Abstract: There has been a significant investment in research to define exposures and potential hazards of pharmaceuticals in freshwater and terrestrial ecosystems. A substantial number of integrated environmental risk assessments have been developed in Europe, North America and many other regions for these situations. In contrast, comparatively few empirical studies have been conducted for human and veterinary pharmaceuticals that are likely to enter coastal and marine ecosystems. This is a critical knowledge gap given the significant increase in coastal human populations around the globe and the growth of coastal megacities, together with the increasing importance of coastal aquaculture around the world. There is increasing evidence that pharmaceuticals are present and are impacting on marine and coastal environments. This paper reviews the sources, impacts and concentrations of pharmaceuticals in marine and coastal environments to identify knowledge gaps and suggests focused case studies as a priority for future research.

Abstract: Medicinal innovation has led to the discovery and use of thousands of human and veterinary drugs. With this comes the potential for unintended effects on non-target organisms exposed to pharmaceuticals inevitably entering the environment. The impracticality of generating whole-organism chronic toxicity data representative of all species in the environment has necessitated prioritization of drugs for focused empirical testing as well as field monitoring. Current prioritization strategies typically emphasize likelihood for exposure (i.e. predicted/measured environmental concentrations), while incorporating only rather limited consideration of potential effects of the drug to non-target organisms. However, substantial mammalian pharmacokinetic and mechanism/mode of action (MOA) data are produced during drug development to understand drug target specificity and efficacy for intended consumers. An integrated prioritization strategy for assessing risks of human and veterinary drugs would leverage available pharmacokinetic and toxicokinetic data for evaluation of the potential for adverse effects to non-target organisms. In this reiview, we demonstrate the utility of read-across approaches to leverage mammalian absorption, distribution, metabolism and elimination data; analyse cross-species molecular target conservation and translate therapeutic MOA to an adverse outcome pathway(s) relevant to aquatic organisms as a means to inform prioritization of drugs for focused toxicity testing and environmental monitoring.

Abstract: Human and veterinary drug development addresses absorption, distribution, metabolism, elimination and toxicology (ADMET) of the Active Pharmaceutical Ingredient (API) in the target species. Metabolism is an important factor in controlling circulating plasma and target tissue API concentrations and in generating metabolites which are more easily eliminated in bile, faeces and urine. The essential purpose of xenobiotic metabolism is to convert lipid-soluble, non-polar and non-excretable chemicals into water soluble, polar molecules that are readily excreted. Xenobiotic metabolism is classified into Phase I enzymatic reactions (which add or expose reactive functional groups on xenobiotic molecules), Phase II reactions (resulting in xenobiotic conjugation with large water-soluble, polar molecules) and Phase III cellular efflux transport processes. The human-fish plasma model provides a useful approach to understanding the pharmacokinetics of APIs (e.g. diclofenac, ibuprofen and propranolol) in freshwater fish, where gill and liver metabolism of APIs have been shown to be of importance. By contrast, wildlife species with low metabolic competency may exhibit zero-order metabolic (pharmacokinetic) profiles and thus high API toxicity, as in the case of diclofenac and the dramatic decline of vulture populations across the Indian subcontinent. A similar threat looms for African Cape Griffon vultures exposed to ketoprofen and meloxicam, recent studies indicating toxicity relates to zero-order metabolism (suggesting P450 Phase I enzyme system or Phase II glucuronidation deficiencies). While all aspects of ADMET are important in toxicity evaluations, these observations demonstrate the importance of methods for predicting API comparative metabolism as a central part of environmental risk assessment.

