Abstract: BACKGROUND: It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. METHODS: In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. RESULTS: The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. CONCLUSION: Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

Abstract: Lung cancer is the leading cause of cancer-related deaths worldwide. The paralogous transcriptional cofactors Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ, also called WWTR1), the main downstream effectors of the Hippo signal transduction pathway, are emerging as pivotal determinants of malignancy in lung cancer. Traditionally, studies have tended to consider YAP and TAZ as functionally redundant transcriptional cofactors with similar biological impact. However, there is growing evidence that each of them also possesses distinct attributes. Here we sought to systematically characterize the division of labor between YAP and TAZ in non-small cell lung cancer (NSCLC), the most common histological subtype of lung cancer. Representative NSCLC cell lines as well as patient-derived data showed that the two paralogs orchestrated nonoverlapping transcriptional programs in this cancer type. YAP preferentially regulated gene sets associated with cell division and cell-cycle progression, whereas TAZ preferentially regulated genes associated with extracellular matrix organization. Depletion of YAP resulted in growth arrest, whereas its overexpression promoted cell proliferation. Likewise, depletion of TAZ compromised cell migration, whereas its overexpression enhanced migration. The differential effects of YAP and TAZ on key cellular processes were also associated with differential response to anticancer therapies. Uncovering the different activities and downstream effects of YAP and TAZ may thus facilitate better stratification of patients with lung cancer for anticancer therapies. SIGNIFICANCE: Thease findings show that oncogenic paralogs YAP and TAZ have distinct roles in NSCLC and are associated with differential response to anticancer drugs, knowledge that may assist lung cancer therapy decisions.

Abstract: In recent decades, many genome-wide association studies on insomnia have reported numerous genes harboring multiple risk variants. Nevertheless, the molecular functions of these risk variants conveying risk to insomnia are still ill-studied. In the present study, we integrated GWAS summary statistics (N=386,533) with two independent brain expression quantitative trait loci (eQTL) datasets (N=329) to determine whether expression-associated SNPs convey risk to insomnia. Furthermore, we applied numerous bioinformatics analyses to highlight promising genes associated with insomnia risk. By using Sherlock integrative analysis, we detected 449 significant insomnia-associated genes in the discovery stage. These identified genes were significantly overrepresented in six biological pathways including Huntington's disease (P=5.58 x 10-5), Alzheimer's disease (P=5.58 x 10-5), Parkinson's disease (P=6.34 x 10-5), spliceosome (P=1.17 x 10-4), oxidative phosphorylation (P=1.09 x 10-4), and wnt signaling pathways (P=2.07 x 10-4). Further, five of these identified genes were replicated in an independent brain eQTL dataset. Through a PPI network analysis, we found that there existed highly functional interactions among these five identified genes. Three genes of LDHA (P=0.044), DALRD3 (P=5.0 x 10-5), and HEBP2 (P=0.032) showed significantly lower expression level in brain tissues of insomnic patients than that in controls. In addition, the expression levels of these five genes showed prominently dynamic changes across different time points between behavioral states of sleep and sleep deprivation in mice brain cortex. Together, the evidence of the present study strongly suggested that these five identified genes may represent candidate genes and contributed risk to the etiology of insomnia.

Abstract: BACKGROUND: Nicotine, the pharmacologically active substance in both tobacco and many electronic cigarette (e-cigarette) liquids, is responsible for the addiction that sustains cigarette smoking. With 8 million deaths worldwide annually, smoking remains one of the major causes of disability and premature death. However, nicotine also plays an important role in smoking cessation strategies. OBJECTIVES: The aim of this study was to develop a comprehensive, whole-body, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of nicotine and its major metabolite cotinine, covering various routes of nicotine administration, and to simulate nicotine brain tissue concentrations after the use of combustible cigarettes, e-cigarettes, nicotine gums, and nicotine patches. METHODS: A parent-metabolite, PBPK/PD model of nicotine for a non-smoking and a smoking population was developed using 91 plasma and brain tissue concentration-time profiles and 11 heart rate profiles. Among others, cytochrome P450 (CYP) 2A6 and 2B6 enzymes were implemented, including kinetics for CYP2A6 poor metabolizers. RESULTS: The model is able to precisely describe and predict both nicotine plasma and brain tissue concentrations, cotinine plasma concentrations, and heart rate profiles. 100% of the predicted area under the concentration-time curve (AUC) and maximum concentration (Cmax) values meet the twofold acceptance criterion with overall geometric mean fold errors of 1.12 and 1.15, respectively. The administration of combustible cigarettes, e-cigarettes, nicotine patches, and nicotine gums was successfully implemented in the model and used to identify differences in steady-state nicotine brain tissue concentration patterns. CONCLUSIONS: Our PBPK/PD model may be helpful in further investigations of nicotine dependence and smoking cessation strategies. As the model represents the first nicotine PBPK/PD model predicting nicotine concentration and heart rate profiles after the use of e-cigarettes, it could also contribute to a better understanding of the recent increase in youth e-cigarette use.

