High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways.

Bioorg Med Chem. 2019 Nov 09;:115209

Authors: Warchal SJ, Dawson JC, Shepherd E, Munro AF, Hughes RE, Makda A, Carragher NO

Abstract
Heterogeneity in disease mechanisms between genetically distinct patients contributes to high attrition rates in late stage clinical drug development. New personalized medicine strategies aim to identify predictive biomarkers which stratify patients most likely to respond to a particular therapy. However, for complex multifactorial diseases not characterized by a single genetic driver, empirical approaches to identifying predictive biomarkers and the most promising therapies for personalized medicine are required. In vitro pharmacogenomics seeks to correlate in vitro drug sensitivity testing across panels of genetically distinct cell models with genomic, gene expression or proteomic data to identify predictive biomarkers of drug response. However, the vast majority of in vitro pharmacogenomic studies performed to date are limited to dose-response screening upon a single viability assay endpoint. In this article we describe the application of multiparametric high content phenotypic screening and the theta comparative cell scoring method to quantify and rank compound hits, screened at a single concentration, which induce a broad variety of divergent phenotypic responses between distinct breast cancer cell lines. High content screening followed by transcriptomic pathway analysis identified serotonin receptor modulators which display selective activity upon breast cancer cell cycle and cytokine signaling pathways correlating with inhibition of cell growth and survival. These methods describe a new evidence-led approach to rapidly identify compounds which display distinct response between different cell types. The results presented also warrant further investigation of the selective activity of serotonin receptor modulators upon breast cancer cell growth and survival as a potential drug repurposing opportunity.

PMID: 31757681 [PubMed - as supplied by publisher]

Role of dimethyl fumarate in the treatment of glioblastoma multiforme: A review article.

Iran J Neurol. 2019 Jul 06;18(3):127-133

Authors: Ahmadi-Beni R, Najafi A, Savar SM, Mohebbi N, Khoshnevisan A

Abstract
Glioblastoma multiforme (GBM), the most frequent malignant and aggressive primary brain tumor, is characterized by genetically unstable heterogeneous cells, diffused growth pattern, microvascular proliferation, and resistance to chemotherapy. Extensive investigations are being carried out to identify the molecular origin of resistance to chemo- and radio-therapy in GBM and find novel targets for therapy to improve overall survival rate. Dimethyl fumarate (DMF) has been shown to be a safe drug with limited short and long-term side effects, and fumaric acid esters (FAEs), including DMF, present both anti-oxidative and anti-inflammatory activity in different cell types and tissues. DMF has also anti-tumoral and neuroprotective effects and so it could be repurposed in the treatment of this invasive tumor in the future. Here, we have reviewed DMF pharmacokinetics and different mechanisms by which DMF could have therapeutic effects on GBM.

PMID: 31749934 [PubMed]

Mapping actionable pathways and mutations in brain tumours using targeted RNA next generation sequencing.

Acta Neuropathol Commun. 2019 Nov 20;7(1):185

Authors: Lenting K, van den Heuvel CNAM, van Ewijk A, ElMelik D, de Boer R, Tindall E, Wei G, Kusters B, Te Dorsthorst M, Ter Laan M, Huynen MA, Leenders WP

Abstract
Many biology-based precision drugs are available that neutralize aberrant molecular pathways in cancer. Molecular heterogeneity and the lack of reliable companion diagnostic biomarkers for many drugs makes targeted treatment of cancer inaccurate for many individuals. Identifying actionable hyperactive biological pathways in individual cancers may improve this situation.To achieve this we applied a novel targeted RNA next generation sequencing (t/RNA-NGS) technique to surgically obtained glioma tissues. The test combines mutation detection with analysis of biological pathway activities that are involved in tumour behavior in many cancer types (e.g. tyrosine kinase signaling, angiogenesis signaling, immune response, metabolism), via quantitative measurement of transcript levels and splice variants of hundreds of genes. We here present proof of concept that the technique, which uses molecular inversion probes, generates a histology-independent molecular diagnosis and identifies classifiers that are strongly associated with conventional histopathology diagnoses and even with patient prognosis. The test not only confirmed known glioma-associated molecular aberrations but also identified aberrant expression levels of actionable genes and mutations that have so far been considered not to be associated with glioma, opening up the possibility of drug repurposing for individual patients. Its cost-effectiveness makes t/RNA-NGS to an attractive instrument to aid oncologists in therapy decision making.

