The U.S. Food and Drug Administration (FDA) approves new drugs every year. Drug targets are some of the most important interactive molecules for drugs, as they have a significant impact on the therapeutic effects of drugs. In this work, we thoroughly analyzed the data of small molecule drugs approved by the U.S. FDA between 2000 and 2015. Specifically, we focused on seven classes of new molecular entity (NME) classified by the anatomic therapeutic chemical (ATC) classification system. They were NMEs and their corresponding targets for the cardiovascular system, respiratory system, nerve system, general anti-infective systemic, genito-urinary system and sex hormones, alimentary tract and metabolisms, and antineoplastic and immunomodulating agents. To study the drug–target interaction on the systems level, we employed network topological analysis and multipartite network projections. As a result, the drug–target relations of different kinds of drugs were comprehensively characterized and global pictures of drug–target, drug–drug, and target–target interactions were visualized and analyzed from the perspective of network models.
BackgroundThe increasing cost on healthcare exposes China’s healthcare budgets and system to financial crisis. To control the excessive growth of healthcare expenditure, China’s healthcare reforms emphasize the control of the global budget for healthcare, which leads to the release of relevant policy and a series of cost-control actions implemented by different hospitals. This work aims to identify the effects brought by the cost-control policy and actions via surveying and analysing feedback from clinicians.MethodsQuestionnaires on the cost-control policy and actions were designed for surveying 110 clinicians in hospitals from different regions of China. The data on the implementation of the cost-control actions and doctors’ feedback on these actions were analysed using descriptive statistics. Pearson’s chi-squared tests were performed to detect associations between doctors’ opinions and specific cost-control actions. A value of p < 0.05 was considered statistically significant. Association relationships between doctors’ opinions and cost-control actions were modelled into network models, and key factors were identified in a multi-variate framework. Last, we visualized our resultant data using a network model, and further multi-variate analysis was performed.ResultsThere were three main findings. (1) The cost-control policy has been widely implemented in the sampled hospitals in different regions of China, with more than 80% of those surveyed acknowledging that their hospitals take actions of reducing average prescription fees for outpatients, drug costs, and in-hospitalization durations. (2) Most doctors have a negative view of some cost-control actions; this is mainly due to concerns about the effects of these actions on the doctors’ own healthcare performance and patient satisfaction. (3) Cost-control actions that had a significant impact on doctors’ performance included limiting average prescription fees for outpatients and limiting the use of examinations/drugs/surgeries. Decreased patient satisfaction was associated with fewer admissions of critically ill patients, reduced use of brand-name drugs, and increased total costs to patients due to increased frequencies of visits to the hospitals.ConclusionsCost-control actions implemented in hospitals in response to the government’s policy to reduce its national healthcare budget affect both doctors and patients in several ways. Moreover, the cost-control policy and actions can be improved.Electronic supplementary materialThe online version of this article (10.1186/s12913-019-3921-8) contains supplementary material, which is available to authorized users.
Herbal formulae have a long history in clinical medicine in Asia. While the complexity of the formulae leads to the complex compound-target interactions and the resultant multi-target therapeutic effects, it is difficult to elucidate the molecular/therapeutic mechanism of action for the many formulae. For example, the Hua-Yu-Qiang-Shen-Tong-Bi-Fang (TBF), an herbal formula of Chinese medicine, has been used for treating rheumatoid arthritis. However, the target information of a great number of compounds from the TBF formula is missing. In this study, we predicted the targets of the compounds from the TBF formula via network analysis and in silico computing. Initially, the information of the phytochemicals contained in the plants of the herbal formula was collected, and subsequently computed to their corresponding fingerprints for the sake of structural similarity calculation. Then a compound structural similarity network infused with available target information was constructed. Five local similarity indices were used and compared for their performance on predicting the potential new targets of the compounds. Finally, the Preferential Attachment Index was selected for it having an area under curve (AUC) of 0.886, which outperforms the other four algorithms in predicting the compound-target interactions. This method could provide a promising direction for identifying the compound-target interactions of herbal formulae in silico.
Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to the health of female. Based on drug repositioning strategy, we trained and benchmarked multiple machine learning models so as to predict potential effective antiviral drugs for HPV infection in this work. Through optimizing models, measuring models’ predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by the United States Food and Drug Administration, and benchmarking different models’ predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naïve Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 57 pairs of antiviral-HPV protein interactions from 864 pairs of antiviral-HPV protein associations. Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.
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