Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy.

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.

Predicting Ovarian/Breast Cancer Pathogenic Risks of Human BRCA1 Gene Variants of Unknown Significance.

High-throughput sequencing is gaining popularity in clinical diagnoses, but more and more novel gene variants with unknown clinical significance are being found, giving difficulties to interpretations of people's genetic data, precise disease diagnoses, and the making of therapeutic strategies and decisions. In order to solve these issues, it is of critical importance to figure out ways to analyze and interpret such variants. In this work, BRCA1 gene variants with unknown clinical significance were identified from clinical sequencing data, and then, we developed machine learning models so as to predict the pathogenicity for variants with unknown clinical significance. Through performance benchmarking, we found that the optimized random forest model scored 0.85 in area under receiver operating characteristic curve, which outperformed other models. Finally, we applied the best random forest model to predict the pathogenicity of 6321 BRCA1 variants from both sequencing data and ClinVar database. As a result, we obtained the predictive pathogenic risks of BRCA1 variants of unknown significance.

Novel Compound-Target Interactions Prediction for the Herbal Formula Hua-Yu-Qiang-Shen-Tong-Bi-Fang.

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.

China's new policy for healthcare cost-control based on global budget: a survey of 110 clinicians in hospitals.

BACKGROUND: The 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. METHODS: Questionnaires 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. RESULTS: There 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. CONCLUSIONS: Cost-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.

Research and development of anti-Alzheimer's disease drugs: an update from the perspective of technology flows.

INTRODUCTION: Today, over 20 million people suffer from Alzheimer's disease (AD) worldwide. AD has become a critical issue to human health, especially in aging societies, and therefore it is a research hotspot in the global scientific community. The technology flow method differs from traditional reviews generating an informative overview of the research and development (R&D) landscape in a specific technological area. We need such an updated method to get a general overview of the R&D of anti-AD drugs in light of the dramatic developments in this area in recent years. AREAS COVERED: This study collects patent data from the Integrity database. A total of 399 patents with 821 internal citation pairs in the US from 1978 to 2017 were analyzed. Patent citation network analysis was used to visualize the technology relationship. EXPERT OPINION: For better production of anti-AD drugs, governments should emphasize the multi-target drug design, provide policy support for private companies, and encourage multilateral cooperation. The ß-amyloid peptide (Aß) theory leaves much to be desired; neurotransmitter and tau protein hypotheses are worth further examination. The use of old drugs for new indications is promising, as are traditional herbal medicines.

Network Analysis of Drug-target Interactions: A Study on FDA-approved New Molecular Entities Between 2000 to 2015.

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.

Research and Development of Hepatitis B Drugs: An Analysis Based on Technology Flows Measured by Patent Citations.

Despite the existence of available therapies, the Hepatitis B virus infection continues to be one of the most serious threats to human health, especially in developing countries such as China and India. To shed light on the improvement of current therapies and development of novel anti-HBV drugs, we thoroughly investigated 212 US patents of anti-HBV drugs and analyzed the technology flow in research and development of anti-HBV drugs based on data from IMS LifeCycle databases. Moreover, utilizing the patent citation method, which is an effective indicator of technology flow, we constructed patent citation network models and performed network analysis in order to reveal the features of different technology clusters. As a result, we identified the stagnant status of anti-HBV drug development and pointed the way for development of domestic pharmaceuticals in developing countries. We also discussed about therapeutic vaccines as the potential next generation therapy for HBV infection. Lastly, we depicted the cooperation between entities and found that novel forms of cooperation added diversity to the conventional form of cooperation within the pharmaceutical industry. In summary, our study provides inspiring insights for investors, policy makers, researchers, and other readers interested in anti-HBV drug development.