High Throughput and Computational Repurposing for Neglected Diseases.

Pharm Res. 2018 Dec 17;36(2):27

Authors: Hernandez HW, Soeung M, Zorn KM, Ashoura N, Mottin M, Andrade CH, Caffrey CR, de Siqueira-Neto JL, Ekins S

Abstract
PURPOSE: Neglected tropical diseases (NTDs) represent are a heterogeneous group of communicable diseases that are found within the poorest populations of the world. There are 23 NTDs that have been prioritized by the World Health Organization, which are endemic in 149 countries and affect more than 1.4 billion people, costing these developing economies billions of dollars annually. The NTDs result from four different causative pathogens: protozoa, bacteria, helminth and virus. The majority of the diseases lack effective treatments. Therefore, new therapeutics for NTDs are desperately needed.
METHODS: We describe various high throughput screening and computational approaches that have been performed in recent years. We have collated the molecules identified in these studies and calculated molecular properties.
RESULTS: Numerous global repurposing efforts have yielded some promising compounds for various neglected tropical diseases. These compounds when analyzed as one would expect appear drug-like. Several large datasets are also now in the public domain and this enables machine learning models to be constructed that then facilitate the discovery of new molecules for these pathogens.
CONCLUSIONS: In the space of a few years many groups have either performed experimental or computational repurposing high throughput screens against neglected diseases. These have identified compounds which in many cases are already approved drugs. Such approaches perhaps offer a more efficient way to develop treatments which are generally not a focus for global pharmaceutical companies because of the economics or the lack of a viable market. Other diseases could perhaps benefit from these repurposing approaches.

PMID: 30560386 [PubMed - indexed for MEDLINE]

Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities.

BMC Med Genomics. 2018 Dec 31;11(Suppl 6):112

Authors: Li H, Fan J, Vitali F, Berghout J, Aberasturi D, Li J, Wilson L, Chiu W, Pumarejo M, Han J, Kenost C, Koripella PC, Pouladi N, Billheimer D, Bedrick EJ, Lussier YA

Abstract
BACKGROUND: Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets.
METHODS: In this work, we integrated genome-wide association study (GWAS) associating diseases and single nucleotide polymorphisms (SNPs) with transcript regulatory activity from expression quantitative trait loci (eQTL). This allowed novel mechanistic insights for noncoding and intergenic regions. We then analyzed pairs of SNPs across diseases to identify shared molecular effectors robust to multiple test correction (False Discovery Rate FDReRNA < 0.05). We hypothesized that disease pairs found to be molecularly convergent would also be significantly overrepresented among comorbidities in clinical datasets. To assess our hypothesis, we used clinical claims datasets from the Healthcare Cost and Utilization Project (HCUP) and calculated significant disease comorbidities (FDRcomorbidity < 0.05). We finally verified if disease pairs resulting molecularly convergent were also statistically comorbid more than by chance using the Fisher's Exact Test.
RESULTS: Our approach integrates: (i) 6175 SNPs associated with 238 diseases from ~ 1000 GWAS, (ii) eQTL associations from 19 tissues, and (iii) claims data for 35 million patients from HCUP. Logistic regression (controlled for age, gender, and race) identified comorbidities in HCUP, while enrichment analyses identified cis- and trans-eQTL downstream effectors of GWAS-identified variants. Among ~ 16,000 combinations of diseases, 398 disease-pairs were prioritized by both convergent eQTL-genetics (RNA overlap enrichment, FDReRNA < 0.05) and clinical comorbidities (OR > 1.5, FDRcomorbidity < 0.05). Case studies of comorbidities illustrate specific convergent noncoding regulatory elements. An intergenic architecture of disease comorbidity was unveiled due to GWAS and eQTL-derived convergent mechanisms between distinct diseases being overrepresented among observed comorbidities in clinical datasets (OR = 8.6, p-value = 6.4 × 10- 5 FET).
CONCLUSIONS: These comorbid diseases with convergent eQTL genetic mechanisms suggest clinical syndromes. While it took over a decade to confirm the genetic underpinning of the metabolic syndrome, this study is likely highlighting hundreds of new ones. Further, this knowledge may improve the clinical management of comorbidities with precision and shed light on novel approaches of drug repositioning or SNP-guided precision molecular therapy inclusive of intergenic risks.

