BackgroundDrugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.ResultsIn this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.ConclusionsThe cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.

BackgroundDrugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.ResultsIn this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.ConclusionsThe cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.

BackgroundDental erosion has been investigated in developed and developing countries and the prevalence varies considerably in different countries, geographic locations, and age groups. With the lifestyle of the Chinese people changing significantly over the decades, dental erosion has begun to receive more attention. However, the information about dental erosion in China is scarce. The purpose of this study was to explore the prevalence of dental erosion and associated risk factors in 12-13-year-old school children in Guangzhou, Southern China.MethodsThis cross-sectional survey was performed by two trained, calibrated examiners. A stratified random sample of 12-13-year-old children (774 boys and 725 girls) from 10 schools was examined for dental erosion using the diagnostic criteria of Eccles and the index of O'Sullivan was applied to record the distribution, severity, and amount of the lesions. Data on the socio-economic status, health behaviours, and general health involved in the etiology of dental erosion were obtained from a self-completed questionnaire. The analyses were performed using SPSS software.ResultsAt least one tooth surface with signs of erosion was found in 416 children (27.3%). The most frequently affected teeth were the central incisors (upper central incisors, 16.3% and 15.9%; lower central incisors, 17.4% and 14.8%). The most frequently affected surface was the incisal or occlusal edge (43.2%). The loss of enamel contour was present in 54.6% of the tooth surfaces with erosion. Of the affected tooth surfaces, 69.3% had greater than one-half of the tooth surface was affected. The results from logistic regression analysis demonstrated that the children who were female, consumed carbonated drinks once a week or more, and those whose mothers were educated to the primary level tended to have more dental erosion.ConclusionsDental erosion in 12-13-year-old Chinese school children is becoming a significant problem. A strategy of offering preventive care, including more campaigns promoting a healthier lifestyle for those at risk of dental erosion should be conducted in Chinese children and their parents.