Abstract: Traditional Chinese medicine (TCM) has a longstanding history and has gained widespread clinical applications. San Cao Decoction (SCD) is an experience prescription first formulated by Prof. Duzhou Liu. We previously demonstrated its antihypertensive effects; however, to systematically explain the underlying mechanisms of action, we employed a systems pharmacology approach for pharmacokinetic screening and target prediction by constructing protein-protein interaction networks of hypertension-related and putative SCD-related targets, and Database for Annotation, Visualization, and Integrated Discovery enrichment analysis. We identified 123 active compounds in SCD and 116 hypertension-related targets. Furthermore, the enrichment analysis of the drug-target network showed that SCD acts in a multidimensional manner to regulate PI3K-Akt-endothelial nitric oxide synthase signaling to maintain blood pressure. Our results highlighted the molecular mechanisms of antihypertensive actions of medicinal herbs at a systematic level.

Abstract: BACKGROUND: Essential medicines lists and related policies are intended to meet the priority health needs of populations and their implementation is associated with more appropriate use of medicines. The World Health Organization (WHO) recommends that countries carefully select the medicines to be included in their national essential medicines lists. Lists that are used to prioritize access to important treatments should not include medicines that have been withdrawn elsewhere because of an unfavourable benefit-to-harm balance; however, countries still list and use medicines that have been withdrawn worldwide. The objective of this study was to determine whether the national essential medicines lists of 137 countries include medicines that have been withdrawn in other countries. METHODS AND FINDINGS: We performed an audit of national essential medicines lists for medicines that had been withdrawn. Medicines withdrawn from worldwide markets between 1953 and 2014 were identified using a systematic review of published literature and regulatory documents. The reviewers used sources including the WHO's database of drugs, PubMed, and the websites of regulatory agencies to obtain information regarding adverse effects associated with the medicines, the year of first withdrawal, markets of withdrawal, and the level of evidence supporting each withdrawal. We recorded the number of countries with a withdrawn medicine included in their national medicines list, the number of withdrawn medicines included in each nation's list, and the number of national essential medicines including each withdrawn medicine. 97 medicines were withdrawn in at least one country but still included in one more national essential medicines list. Of 137 countries with a national essential medicines list, 136 lists included at least one withdrawn medicine, with 54% of the lists containing 5 or fewer withdrawn medicines, and 27% including 10 or more withdrawn medicines. 11 medicines were withdrawn worldwide but still included on at least one national essential medicines list. Countries with longer essential medicines lists had more withdrawn medicines included in their lists. CONCLUSIONS: This study found that withdrawn medicines are included in all but one national essential medicines list, representing a need for more stringent processes for selecting and removing medicines on these lists. Countries may wish to apply special scrutiny to medicines withdrawn in other nations when selecting medicines to include on their lists.

Abstract: Metal ions are involved in many essential biological processes and are crucial for the survival of all organisms. Identification of metal-binding proteins (MBPs) of human affecting pathogens may provide the blueprint for understanding biological metal usage and their putative roles in pathogenesis. This study is focused on the analysis of MBPs from Orientia tsutsugamushi (Ott), a causal agent of scrub typhus in humans. A total of 321 proteins were predicted as putative MBPs, based on sequence search and three-dimensional structure analysis. Majority of proteins could bind with magnesium, and the order of metal binding was Mg > Ca > Zn > Mn > Fe > Cd > Ni > Co > Cu, respectively. The predicted MBPs were functionally classified into nine broad classes. Among them, gene expression and regulation, metabolism, cell signaling, and transport classes were dominant. It was noted that the putative MBPs were localized in all subcellular compartments of Ott, but majorly found in the cytoplasm. Additionally, it was revealed that out of 321 predicted MBPs 245 proteins were putative bacterial toxins and among them, 98 proteins were nonhomologous to human proteome. Sixty putative MBPs showed the ability to interact with drug or drug-like molecules, which indicate that they may be used as broad-spectrum drug targets. These predicted MBPs from Ott could play vital role(s) in various cellular activities and virulence, hence may serve as plausible therapeutic targets to design metal-based drugs to curtail its infection.

Abstract: Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods for drug-drug interaction prediction based on artificial neural networks and factor propagation over graph nodes: adjacency matrix factorization (AMF) and adjacency matrix factorization with propagation (AMFP). We conduct a retrospective analysis by training our models on a previous release of the DrugBank database with 1,141 drugs and 45,296 drug-drug interactions and evaluate the results on a later version of DrugBank with 1,440 drugs and 248,146 drug-drug interactions. Additionally, we perform a holdout analysis using DrugBank. We report an area under the receiver operating characteristic curve score of 0.807 and 0.990 for the retrospective and holdout analyses respectively. Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0.814 and 0.991 for the retrospective and the holdout analyses. We demonstrate that AMF and AMFP provide state of the art results compared to existing methods and that the ensemble-based classifier improves the performance by combining various predictors. Additionally, we compare our methods with multi-source data-based predictors using cross-validation. In the multi-source data comparison, our methods outperform various ensembles created using 29 different predictors based on several data sources. These results suggest that AMF, AMFP, and the proposed ensemble-based classifier can provide important information during drug development and regarding drug prescription given only partial or noisy data. Additionally, the results indicate that the interaction network (known DDIs) is the most useful data source for identifying potential DDIs and that our methods take advantage of it better than the other methods investigated. The methods we present can also be used to solve other link prediction problems. Drug embeddings (compressed representations) created when training our models using the interaction network have been made public.