PMID: 30598089 [PubMed - in process]

Computational drug repositioning using meta-path-based semantic network analysis.

BMC Syst Biol. 2018 Dec 31;12(Suppl 9):134

Authors: Tian Z, Teng Z, Cheng S, Guo M

Abstract
BACKGROUND: Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network. Therefore, it is urgent to develop a rational method to infer new indications for approved drugs.
RESULTS: In this study, we proposed a novel methodology named HeteSim_DrugDisease (HSDD) for the prediction of drug repositioning. Firstly, we build the drug-drug similarity network and disease-disease similarity network by integrating the information of drugs and diseases. Secondly, a drug-disease heterogeneous network is constructed, which combines the drug similarity network, disease similarity network as well as the known drug-disease association network. Finally, HSDD predicts novel drug-disease associations based on the HeteSim scores of different meta-paths. The experimental results show that HSDD performs significantly better than the existing state-of-the-art approaches. HSDD achieves an AUC score of 0.8994 in the leave-one-out cross validation experiment. Moreover, case studies for selected drugs further illustrate the practical usefulness of HSDD.
CONCLUSIONS: HSDD can be an effective and feasible way to infer the associations between drugs and diseases using on meta-path-based semantic network analysis.

PMID: 30598084 [PubMed - in process]

Topiramate protects apoE-deficient mice from kidney damage without affecting plasma lipids.

Pharmacol Res. 2018 Dec 26;:

Authors: Manzini S, Busnelli M, Parolini C, Minoli L, Ossoli A, Brambilla E, Simonelli S, Lekka E, Persidis A, Scanziani E, Chiesa G

Abstract
Topiramate is an anticonvulsant drug also prescribed for migraine prophylaxis that acts through several mechanisms of action. Several studies indicate that topiramate induces weight loss and a moderate reduction of plasma lipids and glucose. Based on these favourable metabolic effects, aim of this study was to evaluate if topiramate could modulate atherosclerosis development and protect target organs of dysmetabolic conditions. Thirty apoE-deficient mice were divided into three groups and fed for 12 weeks a high fat diet (Control) or the same diet containing topiramate at 0.125% and 0.250%. Body weight, water and food intake were monitored throughout the study. Plasma lipids and glucose levels were measured and a glucose tolerance test was performed. Atherosclerosis development was evaluated in the whole aorta and at the aortic sinus. Histological analysis of liver, kidney and adipose tissue was performed. Topiramate did not affect weight gain and food intake. Glucose tolerance and plasma lipids were not changed and, in turn, atherosclerosis development was not different among groups. Topiramate did not modify liver and adipose tissue histology. Conversely, in the kidneys, the treatment reduced the occurrence of glomerular lipidosis by decreasing foam cells accumulation and reducing the expression of inflammatory markers. Blood urea nitrogen levels were also reduced by treatment. Our results indicate that topiramate does not affect atherosclerosis development, but preserves kidney structure and function. The study suggests that topiramate could be investigated in drug repurposing studies for the treatment of glomerular lipidosis.

PMID: 30593851 [PubMed - as supplied by publisher]

Low-cost non-profit drug repurposing.

Nat Rev Drug Discov. 2018 Dec 28;18(1):7

Authors: Mullard A

PMID: 30591726 [PubMed - in process]

A high-throughput chemical-genetics screen in murine adipocytes identifies insulin-regulatory pathways.

