Abstract: Drug-disease treatment relationships, i.e., which drug(s) are indicated to treat which disease(s), are among the most frequently sought information in PubMed(R). Such information is useful for feeding the Google Knowledge Graph, designing computational methods to predict novel drug indications, and validating clinical information in EMRs. Given the importance and utility of this information, there have been several efforts to create repositories of drugs and their indications. However, existing resources are incomplete. Furthermore, they neither label indications in a structured way nor differentiate them by drug-specific properties such as dosage form, and thus do not support computer processing or semantic interoperability. More recently, several studies have proposed automatic methods to extract structured indications from drug descriptions; however, their performance is limited by natural language challenges in disease named entity recognition and indication selection. In response, we report LabeledIn: a human-reviewed, machine-readable and source-linked catalog of labeled indications for human drugs. More specifically, we describe our semi-automatic approach to derive LabeledIn from drug descriptions through human annotations with aids from automatic methods. As the data source, we use the drug labels (or package inserts) submitted to the FDA by drug manufacturers and made available in DailyMed. Our machine-assisted human annotation workflow comprises: (i) a grouping method to remove redundancy and identify representative drug labels to be used for human annotation, (ii) an automatic method to recognize and normalize mentions of diseases in drug labels as candidate indications, and (iii) a two-round annotation workflow for human experts to judge the pre-computed candidates and deliver the final gold standard. In this study, we focused on 250 highly accessed drugs in PubMed Health, a newly developed public web resource for consumers and clinicians on prevention and treatment of diseases. These 250 drugs corresponded to more than 8000 drug labels (500 unique) in DailyMed in which 2950 candidate indications were pre-tagged by an automatic tool. After being reviewed independently by two experts, 1618 indications were selected, and additional 97 (missed by computer) were manually added, with an inter-annotator agreement of 88.35% as measured by the Kappa coefficient. Our final annotation results in LabeledIn consist of 7805 drug-disease treatment relationships where drugs are represented as a triplet of ingredient, dose form, and strength. A systematic comparison of LabeledIn with an existing computer-derived resource revealed significant discrepancies, confirming the need to involve humans in the creation of such a resource. In addition, LabeledIn is unique in that it contains detailed textual context of the selected indications in drug labels, making it suitable for the development of advanced computational methods for the automatic extraction of indications from free text. Finally, motivated by the studies on drug nomenclature and medication errors in EMRs, we adopted a fine-grained drug representation scheme, which enables the automatic identification of drugs with indications specific to certain dose forms or strengths. Future work includes expanding our coverage to more drugs and integration with other resources. The LabeledIn dataset and the annotation guidelines are available at http://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/.

Abstract: Approved drugs target approximately 400 different mechanisms of action, of which as few as 60 are currently used as anti-cancer therapies. Given that on average it takes 10-15 years for a new cancer therapeutic to be approved, and the recent success of drug repurposing for agents such as thalidomide, we hypothesized that effective, safe cancer treatments may be found by testing approved drugs in new therapeutic settings. Here, we report in-vivo testing of a broad compound collection in cancer xenograft models. Using 182 compounds that target 125 unique target mechanisms, we identified 3 drugs that displayed reproducible activity in combination with the chemotherapeutic temozolomide. Candidate drugs appear effective at dose equivalents that exceed current prescription levels, suggesting that additional pre-clinical efforts will be needed before these drugs can be tested for efficacy in clinical trials. In total, we suggest drug repurposing is a relatively resource-intensive method that can identify approved medicines with a narrow margin of anti-cancer activity.

Abstract: The misfolding of intrinsically disordered proteins such as alpha-synuclein, tau and the Abeta peptide has been associated with many highly debilitating neurodegenerative syndromes including Parkinson's and Alzheimer's diseases. Therapeutic targeting of the monomeric state of such intrinsically disordered proteins by small molecules has, however, been a major challenge because of their heterogeneous conformational properties. We show here that a combination of computational and experimental techniques has led to the identification of a drug-like phenyl-sulfonamide compound (ELN484228), that targets alpha-synuclein, a key protein in Parkinson's disease. We found that this compound has substantial biological activity in cellular models of alpha-synuclein-mediated dysfunction, including rescue of alpha-synuclein-induced disruption of vesicle trafficking and dopaminergic neuronal loss and neurite retraction most likely by reducing the amount of alpha-synuclein targeted to sites of vesicle mobilization such as the synapse in neurons or the site of bead engulfment in microglial cells. These results indicate that targeting alpha-synuclein by small molecules represents a promising approach to the development of therapeutic treatments of Parkinson's disease and related conditions.

