profiling To Predict Outcome of Clostridioides difficile Infection.

There is a pressing need for biomarker-based models to predict mortality from and recurrence of Clostridioides difficile infection (CDI). Risk stratification would enable targeted interventions such as fecal microbiota transplant, antitoxin antibodies, and colectomy for those at highest risk. Because severity of CDI is associated with the immune response, we immune profiled patients at the time of diagnosis. The levels of 17 cytokines in plasma were measured in 341 CDI inpatients. The primary outcome of interest was 90-day mortality. Increased tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), C-C motif chemokine ligand 5 (CCL-5), suppression of tumorigenicity 2 receptor (sST-2), IL-8, and IL-15 predicted mortality by univariate analysis. After adjusting for demographics and clinical characteristics, the mortality risk (as indicated by the hazard ratio [HR]) was higher for patients in the top 25th percentile for TNF-α (HR = 8.35, P = 0.005) and IL-8 (HR = 4.45, P = 0.01) and lower for CCL-5 (HR = 0.18, P ≤ 0.008). A logistic regression risk prediction model was developed and had an area under the receiver operating characteristic curve (AUC) of 0.91 for 90-day mortality and 0.77 for 90-day recurrence. While limited by being single site and retrospective, our work resulted in a model with a substantially greater predictive ability than white blood cell count. In conclusion, immune profiling demonstrated differences between patients in their response to CDI, offering the promise for precision medicine individualized treatment.IMPORTANCEClostridioides difficile infection is the most common health care-associated infection in the United States with more than 20% patients experiencing symptomatic recurrence. The complex nature of host-bacterium interactions makes it difficult to predict the course of the disease based solely on clinical parameters. In the present study, we built a robust prediction model using representative plasma biomarkers and clinical parameters for 90-day all-cause mortality. Risk prediction based on immune biomarkers and clinical variables may contribute to treatment selection for patients as well as provide insight into the role of immune system in C. difficile pathogenesis.