Abstract: Drug repurposing techniques allow existing drugs to be tested against diseases outside their initial spectrum, resulting in reduced cost and eliminating the long time-frames of new drug development. In silico drug repurposing further speeds up the process either by proposing drugs suitable to invert the transcriptomic profile of a disease or by indicating drugs based on their common targets or structural similarity with other drugs with similar mode of action. Such methods usually return a number of potential repurposed drugs that need to be tested against the disease in in vitro, pre-clinical and clinical studies. Thus, it is crucial to have a more sophisticated candidate drug ranking in order to start testing from the most promising chemical substances. As a means to enhance the above decision process, we present CoDReS (Composite Drug Reranking Scoring), a drug (re-)ranking web-based tool, which combines an initial drug ranking (i.e. repurposing score or hypothesis/potentiality score) with a functional score of each drug considered in conjunction with the disease under study as well as with a structural score derived from potential drugability violations. Furthermore, a structural similarity clustering is applied on the considered drugs and a handful of structural exemplars are suggested for further in vitro and in vivo validation. The user is able to filter the results further, through structural similarity examination of the candidate drugs with drugs that have failed against the queried disease where related clinical trials have been carried out. CoDReS is publicly available online at http://bioinformatics.cing.ac.cy/codres.

Abstract: The juvenile conventional pig has been suggested as a preclinical animal model to evaluate pharmacokinetic (PK), pharmacodynamic (PD), and safety parameters in children. However, a lot of developmental changes in pig physiology still need to be unraveled. While the in vitro ontogeny of pig biotransformation enzymes is getting more attention in literature, the in vivo developmental changes have not yet been investigated. Therefore, the aim of the current study was to evaluate the biotransformation of ibuprofen (IBU) in conventional pigs aged 1 week, 4 weeks, 8 weeks, and 6-7 months after a single intravenous and oral administration of 5 mg/kg body weight (BW) of IBU, using a PK approach in a crossover design for each age group. An ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated to determine 2-hydroxyibuprofen (2OH-IBU), carboxyibuprofen (COOH-IBU), and ibuprofen glucuronide (IBU-GlcA) in pig plasma. All three metabolites could be quantified in plasma and the following PK parameters were determined: C max, T max, AUC0-->6h, area under plasma concentration-time curve (AUC) ratio between parent drug and metabolite, and the absolute oral bioavailability of the parent drug IBU. The plasma concentrations of the metabolites were always lower than those of IBU. The bioavailability was high, indicating limited pre-systemic biotransformation. The AUC ratio of 2OH-IBU and COOH-IBU/IBU showed a significant increase at 4 weeks of age compared to the 1-week-old and 6- to 7-month-old pigs. Interestingly, the IBU-GlcA/IBU AUC ratio did not change with age. The present study demonstrated that the main metabolites of IBU in human are also present in growing pigs. The oxidative phase I metabolism of IBU in growing conventional pigs did change with age. In contrast, age did not seem to affect the glucuronidation capacity of IBU in conventional pigs, although more studies with other substrate drugs are needed to confirm this.

Abstract: Drug response variations amongst different individuals/populations are influenced by several factors including allele frequency differences of single nucleotide polymorphisms (SNPs) that functionally affect drug-response genes. Here, we aim to identify drugs that potentially exhibit population differences in response using SNP data mining and analytics. Ninety-one pairwise-comparisons of >22,000,000 SNPs from the 1000 Genomes Project, across 14 different populations, were performed to identify 'population-differentiated' SNPs (pdSNPs). Potentially-functional pdSNPs (pf-pdSNPs) were then selected, mapped into genes, and integrated with drug-gene databases to identify 'population-differentiated' drugs enriched with genes carrying pf-pdSNPs. 1191 clinically-approved drugs were found to be significantly enriched (Z > 2.58) with genes carrying SNPs that were differentiated in one or more population-pair comparisons. Thirteen drugs were found to be enriched with such differentiated genes across all 91 population-pairs. Notably, 82% of drugs, which were previously reported in the literature to exhibit population differences in response were also found by this method to contain a significant enrichment of population specific differentiated SNPs. Furthermore, drugs with genetic testing labels, or those suspected to cause adverse reactions, contained a significantly larger number (P < 0.01) of population-pairs with enriched pf-pdSNPs compared with those without these labels. This pioneering effort at harnessing big-data pharmacogenomics to identify 'population differentiated' drugs could help to facilitate data-driven decision-making for a more personalized medicine.

Abstract: The surge of public disease and drug-related data availability has facilitated the application of computational methodologies to transform drug discovery. In the current chapter, we outline and detail the various resources and tools one can leverage in order to perform such analyses. We further describe in depth the in silico workflows of two recent studies that have identified possible novel indications of existing drugs. Lastly, we delve into the caveats and considerations of this process to enable other researchers to perform rigorous computational drug discovery experiments of their own.

Abstract: Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand- and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2A). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modeling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.

Abstract: Different genes have their protein products localized in various subcellular compartments. The diversity in protein localization may serve as a gene characteristic, revealing gene essentiality from a subcellular perspective. To measure this diversity, we introduced a Subcellular Diversity Index (SDI) based on the Gene Ontology-Cellular Component Ontology (GO-CCO) and a semantic similarity measure of GO terms. Analyses revealed that SDI of human genes was well correlated with some known measures of gene essentiality, including protein-protein interaction (PPI) network topology measurements, dN/dS ratio, homologous gene number, expression level and tissue specificity. In addition, SDI had a good performance in predicting human essential genes (AUC = 0.702) and drug target genes (AUC = 0.704), and drug targets with higher SDI scores tended to cause more side-effects. The results suggest that SDI could be used to identify novel drug targets and to guide the filtering of drug targets with fewer potential side effects. Finally, we developed a user-friendly online database for querying SDI score for genes across eight species, and the predicted probabilities of human drug target based on SDI. The online database of SDI is available at: http://www.cuilab.cn/sdi.