Abstract: Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics. To this end, we have developed a new tool, GLORY, which combines SoM prediction with FAME 2 and a new collection of rules for metabolic reactions mediated by the cytochrome P450 enzyme family. GLORY has two modes: MaxEfficiency and MaxCoverage. For MaxEfficiency mode, the use of predicted SoMs to restrict the locations in the molecule at which the reaction rules could be applied was explored. For MaxCoverage mode, the predicted SoM probabilities were instead used to develop a new scoring approach for the predicted metabolites. With this scoring approach, GLORY achieves a recall of 0.83 and can predict at least one known metabolite within the top three ranked positions for 76% of the molecules of a new, manually curated test set. GLORY is freely available as a web server at https://acm.zbh.uni-hamburg.de/glory/, and the datasets and reaction rules are provided in the Supplementary Material.

Abstract: Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Abstract: A subtractive genomic approach has been utilized for the identification of potential drug targets and vaccine candidates in Clostridium botulinum, the causative agent of flaccid paralysis in humans. The emergence of drug-resistant pathogenic strains has become a significant global public health threat. Treatment with antitoxin can target the neurotoxin at the extracellular level, however, can't converse the paralysis caused by botulism. Therefore, identification of drug targets and vaccine candidates in C. botulinum would be crucial to overcome drug resistance to existing antibiotic therapy. A total of 1729 crucial proteins, including chokepoint, virulence, plasmid and resistance proteins were mined and used for subtractive channel of analysis. This analysis disclosed 15 potential targets, which were non-similar to human, gut micro flora, and anti-targets in the host. The cellular localization of 6 targets was observed in the cytoplasm and might be used as a drug target, whereas 9 targets were localized in extracellular and membrane bound proteins and can be used as vaccine candidates. Furthermore, 4 targets were observed to be homologous to more than 75 pathogens and hence are considered as broad-spectrum antibiotic targets. The identified drug and vaccine targets in this study would be useful in the design and discovery of novel therapeutic compounds against botulism.

Abstract: BACKGROUND: In the literature-based discovery, considerable research has been done based on the ABC model developed by Swanson. ABC model hypothesizes that there is a meaningful relation between entity A extracted from document set 1 and entity C extracted from document set 2 through B entities that appear commonly in both document sets. The results of ABC model are relations among entity A, B, and C, which is referred as paths. A path allows for hypothesizing the relationship between entity A and entity C, or helps discover entity B as a new evidence for the relationship between entity A and entity C. The co-occurrence based approach of ABC model is a well-known approach to automatic hypothesis generation by creating various paths. However, the co-occurrence based ABC model has a limitation, in that biological context is not considered. It focuses only on matching of B entity which commonly appears in relation between two entities. Therefore, the paths extracted by the co-occurrence based ABC model tend to include a lot of irrelevant paths, meaning that expert verification is essential. METHODS: In order to overcome this limitation of the co-occurrence based ABC model, we propose a context-based approach to connecting one entity relation to another, modifying the ABC model using biological contexts. In this study, we defined four biological context elements: cell, drug, disease, and organism. Based on these biological context, we propose two extended ABC models: a context-based ABC model and a context-assignment-based ABC model. In order to measure the performance of the both proposed models, we examined the relevance of the B entities between the well-known relations "APOE-MAPT" as well as "FUS-TARDBP". Each relation means interaction between neurodegenerative disease associated with proteins. The interaction between APOE and MAPT is known to play a crucial role in Alzheimer's disease as APOE affects tau-mediated neurodegeneration. It has been shown that mutation in FUS and TARDBP are associated with amyotrophic lateral sclerosis(ALS), a motor neuron disease by leading to neuronal cell death. Using these two relations, we compared both of proposed models to co-occurrence based ABC model. RESULTS: The precision of B entities by co-occurrence based ABC model was 27.1% for "APOE-MAPT" and 22.1% for "FUS-TARDBP", respectively. In context-based ABC model, precision of extracted B entities was 71.4% for "APOE-MAPT", and 77.9% for "FUS-TARDBP". Context-assignment based ABC model achieved 89% and 97.5% precision for the two relations, respectively. Both proposed models achieved a higher precision than co-occurrence-based ABC model.

Abstract: The classical form of alpha1-antitrypsin deficiency (ATD) is characterized by intracellular accumulation of the misfolded variant alpha1-antitrypsin Z (ATZ) and severe liver disease in some of the affected individuals. In this study, we investigated the possibility of discovering novel therapeutic agents that would reduce ATZ accumulation by interrogating a C. elegans model of ATD with high-content genome-wide RNAi screening and computational systems pharmacology strategies. The RNAi screening was utilized to identify genes that modify the intracellular accumulation of ATZ and a novel computational pipeline was developed to make high confidence predictions on repurposable drugs. This approach identified glibenclamide (GLB), a sulfonylurea drug that has been used broadly in clinical medicine as an oral hypoglycemic agent. Here we show that GLB promotes autophagic degradation of misfolded ATZ in mammalian cell line models of ATD. Furthermore, an analog of GLB reduces hepatic ATZ accumulation and hepatic fibrosis in a mouse model in vivo without affecting blood glucose or insulin levels. These results provide support for a drug discovery strategy using simple organisms as human disease models combined with genetic and computational screening methods. They also show that GLB and/or at least one of its analogs can be immediately tested to arrest the progression of human ATD liver disease.