J Biol Chem. 2018 Dec 27;:

Authors: Brewer PD, Romenskaia I, Mastick CC

Abstract
Pathways linking activation of the insulin receptor to downstream targets of insulin have traditionally been studied using a candidate gene approach. To elucidate additional pathways regulating insulin activity, we performed a forward chemical-genetics screen based on translocation of a glucose transporter 4 (Glut4) reporter expressed in murine 3T3-L1 adipocytes. To identify compounds with known targets, we screened drug repurposing and natural product libraries. We identified, confirmed, and validated 64 activators and 65 inhibitors that acutely increase or rapidly decrease cell surface Glut4 in adipocytes stimulated with submaximal insulin concentrations. These agents were grouped by target, chemical class, and mechanism of action. All groups contained multiple hits from a single drug class, and several comprised multiple structurally unrelated hits for a single target. Targets include the β-adrenergic and adenosine receptors. Agonists of these receptors increased and inverse agonists/antagonists decreased cell-surface Glut4 independently of insulin. Additional activators include insulin sensitizers (thiazolidinediones), insulin mimetics, dis-inhibitors (the mTORC1 inhibitor rapamycin), cardiotonic steroids (the Na+/K+ATPase inhibitor ouabain) and corticosteroids (dexamethasone). Inhibitors include heterocyclic amines (tricyclic antidepressants) and 21 natural product supplements and herbal extracts. Mechanisms of action include effects on Glut4 trafficking, signal transduction, inhibition of protein synthesis, and dissipation of proton gradients. Two pathways that acutely regulate Glut4 translocation were discovered: de novo protein synthesis and endocytic acidification. The mechanism of action of additional classes of activators (tanshinones, dalbergiones, and coumarins) and inhibitors (flavonoids, resveratrol) remain to be determined. These tools are among the most sensitive, responsive, and reproducible insulin-activity assays described to date.

PMID: 30591588 [PubMed - as supplied by publisher]

On the Mechanism of the Cardioprotective Action of σ1 Receptor Agonist Anxiolytic Fabomotizole Hydrochloride (Afobazole).

Bull Exp Biol Med. 2018 Sep;165(5):660-664

Authors: Kryzhanovskii SA, Kozhevnikova LM, Tsorin IB, Sukhanova IF, Ionova EO, Stolyaruk VN, Vititnova MB, Miroshkina IA, Seredenin SB

Abstract
Original translational rat model of chronic heart failure provoked by experimental anterior transmural myocardium infarction was employed to examine the preventive action of anxiolytic Afobazole (15 mg/kg/day administered intraperitoneally during the first 15 days after coronary occlusion) on the development of the heart failure assessed in 3 months after infarction. Afobazole prevented the development of pathologic remodeling of the myocardium, maintained its inotropic function, and decreased the plasma level of brain natriuretic peptide known as a biochemical marker of chronic heart failure. In the myocardium, Afobazole down-regulated overexpression of the genes induced in chronic heart failure and assessed by corresponding RNA levels, which code angiotensin (AT1A-R), vasopressin (V1A-R), and glucocorticoid (GR) receptors as well as Epac2 protein. The revealed biochemical changes are consistent with the data on cardioprotective action of Afobazole.

PMID: 30225699 [PubMed - indexed for MEDLINE]

Antidiabetic Activity of Afobazole in Wistar Rats.

Bull Exp Biol Med. 2018 Sep;165(5):649-652

Authors: Ostrovskaya RU, Ivanov SV, Voronin MV, Ozerova IV, Zolotov NN, Seredenin SB

Abstract
Using the streptozotocin model of type 2 diabetes mellitus in Wistar rats, we compared antidiabetic activity of anxiolytic Afobazole with that of metformin. Afobazole in a dose of 10 mg/kg reduced streptozotocin-induced hyperglycemia and polyphagia and prevented accumulation of malonic dialdehyde, being not inferior to metformin in a dose of 300 mg/kg, and was even more effective than metformin in body weight recovery, elimination of polydipsia, and preservation of these effects after treatment withdrawal.

PMID: 30225697 [PubMed - indexed for MEDLINE]

An Epithelial-to-Mesenchymal transcriptional switch triggers evolution of Pulmonary Sarcomatoid Carcinoma (PSC) and identifies dasatinib as new therapeutic option.