PMID: 31747973 [PubMed - in process]

Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks.

J Transl Med. 2019 Nov 20;17(1):382

Authors: Jiang HJ, You ZH, Huang YA

Abstract
BACKGROUND: In the process of drug development, computational drug repositioning is effective and resource-saving with regards to its important functions on identifying new drug-disease associations. Recent years have witnessed a great progression in the field of data mining with the advent of deep learning. An increasing number of deep learning-based techniques have been proposed to develop computational tools in bioinformatics.
METHODS: Along this promising direction, we here propose a drug repositioning computational method combining the techniques of Sigmoid Kernel and Convolutional Neural Network (SKCNN) which is able to learn new features effectively representing drug-disease associations via its hidden layers. Specifically, we first construct similarity metric of drugs using drug sigmoid similarity and drug structural similarity, and that of disease using disease sigmoid similarity and disease semantic similarity. Based on the combined similarities of drugs and diseases, we then use SKCNN to learn hidden representations for each drug-disease pair whose labels are finally predicted by a classifier based on random forest.
RESULTS: A series of experiments were implemented for performance evaluation and their results show that the proposed SKCNN improves the prediction accuracy compared with other state-of-the-art approaches. Case studies of two selected disease are also conducted through which we prove the superior performance of our method in terms of the actual discovery of potential drug indications.
CONCLUSION: The aim of this study was to establish an effective predictive model for finding new drug-disease associations. These experimental results show that SKCNN can effectively predict the association between drugs and diseases.

PMID: 31747915 [PubMed - in process]

Leveraging the Medicines for Malaria Venture malaria and pathogen boxes to discover chemical inhibitors of East Coast fever.

Int J Parasitol Drugs Drug Resist. 2019 04;9:80-86

Authors: Nyagwange J, Awino E, Tijhaar E, Svitek N, Pelle R, Nene V

Abstract
Chemotherapy of East Coast fever, a lymphoproliferative cancer-like disease of cattle causing significant economic losses in Africa, is largely dependent on the use of buparvaquone, a drug that was developed in the late 1980's. The disease is caused by the tick-borne protozoan pathogen Theileria parva. Buparvaquone can be used prophylactically and it is also active against tropical theileriosis, caused by the related parasite Theileria annulata. Recently, drug resistance was reported in T. annulata, and could occur in T. parva. Using a 3H-thymidine incorporation assay we screened 796 open source compounds from the Medicines for Malaria Venture (MMV) to discover novel chemicals with potential inhibitory activity to T. parva. We identified nine malaria box compounds and eight pathogen box compounds that inhibited the proliferation of F100TpM, a T. parva infected lymphocyte cell line. However, only two compounds, MMV008212 and MMV688372 represent promising leads with IC50 values of 0.78 and 0.61 μM, respectively, and CC50 values > 5 μM. The remaining compounds exhibited a high degree of toxicity (CC50 values < 1.09 μM) on the proliferation of bovine peripheral blood mononuclear cells stimulated with concanavalin A. We also tested the anti-cancer drug, dasatinib, used in the chemotherapy of some leukemias. Dasatinib was as active and safe as buparvaquone in vitro, with an IC50 of 5 and 4.2 nM, respectively, and CC50 > 10 μM. Our preliminary data suggest that it may be possible to repurpose compounds from the cancer field as well as MMV as novel anti-T. parva molecules.

