Early Experience With an Occupational JYNNEOS ( Orthopoxvirus ) Vaccination Program.

OBJECTIVE: The aim of the study is to identify lessons learned implementing JYNNEOS vaccination for laboratory workers exposed to orthopoxviruses such as mpox. METHODS: Workers at risk of laboratory exposure were offered vaccine in a carefully planned occupational health program. Vaccine was procured from the Centers for Disease Control and Prevention (CDC) Drug Service, which has special requirements. Reasons for accepting or declining vaccine and adverse effects were obtained by survey. RESULTS: Most workers accepted JYNNEOS, and occupational risk was the most commonly cited reason for acceptance. Most experienced mild local adverse effects. The administrative requirements of the Centers for Disease Control and Prevention Drug Service are documented. CONCLUSIONS: Occupational health programs caring for laboratory workers handling unusual biological agents require careful planning and coordination to facilitate access to vaccines that are not commercially available, anticipate and mitigate barriers to vaccination, and comply with special Centers for Disease Control and Prevention requirements.

TAK-676: A Novel Stimulator of Interferon Genes (STING) Agonist Promoting Durable IFN-dependent Antitumor Immunity in Preclinical Studies.

Oncology therapies targeting the immune system have improved patient outcomes across a wide range of tumor types, but resistance due to an inadequate T-cell response in a suppressive tumor microenvironment (TME) remains a significant problem. New therapies that activate an innate immune response and relieve this suppression may be beneficial to overcome this hurdle. TAK-676 is a synthetic novel stimulator of interferon genes (STING) agonist designed for intravenous administration. Here we demonstrate that TAK-676 dose-dependently triggers activation of the STING signaling pathway and activation of type I interferons. Furthermore, we show that TAK-676 is a highly potent modulator of both the innate and adaptive immune system and that it promotes the activation of dendritic cells, natural killer cells, and T cells in preclinical models. In syngeneic murine tumor models in vivo, TAK-676 induces dose-dependent cytokine responses and increases the activation and proliferation of immune cells within the TME and tumor-associated lymphoid tissue. We also demonstrate that TAK-676 dosing results in significant STING-dependent antitumor activity, including complete regressions and durable memory T-cell immunity. We show that TAK-676 is well tolerated, exhibits dose-proportional pharmacokinetics in plasma, and exhibits higher exposure in tumor. The intravenous administration of TAK-676 provides potential treatment benefit in a broad range of tumor types. Further study of TAK-676 in first-in-human phase I trials is ongoing. Significance: TAK-676 is a novel systemic STING agonist demonstrating robust activation of innate and adaptive immune activity resulting in durable antitumor responses within multiple syngeneic tumor models. Clinical investigation of TAK-676 is ongoing.

Description of Pharmacogenomic Testing Among Patients Admitted to the Intensive Care Unit After Cardiovascular Surgery.

BACKGROUND: Pharmacogenomic (PGx) testing has the potential to provide information on specific drug-metabolizing enzymes that may lead to an absence, reduction, or increase in medication effect in patients. There is a paucity of prospective studies examining PGx testing in the intensive care unit (ICU) setting. RESEARCH AIMS: To (1) obtain a PGx panel in a sample of cardiovascular (CV) surgical patients with a planned ICU stay and identify phenotypes, and (2) identify PGx variants that may inform treatment regimens and may warrant prescribing adjustments. DESIGN AND METHODS: Descriptive, single cohort cross-sectional design. Adult (≥18 years) CV patients with an anticipated postoperative ICU stay were enrolled from a large Midwestern tertiary academic medical center. Eligible patients provided informed consent at the time of their CV clinic appointment; PGx testing was then ordered. Pharmacogenomic testing consisted of the Focused Pharmacogenomics panel which included 10 genes and 55 medications. RESULTS: Of the 272 patients screened, 100 (68% male) patients completed PGx testing (mean age 66.2 ± 9.6 years, mean Acute Physiology, Age and Chronic Health Evaluation III score 76.1 ± standard deviation). Pharmacogenomic results were available in the medical record within a median of 52.4 hours (interquartile range: 33.4-80.3). Pharmacogenomic testing results identified 5 CYP2C19 poor metabolizers, 26 CYP2C19 rapid metabolizers, 5 CYP2C19 ultrarapid metabolizers, 6 CYP2D6 poor metabolizers, 5 CYP2D6 poor to intermediate metabolizers, and 2 CYP2D6 rapid metabolizers identified. Overall, 98% of patients had actionable or potentially actionable PGx results, including 82% for warfarin, 65% for propafenone, 65% for tramadol, 46% for oxycodone, 45% for metoprolol, 33% for clopidogrel, 32% for proton pump inhibitors, 25% for statins, and 12% for haloperidol. CONCLUSIONS: A significant portion of patients had identified genetic variants that may warrant changes in medication management during and after CV-ICU stay. It remains to be seen if PGx testing leads to improvements in ICU patient outcomes.

