Abstract: The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.

Abstract: The intrinsic value of biodiversity extends beyond species diversity, genetic heritage, ecosystem variability and ecological services, such as climate regulation, water quality, nutrient cycling and the provision of reproductive habitats it is also an inexhaustible source of molecules and products beneficial to human well-being. To uncover the chemistry of Brazilian natural products, the Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products Database (NuBBEDB) was created as the first natural product library from Brazilian biodiversity. Since its launch in 2013, the NuBBEDB has proven to be an important resource for new drug design and dereplication studies. Consequently, continuous efforts have been made to expand its contents and include a greater diversity of natural sources to establish it as a comprehensive compendium of available biogeochemical information about Brazilian biodiversity. The content in the NuBBEDB is freely accessible online (https://nubbe.iq.unesp.br/portal/nubbedb.html) and provides validated multidisciplinary information, chemical descriptors, species sources, geographic locations, spectroscopic data (NMR) and pharmacological properties. Herein, we report the latest advancements concerning the interface, content and functionality of the NuBBEDB. We also present a preliminary study on the current profile of the compounds present in Brazilian territory.

Abstract: Drug repositioning strategies have improved substantially in recent years. At present, two advances are poised to facilitate new strategies. First, the LINCS project can provide rich transcriptome data that reflect the responses of cells upon exposure to various drugs. Second, machine learning algorithms have been applied successfully in biomedical research. In this paper, we developed a systematic method to discover novel indications for existing drugs by approaching drug repositioning as a multi-label classification task and used a Softmax regression model to predict previously unrecognized therapeutic properties of drugs based on LINCS transcriptome data. This approach to complete the said task has not been achieved in previous studies. By performing in silico comparison, we demonstrated that the proposed Softmax method showed markedly superior performance over those of other methods. Once fully trained, the method showed a training accuracy exceeding 80% and a validation accuracy of approximately 70%. We generated a highly credible set of 98 drugs with high potential to be repositioned for novel therapeutic purposes. Our case studies included zonisamide and brinzolamide, which were originally developed to treat indications of the nervous system and sensory organs, respectively. Both drugs were repurposed to the cardiovascular category.

Abstract: Motivation: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. Results: We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. Availability and Implementation: The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . Contact: nazong@ucsd.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract: Screening for drug compounds that exhibit therapeutic properties in the treatment of various diseases remains a challenge even after considerable advancements in biomedical research. Here, we introduce an integrated platform that exploits gene expression compendia generated from drug-treated cell lines and primary tumor tissue to identify therapeutic candidates that can be used in the treatment of acute myeloid leukemia (AML). Our framework combines these data with patient survival information to identify potential candidates that presumably have a significant impact on AML patient survival. We use a drug regulatory score (DRS) to measure the similarity between drug-induced cell line and patient tumor gene expression profiles, and show that these computed scores are highly correlated with in vitro metrics of pharmacological activity. Furthermore, we conducted several in vivo validation experiments of our potential candidate drugs in AML mouse models to demonstrate the accuracy of our in silico predictions.

Abstract: Disorders of iron metabolism are largely attributed to an excessive or insufficient expression of hepcidin, the master regulator of systemic iron homeostasis. Here, we investigated whether drugs targeting genetic regulators of hepcidin can affect iron homeostasis. We focused our efforts on drugs approved for clinical use to enable repositioning strategies and/or to reveal iron-related side effects of widely prescribed therapeutics. To identify hepcidin-modulating therapeutics, we re-evaluated data generated by a genome-wide RNAi screen for hepcidin regulators. We identified 'druggable' screening hits and validated those by applying RNAi of potential drug targets and small-molecule testing in a hepatocytic cell line, in primary murine and human hepatocytes and in mice. We initially identified spironolactone, diclofenac, imatinib and Suberoylanilide hydroxamic acid (SAHA) as hepcidin modulating drugs in cellular assays. Among these, imatinib and spironolactone further suppressed liver hepcidin expression in mice. Our results demonstrate that a commonly used anti-hypertensive drug, spironolactone, which is prescribed for the treatment of heart failure, acne and female hirsutism, as well as imatinib, a first-line, lifelong therapeutic option for some frequent cancer types suppress hepcidin expression in cultured cells and in mice. We expect these results to be of relevance for patient management, which needs to be addressed in prospective clinical studies.

Abstract: The photocatalytic degradation of the model pollutant carbamazepine (CBZ) was investigated under simulated solar irradiation with an N-doped TiO(2)-coated Al(2)O(3) photocatalytic membrane, using different water types. The photocatalytic membrane combines photocatalysis and membrane filtration in a single step. The impact of each individual constituent such as acidity, alkalinity, dissolved organic matter (DOM), divalent cations (Mg(2+) and Ca(2+)), and Cl(-) on the degradation of CBZ was examined. CBZ in water was efficiently degraded by an N-doped TiO(2)-coated Al(2)O(3) membrane. However, elements added to the water, which simulate the constituents of natural water, had an impact on the CBZ degradation. Water alkalinity inhibited CBZ degradation mostly due to increase in pH while radical scavenging by carbonate was more dominant at higher values (>200 mg/L as CaCO(3)). A negative effect of Ca(2+) addition on photocatalytic degradation was found only in combination with phosphate buffer, probably caused by deposition of CaHPO(4) or CaHPO(4).2H(2)O on the catalyst surface. The presence of Cl(-) and Mg(2+) ions had no effect on CBZ degradation. DOM significantly inhibited CBZ degradation for all tested background organic compounds. The photocatalytic activity of N-doped TiO(2)-coated Al(2)O(3) membranes gradually decreased after continuous use; however, it was successfully regenerated by 0.1% HCl chemical cleaning. Nevertheless, dissolution of metals like Al and Ti should be monitored following acid cleaning.