Clin Cancer Res. 2018 Dec 26;:

Authors: Manzotti G, Torricelli F, Donati B, Lococo F, Sancisi V, Rossi G, Piana S, Ciarrocchi A

Abstract
Purpose: Pulmonary Sarcomatoid Carcinoma (PSC) is an aggressive form of NSCLC. Rarity and poor characterization have limited development of PSC-tailored treatment protocols, leaving patients with inadequate therapeutic options. In this study, we investigated the gene expression profile of PSCs, with the aim to characterize the molecular mechanisms responsible for their evolution and to identify new drugs for their treatment. Experimental Design: A training set of 17 biphasic PSCs was tested for the expression of a large panel of 770 genes related to cancer progression using Nanostring technology. Computational analyses were used to characterize a PSCs-gene specific signature from which pathways and drivers of PSC evolution were identified and validated using functional assays in vitro. This signature was validated in a separate set of 15 PSCs and 8 differentiated NSCLC and used to interrogate the cMAP database searching for FDA-approved small molecules able to counteract PSC phenotype. Results: We demonstrated that transcriptional activation of an Epithelial Mesenchymal Transition (EMT) program drives PSC phylogeny in vivo We showed, that loss of the epithelial-associated transcription factor (TF) OVOL2 characterizes the transition to sarcomatoid phenotype triggering the expression of EMT promoting TFs, including TWIST,ZEB and the expression of the membrane kinase DDR2. Finally, using a drug repurposing approach, we identified dasatinib as potential inhibitor of the PSC-gene expression signature and we confirmed in vitro that this drug efficiently restrains proliferation and reverts the sarcomatoid-associated phenotype. Conclusions: Our data provide new insights into PSC evolution and provide the rationale for further clinical studies with dasatinib.

PMID: 30587547 [PubMed - as supplied by publisher]

Drug-Repositioning Approach for the Discovery of Anti-Influenza Virus Activity of Japanese Herbal (Kampo) Medicines In Vitro: Potent High Activity of Daio-Kanzo-To.

Evid Based Complement Alternat Med. 2018;2018:6058181

Authors: Watanabe K

Abstract
Influenza virus infections are a serious public health concern throughout the world. Emergence of viral resistance to the currently approved anti-influenza drugs warrants the development of new antiviral agents. Japanese herbal medicines called Kampo are very commonly used as prescription medication in Japan, and Mao-to is known to be effective against influenza that is caused by oseltamivir-resistant viruses. However, influenza-related death occurs mainly among the elderly, and for patients with hypertension and diabetes, Mao-to may cause these diseases to worsen. Therefore, the exploration of more potent and safe Kampo medicines may be a good strategy for developing new influenza medicines. Here cell-based screening of anti-influenza virus activity for 42 approved Kampo medicines was performed using the drug-repositioning approach. As a result, four Kampo medicines were selected as potent anti-influenza agents against the A/WSN/33 strain. It was found that Daio-kanzo-to [50% inhibitory concentration (IC50) = 10.5 μg/mL; 50% cytotoxic concentration (CC50) = 71.6 μg/mL; selective index = 6.8] is more effective than Mao-to. Daio-kanzo-to and its constituent Japanese Pharmacopoeia (JP) Rhubarb were also effective against H3N2 and H1N1 subtypes of influenza viruses, including oseltamivir-insensitive-2009 pandemic clinical isolates. These data suggest the potential application of Daio-kanzo-to for influenza treatment.

PMID: 30584454 [PubMed]

Identifying Potential Ageing-Modulating Drugs In Silico.

Trends Endocrinol Metab. 2018 Dec 20;:

Authors: Dönertaş HM, Fuentealba M, Partridge L, Thornton JM

Abstract
Increasing human life expectancy has posed increasing challenges for healthcare systems. As people age, they become more susceptible to chronic diseases, with an increasing burden of multimorbidity, and the associated polypharmacy. Accumulating evidence from work with laboratory animals has shown that ageing is a malleable process that can be ameliorated by genetic and environmental interventions. Drugs that modulate the ageing process may delay or even prevent the incidence of multiple diseases of ageing. To identify novel, anti-ageing drugs, several studies have developed computational drug-repurposing strategies. We review published studies showing the potential of current drugs to modulate ageing. Future studies should integrate current knowledge with multi-omics, health records, and drug safety data to predict drugs that can improve health in late life.