Role of tacrolimus in return of hand function after brachial plexus injury in a lung transplantation patient.

We report a patient who has been on tacrolimus for bilateral lung transplantation and presented with a brachial plexus injury (BPI), with unusual improvement of lower trunk innervated hand function. The lower trunk injury with resultant left hand paralysis had developed after his sternotomy 18 months ago. He has been treated with tacrolimus as part of his immunosuppression protocol since the surgery, without severe side effects. Physical examination at 18 months demonstrated unusual excellent grip pattern and full opposition of his thumb with slight claw deformity of his ulnar two digits. While the neurotoxic effects of tacrolimus are more emphasised, the neuroregenerative properties have been recently explored. The recovery in this patient is unique and unusual after BPI and is most likely as a result of the low dose tacrolimus treatment.

Sample size re-estimation in a superiority clinical trial using a hybrid classical and Bayesian procedure.

When designing studies involving a continuous endpoint, the hypothesized difference in means ( θA ) and the assumed variability of the endpoint ( σ2 ) play an important role in sample size and power calculations. Traditional methods of sample size re-estimation often update one or both of these parameters using statistics observed from an internal pilot study. However, the uncertainty in these estimates is rarely addressed. We propose a hybrid classical and Bayesian method to formally integrate prior beliefs about the study parameters and the results observed from an internal pilot study into the sample size re-estimation of a two-stage study design. The proposed method is based on a measure of power called conditional expected power (CEP), which averages the traditional power curve using the prior distributions of θ and σ2 as the averaging weight, conditional on the presence of a positive treatment effect. The proposed sample size re-estimation procedure finds the second stage per-group sample size necessary to achieve the desired level of conditional expected interim power, an updated CEP calculation that conditions on the observed first-stage results. The CEP re-estimation method retains the assumption that the parameters are not known with certainty at an interim point in the trial. Notional scenarios are evaluated to compare the behavior of the proposed method of sample size re-estimation to three traditional methods.

Time to achieving therapeutic international normalized ratio increases hospital length of stay after heart valve replacement surgery.

BACKGROUND: Achieving a therapeutic international normalized ratio (INR) before hospital discharge is an important inpatient goal for patients undergoing mechanical cardiac valve replacement (MCVR). The use of clinical algorithms has reduced the time to achieve therapeutic INR (TTI) with warfarin therapy. Whether TTI prolongs length of stay (LOS) is unknown. METHODS: Patients who underwent MCVR over a consecutive 42-month period were included. Clinical data were obtained from the Society of Thoracic Surgeons Adult Cardiac Surgery database and electronic medical records. Therapeutic INR was defined as per standard guidelines. Warfarin dose was prescribed using an inpatient pharmacy-managed algorithm and computer-based dosing tool. International normalized ratio trajectory, procedural needs, and drug interactions were included in warfarin dose determination. RESULTS: There were 708 patients who underwent MCVR, of which 159 were excluded for reasons that would preclude or interrupt warfarin use. Among the remainder of 549 patients, the average LOS was 6.4days and mean TTI was 3.5days. Landmark analysis showed that subjects in hospital on day 4 (n=542) who achieved therapeutic INR were more likely to be discharged by day 6 compared with those who did not achieve therapeutic INR (75% vs 59%, P<.001). Multivariable proportional hazards regression with TTI as a time-dependent effect showed a strong association with discharge (P=.0096, hazard ratio1.3) after adjustment for other significant clinical covariates. CONCLUSIONS: Time to achieve therapeutic INR is an independent predictor of LOS in patients requiring anticoagulation with warfarin after MCVR surgery. Alternative dosing and anticoagulation strategies will need to be adopted to reduce LOS in these patients.