Abstract: Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimensional vectors, so as to avoid the sparseness and bit collisions from the cumbersomely manually extracted features. Visualization of SPVec nicely illustrated that the similar compounds or proteins occupy similar vector space, which indicated that SPVec not only encodes compound substructures or protein sequences efficiently, but also implicitly reveals some important biophysical and biochemical patterns. Compared with manually-designed features like MACCS fingerprints and amino acid composition (AAC), SPVec showed better performance with several state-of-art machine learning classifiers such as Gradient Boosting Decision Tree, Random Forest and Deep Neural Network on BindingDB. The performance and robustness of SPVec were also confirmed on independent test sets obtained from DrugBank database. Also, based on the whole DrugBank dataset, we predicted the possibilities of all unlabeled DTIs, where two of the top five predicted novel DTIs were supported by external evidences. These results indicated that SPVec can provide an effective and efficient way to discover reliable DTIs, which would be beneficial for drug reprofiling.

Abstract: Paracoccidioidomycosis (PCM) is the most prevalent endemic mycosis in Latin America. The disease is caused by fungi of the genus Paracoccidioides and mainly affects low-income rural workers after inhalation of fungal conidia suspended in the air. The current arsenal of chemotherapeutic agents requires long-term administration protocols. In addition, chemotherapy is related to a significantly increased frequency of disease relapse, high toxicity, and incomplete elimination of the fungus. Due to the limitations of current anti-PCM drugs, we developed a computational drug repurposing-chemogenomics approach to identify approved drugs or drug candidates in clinical trials with anti-PCM activity. In contrast to the one-drug-one-target paradigm, our chemogenomics approach attempts to predict interactions between drugs, and Paracoccidioides protein targets. To achieve this goal, we designed a workflow with the following steps: (a) compilation and preparation of Paracoccidioides spp. genome data; (b) identification of orthologous proteins among the isolates; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Paracoccidioides essential targets using validated genes from Saccharomyces cerevisiae; (e) homology modeling and molecular docking studies; and (f) experimental validation of selected candidates. We prioritized 14 compounds. Two antineoplastic drug candidates (vistusertib and BGT-226) predicted to be inhibitors of phosphatidylinositol 3-kinase TOR2 showed antifungal activity at low micromolar concentrations (<10 muM). Four antifungal azole drugs (bifonazole, luliconazole, butoconazole, and sertaconazole) showed antifungal activity at low nanomolar concentrations, validating our methodology. The results suggest our strategy for predicting new anti-PCM drugs is useful. Finally, we could recommend hit-to-lead optimization studies to improve potency and selectivity, as well as pharmaceutical formulations to improve oral bioavailability of the antifungal azoles identified.

Abstract: Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

Abstract: As the leading cause of cancer death worldwide, lung cancer (LC) has seriously affected human health and longevity. Chinese medicine is a complex system guided by traditional Chinese medicine theories (TCM). Nowadays, the clinical application of TCM for LC patients has become the focus for its effectiveness and security. In this paper, we will analyze and study the mechanism of Xia Qi Decoction (XQD) in the treatment of LC. The results collectively show that XQD could act on 41 therapeutic targets of LC. At the same time, 8 of 41 targets were significantly expressed in immune tissues and cells by activating CD8+T cells to promote apoptosis of cancer cells. It reveals the molecular mechanism of XQD in the treatment of LC from the perspective of network pharmacology. In addition, in the treatment of LC, XQD can activate (up-regulate) the function of immune cells, promote the apoptosis of tumor cells, and have an active anti-tumor immune effect. In conclusion, this study reveals the unique advantages of traditional Chinese medicine in the treatment of cancer, in reinforcing the healthy qi and eliminating the pathogenic factors. More research, however, is needed to verify the potential mechanisms.

Abstract: Due to the aging world population and increasing trend in clinical practice to treat patients with multiple drugs, adverse events (AEs) are becoming a major challenge in drug discovery and public health. In particular, identifying AEs caused by drug combinations remains a challenging task. Clinical trials typically focus on individual drugs rather than drug combinations and animal models are unreliable. An added difficulty is the combinatorial explosion in the number of possible combinations that can be made using the increasingly large set of FDA approved chemicals. We present a statistical and computational technique for identifying AEs caused by two-drug combinations. Taking advantage of the large and increasing data deposited in FDA's postmarketing reports, we demonstrate that the task of predicting AEs for 2-drug combinations is amenable to the Likelihood Ratio Test (LRT). Our pAERS database constructed with LRT contains almost 77 thousand associations between pairs of drugs and corresponding AEs caused solely by drug-drug interactions (DDIs). The DDIs stored in pAERS complement the existing data sets. Due to our stringent statistical test, we expect many of the associations in pAERS to be unrecorded or poorly documented in the literature.