Abstract: Fish represent the planet's most diverse group of vertebrates and they can be exposed to a wide range of pharmaceuticals. For practical reasons, extrapolation of pharmaceutical effects from 'model' species to other fish species is adopted in risk assessment. Here, we critically assess this approach. First, we show that between 65% and 86% of human drug targets are evolutionarily conserved in 12 diverse fish species. Focusing on nuclear steroid hormone receptors, we further show that the sequence of the ligand binding domain that plays a key role in drug potency is highly conserved, but there is variation between species. This variation for the oestrogen receptor, however, does not obviously account for observed differences in receptor activation. Taking the synthetic oestrogen ethinyloestradiol as a test case, and using life-table-response experiments, we demonstrate significant reductions in population growth in fathead minnow and medaka, but not zebrafish, for environmentally relevant exposures. This finding contrasts with zebrafish being ranked as more ecologically susceptible, according to two independent life-history analyses. We conclude that while most drug targets are conserved in fish, evolutionary divergence in drug-target activation, physiology, behaviour and ecological life history make it difficult to predict population-level effects. This justifies the conventional use of at least a 10x assessment factor in pharmaceutical risk assessment, to account for differences in species susceptibility.

Abstract: BACKGROUND: Nowadays a broad collection of transcriptomics data is publicly available in online repositories. Methods for analyzing these data often aim at deciphering the influence of gene expression at the process level. Biological pathway diagrams depict known processes and capture the interactions of gene products and metabolites, information that is essential for the computational analysis and interpretation of transcriptomics data.The present study describes a comprehensive network biology workflow that integrates differential gene expression in the human diabetic liver with pathway information by building a network of interconnected pathways. Worldwide, the incidence of type 2 diabetes mellitus is increasing dramatically, and to better understand this multifactorial disease, more insight into the concerted action of the disease-related processes is needed. The liver is a key player in metabolic diseases and diabetic patients often develop non-alcoholic fatty liver disease. RESULTS: A publicly available dataset comparing the liver transcriptome from lean and healthy vs. obese and insulin-resistant subjects was selected after a thorough analysis. Pathway analysis revealed seven significantly altered pathways in the WikiPathways human pathway collection. These pathways were then merged into one combined network with 408 gene products, 38 metabolites and 5 pathway nodes. Further analysis highlighted 17 nodes present in multiple pathways, and revealed the connections between different pathways in the network. The integration of transcription factor-gene interactions from the ENCODE project identified new links between the pathways on a regulatory level. The extension of the network with known drug-target interactions from DrugBank allows for a more complete study of drug actions and helps with the identification of other drugs that target proteins up- or downstream which might interfere with the action or efficiency of a drug. CONCLUSIONS: The described network biology workflow uses state-of-the-art pathway and network analysis methods to study the rewiring of the diabetic liver. The integration of experimental data and knowledge on disease-affected biological pathways, including regulatory elements like transcription factors or drugs, leads to improved insights and a clearer illustration of the overall process. It also provides a resource for building new hypotheses for further follow-up studies. The approach is highly generic and can be applied in different research fields.

Abstract: Drug targets and disease genes may work as driver factors at the transcriptional level, which propagate signals through gene regulatory network and cause the downstream genes' differential expression. How to analyze transcriptional response data to identify meaningful gene modules shared by both drugs and diseases is still a critical issue for drug-disease associations and molecular mechanism. In this article, we propose the drug-gene-disease coherent subnetwork concept to group the biological function related drugs, diseases, and genes. It was defined as the subnetwork with drug, gene, and disease as nodes and their interactions coherently crossing three data layers as edges. Integrating differential expression profiles of 418 drugs and 84 diseases, we develop a computational framework and identify 13 coherent subnetworks such as inflammatory bowel disease and melanoma relevant subnetwork. The results demonstrate that our coherent subnetwork approach is able to identify novel drug indications and highlight their molecular basis.

Abstract: BCR-ABL tyrosine kinase plays a major role in the pathogenesis of chronic myeloid leukemia (CML) and is a proven target for drug development. Currently available drugs in the market are effective against CML; however, side-effects and drug-resistant mutations in BCR-ABL limit their full potential. Using high throughput virtual screening approach, we have screened several small molecule databases and docked against wild-type and drug resistant T315I mutant BCR-ABL. Drugs that are currently available, such as imatinib and ponatinib, were also docked against BCR-ABL protein to set a cutoff value for our screening. Selected lead compounds were further evaluated for chemical reactivity employing density functional theory approach, all selected ligands shows HLG value > 0.09900 and the binding free energy between protein-ligand complex interactions obtained was rescored using MM-GBSA. The selected compounds showed least DeltaG score -71.53 KJ/mol to maximum -126.71 KJ/mol in both wild type and drug resistant T315I mutant BCR-ABL. Following which, the stability of the docking complexes were evaluated by molecular dynamics simulation (MD) using GROMACS4.5.5. Results uncovered seven lead molecules, designated with Drug-Bank and PubChem ids as DB07107, DB06977, ST013616, DB04200, ST007180 ST019342, and DB01172, which shows docking scores higher than imatinib and ponatinib.