PMID: 30771616 [PubMed - indexed for MEDLINE]

New life for an old drug: In vitro and in vivo effects of the anthelmintic drug niclosamide against Toxoplasma gondii RH strain.

Int J Parasitol Drugs Drug Resist. 2019 04;9:27-34

Authors: Zhang JL, Si HF, Shang XF, Zhang XK, Li B, Zhou XZ, Zhang JY

Abstract
Toxoplasma gondii is the causative agent of toxoplasmosis and causes serious public health problems. However, the current treatment drugs have many limitations, such as serious side effects. Niclosamide is a salicylanilide drug commonly used to treat worm infections. Herein, the effectiveness of niclosamide for the treatment of T. gondii infection was demonstrated. This study was to evaluate the in vitro and in vivo activities of niclosamide against T. gondii and to explore its mechanism of action. The in vitro cytotoxicity of niclosamide on human foreskin fibroblast cells was evaluated by MTT test. Niclosamide displayed low host toxicity and its 50% inhibitory concentration was 8.3 μg/mL. The in vitro anti-proliferation and anti-invasion effects of niclosamide on T. gondii were determined by quantitative PCR and Giemsa staining. Niclosamide also inhibited T. gondii tachyzoite proliferation, with a 50% effective concentration of 45.3 ng/mL, and reduced the invasion of cells by tachyzoites (17.8% for the parasite control versus 1.9% for the niclosamide group treated with 100 ng/mL). A model was established by infecting BALB/c mice with the virulent RH strain of T. gondii and used to determine the in vivo effects of niclosamide on acute infection. The mice infected with tachyzoites and treated with 160, 200 or 240 mg/kg·bw niclosamide for 7 days exhibited 20%, 40% and 50% survival, respectively. In addition, niclosamide reduced the parasite burden in the blood and tissues of acutely infected mice, and niclosamide induced decreases in the mitochondrial membrane potential (ΔΨm) and adenosine triphosphate (ATP) levels in extracellular tachyzoites, as assessed by laser confocal microscopy and a multilabel reader. These findings indicated that the mechanism of action of niclosamide might be associated with T. gondii mitochondria oxidative phosphorylation. In conclusion, our results support the efficacy of niclosamide as a potential compound for the treatment of T. gondii infection.

PMID: 30599391 [PubMed - indexed for MEDLINE]

A Bayesian machine learning approach for drug target identification using diverse data types.

Nat Commun. 2019 Nov 19;10(1):5221

Authors: Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, Allen JE, Giannakakou P, Elemento O

Abstract
Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201-an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201's target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application.

PMID: 31745082 [PubMed - in process]

The Drug Rediscovery protocol facilitates the expanded use of existing anticancer drugs.

Nature. 2019 10;574(7776):127-131

Authors: van der Velden DL, Hoes LR, van der Wijngaart H, van Berge Henegouwen JM, van Werkhoven E, Roepman P, Schilsky RL, de Leng WWJ, Huitema ADR, Nuijen B, Nederlof PM, van Herpen CML, de Groot DJA, Devriese LA, Hoeben A, de Jonge MJA, Chalabi M, Smit EF, de Langen AJ, Mehra N, Labots M, Kapiteijn E, Sleijfer S, Cuppen E, Verheul HMW, Gelderblom H, Voest EE

