Abstract: BACKGROUND: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. METHODS: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. RESULTS: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.

Abstract: Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 diseases and complex traits (average N = 323K) to identify enriched annotations. First, we analyzed 18,119 biological pathways. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and 75 known functional annotations (from the baseline-LD model), a stringent step that greatly reduced the number of pathways detected; most significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, emphasizing the importance of accounting for known annotations.

Abstract: Effective therapeutic regimens for the treatment of tuberculosis (TB) are limited. They are comprised of multiple drugs that inhibit the essential cellular pathways in Mycobacterium tuberculosis (Mtb). The present study investigates an approach which enables a combination of Amoxicillin-Clavulanic acid (AMC) and a repurposed drug for its synergistic effect towards TB treatment. We identified Diosmin (DIO), by targeting the active site residues of L,D-transpeptidase (Ldt) enzymes involved in Mtb cell wall biosynthesis by using a structure-based drug design method. DIO is rapidly converted into aglycone form Diosmetin (DMT) after oral administration. Binding of DIO or DMT towards Ldt enzymes was studied using molecular docking and bioassay techniques. Combination of DIO (or DMT) and AMC exhibited higher mycobactericidal activity against Mycobacterium marinum as compared to individual drugs. Scanning electron microscopy study of M. marinum treated with AMC-DIO and AMC-DMT showed marked cellular leakage. M. marinum infected Drosophila melanogaster fly model showed an increased fly survival of ~60% upon treatment with a combination of AMC and DIO (or DMT). Finally, the enhanced in vitro antimicrobial activity of AMC-DIO was validated against Mtb H37Ra and a MDR clinical isolate. Our results demonstrate the potential for AMC and DIO (or DMT) as a synergistic combination for the treatment of TB.

Abstract: MOTIVATION: Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction. RESULTS: We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies. AVAILABILITY AND IMPLEMENTATION: Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: Iatrogenic hookworm therapy shows promise for treating disorders that result from a dysregulated immune system, including inflammatory bowel disease (IBD). Using a murine model of trinitrobenzenesulfonic acid-induced colitis and human peripheral blood mononuclear cells, we demonstrated that low-molecular-weight metabolites derived from both somatic extracts (LMWM-SE) and excretory-secretory products (LMWM-ESP) of the hookworm, Ancylostoma caninum, display anti-inflammatory properties. Administration to mice of LMWM-ESP as well as sequentially extracted fractions of LMWM-SE using both methanol (SE-MeOH) and hexane-dichloromethane-acetonitrile (SE-HDA) resulted in significant protection against T cell-mediated immunopathology, clinical signs of colitis, and impaired histological colon architecture. To assess bioactivity in human cells, we stimulated primary human leukocytes with lipopolysaccharide in the presence of hookworm extracts and showed that SE-HDA suppressed ex vivo production of inflammatory cytokines. Gas chromatography-mass spectrometry (MS) and liquid chromatography-MS analyses revealed the presence of 46 polar metabolites, 22 fatty acids, and five short-chain fatty acids (SCFAs) in the LMWM-SE fraction and 29 polar metabolites, 13 fatty acids, and six SCFAs in the LMWM-ESP fraction. Several of these small metabolites, notably the SCFAs, have been previously reported to have anti-inflammatory properties in various disease settings, including IBD. This is the first report showing that hookworms secrete small molecules with both ex vivo and in vivo anti-inflammatory bioactivity, and this warrants further exploration as a novel approach to the development of anti-inflammatory drugs inspired by coevolution of gut-dwelling hookworms with their vertebrate hosts.

Abstract: In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule-based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

Abstract: The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.

Abstract: The synthesis of 2,3-disubstituted benzo[b]thiophenes with selective placement of a chlorine moiety at the 3 position while maintaining diversity at the 2 position has only been accomplished by a handful of conditions in the past. The development of a greener, less expensive, and simpler method is paramount for the exploration of innovative compounds for application in medicinal and materials chemistry. Herein, the first reported copper-catalyzed electrophilic chlorocyclization method was developed and employed across diverse substrates to generate highly functionalized 2,3-disubstituted benzo[b]thiophenes and 2,3,5-trisubstituted thiophenes in very high yields. This method was optimized in both ethanol and acetonitrile in a comparative solvent study. The utility of this method was further expanded beyond chlorocyclization by changing the sodium halide to generate bromo- and iodocyclization products in excellent yields.

Abstract: Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing-the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues.

Abstract: Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.