Abstract: Existing treatments against drug addiction are often ineffective due to the complexity of the networks of protein-drug and protein-protein interactions (PPIs) that mediate the development of drug addiction and related neurobiological disorders. There is an urgent need for understanding the molecular mechanisms that underlie drug addiction toward designing novel preventive or therapeutic strategies. The rapidly accumulating data on addictive drugs and their targets as well as advances in machine learning methods and computing technology now present an opportunity to systematically mine existing data and draw inferences on potential new strategies. To this aim, we carried out a comprehensive analysis of cellular pathways implicated in a diverse set of 50 drugs of abuse using quantitative systems pharmacology methods. The analysis of the drug/ligand-target interactions compiled in DrugBank and STITCH databases revealed 142 known and 48 newly predicted targets, which have been further analyzed to identify the KEGG pathways enriched at different stages of drug addiction cycle, as well as those implicated in cell signaling and regulation events associated with drug abuse. Apart from synaptic neurotransmission pathways detected as upstream signaling modules that "sense" the early effects of drugs of abuse, pathways involved in neuroplasticity are distinguished as determinants of neuronal morphological changes. Notably, many signaling pathways converge on important targets such as mTORC1. The latter emerges as a universal effector of the persistent restructuring of neurons in response to continued use of drugs of abuse.

Abstract: Histamine-1 receptor (H1R) belongs to the family of rhodopsin-like G-protein-coupled receptors expressed in cells that mediates allergies and other pathophysiological diseases. For alleviation of allergic symptoms, H1R antagonists are therapeutic drugs; of which the most frequently prescribed are second generation drugs, such as; Cetirizine, Loratadine, Hydroxyzine, Desloratadine, Bepotastine, Acrivastine and Rupatadine. To understand their potency, binding affinity and interaction; we have employed molecular docking and quantum chemical study such as; Induced-fit docking and calculation of quantum chemical descriptors. This study also introduces the binding site characterization of H1R, with its known antagonists and Curcumin (our proposed alternative H1R antagonist); useful for future drug target site. The interactive binding site residues of H1R are found to be; Lys-191, Tyr-108, Asp-107, Tyr-100, Lys-179, Lys-191, Thr-194, Trp-428, Phe-432, Tyr-458, Hie-450, with most of these shown to be inhibited by naturally-occurring compound curcumin. Amongst the FDA approved drugs, Hydroxyzine showed best ligand binding affinity, calculated as - 141.491 kcal/mol and naturally occurring compound, Curcumin showed binding affinity of - 87.046 kcal/mol. The known antagonists of H1R has been used for hypothesizing curcumin as naturally occurring lead compound for the target using accurate molecular docking simulation study. Though the pharmacological action of known inhibitor is already established, they could differ from their reactivity, which we have also focused in our study for predicting drug reactivity.

Abstract: Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Abstract: Antimicrobial efflux is one of the important mechanisms causing multi-drug resistance (MDR) in bacteria. Chemosensitizers like 1-(1-naphthylmethyl)-piperazine (NMP) can inhibit an efflux pump and therefore can overcome MDR. However, secondary effects of NMP other than efflux pump inhibition are rarely investigated. Here, using phenotypic assays, phenotypic microarray and transcriptomic assays we show that NMP creates membrane destabilization in MDR Klebsiella pneumoniae MGH 78578 strain. The NMP mediated membrane destabilization activity was measured using beta-lactamase activity, membrane potential alteration studies, and transmission electron microscopy assays. Results from both beta-lactamase and membrane potential alteration studies shows that both outer and inner membranes are destabilized in NMP exposed K. pneumoniae MGH 78578 cells. Phenotypic Microarray and RNA-seq were further used to elucidate the metabolic and transcriptional signals underpinning membrane destabilization. Membrane destabilization happens as early as 15 min post-NMP treatment. Our RNA-seq data shows that many genes involved in envelope stress response were differentially regulated in the NMP treated cells. Up-regulation of genes encoding the envelope stress response and repair systems show the distortion in membrane homeostasis during survival in an environment containing sub-inhibitory concentration of NMP. In addition, the lsr operon encoding the production of autoinducer-2 responsible for biofilm production was down-regulated resulting in reduced biofilm formation in NMP treated cells, a phenotype confirmed by crystal violet-based assays. We postulate that the early membrane disruption leads to destabilization of inner membrane potential, impairing ATP production and consequently resulting in efflux pump inhibition.