Abstract
The large-scale genetic profiling of tumours can identify potentially actionable molecular variants for which approved anticancer drugs are available1-3. However, when patients with such variants are treated with drugs outside of their approved label, successes and failures of targeted therapy are not systematically collected or shared. We therefore initiated the Drug Rediscovery protocol, an adaptive, precision-oncology trial that aims to identify signals of activity in cohorts of patients, with defined tumour types and molecular variants, who are being treated with anticancer drugs outside of their approved label. To be eligible for the trial, patients have to have exhausted or declined standard therapies, and have malignancies with potentially actionable variants for which no approved anticancer drugs are available. Here we show an overall rate of clinical benefit-defined as complete or partial response, or as stable disease beyond 16 weeks-of 34% in 215 treated patients, comprising 136 patients who received targeted therapies and 79 patients who received immunotherapy. The overall median duration of clinical benefit was 9 months (95% confidence interval of 8-11 months), including 26 patients who were experiencing ongoing clinical benefit at data cut-off. The potential of the Drug Rediscovery protocol is illustrated by the identification of a successful cohort of patients with microsatellite instable tumours who received nivolumab (clinical benefit rate of 63%), and a cohort of patients with colorectal cancer with relatively low mutational load who experienced only limited clinical benefit from immunotherapy. The Drug Rediscovery protocol facilitates the defined use of approved drugs beyond their labels in rare subgroups of cancer, identifies early signals of activity in these subgroups, accelerates the clinical translation of new insights into the use of anticancer drugs outside of their approved label, and creates a publicly available repository of knowledge for future decision-making.

PMID: 31570881 [PubMed - indexed for MEDLINE]

A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome.

Nat Commun. 2019 Nov 18;10(1):5215

Authors: Misselbeck K, Parolo S, Lorenzini F, Savoca V, Leonardelli L, Bora P, Morine MJ, Mione MC, Domenici E, Priami C

Abstract
Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.

PMID: 31740673 [PubMed - in process]

Discovery of disease- and drug-specific pathways through community structures of a literature network.

Bioinformatics. 2019 Nov 18;:

Authors: Pham M, Wilson S, Govindarajan H, Lin CH, Lichtarge O

Abstract
MOTIVATION: In light of the massive growth of the scientific literature, text mining is increasingly used to extract biological pathways. Though multiple tools explore individual connections between genes, diseases, and drugs, few extensively synthesize pathways for specific diseases and drugs.
RESULTS: Through community detection of a literature network, we extracted 3,444 functional gene groups that represented biological pathways for specific diseases and drugs. The network linked Medical Subject Headings (MeSH) terms of genes, diseases, and drugs that co-occurred in publications. The resulting communities detected highly associated genes, diseases, and drugs. These significantly matched current knowledge of biological pathways and predicted future ones in time-stamped experiments. Likewise, disease- and drug-specific communities also recapitulated known pathways for those given diseases and drugs. Moreover, diseases sharing communities had high comorbidity with each other and drugs sharing communities had many common side effects, consistent with related mechanisms. Indeed, the communities robustly recovered mutual targets for drugs (AUROC = 0.75) and shared pathogenic genes for diseases (AUROC = 0.82). These data show that literature communities inform not just known biological processes but also suggest novel disease- and drug-specific mechanisms that may guide disease gene discovery and drug repurposing.
AVAILABILITY: Application tools are available at http://meteor.lichtargelab.org.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 31738408 [PubMed - as supplied by publisher]

Sertraline Delivered in Phosphatidylserine Liposomes Is Effective in an Experimental Model of Visceral Leishmaniasis.

Front Cell Infect Microbiol. 2019;9:353

Authors: Romanelli MM, da Costa-Silva TA, Cunha-Junior E, Dias Ferreira D, Guerra JM, Galisteo AJ, Pinto EG, Barbosa LRS, Torres-Santos EC, Tempone AG