Sample size determination for a binary response in a superiority clinical trial using a hybrid classical and Bayesian procedure.

BACKGROUND: When designing studies that have a binary outcome as the primary endpoint, the hypothesized proportion of patients in each population experiencing the endpoint of interest (i.e., π 1,π 2) plays an important role in sample size and power calculations. Point estimates for π 1 and π 2 are often calculated using historical data. However, the uncertainty in these estimates is rarely addressed. METHODS: This paper presents a hybrid classical and Bayesian procedure that formally integrates prior information on the distributions of π 1 and π 2 into the study's power calculation. Conditional expected power (CEP), which averages the traditional power curve using the prior distributions of π 1 and π 2 as the averaging weight conditional on the presence of a positive treatment effect (i.e., π 2>π 1), is used, and the sample size is found that equates the pre-specified frequentist power (1-ß) and the conditional expected power of the trial. RESULTS: Notional scenarios are evaluated to compare the probability of achieving a target value of power with a trial design based on traditional power and a design based on CEP. We show that if there is uncertainty in the study parameters and a distribution of plausible values for π 1 and π 2, the performance of the CEP design is more consistent and robust than traditional designs based on point estimates for the study parameters. Traditional sample size calculations based on point estimates for the hypothesized study parameters tend to underestimate the required sample size needed to account for the uncertainty in the parameters. The greatest marginal benefit of the proposed method is achieved when the uncertainty in the parameters is not large. CONCLUSIONS: Through this procedure, we are able to formally integrate prior information on the uncertainty and variability of the study parameters into the design of the study while maintaining a frequentist framework for the final analysis. Solving for the sample size that is necessary to achieve a high level of CEP given the available prior information helps protect against misspecification of hypothesized treatment effect and provides a substantiated estimate that forms the basis for discussion about the study's feasibility during the design phase.

Gene signature-based mapping of immunological systems and diseases.

BACKGROUND: The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease. RESULT: We focus on gene signatures, representing highly enriched genes of immune cell subsets from both diseased and healthy tissues. From these, we construct a series of biomaps that illustrate the molecular linkages between cell subsets from different lineages, the connectivity between different immunological diseases, and the enrichment of cell subset signatures in diseased tissues. Finally, we overlay the downstream genes of drug targets with disease gene signatures to display the potential therapeutic applications for these approaches. CONCLUSION: An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states. This modular 'biomap' reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues. The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease.

Selection of the effect size for sample size determination for a continuous response in a superiority clinical trial using a hybrid classical and Bayesian procedure.

BACKGROUND: When designing studies that have a continuous outcome as the primary endpoint, the hypothesized effect size ([Formula: see text]), that is, the hypothesized difference in means ([Formula: see text]) relative to the assumed variability of the endpoint ([Formula: see text]), plays an important role in sample size and power calculations. Point estimates for [Formula: see text] and [Formula: see text] are often calculated using historical data. However, the uncertainty in these estimates is rarely addressed. METHODS: This article presents a hybrid classical and Bayesian procedure that formally integrates prior information on the distributions of [Formula: see text] and [Formula: see text] into the study's power calculation. Conditional expected power, which averages the traditional power curve using the prior distributions of [Formula: see text] and [Formula: see text] as the averaging weight, is used, and the value of [Formula: see text] is found that equates the prespecified frequentist power ([Formula: see text]) and the conditional expected power of the trial. This hypothesized effect size is then used in traditional sample size calculations when determining sample size for the study. RESULTS: The value of [Formula: see text] found using this method may be expressed as a function of the prior means of [Formula: see text] and [Formula: see text], [Formula: see text], and their prior standard deviations, [Formula: see text]. We show that the "naïve" estimate of the effect size, that is, the ratio of prior means, should be down-weighted to account for the variability in the parameters. An example is presented for designing a placebo-controlled clinical trial testing the antidepressant effect of alprazolam as monotherapy for major depression. CONCLUSION: Through this method, we are able to formally integrate prior information on the uncertainty and variability of both the treatment effect and the common standard deviation into the design of the study while maintaining a frequentist framework for the final analysis. Solving for the effect size which the study has a high probability of correctly detecting based on the available prior information on the difference [Formula: see text] and the standard deviation [Formula: see text] provides a valuable, substantiated estimate that can form the basis for discussion about the study's feasibility during the design phase.