Abstract: Large-scale screening has revealed that human hematopoietic cancer cell lines are generally more sensitive to various classes of drugs than cell lines established from solid tumors. A detailed examination of data in the Cancer Therapeutics Response Portal (http://portals.broadinstitute.org/ctrp/) suggests that this enhanced sensitivity is due to lower basal levels of activation of TAZ-TEAD mechanotransduction pathways in hematopoietic versus non-hematopoietic cells. Translation inhibitors such as omacetaxine mepesuccinate (homoharringtonine) fall into this category of hematopoietic-selective compounds. Moreover, additional molecular determinants of sensitivity suggest that homoharringtonine might show therapeutic efficacy in certain patients with advanced hematologic malignancies despite activation of these pathways.

Abstract: BACKGROUND: This study aimed to identify potential prostate cancer (PC)-related variations in gene expression profiles. METHODS: Microarray data from the GSE21032 dataset that contained the whole-transcript and exon-level expression profile (GSE21034) and Agilent 244K array-comparative genomic hybridization data (GSE21035) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and copy-number variations (CNVs) were identified between PC and normal tissue samples. Coexpression interactions of DEGs that contained CNVs (CNV-DEGs) were analyzed. Pathway enrichment analysis of CNV-DEGs was performed. Drugs targeting CNV-DEGs were searched using the Drug-Gene Interaction database. RESULTS: In total, 679 DEGs were obtained, including 182 upregulated genes and 497 downregulated genes. A total of 48 amplified CNV regions and 45 deleted regions were determined. The number of CNVs at 8q and 8p was relatively higher in PC tissue. Only 16 DEGs, including 4 upregulated and 12 downregulated genes, showed a positive correlation with CNVs. In the coexpression network, 3 downregulated CNV-DEGs, including FAT4 (FAT atypical cadherin 4), PDE5A (phosphodiesterase 5A, cGMP-specific), and PCP4 (Purkinje cell protein 4), had a higher degree, and were enriched in specific pathways such as the calmodulin signaling pathway. Five of the 16 CNV-DEGs (e.g., PDE5A) were identified as drug targets. CONCLUSION: The identified CNV-DEGs could be implicated in the progression of human PC. The findings could lead to a better understanding of PC pathogenesis.

Abstract: New, safe and effective drugs are urgently needed to treat and control malaria and tuberculosis, which affect millions of people annually. However, financial return on investment in the poor settings where these diseases are mostly prevalent is very minimal to support market-driven drug discovery and development. Moreover, the imminent loss of therapeutic lifespan of existing therapies due to evolution and spread of drug resistance further compounds the urgency to identify novel effective drugs. However, the advent of new public-private partnerships focused on tropical diseases and the recent release of large data sets by pharmaceutical companies on antimalarial and antituberculosis compounds derived from phenotypic whole cell high throughput screening have spurred renewed interest and opened new frontiers in malaria and tuberculosis drug discovery. This Account recaps the existing challenges facing antimalarial and antituberculosis drug discovery, including limitations associated with experimental animal models as well as biological complexities intrinsic to the causative pathogens. We enlist various highlights from a body of work within our research group aimed at identifying and characterizing new chemical leads, and navigating these challenges to contribute toward the global drug discovery and development pipeline in malaria and tuberculosis. We describe a catalogue of in-house efforts toward deriving safe and efficacious preclinical drug development candidates via cell-based medicinal chemistry optimization of phenotypic whole-cell medium and high throughput screening hits sourced from various small molecule chemical libraries. We also provide an appraisal of target-based screening, as invoked in our laboratory for mechanistic evaluation of the hits generated, with particular focus on the enzymes within the de novo pyrimidine biosynthetic and hemoglobin degradation pathways, the latter constituting a heme detoxification process and an associated cysteine protease-mediated hydrolysis of hemoglobin. We further expound on the recombinant enzyme assays, heme fractionation experiments, and genomic and chemoproteomic methods that we employed to identify Plasmodium falciparum falcipain 2 (PfFP2), hemozoin formation, phosphatidylinositol 4-kinase (PfPI4K) and Mycobacterium tuberculosis cytochrome bc1 complex as the targets of the antimalarial chalcones, pyrido[1,2-a]benzimidazoles, aminopyridines, and antimycobacterial pyrrolo[3,4-c]pyridine-1,3(2H)-diones, respectively. In conclusion, we argue for the expansion of chemical space through exploitation of privileged natural product scaffolds and diversity-oriented synthesis, as well as the broadening of druggable spaces by exploiting available protein crystal structures, -omics data, and bioinformatics infrastructure to explore hitherto untargeted spaces like lipid metabolism and protein kinases in P. falciparum. Finally, we audit the merits of both target-based and whole-cell phenotypic screening in steering antimalarial and antituberculosis chemical matter toward populating drug discovery pipelines with new lead molecules.