Abstract: INTRODUCTION: In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED: In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION: Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Abstract: Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.

Abstract: Initially recognised as an important factor for bone health, vitamin D is now known to have a range of effects on the immune system. Vitamin D deficiency is associated with an increased risk of multiple sclerosis (MS), a chronic immune-mediated demyelinating disease of the CNS. In this review, we explore the links between vitamin D deficiency, MS risk, and disease activity. We also discuss the known immune effects of vitamin D supplementation and the relevance of these observations to the immunopathology of MS. Finally, we review the existing evidence for vitamin D supplementation as an MS therapy, highlighting several recent clinical studies and trials.

Abstract: Coronaviruses are enveloped positive-strand RNA viruses belonging to family Coronaviridae and order Nidovirales which cause infections in birds and mammals. Among the human coronaviruses, highly pathogenic ones are Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome coronavirus (MERS-CoV) which have been implicated in severe respiratory syndrome in humans. There are no approved antiviral drugs or vaccines for the treatment of human CoV infection to date. The recent outbreak of new coronavirus pandemic, coronavirus disease 2019 (COVID-19) has caused a high mortality rate and infections around the world which necessitates the need for the discovery of novel anti-coronaviral drugs. Among the coronaviruses proteins, 3C-like protease (3CL(pro)) is an important drug target against coronaviral infection as the auto-cleavage process catalysed by the enzyme is crucial for viral maturation and replication. The present work is aimed at the identification of suitable lead molecules for the inhibition of 3CL(pro) enzyme via a computational screening of the Food and Drug Administration (FDA) approved antiviral drugs and phytochemicals. Based on binding energies and molecular interaction studies, we shortlisted five lead molecules (both FDA approved drugs and phytochemicals) for each enzyme targets (SARS-CoV-2 3CL(pro), SARS-CoV 3CL(pro) and MERS-CoV 3CL(pro)). The lead molecules showed higher binding affinity compared to the standard inhibitors and exhibited favourable hydrophobic interactions and a good number of hydrogen bonds with their respective targets. A few promising leads with dual inhibition potential were identified among FDA approved antiviral drugs which include DB13879 (Glecaprevir), DB09102 (Daclatasvir), molecule DB09297 (Paritaprevir) and DB01072 (Atazanavir). Among the phytochemicals, 11,646,359 (Vincapusine), 120,716 (Alloyohimbine) and 10,308,017 (Gummadiol) showed triple inhibition potential against all the three targets and 102,004,710 (18-Hydroxy-3-epi-alpha-yohimbine) exhibited dual inhibition potential. Hence, the proposed lead molecules from our findings can be further investigated through in vitro and in vivo studies to develop into potential drug candidates against human coronaviral infections.

Abstract: During these days of global emergency for the COVID-19 disease outbreak, there is an urgency to share reliable information able to help worldwide life scientists to get better insights and make sense of the large amount of data currently available. In this study we used the results presented in [1] to perform two different Systems Biology analyses on the HCoV-host interactome. In the first one, we reconstructed the interactome of the HCoV-host proteins, integrating it with highly reliable miRNA and drug interactions information. We then added the IL-6 gene, identified in recent publications [2] as heavily involved in the COVID-19 progression and, interestingly, we identified several interactions with the reconstructed interactome. In the second analysis, we performed a Gene Ontology and a Pathways enrichment analysis on the full set of the HCoV-host interactome proteins and on the ones belonging to a significantly dense cluster of interacting proteins identified in the first analysis. Results of the two analyses provide a compact but comprehensive glance on some of the current state-of-the-art regulations, GO, and pathways involved in the HCoV-host interactome, and that could support all scientists currently focusing on SARS-CoV-2 research.

Abstract: Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.