Abstract: To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes.

Abstract: Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 degrees C for 105 days were used to predicted stability by applying rule-embedded naive Bayesian learning, based upon atom center fragment (ACF) features. To build the naive Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naive Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

Abstract: Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.

Abstract: In drug discovery and development, the conventional "single drug, single target" concept has been shifted to "single drug, multiple targets"--a concept coined as polypharmacology. For studies in this emerging field, dedicated and high-quality databases of multitargeting ligands would be exceedingly beneficial. To this end, we conducted a comprehensive analysis of the structural and chemical/biological profiles of polypharmacological agents and present a Web-based database (Polypharma). All of these compounds curated herein have been cocrystallized with more than one unique protein with intensive reports of their multitargeting activities. The present study provides more insight of drug multitargeting and is particularly useful for polypharmacology modeling. This specialized curation has been made publically available at http:/imdlab.org/polypharma/

Abstract: Polypharmacology is beginning to emerge as an important concept in the field of drug discovery. However, there are no established approaches to either select appropriate target sets or design polypharmacological drugs. Here, we propose a structural-proteomics approach that utilizes the structural information of the binding sites at a genome-scale obtained through in-house algorithms to characterize the pocketome, yielding a list of ligands that can participate in various biochemical events in the mycobacterial cell. The pocket-type space is seen to be much larger than the sequence or fold-space, suggesting that variations at the site-level contribute significantly to functional repertoire of the organism. All-pair comparisons of binding sites within Mycobacterium tuberculosis (Mtb), pocket-similarity network construction and clustering result in identification of binding-site sets, each containing a group of similar binding sites, theoretically having a potential to interact with a common set of compounds. A polypharmacology index is formulated to rank targets by incorporating a measure of druggability and similarity to other pockets within the proteome. This study presents a rational approach to identify targets with polypharmacological potential along with possible drugs for repurposing, while simultaneously, obtaining clues on lead compounds for use in new drug-discovery pipelines.

Abstract: The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to establish an open-source community effort to develop a global knowledge base, one that brings together and standardizes all available information for all drugs and all health outcomes of interest (HOIs) from all electronic sources pertinent to drug safety. To make this vision a reality, we have established a workgroup within the Observational Health Data Sciences and Informatics (OHDSI, http://ohdsi.org) collaborative. The workgroup's mission is to develop an open-source standardized knowledge base for the effects of medical products and an efficient procedure for maintaining and expanding it. The knowledge base will make it simpler for practitioners to access, retrieve, and synthesize evidence so that they can reach a rigorous and accurate assessment of causal relationships between a given drug and HOI. Development of the knowledge base will proceed with the measureable goal of supporting an efficient and thorough evidence-based assessment of the effects of 1,000 active ingredients across 100 HOIs. This non-trivial task will result in a high-quality and generally applicable drug safety knowledge base. It will also yield a reference standard of drug-HOI pairs that will enable more advanced methodological research that empirically evaluates the performance of drug safety analysis methods.

Abstract: Parasitic diseases have an enormous health, social and economic impact and are a particular problem in tropical regions of the world. Diseases caused by protozoa and helminths, such as malaria and schistosomiasis, are the cause of most parasite related morbidity and mortality, with an estimated 1.1 million combined deaths annually. The global burden of these diseases is exacerbated by the lack of licensed vaccines, making safe and effective drugs vital to their prevention and treatment. Unfortunately, where drugs are available, their usefulness is being increasingly threatened by parasite drug resistance. The need for new drugs drives antiparasitic drug discovery research globally and requires a range of innovative strategies to ensure a sustainable pipeline of lead compounds. In this review we discuss one of these approaches, drug repurposing or repositioning, with a focus on major human parasitic protozoan diseases such as malaria, trypanosomiasis, toxoplasmosis, cryptosporidiosis and leishmaniasis.

Abstract: Three quinolone compounds were synthesized and crystallized in an effort to study the structure-activity relationship of these calcium-channel antagonists. In all three quinolones, viz. ethyl 4-(4-bromophenyl)-2,7,7-trimethyl-5-oxo-1,4,5,6,7,8-hexahydroquinoline-3-carboxyl ate, (I), ethyl 4-(3-bromophenyl)-2,7,7-trimethyl-5-oxo-1,4,5,6,7,8-hexahydroquinoline-3-carboxyl ate, (II), and ethyl 4-(2-bromophenyl)-2,7,7-trimethyl-5-oxo-1,4,5,6,7,8-hexahydroquinoline-3-carboxyl ate, (III), all C21H24BrNO3, common structural features such as a flat boat conformation of the 1,4-dihydropyridine (1,4-DHP) ring, an envelope conformation of the fused cyclohexanone ring and a bromophenyl ring at the pseudo-axial position and orthogonal to the 1,4-DHP ring are retained. However, due to the different packing interactions in each compound, halogen bonds are observed in (I) and (III). Compound (III) crystallizes with two molecules in the asymmetric unit. All of the prepared derivatives satisfy the basic structural requirements to possess moderate activity as calcium-channel antagonists.

Abstract: BACKGROUND: In the treatment of depression, it is essential to monitor for early warnings of suicide. OBJECTIVE: The aim of this study was to identify the symptoms that would suggest a high suicide risk by analyzing data obtained from the US Food and Drug Administration Adverse Event Reporting System (FAERS) of selective serotonin reuptake inhibitors. METHODS: Using FAERS reports from 1997 to the second quarter of 2012, we constructed the co-occurrence network of adverse events. From this network, we extracted the events that were strongly connected to suicidal events (suicidal attempts, suicidal ideation, suicidal behavior, and complete suicide) by means of the community detection method. RESULTS: We succeeded in obtaining a list of suicide-related adverse events. Owing to the randomness inherent in the algorithms of community detection, we found that the obtained list differed according to each trial of analysis. However, the lists we derived show considerable efficiency in identifying suicidal events. CONCLUSION: The network analysis appears to be a promising method for identifying signals of suicide.