Abstract: Many clinically available anticancer compounds are designed to target DNA. This commonality of action often yields overlapping cellular response mechanisms and can thus detract from drug efficacy. New compounds are required to overcome resistance mechanisms that effectively neutralise compounds like cisplatin and those with similar chemical structures. Studies have shown that 56MESS is a novel compound which, unlike cisplatin, does not covalently bind to DNA, but is more toxic to many cell lines and active against cisplatin-resistant cells. Furthermore, a transcriptional study of 56MESS in yeast has implicated iron and copper metabolism as well as the general yeast stress response following challenge with 56MESS. Beyond this, the cytotoxicity of 56MESS remains largely uncharacterised. Here, yeast was used as a model system to facilitate a systems-level comparison between 56MESS and cisplatin. Preliminary experiments indicated that higher concentrations than seen in similar studies be used. Although a DNA interaction with 56MESS had been theorized, this work indicated that an effect on protein synthesis/ degradation was also implicated in the mechanism(s) of action of this novel anticancer compound. In contrast to cisplatin, the different mechanisms of action that are indicated for 56MESS suggest that this compound could overcome cisplatin resistance either as a stand-alone treatment or a synergistic component of therapeutics.

Abstract: BACKGROUND: Bacterial respiratory tract infections, mainly caused by Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis are among the leading causes of global mortality and morbidity. Increased resistance of these pathogens to existing antibiotics necessitates the search for novel targets to develop potent antimicrobials. RESULT: Here, we report a proof of concept study for the reliable identification of potential drug targets in these human respiratory pathogens by combining high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics. Approximately 20% of all genes in these three species were essential for growth and viability, including 128 essential and conserved genes, part of 47 metabolic pathways. By comparing these essential genes to the human genome, and a database of genes from commensal human gut microbiota, we identified and excluded potential drug targets in respiratory tract pathogens that will have off-target effects in the host, or disrupt the natural host microbiota. We propose 249 potential drug targets, 67 of which are targets for 75 FDA-approved antimicrobials and 35 other researched small molecule inhibitors. Two out of four selected novel targets were experimentally validated, proofing the concept. CONCLUSION: Here we have pioneered an attempt in systematically combining the power of high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics to discover potential drug targets at genome-scale. By circumventing the time-consuming and expensive laboratory screens traditionally used to select potential drug targets, our approach provides an attractive alternative that could accelerate the much needed discovery of novel antimicrobials.

Abstract: BACKGROUND: Compared with the traditional oral administration form, injection administration is basically superior in terms of both biological availability and therapeutic effects. However, few researches have focused on the traditional Chinese medicinal injection due to the complicated constituents and the intricate mechanism of action. METHODS: In the present work, a novel systems pharmacology model, integrating ADME (absorption, distribution, metabolism, and excretion) filtering such as half-life evaluation, network targeting, pathway and systems analyses, is specifically developed for the identification of active compounds and the study of the mechanism of action of TCM injection, which is exemplified by Reduning injection confronting the influenza. RESULTS: The ADME filter successfully identifies 35 bioactive compounds (31 molecules and 4 metabolites) from the Reduning injection. The systems analysis and experimental validation further reveal a new way of confronting influenza disease of this injection: 1) stimulating the immunomodulatory agents for immune response activation, and 2) regulating the inflammatory agents for anti-inflammation. CONCLUSIONS: The novel systems pharmacology method used in this study has the potential to advance the understanding of the molecular mechanisms of action of multicomponent herbal injections, and provide clues to discovering more effective drugs against complex diseases.