Insulin, Not the Preservative m-cresol, Instigates Loss of Infusion Site Patency Over Extended Durations of CSII in Diabetic Swine.

Insulin infusion sets worn for more than 4-5 days have been associated with a greater risk of unexplained hyperglycemia, a phenomenon that has been hypothesized to be caused by an inflammatory response to preservatives such as m-cresol and phenol. In this cross-over study in diabetic swine, we examined the role of the preservative m-cresol in inflammation and changes in infusion site patency. Insulin pharmacokinetics (PK) and glucose pharmacodynamics (PD) were measured on delivery of a bolus of regular human insulin U-100 (U-100R), formulated with or without 2.5 mg/mL m-cresol, to fasted swine following 0, 3, 5, 7, and 10 days of continuous subcutaneous insulin infusion (CSII). In a subsequent study with the same animals, biopsies were evaluated from swine wearing infusion sets infusing nothing, saline, or U-100R either with or without 2.5 mg/mL m-cresol, following 3, 7, and 10 days of CSII. Exposure to m-cresol did not impact any PK or PD endpoints. PK and PD responses dropped markedly from Days 7-10, regardless of the presence of m-cresol. Histopathology results suggest an additive inflammatory response to both the infusion set and the insulin protein itself, peaking at Day 7 and remaining stable beyond.

Synthesis and Pharmacological Characterization of C4ß-Amide-Substituted 2-Aminobicyclo[3.1.0]hexane-2,6-dicarboxylates. Identification of (1 S,2 S,4 S,5 R,6 S)-2-Amino-4-[(3-methoxybenzoyl)amino]bicyclo[3.1.0]hexane-2,6-dicarboxylic Acid (LY2794193), a Highly Potent and Selective mGlu3 Receptor Agonist.

Multiple therapeutic opportunities have been suggested for compounds capable of selective activation of metabotropic glutamate 3 (mGlu3) receptors, but small molecule tools are lacking. As part of our ongoing efforts to identify potent, selective, and systemically bioavailable agonists for mGlu2 and mGlu3 receptor subtypes, a series of C4ß-N-linked variants of (1 S,2 S,5 R,6 S)-2-amino-bicyclo[3.1.0]hexane-2,6-dicarboxylic acid 1 (LY354740) were prepared and evaluated for both mGlu2 and mGlu3 receptor binding affinity and functional cellular responses. From this investigation we identified (1 S,2 S,4 S,5 R,6 S)-2-amino-4-[(3-methoxybenzoyl)amino]bicyclo[3.1.0]hexane-2,6-dicarboxylic acid 8p (LY2794193), a molecule that demonstrates remarkable mGlu3 receptor selectivity. Crystallization of 8p with the amino terminal domain of hmGlu3 revealed critical binding interactions for this ligand with residues adjacent to the glutamate binding site, while pharmacokinetic assessment of 8p combined with its effect in an mGlu2 receptor-dependent behavioral model provides estimates for doses of this compound that would be expected to selectively engage and activate central mGlu3 receptors in vivo.

Risk-stratified imputation in survival analysis.

BACKGROUND: Censoring that is dependent on covariates associated with survival can arise in randomized trials due to changes in recruitment and eligibility criteria to minimize withdrawals, potentially leading to biased treatment effect estimates. Imputation approaches have been proposed to address censoring in survival analysis; while these approaches may provide unbiased estimates of treatment effects, imputation of a large number of outcomes may over- or underestimate the associated variance based on the imputation pool selected. PURPOSE: We propose an improved method, risk-stratified imputation, as an alternative to address withdrawal related to the risk of events in the context of time-to-event analyses. METHODS: Our algorithm performs imputation from a pool of replacement subjects with similar values of both treatment and covariate(s) of interest, that is, from a risk-stratified sample. This stratification prior to imputation addresses the requirement of time-to-event analysis that censored observations are representative of all other observations in the risk group with similar exposure variables. We compared our risk-stratified imputation to case deletion and bootstrap imputation in a simulated dataset in which the covariate of interest (study withdrawal) was related to treatment. A motivating example from a recent clinical trial is also presented to demonstrate the utility of our method. RESULTS: In our simulations, risk-stratified imputation gives estimates of treatment effect comparable to bootstrap and auxiliary variable imputation while avoiding inaccuracies of the latter two in estimating the associated variance. Similar results were obtained in analysis of clinical trial data. LIMITATIONS: Risk-stratified imputation has little advantage over other imputation methods when covariates of interest are not related to treatment. Risk-stratified imputation is intended for categorical covariates and may be sensitive to the width of the matching window if continuous covariates are used. CONCLUSIONS: The use of the risk-stratified imputation should facilitate the analysis of many clinical trials, in which one group has a higher withdrawal rate that is related to treatment.

Sufficient dimension reduction and prediction in regression.

Dimension reduction for regression is a prominent issue today because technological advances now allow scientists to routinely formulate regressions in which the number of predictors is considerably larger than in the past. While several methods have been proposed to deal with such regressions, principal components (PCs) still seem to be the most widely used across the applied sciences. We give a broad overview of ideas underlying a particular class of methods for dimension reduction that includes PCs, along with an introduction to the corresponding methodology. New methods are proposed for prediction in regressions with many predictors.