Abstract: Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.

Abstract: AIM: Computational exploration of small-molecule-based relationships between target proteins from different families. MATERIALS & METHODS: Target annotations of drugs and other bioactive compounds were systematically analyzed on the basis of high-confidence activity data. RESULTS: A total of 286 novel chemical links were established between distantly related or unrelated target proteins. These relationships involved a total of 1859 bioactive compounds including 147 drugs and 141 targets. CONCLUSION: Computational analysis of large amounts of compounds and activity data has revealed unexpected relationships between diverse target proteins on the basis of compounds they share. These relationships are relevant for drug discovery efforts. Target pairs that we have identified and associated compound information are made freely available.

Abstract: American trypanosomiasis or Chagas disease caused by the protozoan Trypanosoma cruzi (T. cruzi) is an important endemic trypanosomiasis in Central and South America. This disease was considered to be a priority in the global plan to combat neglected tropical diseases, 2008-2015, which indicates that there is an urgent need to develop more effective drugs. The development of new chemotherapeutic agents against Chagas disease can be related to an important biochemical feature of T. cruzi: its redox defense system. This system is based on trypanothione ([Formula: see text],[Formula: see text]-bis(glutathyonil)spermidine) and trypanothione reductase (TR), which are rather unique to trypanosomes and completely absent in mammalian cells. In this regard, tricyclic compounds have been studied extensively due to their ability to inhibit the T. cruzi TR. However, synthetic derivatives of natural products, such as [Formula: see text]-carboline derivatives ([Formula: see text]-CDs), as potential TR inhibitors, has received little attention. This study presents an analysis of the structural and physicochemical properties of commercially available [Formula: see text]-CDs in relation to compounds tested against T. cruzi in previously reported enzymatic assays and shows that [Formula: see text]-CDs cover chemical space that has not been considered for the design of TR inhibitors. Moreover, this study presents a ligand-based approach to discover potential TR inhibitors among commercially available [Formula: see text]-CDs, which could lead to the generation of promising [Formula: see text]-CD candidates.

Abstract: BACKGROUND: Accessibility to essential cancer medications in low- and middle-income countries is threatened by insufficient availability and affordability. The objective of this study is to characterize variation in transactional prices for essential cancer medications across geographies, medication type, and time. METHODS: Drug purchase prices for 19 national and international buyers (representing 29 total countries) between 2010 and 2014 were obtained from Management Sciences for Health. Median values for drug pricing were computed, to address outliers in the data. For comparing purchase prices across geographic units, medications, and over time; Mann-Whitney U tests were used to compare two groups, Kruskal Wallis H tests were used to compare more than two groups, and linear regression was used to compare across continuous independent variables. RESULTS: During the five-year data period examined, the median price paid for a package of essential cancer medication was $12.63. No significant differences in prices were found based on country-level wealth, country-level disease burden, drug formulation, or year when medication was purchased. Statistical tests found significant differences in prices paid across countries, regions, individual medications, and medication categories. Specifically, countries in the Africa region appeared to pay more for a package of essential cancer medication than countries in the Latin America region, and cancer medications tended to be more expensive than anti-infective medications and cardiovascular medications. CONCLUSIONS: Though preliminary, our study found evidence of variation in prices paid by health systems to acquire essential cancer medications. Primarily, variations in pricing based on geographic location and cancer medication type (including when comparing to essential medicines that treat cardiovascular and infectious diseases) indicate that these factors may impact availability, affordability and access to essential cancer drugs. These factors should be taken into consideration when countries assess formulary decisions, negotiate drug procurement terms, and when formulating health and cancer policy.

