3D-QSAR studies on fluroquinolones derivatives as inhibitors for tuberculosis

A quantitative structure activity relationship (QSAR) study was performed on the fluroquinolones known to have anti-tuberculosis activity. The 3D-QSAR models were generated using stepwise variable selection of the four methods - multiple regression (MR), partial least square regression (PLSR), principal component regression (PCR) and artificial neural networks (kNN-MFA). The statistical result showed a significant correlation coefficient q(2) (90%) for MR model and an external test set of (pred_r(2)) -1.7535, though the external predictivity showed to improve using kNN-MFA method with pred_r(2) of -0.4644. Contour maps showed that steric effects dominantly determine the binding affinities. The QSAR models may lead to a better understanding of the structural requirements of anti-tuberculosis compounds and also help in the design of novel molecules.     

New QSAR combined strategy for the design of A1 adenosine receptor agonists

Combined discriminant and regression analysis was carried out on a series of 167 A1 adenosine receptor agonists to identify the best linear and nonlinear models for the design of new compounds with a better biological profile. On the basis of the best linear discriminant analysis and both linear and nonlinear Multi Layer Perceptron neural networks regression, we have designed and synthesized 14 carbonucleoside analogues of adenosine. Their biological activities were predicted and experimentally measured to demonstrate the capability of our model to avoid the prediction of false positives. A good agreement was found between the calculated and observed biological activity.     

Identifying Populations at High Risk for Occupational Back Injury with Neutral Networks

For this study a simulation is conducted to investigate the accuracy of neural networks and logistic regression in identifying populations at high risk for occupational back injury. In contrast to most standard regression techniques, neural networks do not rely on linearity or explicitly specifying the nature of the association. Because the underlying relationships between work exposures, personal risk factors, and injury are often not well defined, neural networks may prove useful for injury risk assessment. Accuracy was assessed by comparing the injury status to the predicted level of risk in each worker. In simulations of a non-linear association, workers (used in the training data) were correctly classified 85% of the time with neural networks, 74% of the time with the main effects logistic model, and 79% of the time with the fully-specified logistic model. Using the test data, however, workers were correctly classified 67% of the time with neural networks, and 71% and 69% of the time with the main effects and fully specified logistic models, respectively. Simulations of a null association indicated that neural networks may be more likely to overfit random associations. These findings provide a valuable guide concerning statistical methodology for identifying high-risk worker populations.     

Empirical modelling of lake water-temperature relationships: a comparison of approaches

1.  As a result of the role that temperature plays in many aquatic processes, good predictive models of annual maximum near-surface lake water temperature across large spatial scales are needed, particularly given concerns regarding climate change. Comparisons of suitable modelling approaches are required to determine their relative merit and suitability for providing good predictions of current conditions. We developed models predicting annual maximum near-surface lake water temperatures for lakes across Canada using four statistical approaches: multiple regression, regression tree, artificial neural networks and Bayesian multiple regression.
2.  Annual maximum near-surface (from 0 to 2 m) lake water-temperature data were obtained for more than 13 000 lakes and were matched to geographic, climatic, lake morphology, physical habitat and water chemistry data. We modelled 2348 lakes and three subsets thereof encompassing different spatial scales and predictor variables to identify the relative importance of these variables at predicting lake temperature.
3.  Although artificial neural networks were marginally better for three of the four data sets, multiple regression was considered to provide the best solution based on the combination of model performance and computational complexity. Climatic variables and date of sampling were the most important variables for predicting water temperature in our models.
4.  Lake morphology did not play a substantial role in predicting lake temperature across any of the spatial scales. Maximum near-surface temperatures for Canadian lakes appeared to be dominated by large-scale climatic and geographic patterns, rather than lake-specific variables, such as lake morphology and water chemistry.     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−¹³C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.We acknowledge the support of NIH Grant DK58533 and the DuPont-MIT Alliance.     