TLR3 Signaling Promotes the Induction of Unique Human BDCA-3 Dendritic Cell Populations.

Conventional and plasmacytoid dendritic cells (cDCs and pDCs) are the two populations of DCs that can be readily identified in human blood. Conventional DCs have been subdivided into CD1c(+), or blood dendritic cells antigen (BDCA) 1 and CD141(+), or BDCA-3, DCs, each having both unique gene expression profiles and functions. BDCA-3 DCs express high levels of toll-like receptor 3 and upon stimulation with Poly I:C secrete IFN-ß, CXCL10, and IL-12p70. In this article, we show that activation of human BDCA-3 DCs with Poly I:C induces the expression of activation markers (CD40, CD80, and CD86) and immunoglobulin-like transcript (ILT) 3 and 4. This Poly I:C stimulation results in four populations identifiable by flow cytometry based on their expression of ILT3 and ILT4. We focused our efforts on profiling the ILT4(-) and ILT4(+) DCs. These ILT-expressing BDCA-3 populations exhibit similar levels of activation as measured by CD40, CD80, and CD86; however, they exhibit differential cytokine secretion profiles, unique gene signatures, and vary in their ability to prime allogenic naïve T cells. Taken together, these data illustrate that within a pool of BDCA-3 DCs, there are cells poised to respond differently to a given input stimulus with unique output of immune functions.

Complementarity and redundancy of IL-22-producing innate lymphoid cells.

Intestinal T cells and group 3 innate lymphoid cells (ILC3 cells) control the composition of the microbiota and gut immune responses. Within the gut, ILC3 subsets coexist that either express or lack the natural cytoxicity receptor (NCR) NKp46. We identified here the transcriptional signature associated with the transcription factor T-bet-dependent differentiation of NCR(-) ILC3 cells into NCR(+) ILC3 cells. Contrary to the prevailing view, we found by conditional deletion of the key ILC3 genes Stat3, Il22, Tbx21 and Mcl1 that NCR(+) ILC3 cells were redundant for the control of mouse colonic infection with Citrobacter rodentium in the presence of T cells. However, NCR(+) ILC3 cells were essential for cecal homeostasis. Our data show that interplay between intestinal ILC3 cells and adaptive lymphocytes results in robust complementary failsafe mechanisms that ensure gut homeostasis.

Selection of the treatment effect for sample size determination in a superiority clinical trial using a hybrid classical and Bayesian procedure.

Specification of the treatment effect that a clinical trial is designed to detect (θA) plays a critical role in sample size and power calculations. However, no formal method exists for using prior information to guide the choice of θA. This paper presents a hybrid classical and Bayesian procedure for choosing an estimate of the treatment effect to be detected in a clinical trial that formally integrates prior information into this aspect of trial design. The value of θA is found that equates the pre-specified frequentist power and the conditional expected power of the trial. The conditional expected power averages the traditional frequentist power curve using the conditional prior distribution of the true unknown treatment effect θ as the averaging weight. The Bayesian prior distribution summarizes current knowledge of both the magnitude of the treatment effect and the strength of the prior information through the assumed spread of the distribution. By using a hybrid classical and Bayesian approach, we are able to formally integrate prior information on the uncertainty and variability of the treatment effect into the design of the study, mitigating the risk that the power calculation will be overly optimistic while maintaining a frequentist framework for the final analysis. The value of θA found using this method may be written as a function of the prior mean µ0 and standard deviation τ0, with a unique relationship for a given ratio of µ0/τ0. Results are presented for Normal, Uniform, and Gamma priors for θ.

Teriflunomide attenuates immunopathological changes in the dark agouti rat model of experimental autoimmune encephalomyelitis.

Teriflunomide is an oral disease-modifying therapy recently approved in several locations for relapsing-remitting multiple sclerosis. To gain insight into the effects of teriflunomide, immunocyte population changes were measured during progression of experimental autoimmune encephalomyelitis in Dark Agouti rats. Treatment with teriflunomide attenuated levels of spinal cord-infiltrating T cells, natural killer cells, macrophages, and neutrophils. Teriflunomide also mitigated the disease-induced changes in immune cell populations in the blood and spleen suggesting an inhibitory effect on pathogenic immune responses.

The effects of teriflunomide on lymphocyte subpopulations in human peripheral blood mononuclear cells in vitro.