Abstract: Finding effective drugs to treat fungal infections has important clinical significance based on high mortality rates, especially in an immunodeficient population. Traditional antifungal drugs with single targets have been reported to cause serious side effects and drug resistance. Nowadays, however, drug combinations, particularly with respect to synergistic interaction, have attracted the attention of researchers. In fact, synergistic drug combinations could simultaneously affect multiple subpopulations, targets, and diseases. Therefore, a strategy that employs synergistic antifungal drug combinations could eliminate the limitations noted above and offer the opportunity to explore this emerging bioactive chemical space. However, it is first necessary to build a powerful database in order to facilitate the analysis of drug combinations. To address this gap in our knowledge, we have built the first Antifungal Synergistic Drug Combination Database (ASDCD), including previously published synergistic antifungal drug combinations, chemical structures, targets, target-related signaling pathways, indications, and other pertinent data. Its current version includes 210 antifungal synergistic drug combinations and 1225 drug-target interactions, involving 105 individual drugs from more than 12,000 references. ASDCD is freely available at http://ASDCD.amss.ac.cn.

Abstract: BACKGROUND: QSAR is an established and powerful method for cheap in silico assessment of physicochemical properties and biological activities of chemical compounds. However, QSAR models are rather complex mathematical constructs that cannot easily be interpreted. Medicinal chemists would benefit from practical guidance regarding which molecules to synthesize. Another possible approach is analysis of pairs of very similar molecules, so-called matched molecular pairs (MMPs). Such an approach allows identification of molecular transformations that affect particular activities (e.g. toxicity). In contrast to QSAR, chemical interpretation of these transformations is straightforward. Furthermore, such transformations can give medicinal chemists useful hints for the hit-to-lead optimization process. RESULTS: The current study suggests a combination of QSAR and MMP approaches by finding MMP transformations based on QSAR predictions for large chemical datasets. The study shows that such an approach, referred to as prediction-driven MMP analysis, is a useful tool for medicinal chemists, allowing identification of large numbers of "interesting" transformations that can be used to drive the molecular optimization process. All the methodological developments have been implemented as software products available online as part of OCHEM (http://ochem.eu/). CONCLUSIONS: The prediction-driven MMPs methodology was exemplified by two use cases: modelling of aquatic toxicity and CYP3A4 inhibition. This approach helped us to interpret QSAR models and allowed identification of a number of "significant" molecular transformations that affect the desired properties. This can facilitate drug design as a part of molecular optimization process. Graphical AbstractMolecular matched pairs and transformation graphs facilitate interpretable molecular optimisation process.

Abstract: In response to the high cost and high risk associated with traditional de novo drug discovery, investigation of potential additional uses for existing drugs, also known as drug repositioning, has attracted increasing attention from both the pharmaceutical industry and the research community. In this paper, we propose a unified computational framework, called DDR, to predict novel drug-disease associations. DDR formulates the task of hypothesis generation for drug repositioning as a constrained nonlinear optimization problem. It utilizes multiple drug similarity networks, multiple disease similarity networks, and known drug-disease associations to explore potential new associations among drugs and diseases with no known links. A large-scale study was conducted using 799 drugs against 719 diseases. Experimental results demonstrated the effectiveness of the approach. In addition, DDR ranked drug and disease information sources based on their contributions to the prediction, thus paving the way for prioritizing multiple data sources and building more reliable drug repositioning models. Particularly, some of our novel predictions of drug-disease associations were supported by clinical trials databases, showing that DDR could serve as a useful tool in drug discovery to efficiently identify potential novel uses for existing drugs.

Abstract: Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them.

Abstract: Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.

Abstract: Ever increasing propensity of antibiotic resistance among pathogenic bacteria raises the demand for the development of novel therapeutic agents to control this grave problem. Advances in the field of bioinformatics, genomics, and proteomics have greatly facilitated the discovery of alternative drugs by swift identification of new drug targets. In the present study, we employed comparative genomics and metabolic pathway analysis with an aim of identifying therapeutic targets in Mycoplasma hominis. Our study has revealed 40 annotated metabolic pathways, including five unique pathways of M. hominis. Our study also identified 179 essential proteins, including 59 proteins having no similarity with human proteins. Further filtering by molecular weight, subcellular localization, functional analysis, and protein network interaction, we identified 57 putative candidates for which new drugs can be developed. Druggability analysis for each of the identified targets has prioritized 16 proteins as suitable for potential drug development.

Abstract: A major part of membrane function is conducted by proteins, both integral and peripheral. Peripheral membrane proteins temporarily adhere to biological membranes, either to the lipid bilayer or to integral membrane proteins with noncovalent interactions. The aim of this study was to construct and analyze the interactions of the human plasma membrane peripheral proteins (peripherome hereinafter). For this purpose, we collected a dataset of peripheral proteins of the human plasma membrane. We also collected a dataset of experimentally verified interactions for these proteins. The interaction network created from this dataset has been visualized using Cytoscape. We grouped the proteins based on their subcellular location and clustered them using the MCL algorithm in order to detect functional modules. Moreover, functional and graph theory based analyses have been performed to assess biological features of the network. Interaction data with drug molecules show that ~10% of peripheral membrane proteins are targets for approved drugs, suggesting their potential implications in disease. In conclusion, we reveal novel features and properties regarding the protein-protein interaction network created by peripheral proteins of the human plasma membrane.