PMID: 30581056 [PubMed - as supplied by publisher]

Tamoxifen mechanically deactivates hepatic stellate cells via the G protein-coupled estrogen receptor.

Oncogene. 2018 Dec 21;:

Authors: Cortes E, Lachowski D, Rice A, Thorpe SD, Robinson B, Yeldag G, Lee DA, Ghemtio L, Rombouts K, Del Río Hernández AE

Abstract
Tamoxifen has been used for many years to target estrogen receptor signalling in breast cancer cells. Tamoxifen is also an agonist of the G protein-coupled estrogen receptor (GPER), a GPCR ubiquitously expressed in tissues that mediates the acute response to estrogens. Here we report that tamoxifen promotes mechanical quiescence in hepatic stellate cells (HSCs), stromal fibroblast-like cells whose activation triggers and perpetuates liver fibrosis in hepatocellular carcinomas. This mechanical deactivation is mediated by the GPER/RhoA/myosin axis and induces YAP deactivation. We report that tamoxifen decreases the levels of hypoxia-inducible factor-1 alpha (HIF-1α) and the synthesis of extracellular matrix proteins through a mechanical mechanism that involves actomyosin-dependent contractility and mechanosensing of tissue stiffness. Our results implicate GPER-mediated estrogen signalling in the mechanosensory-driven activation of HSCs and put forward estrogenic signalling as an option for mechanical reprogramming of myofibroblast-like cells in the tumour microenvironment. Tamoxifen, with half a century of safe clinical use, might lead this strategy of drug repositioning.

PMID: 30575816 [PubMed - as supplied by publisher]

Genome-wide meta-analysis reveals shared new loci in systemic seropositive rheumatic diseases.

Ann Rheum Dis. 2018 Dec 20;:

Authors: Acosta-Herrera M, Kerick M, González-Serna D, Wijmenga C, Franke A, Gregersen PK, Padyukov L, Worthington J, Vyse TJ, Alarcón-Riquelme ME, Mayes MD, Martin J, Myositis Genetics Consortium

Abstract
OBJECTIVE: Immune-mediated inflammatory diseases (IMIDs) are heterogeneous and complex conditions with overlapping clinical symptoms and elevated familial aggregation, which suggests the existence of a shared genetic component. In order to identify this genetic background in a systematic fashion, we performed the first cross-disease genome-wide meta-analysis in systemic seropositive rheumatic diseases, namely, systemic sclerosis, systemic lupus erythematosus, rheumatoid arthritis and idiopathic inflammatory myopathies.
METHODS: We meta-analysed ~6.5 million single nucleotide polymorphisms in 11 678 cases and 19 704 non-affected controls of European descent populations. The functional roles of the associated variants were interrogated using publicly available databases.
RESULTS: Our analysis revealed five shared genome-wide significant independent loci that had not been previously associated with these diseases: NAB1, KPNA4-ARL14, DGQK, LIMK1 and PRR12. All of these loci are related with immune processes such as interferon and epidermal growth factor signalling, response to methotrexate, cytoskeleton dynamics and coagulation cascade. Remarkably, several of the associated loci are known key players in autoimmunity, which supports the validity of our results. All the associated variants showed significant functional enrichment in DNase hypersensitivity sites, chromatin states and histone marks in relevant immune cells, including shared expression quantitative trait loci. Additionally, our results were significantly enriched in drugs that are being tested for the treatment of the diseases under study.
CONCLUSIONS: We have identified shared new risk loci with functional value across diseases and pinpoint new potential candidate loci that could be further investigated. Our results highlight the potential of drug repositioning among related systemic seropositive rheumatic IMIDs.

PMID: 30573655 [PubMed - as supplied by publisher]

Meta-analysis of Immunochip data of four autoimmune diseases reveals novel single-disease and cross-phenotype associations.