Teriflunomide is an inhibitor of dihydro-orotate dehydrogenase (DHODH), and is hypothesized to ameliorate multiple sclerosis by reducing proliferation of stimulated lymphocytes. We investigated teriflunomide's effects on proliferation, activation, survival, and function of stimulated human peripheral blood mononuclear cell subsets in vitro. Teriflunomide had little/no impact on lymphocyte activation but exerted significant dose-dependent inhibition of T- and B-cell proliferation, which was uridine-reversible (DHODH-dependent). Viability analyses showed no teriflunomide-associated cytotoxicity. Teriflunomide significantly decreased release of several pro-inflammatory cytokines from activated monocytes in a DHODH-independent fashion. In conclusion, teriflunomide acts on multiple immune cell types and processes via DHODH-dependent and independent mechanisms.

Robust methods for population stratification in genome wide association studies.

BACKGROUND: Genome-wide association studies can provide novel insights into diseases of interest, as well as to the responsiveness of an individual to specific treatments. In such studies, it is very important to correct for population stratification, which refers to allele frequency differences between cases and controls due to systematic ancestry differences. Population stratification can cause spurious associations if not adjusted properly. The principal component analysis (PCA) method has been relied upon as a highly useful methodology to adjust for population stratification in these types of large-scale studies. Recently, the linear mixed model (LMM) has also been proposed to account for family structure or cryptic relatedness. However, neither of these approaches may be optimal in properly correcting for sample structures in the presence of subject outliers. RESULTS: We propose to use robust PCA combined with k-medoids clustering to deal with population stratification. This approach can adjust for population stratification for both continuous and discrete populations with subject outliers, and it can be considered as an extension of the PCA method and the multidimensional scaling (MDS) method. Through simulation studies, we compare the performance of our proposed methods with several widely used stratification methods, including PCA and MDS. We show that subject outliers can greatly influence the analysis results from several existing methods, while our proposed robust population stratification methods perform very well for both discrete and admixed populations with subject outliers. We illustrate the new method using data from a rheumatoid arthritis study. CONCLUSIONS: We demonstrate that subject outliers can greatly influence the analysis result in GWA studies, and propose robust methods for dealing with population stratification that outperform existing population stratification methods in the presence of subject outliers.

Evaluating translocation gene fusions by SNP array data.

Somatic cell genetic alterations are a hallmark of tumor development and progression. Although various technologies have been developed and utilized to identify genetic aberrations, identifying genetic translocations at the chromosomal level is still a challenging task. High density SNP microarrays are useful to measure DNA copy number variation (CNV) across the genome. Utilizing SNP array data of cancer cell lines and patient samples, we evaluated the CNV and copy number breakpoints for several known fusion genes implicated in tumorigenesis. This analysis demonstrated the potential utility of SNP array data for the prediction of genetic aberrations via translocations based on identifying copy number breakpoints within the target genes. Genome-wide analysis was also performed to identify genes harboring copy number breakpoints across 820 cancer cell lines. Candidate oncogenes were identified that are linked to potential translocations in specific cancer cell lines.

Naive precursors of human regulatory T cells require FoxP3 for suppression and are susceptible to HIV infection.

CD4+CD25+ human regulatory T cells (Treg cells), which express the transcription factor FoxP3, suppress T cell activation. In this study, we sought to define cellular and molecular mechanisms of human Treg cell differentiation. A subset of human naive CD4+ T cells that are CD25+ express high levels of FoxP3. We show that upon activation through the TCR, these FoxP3-expressing naive T cells (termed TNreg cells) greatly expand in vitro. Expanded TNreg cells acquire a full Treg phenotype with potent suppressive activity and display low IL-2 production upon TCR stimulation. TNreg cells in which FoxP3 expression was reduced through RNA interference lost their suppressive activity, but retained their low IL-2 secretion in response to TCR stimulation. Furthermore, in support of the notion that TNreg cells represent a separate lineage of naive cells, we found that they were more susceptible to HIV infection as compared with naive CD4+ T cells. Based on these findings, we propose that TNreg cells are precursors for human Treg cells and that these cells require a high level of FoxP3 expression to maintain their suppressive function. Accordingly, modulation of TNreg cell numbers during infections such as HIV may disrupt human Treg cell development, and contribute to chronic immune activation.