Ayasdi (United States)

Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence supporting a specific clinical pathway for a patient or patient population is often imperfect limiting adoption and efficacy of clinical pathway. Machine intelligence can potentially identify clinical variation and may provide useful insights to create and optimize clinical pathways. In this quality improvement project we analyzed the inpatient care of 1786 patients undergoing colorectal surgery from 2015 to 2016 across multiple Ohio hospitals in the Cleveland Clinic System. Data from four information subsystems was loaded in the Clinical Variation Management (CVM) application (Ayasdi, Inc., Menlo Park, CA). The CVM application uses machine intelligence and topological data analysis methods to identify groups of similar patients based on the treatment received. We defined "favorable performance" as groups with lower direct variable cost, lower length of stay, and lower 30-day readmissions. The software auto-generated 9 distinct groups of patients based on similarity analysis. Overall, favorable performance was seen with ketorolac use, lower intra-operative fluid use (< 2000cc) and surgery for cancer. Multiple sub-groups were easily created and analyzed. Adherence reporting tools were easy to use enabling almost real time monitoring. Machine intelligence provided useful insights to create and monitor care pathways with several advantages over traditional analytic approaches including: (1) analysis across disparate data sets, (2) unsupervised discovery, (3) speed and auto-generation of clinical pathways, (4) ease of use by team members, and (5) adherence reporting.

Highly potent human antibodies are required to therapeutically neutralize cytokines such as interleukin-6 (IL-6) that is involved in many inflammatory diseases and malignancies. Although a number of mutagenesis approaches exist to perform antibody affinity maturation, these may cause antibody instability and production issues. Thus, a robust and easy antibody affinity maturation strategy to increase antibody potency remains highly desirable. By immunizing llama, cloning the 'immune' antibody repertoire and using phage display, we selected a diverse set of IL-6 antagonistic Fabs. Heavy chain shuffling was performed on the Fab with lowest off-rate, resulting in a panel of variants with even lower off-rate. Structural analysis of the Fab:IL-6 complex suggests that the increased affinity was partly due to a serine to tyrosine switch in HCDR2. This translated into neutralizing capacity in an in vivo model of IL-6 induced SAA production. Finally, a novel Fab library was designed, encoding all variations found in the natural repertoire of VH genes identified after heavy chain shuffling. High stringency selections resulted in identification of a Fab with 250-fold increased potency when re-formatted into IgG1. Compared with a heavily engineered anti-IL-6 monoclonal antibody currently in clinical development, this IgG was at least equally potent, showing the engineering process to have had led to a highly potent anti-IL-6 antibody.

Abstract Routine methods for enumerating antigen-specific T-helper cells may not identify low-frequency phenotypes such as Th2 cells. We compared methods of evaluating such responses to identify TT-specific Th1, Th2, Th17 and IL10+ cells. Eight healthy subjects were given a TT booster vaccination. Blood was drawn before, 3, 7, 14, and 28d after vaccination. PBMC cultures were stimulated with TT or PBS. Activation markers (CD25 and CD69) were measured after 44h (n=8), cytokines in supernatant after 3 and 7d of culture, and intracellular cytokine staining (ICS) was performed in proliferated cells (PKH67 dye dilution) after 7d of culture (n=6). Vaccination increased TT-specific expression of CD25 and CD69 on CD3+CD4+ lymphocytes, and TT-specific proliferation at 7, 14 and 28d post vaccination. Vaccination induced TT-specific Th1 (IFNγ, TNFα, and IL2) Th2 (IL13, IL5, and IL4), Th17 (IL17A) and IL10+ cells as measured by ICS. TT-specific Th1 cells were the most abundant (12-15% of all CD3+CD4+ T-cells) while IL10+ (1.8%) Th17 (1.1%) and Th2 cells (0.2-0.6%) were less abundant. TT-specific cytokine concentrations in PBMC supernatants followed the same pattern. A TT-specific IL-9 response was also seen in the supernatants. In conclusion, TT booster vaccination induced a broad T-helper cell response. This method of evaluating cytokine phenotypes may be useful in examining the impact of nutrition and environmental conditions on the plasticity of T-helper cell memory responses.