Genome Med. 2018 Dec 20;10(1):97

Authors: Márquez A, Kerick M, Zhernakova A, Gutierrez-Achury J, Chen WM, Onengut-Gumuscu S, González-Álvaro I, Rodriguez-Rodriguez L, Rios-Fernández R, González-Gay MA, Coeliac Disease Immunochip Consortium, Rheumatoid Arthritis Consortium International for Immunochip (RACI), International Scleroderma Group, Type 1 Diabetes Genetics Consortium, Mayes MD, Raychaudhuri S, Rich SS, Wijmenga C, Martín J

Abstract
BACKGROUND: In recent years, research has consistently proven the occurrence of genetic overlap across autoimmune diseases, which supports the existence of common pathogenic mechanisms in autoimmunity. The objective of this study was to further investigate this shared genetic component.
METHODS: For this purpose, we performed a cross-disease meta-analysis of Immunochip data from 37,159 patients diagnosed with a seropositive autoimmune disease (11,489 celiac disease (CeD), 15,523 rheumatoid arthritis (RA), 3477 systemic sclerosis (SSc), and 6670 type 1 diabetes (T1D)) and 22,308 healthy controls of European origin using the R package ASSET.
RESULTS: We identified 38 risk variants shared by at least two of the conditions analyzed, five of which represent new pleiotropic loci in autoimmunity. We also identified six novel genome-wide associations for the diseases studied. Cell-specific functional annotations and biological pathway enrichment analyses suggested that pleiotropic variants may act by deregulating gene expression in different subsets of T cells, especially Th17 and regulatory T cells. Finally, drug repositioning analysis evidenced several drugs that could represent promising candidates for CeD, RA, SSc, and T1D treatment.
CONCLUSIONS: In this study, we have been able to advance in the knowledge of the genetic overlap existing in autoimmunity, thus shedding light on common molecular mechanisms of disease and suggesting novel drug targets that could be explored for the treatment of the autoimmune diseases studied.

PMID: 30572963 [PubMed - in process]

Metabolic Network Prediction of Drug Side Effects.

Cell Syst. 2016 Mar 23;2(3):209-13

Authors: Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E

Abstract
Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.7) for >70 drug side effects, including very serious ones such as interstitial nephritis and extrapyramidal disorders. We evaluate AMPP's predictive signal through cross-validation, comparison across multiple versions of a side effects database, and co-occurrence analysis of drug side effect associations in scientific abstracts (hypergeometric p value = 2.2e-40). AMPP outperforms a previous biochemical structure-based method in predicting metabolically based side effects (aggregate AUC = 0.65 versus 0.59). Importantly, AMPP enables the identification of key metabolic reactions and biomarkers that are predictive of specific side effects. Taken together, this work lays a foundation for future detection of metabolically grounded side effects during early stages of drug development.

PMID: 27135366 [PubMed - indexed for MEDLINE]

Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning.

Psychol Med. 2018 Dec 20;:1-17

Authors: So HC, Chau CK, Lau A, Wong SY, Zhao K

Abstract
BACKGROUND: Depression and anxiety disorders (AD) are the first and sixth leading causes of disability worldwide. Despite their high prevalence and significant disability resulted, there are limited advances in new drug development. Recently, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic basis underlying psychiatric disorders.
METHODS: Here we employed gene-set analyses of GWAS summary statistics for drug repositioning. We explored five related GWAS datasets, including two on major depressive disorder (MDD2018 and MDD-CONVERGE, with the latter focusing on severe melancholic depression), one on AD, and two on depressive symptoms and neuroticism in the population. We extracted gene-sets associated with each drug from DSigDB and examined their association with each GWAS phenotype. We also performed repositioning analyses on meta-analyzed GWAS data, integrating evidence from all related phenotypes.
RESULTS: Importantly, we showed that the repositioning hits are generally enriched for known psychiatric medications or those considered in clinical trials. Enrichment was seen for antidepressants and anxiolytics but also for antipsychotics. We also revealed new candidates or drug classes for repositioning, some of which were supported by experimental or clinical studies. For example, the top repositioning hit using meta-analyzed p values was fendiline, which was shown to produce antidepressant-like effects in mouse models by inhibition of acid sphingomyelinase.
CONCLUSION: Taken together, our findings suggest that human genomic data such as GWAS are useful in guiding drug discoveries for depression and AD.

