The design and synthesis of novel adenosine-derived inhibitors of HSP70, guided by modeling and X-ray crystallographic structures of these compounds in complex with HSC70/BAG-1, is described. Examples exhibited submicromolar affinity for HSP70, were highly selective over HSP90, and some displayed potency against HCT116 cells. Exposure of compound 12 to HCT116 cells caused significant reduction in cellular levels of Raf-1 and Her2 at concentrations similar to that which caused cell growth arrest.

Purpose The anti-apoptotic function of the 70 kDa family of heat shock proteins and their role in cancer is well documented. Dual targeting of Hsc70 and Hsp70 with siRNA induces proteasome-dependent degradation of Hsp90 client proteins and extensive tumor specific apoptosis as well as the potentiation of tumor cell apoptosis following pharmacological Hsp90 inhibition. Methods We have previously described the discovery and synthesis of novel adenosine-derived inhibitors of the 70 kDa family of heat shock proteins; the first inhibitors described to target the ATPase binding domain. The in vitro activity of VER-155008 was evaluated in HCT116, HT29, BT474 and MDA-MB-468 carcinoma cell lines. Cell proliferation, cell apoptosis and caspase 3/7 activity was determined for VER-155008 in the absence or presence of small molecule Hsp90 inhibitors. Results VER-155008 inhibited the proliferation of human breast and colon cancer cell lines with GI 50 s in the range 5.3-14.4 lM, and induced Hsp90 client protein degradation in both HCT116 and BT474 cells. As a single agent, VER-155008 induced caspase-3/7 dependent apoptosis in BT474 cells and non-caspase dependent cell death in HCT116 cells. VER-155008 potentiated the apoptotic potential of a small molecule Hsp90 inhibitor in HCT116 but not HT29 or MDA-MB-468 cells. In vivo, VER-155008 demonstrated rapid metabolism and clearance, along with tumor levels below the predicted pharmacologically active level. Conclusion These data suggest that small molecule inhibitors of Hsc70/Hsp70 phenotypically mimic the cellular mode of action of a small molecule Hsp90 inhibitor and can potentiate the apoptotic potential of a small molecule Hsp90 inhibitor in certain cell lines. The factors determining whether or not cells apoptose in response to Hsp90 inhibition or the combination of Hsp90 plus Hsc70/ Hsp70 inhibition remain to be determined.

78 kDa glucose-regulated protein (Grp78) is a heat shock protein (HSP) involved in protein folding that plays a role in cancer cell proliferation. Binding of adenosine-derived inhibitors to Grp78 was characterized by surface plasmon resonance and isothermal titration calorimetry. The most potent compounds were 13 (VER-155008) with K(D) = 80 nM and 14 with K(D) = 60 nM. X-ray crystal structures of Grp78 bound to ATP, ADPnP, and adenosine derivative 10 revealed differences in the binding site between Grp78 and homologous proteins.

Accurately predicting the arrival of coronal mass ejections (CMEs) at the Earth based on remote images is of critical significance in the study of space weather. In this paper, we make a statistical study of 21 Earth directed CMEs, exploring in particular the relationship between CME initial speeds and transit times. The initial speed of a CME is obtained by fitting the CME with the Graduated Cylindrical Shell model and is thus free of projection effects. We then use the drag force model to fit results of the transit time versus the initial speed. By adopting different drag regimes, i.e., the viscous, aerodynamics, and hybrid regimes, we get similar results, with the least mean estimation error of the hybrid model of 12.9 hours. CMEs with a propagation angle (the angle between the propagation direction and the Sun-Earth line) larger than its half angular width arrive at the Earth with an angular deviation caused by factors other than the radial solar wind drag. The drag force model cannot be well applied to such events. If we exclude these events in the sample, the prediction accuracy can be improved, i.e., the estimation error reduces to 6.8 hours. This work suggests that it is viable to predict the arrival time of CMEs at the Earth based on the initial parameters with a fairly good accuracy. Thus, it provides a method of space weather forecast of 1-5 days following the occurrence of CMEs.

With the development of data mining, machine learning offers opportunities to improve discrimination by analyzing complex interactions among massive variables. To test the ability of machine learning algorithms for predicting risk of type 2 diabetes mellitus (T2DM) in a rural Chinese population, we focus on a total of 36,652 eligible participants from the Henan Rural Cohort Study. Risk assessment models for T2DM were developed using six machine learning algorithms, including logistic regression (LR), classification and regression tree (CART), artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM). The model performance was measured in an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value and area under precision recall curve. The importance of variables was identified based on each classifier and the shapley additive explanations approach. Using all available variables, all models for predicting risk of T2DM demonstrated strong predictive performance, with AUCs ranging between 0.811 and 0.872 using laboratory data and from 0.767 to 0.817 without laboratory data. Among them, the GBM model performed best (AUC: 0.872 with laboratory data and 0.817 without laboratory data). Performance of models plateaued when introduced 30 variables to each model except CART model. Among the top-10 variables across all methods were sweet flavor, urine glucose, age, heart rate, creatinine, waist circumference, uric acid, pulse pressure, insulin, and hypertension. New important risk factors (urinary indicators, sweet flavor) were not found in previous risk prediction methods, but determined by machine learning in our study. Through the results, machine learning methods showed competence in predicting risk of T2DM, leading to greater insights on disease risk factors with no priori assumption of causality.