Development of adenosine responsiveness after isolation of Leydig cells

Effects of adenosine and related compounds on the regulation of steroid production by isolated Leydig cells have been investigated. Steroid production by freshly isolated Leydig cells from testes of immature or mature rats and mice, or from Leydig tumor tissue could not be stimulated with adenosine, nicotinamide-adenine dinucleotide (phosphate) [NADPH, NAD(P)] or N6-(1-2-phenylisopropyl)-adenosine (PIA) (50 microM), whereas luteinizing hormone (LH) stimulated steroid production more than 10-fold. After 24 h incubation all adenosine-related compounds, but not inosine, stimulated steroid production to 20-100% of the maximal LH-stimulated activity. LH- or 22R -hydroxycholesterol-stimulated steroidogenesis in Leydig cells from immature rats did not decrease during the 24-h culture period, whereas ATP levels increased. The first significant effect of adenosine on steroid production in these cells was found after an incubation period of 3 h. In cells incubated for 1 h and 24 h, LH stimulated cyclic adenosine 3':5'-monophosphoric acid (cAMP) production 10-fold. Significant effects of adenosine and PIA on cAMP production or protein phosphorylation could only be shown in cells incubated for 24 h. Effects of adenosine on Leydig cells in intact testis tissue of immature rats could not be determined. The results suggest that after isolation of Leydig cells, specific alterations in the cell membrane occur, causing increased sensitivity to adenosine and related compounds. Adenosine apparently does not play a role in the role of steroid production in Leydig cells in vivo.     

Phenylethyl-substituted pyrimido[2,1-f]purinediones and related compounds: structure-activity relationships as adenosine A(1) and A(2A) receptor ligands

The synthesis of N-(un)substituted-phenylalkylpyrimido[2,1-f]purinediones was performed starting with 7-(3-chloropropyl)-8-bromotheophylline and 7-(3-chloropropyl)-8-bromo-1,3-dipropylxanthine. Compounds with unsubstituted or substituted ethylene spacer to an aromatic ring were synthesized. Additionally variations in the spacer-elongation of the linker containing more than two atoms, introduction of a double bond or heteroatoms were performed. Physicochemical properties of the synthesized compounds were described. The obtained compounds envisaged as sterically fixed and configurationally stable analogs of 8-styrylxanthines, were evaluated for their affinity to adenosine A(1) and A(2A) receptors, the receptor subtypes that are predominant in the brain. Selected compounds were also investigated for the affinity to the A(2B) and A(3) receptor subtypes. It was stated that phenylethyl pyrimido[2,1-f]purinediones and their analogs with variations of the ethylene spacer (substituted or extended) exhibit micromolar or submicromolar affinity for A(2A) ARs (adenosine receptors); for example compound 2Ac with p-hydroxy substituent displayed a K(i) value of 0.23 microM at the rat A(2A) receptor. In comparison to the previously obtained phenyl and benzyl pyrimido[2,1-f]purinediones compounds with a shorter spacer, phenethyl derivatives were optimal for A(2A) AR. The kind of substituent at the aromatic ring was important for the affinity. Oxygen and nitrogen atoms in the spacer resulted frequently in a slight decrease of the A(2A) AR affinity, introduction of more heteroatoms into the spacer-in carbamates-caused distinctly negative effect on the activity. In this series of compounds more frequently the adenosine A(1) activity was observed, also in submicromolar range as for dipropyl derivative 2Ba with K(i) value of 0.62 microM at the rat A(2A) AR. 3D-QSAR models were developed for the compounds presented in this paper as well as in the previous publications showing activity at adenosine A(1) and A(2A) ARs. It was concluded that for the activity at adenosine A(1) and A(2A) receptors lipophilicity, steric effects along with the molecule's electrostatic surface properties had greatest value. Chosen compounds were evaluated in vivo as anticonvulsants in MES, scMet tests and examined for neurotoxicity. Contrary to previously obtained phenyl and benzyl pyrimido[2,1-f]purinediones, all tested compounds were inactive as anticonvulsants.