PMID: 30569882 [PubMed - as supplied by publisher]

Activity of fenticonazole, tioconazole and nystatin on New World Leishmania species.

Curr Top Med Chem. 2018 Dec 19;:

Authors: Yamamoto ES, Jesus JA, Bezerra-Souza A, Laurenti MD, Ribeiro SP, Passero LFD

Abstract
Leishmaniasis is an infectious disease caused by protozoal parasites belonging to Leishmania genus. Different clinical outcomes can be observed depending on the parasite species and health condition of patients. It can range from single cutaneous lesion until deadly visceral form. The treatment of all forms of leishmaniasis is based on pentavalent antimonials, and in some cases, the second-line drug, amphotericin B is used. Beside the toxicity of both drugs, parasites can be resistant to antimonial in some areas of the world. This makes fundamental the characterization of new drugs with leishmanicidal effect. Thus, the aim of the present work was to study the leishmanicidal activity of drugs able to interfere with ergosterol pathway (fenticonazole, tioconazole, nystatin, rosuvastatin and voriconazole) against promastigote and amastigote forms of L. (L.) amazonensis, L. (V.) braziliensis and L. (L.) infantum, and its impact on morphological and physiological changes in L. (L.) amazonensis or in host macrophages. We observed that fenticonazole, tioconazole and nystatin drugs eliminated promastigote and intracellular amastigotes, being fenticonazole and nystatin the most selective towards amastigote forms. Rosuvastatin and voriconazole did not present activity against amastigote forms of Leishmania sp. In addition, the drugs with leishmanicidal activity interfered with parasite mitochondrion. Although drugs did not stimulate NO and H2O2, specially fenticonazole was able to alkalize infected host macrophages. These results suggest well established and non-toxic antifungal drugs can be repurposed and used in leishmaniasis.

PMID: 30569856 [PubMed - as supplied by publisher]

Drug Repurposing for Japanese Encephalitis Virus Infection by Systems Biology Methods.

Molecules. 2018 Dec 18;23(12):

Authors: Lv BM, Tong XY, Quan Y, Liu MY, Zhang QY, Song YF, Zhang HY

Abstract
Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection.

PMID: 30567313 [PubMed - in process]

[Continued development of drugs: the path of thioguanine].

Ned Tijdschr Geneeskd. 2018;162:D1757

Authors: Simsek M, de Boer NKH, Mulder CJJ

Abstract
Continued development of existing drugs ('drug rediscovery') may offer new therapeutic options and be cost-effective. Rediscovered drugs are commonly prescribed off-label, although licensing can be important to allow safe and controlled prescription of the drugs to patients. Licensing of a new indication for a generic drug, however, is a complicated process since there is no blueprint for this and there is little interest from the pharmaceutical industry due to an unattractive cost-recovery model. In this article, we illustrate the successful license-extension for thioguanine - initially developed in 1950 for leukaemia - as a new treatment for patients with inflammatory bowel disease.

PMID: 29350120 [PubMed - indexed for MEDLINE]

Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing.

Brief Bioinform. 2018 Dec 18;:

Authors: Tanoli Z, Alam Z, Ianevski A, Wennerberg K, Vähä-Koskela M, Aittokallio T

Abstract
Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. In this work, we demonstrate an integrated use of the rich bioactivity data from DTC and related drug databases using Drug Target Profiler (DTP), an open-source software and web tool for interactive exploration of drug-target interaction networks. DTP was designed for network-centric modeling of mode-of-action of multi-targeting anticancer compounds, especially for precision oncology applications. DTP enables users to construct an interaction network based on integrated bioactivity data across selected chemical compounds and their protein targets, further customizable using various visualization and filtering options, as well as cross-links to several drug and protein databases to provide comprehensive information of the network nodes and interactions. We demonstrate here the operation of the DTP tool and its unique features by several use cases related to both drug discovery and drug repurposing applications, using examples of anticancer drugs with shared target profiles. DTP is freely accessible at http://drugtargetprofiler.fimm.fi/.

PMID: 30566623 [PubMed - as supplied by publisher]