Development and Validation of a Predictive Model of Acute Glucose Response to Exercise in Individuals With Type 2 Diabetes

Background: Our purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes. Design and methods: Data from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation. Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤ 1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals\u27 attributes with model error. Results: Minutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for \u3c1.0% of the variance. In the development dataset, a model with random slopes for pre-exercise glucose improved fit over a model with random intercepts only (likelihood ratio 34.5, p \u3c 0.001). Cross-validated model accuracy was 83.3%. In the test dataset, overall accuracy was 80.2%. The model was more accurate in pre-prandial than postprandial exercise (83.6% vs. 74.5% accuracy respectively). 31/47 subjects had ≤1 model error after the third exercise session. Model error varied across individuals and was weakly associated with within-subject variability in pre-exercise glucose (Odds ratio 1.49, 95% Confidence interval 1.23-1.75). Conclusions: The preliminary development and test of a predictive model of acute glucose response to exercise is presented. Further work to improve this model is discussed

Modification of a Hydrophobic Layer by a Point Mutation in Syntaxin 1A Regulates the Rate of Synaptic Vesicle Fusion

Both constitutive secretion and Ca(2+)-regulated exocytosis require the assembly of the soluble N-ethylmaleimide–sensitive factor attachment protein receptor (SNARE) complexes. At present, little is known about how the SNARE complexes mediating these two distinct pathways differ in structure. Using the Drosophila neuromuscular synapse as a model, we show that a mutation modifying a hydrophobic layer in syntaxin 1A regulates the rate of vesicle fusion. Syntaxin 1A molecules share a highly conserved threonine in the C-terminal +7 layer near the transmembrane domain. Mutation of this threonine to isoleucine results in a structural change that more closely resembles those found in syntaxins ascribed to the constitutive secretory pathway. Flies carrying the I254 mutant protein have increased levels of SNARE complexes and dramatically enhanced rate of both constitutive and evoked vesicle fusion. In contrast, overexpression of the T254 wild-type protein in neurons reduces vesicle fusion only in the I254 mutant background. These results are consistent with molecular dynamics simulations of the SNARE core complex, suggesting that T254 serves as an internal brake to dampen SNARE zippering and impede vesicle fusion, whereas I254 favors fusion by enhancing intermolecular interaction within the SNARE core complex

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Evacetrapib and Cardiovascular Outcomes in High-Risk Vascular Disease

BACKGROUND: The cholesteryl ester transfer protein inhibitor evacetrapib substantially raises the high-density lipoprotein (HDL) cholesterol level, reduces the low-density lipoprotein (LDL) cholesterol level, and enhances cellular cholesterol efflux capacity. We sought to determine the effect of evacetrapib on major adverse cardiovascular outcomes in patients with high-risk vascular disease. METHODS: In a multicenter, randomized, double-blind, placebo-controlled phase 3 trial, we enrolled 12,092 patients who had at least one of the following conditions: an acute coronary syndrome within the previous 30 to 365 days, cerebrovascular atherosclerotic disease, peripheral vascular arterial disease, or diabetes mellitus with coronary artery disease. Patients were randomly assigned to receive either evacetrapib at a dose of 130 mg or matching placebo, administered daily, in addition to standard medical therapy. The primary efficacy end point was the first occurrence of any component of the composite of death from cardiovascular causes, myocardial infarction, stroke, coronary revascularization, or hospitalization for unstable angina. RESULTS: At 3 months, a 31.1% decrease in the mean LDL cholesterol level was observed with evacetrapib versus a 6.0% increase with placebo, and a 133.2% increase in the mean HDL cholesterol level was seen with evacetrapib versus a 1.6% increase with placebo. After 1363 of the planned 1670 primary end-point events had occurred, the data and safety monitoring board recommended that the trial be terminated early because of a lack of efficacy. After a median of 26 months of evacetrapib or placebo, a primary end-point event occurred in 12.9% of the patients in the evacetrapib group and in 12.8% of those in the placebo group (hazard ratio, 1.01; 95% confidence interval, 0.91 to 1.11; P=0.91). CONCLUSIONS: Although the cholesteryl ester transfer protein inhibitor evacetrapib had favorable effects on established lipid biomarkers, treatment with evacetrapib did not result in a lower rate of cardiovascular events than placebo among patients with high-risk vascular disease. (Funded by Eli Lilly; ACCELERATE ClinicalTrials.gov number, NCT01687998 .)

Doxorubicin induces de novo expression of N-terminal truncated MMP-2 in cardiac myocytes

Anthracyclines, such as doxorubicin, are commonly prescribed antineoplastic agents which cause irreversible cardiac injury. Doxorubicin cardiotoxicity is initiated by increased oxidative stress in cardiomyocytes. Oxidative stress enhances intracellular matrix metalloproteinase-2 (MMP-2) by direct activation of its full length isoform and/or de novo expression of an N-terminal truncated (NTT-MMP-2) isoform. As MMP-2 is localized to the sarcomere we tested whether doxorubicin activates intracellular MMP-2 in neonatal rat ventricular myocytes (NRVM) and if it thereby proteolyzes two of its identified sarcomeric targets, α-actinin and troponin I. Doxorubicin increased oxidative stress within 12 hr as indicated by reduced aconitase activity. This was associated with a twofold increase in MMP-2 protein levels and threefold higher gelatinolytic activity. MMP inhibitors ARP-100 or ONO-4817 (1 μM) prevented doxorubicin-induced MMP-2 activation. Doxorubicin also increased the levels and activity of MMP-2 secreted into the conditioned media. Doxorubicin upregulated the mRNA expression of both full length MMP-2 and NTT-MMP-2. α-Actinin levels remained unchanged, whereas doxorubicin downregulated troponin I in an MMP-independent manner. Doxorubicin induces oxidative stress and stimulates a robust increase in MMP-2 expression and activity in NRVM, including NTT-MMP-2. The sarcomeric proteins α-actinin and troponin I are, however, not targeted by MMP-2 under these conditions.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author