Abstract: BACKGROUND: Autophagy and apoptosis are the basic physiological processes in cells that clean up aged and mutant cellular components or even the entire cells. Both autophagy and apoptosis are disrupted in most major diseases such as cancer and neurological disorders. Recently, increasing attention has been paid to understand the crosstalk between autophagy and apoptosis due to their tightly synergetic or opposite functions in several pathological processes. OBJECTIVE: This study aims to assist autophagy and apoptosis-related drug research, clarify the intense and complicated connections between two processes, and provide a guide for novel drug development. METHODS: We established two chemical-genomic databases which are specifically designed for autophagy and apoptosis, including autophagy- and apoptosis-related proteins, pathways and compounds. We then performed network analysis on the apoptosis- and autophagy-related proteins and investigated the full protein-protein interaction (PPI) network of these two closely connected processes for the first time. RESULTS: The overlapping targets we discovered show a more intense connection with each other than other targets in the full network, indicating a better efficacy potential for drug modulation. We also found that Death-associated protein kinase 1 (DAPK1) is a critical point linking autophagy- and apoptosis-related pathways beyond the overlapping part, and this finding may reveal some delicate signaling mechanism of the process. Finally, we demonstrated how to utilize our integrated computational chemogenomics tools on in silico target identification for small molecules capable of modulating autophagy- and apoptosis-related pathways. CONCLUSION: The knowledge-bases for apoptosis and autophagy and the integrated tools will accelerate our work in autophagy and apoptosis-related research and can be useful sources for information searching, target prediction, and new chemical discovery.

Abstract: As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.

Abstract: Influenza viruses still constitute a real public health problem today. To cope with the emergence of new circulating strains, but also the emergence of resistant strains to classic antivirals, it is necessary to develop new antiviral approaches. This review summarizes the state-of-the-art of current antiviral options against influenza infection, with a particular focus on the recent advances of anti-influenza drug repurposing strategies and their potential therapeutic, regulatory and economic benefits. The review will illustrate the multiple ways to reposition molecules for the treatment of influenza, from adventitious discovery to in silico-based screening. These novel antiviral molecules, many of which targeting the host cell, in combination with conventional antiviral agents targeting the virus, will ideally enter the clinics and reinforce the therapeutic arsenal to combat influenza virus infections.

Abstract: Small nuclear ribonucleoprotein polypeptide G (SNRPG), often referred to as Smith protein G (SmG), is an indispensable component in the biogenesis of spliceosomal uridyl-rich small nuclear ribonucleoprotein particles (U snRNPs; U1, U2, U4 and U5), which are precursors of both the major and minor spliceosome. SNRPG has attracted significant attention because of its implicated roles in tumorigenesis and tumor development. Suggestive evidence of its varying expression levels has been reported in different types of cancers, which include breast cancer, lung cancer, prostate cancer and colon cancer. The accumulating evidence suggests that the splicing machinery component plays a significant role in the initiation and progression of cancers. SNRPG has a wide interaction network, and its functions are predominantly mediated by protein-protein interactions (PPIs), making it a promising anti-cancer therapeutic target in PPI-focused drug technology. Understanding its roles in tumorigenesis and tumor development is an indispensable arsenal in the development of molecular-targeted therapies. Several antitumor drugs linked to splicing machinery components have been reported in different types of cancers and some have already entered the clinic. However, targeting SNRPG as a drug development tool has been an overlooked and underdeveloped strategy in cancer therapy. In this article, we present a comprehensive and perspective view on the oncogenic potential of SNRPG in PPI-focused drug discovery.

Abstract: The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to reenter the system at a costlier level. We present a large-scale longitudinal study (18 months) of the DDI phenomenon at the primary- and secondary-care level using electronic health records (EHR) from the city of Blumenau in Southern Brazil (pop. approximately 340,000). We found that 181 distinct drug pairs known to interact were dispensed concomitantly to 12% of the patients in the city's public health-care system. Further, 4% of the patients were dispensed drug pairs that are likely to result in major adverse drug reactions (ADR)-with costs estimated to be much larger than previously reported in smaller studies. The large-scale analysis reveals that women have a 60% increased risk of DDI as compared to men; the increase becomes 90% when considering only DDI known to lead to major ADR. Furthermore, DDI risk increases substantially with age; patients aged 70-79 years have a 34% risk of DDI when they are dispensed two or more drugs concomitantly. Interestingly, a statistical null model demonstrates that age- and female-specific risks from increased polypharmacy fail by far to explain the observed DDI risks in those populations, suggesting unknown social or biological causes. We also provide a network visualization of drugs and demographic factors that characterize the DDI phenomenon and demonstrate that accurate DDI prediction can be included in health care and public-health management, to reduce DDI-related ADR and costs.

Abstract: A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.