Abstract: Osteoarthritis (OA) is a common disease characterized by cartilage degeneration and joint remodeling. The underlying molecular changes underpinning disease progression are incompletely understood. We investigated genes and pathways that mark OA progression in isolated primary chondrocytes taken from paired intact versus degraded articular cartilage samples across 38 patients undergoing joint replacement surgery (discovery cohort: 12 knee OA, replication cohorts: 17 knee OA, 9 hip OA patients). We combined genome-wide DNA methylation, RNA sequencing, and quantitative proteomics data. We identified 49 genes differentially regulated between intact and degraded cartilage in at least two -omics levels, 16 of which have not previously been implicated in OA progression. Integrated pathway analysis implicated the involvement of extracellular matrix degradation, collagen catabolism and angiogenesis in disease progression. Using independent replication datasets, we showed that the direction of change is consistent for over 90% of differentially expressed genes and differentially methylated CpG probes. AQP1, COL1A1 and CLEC3B were significantly differentially regulated across all three -omics levels, confirming their differential expression in human disease. Through integration of genome-wide methylation, gene and protein expression data in human primary chondrocytes, we identified consistent molecular players in OA progression that replicated across independent datasets and that have translational potential.

Abstract: The prediction of drug-target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug-target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns. Simultaneously, we apply the discrete wavelet transform (DWT) to extract features from target sequences. Then, we concatenate and normalize the target, drug, and network features to construct feature vectors. The prediction model is obtained by feeding these feature vectors into the support vector machine (SVM) classifier. Extensive experimental results show that the prediction ability of DAWN has a compatibility among other DTI prediction schemes. The prediction areas under the precision-recall curves (AUPRs) of four datasets are 0 . 895 (Enzyme), 0 . 921 (Ion Channel), 0 . 786 (guanosine-binding protein coupled receptor, GPCR), and 0 . 603 (Nuclear Receptor), respectively.

Abstract: BACKGROUND: A fundamental understanding of live-cell dynamics is necessary in order to advance scientific techniques and personalized medicine. For this understanding to be possible, image processing techniques, probes, tracking algorithms and many other methodologies must be improved. Currently there are no large open-source datasets containing live-cell imaging to act as a standard for the community. As a result, researchers cannot evaluate their methodologies on an independent benchmark or leverage such a dataset to formulate scientific questions. FINDINGS: Here we present T-Time, the largest free and publicly available data set of T cell phase contrast imagery designed with the intention of furthering live-cell dynamics research. T-Time consists of over 40 GB of imagery data, and includes annotations derived from these images using a custom T cell identification and tracking algorithm. The data set contains 71 time-lapse sequences containing T cell movement and calcium release activated calcium channel activation, along with 50 time-lapse sequences of T cell activation and T reg interactions. The database includes a user-friendly web interface, summary information on the time-lapse images, and a mechanism for users to download tailored image datasets for their own research. T-Time is freely available on the web at http://ttime.mlatlab.org . CONCLUSIONS: T-Time is a novel data set of T cell images and associated metadata. It allows users to study T cell interaction and activation.

Abstract: Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.

Abstract: Over the past few decades, (poly)peptide block copolymers have been widely employed in generating well-defined nanostructures as vehicles for targeted drug delivery applications. We previously reported the assembly of thermoresponsive nanoscale vesicles from an elastin-b-collagen-like peptide (ELP-CLP). The vesicles were observed to dissociate at elevated temperatures, despite the LCST-like behavior of the tethered ELP domain, which is suggested to be triggered by the unfolding of the CLP domain. Here, the potential of using the vesicles as drug delivery vehicles for targeting collagen-containing matrices is evaluated. The sustained release of an encapsulated model drug was achieved over a period of 3 weeks, following which complete release could be triggered via heating. The ELP-CLP vesicles show strong retention on a collagen substrate, presumably through collagen triple helix interactions. Cell viability and proliferation studies using fibroblasts and chondrocytes suggest that the vesicles are highly cytocompatible. Additionally, essentially no activation of a macrophage-like cell line is observed, suggesting that the vesicles do not initiate an inflammatory response. Endowed with thermally controlled delivery, the ability to bind collagen, and excellent cytocompatibility, these ELP-CLP nanovesicles are suggested to have significant potential in the controlled delivery of drugs to collagen-containing matrices and tissues.

Abstract: BACKGROUND: Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. RESULTS: We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. CONCLUSIONS: Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.