Abstract
Liposomes containing phosphatidylserine (PS) has been used for the delivery of drugs into the intramacrophage milieu. Leishmania (L.) infantum parasites live inside macrophages and cause a fatal and neglected viscerotropic disease, with a toxic treatment. Sertraline was studied as a free formulation (SERT) and also entrapped into phosphatidylserine liposomes (LP-SERT) against intracellular amastigotes and in a murine model of visceral leishmaniasis. LP-SERT showed a potent activity against intracellular amastigotes with an EC50 value of 2.5 μM. The in vivo efficacy of SERT demonstrated a therapeutic failure. However, when entrapped into negatively charged liposomes (-58 mV) of 125 nm, it significantly reduced the parasite burden in the mice liver by 89% at 1 mg/kg, reducing the serum levels of the cytokine IL-6 and upregulating the levels of the chemokine MCP-1. Histopathological studies demonstrated the presence of an inflammatory infiltrate with the development of granulomas in the liver, suggesting the resolution of the infection in the treated group. Delivery studies showed fluorescent-labeled LP-SERT in the liver and spleen of mice even after 48 h of administration. This study demonstrates the efficacy of PS liposomes containing sertraline in experimental VL. Considering the urgent need for VL treatments, the repurposing approach of SERT could be a promising alternative.

PMID: 31737574 [PubMed - in process]

Design and optimization strategies for the development of new drugs that treat chronic kidney disease.

Expert Opin Drug Discov. 2019 Nov 18;:1-15

Authors: Ramos AM, Fernández-Fernández B, Pérez-Gómez MV, Carriazo Julio SM, Sanchez-Niño MD, Sanz A, Ruiz-Ortega M, Ortiz A

Abstract
Introduction: Chronic kidney disease (CKD) is characterized by increased risks of progression to end-stage kidney disease requiring dialysis and cardiovascular mortality, predicted to be among the five top causes of death by 2040. Only the design and optimization of novel strategies to develop new drugs to treat CKD will contain this trend. Current therapy for CKD includes nonspecific therapy targeting proteinuria and/or hypertension and cause-specific therapies for diabetic kidney disease, autosomal dominant polycystic kidney disease, glomerulonephritides, Fabry nephropathy, hemolytic uremic syndrome and others.Areas covered: Herein, the authors review the literature on new drugs under development for CKD as well as novel design and development strategies.Expert opinion: New therapies for CKD have become a healthcare priority. Emerging therapies undergoing clinical trials are testing expanded renin-angiotensin system blockade with double angiotensin receptor/endothelin receptor blockers, SGLT2 inhibition, and targeting inflammation, the immune response, fibrosis and the Nrf2 transcription factor. Emerging therapeutic targets include cell senescence, complement activation, Klotho expression preservation and microbiota. Novel approaches include novel model systems that can be personalized (e.g. organoids), unbiased systems biology-based identification of new therapeutic targets, drug databases that speed up drug identification and repurposing, nanomedicines that improve drug delivery and RNA targeting to expand the number of targetable proteins.

PMID: 31736379 [PubMed - as supplied by publisher]

Current state and future perspective of drug repurposing in malignant glioma.

Semin Cancer Biol. 2019 Nov 14;:

Authors: Siegelin MD, Schneider E, Westhoff MA, Wirtz CR, Karpel-Massler G

Abstract
Malignant gliomas are still extremely difficult to treat because complete surgical resection is biologically not feasible due to the invasive nature of the disease and the proximity of tumors to functionally sensitive areas. Moreover, adjuvant therapies are facing a strong therapeutic resistance since the central nervous system is a highly protected environment and the tumor cells display a vast intra-tumoral genetic and epigenetic variation. As a consequence, new therapeutics are urgently needed but the process of developing novel compounds that finally reach clinical application is highly time-consuming and expensive. Drug repurposing is an approach to facilitate and accelerate the discovery of new cancer treatments. In malignant glioma, like in other cancers, pre-existing physiological pathways that regulate cell growth, cell death or cell migration are dysregulated causing malignant transformation. A wide variety of drugs are clinically used to treat non-cancerous diseases interfering with these malignancy-associated pathways. Repurposed drugs have key advantages: They already have approval for clinical use by national regulatory authorities. Moreover, they are for the most part inexpensive and their side effect and safety profiles are well characterized. In this work, we provide an overview on current repurposing strategies for the treatment of malignant glioma.

PMID: 31734137 [PubMed - as supplied by publisher]

Orabase-formulated Gentian Violet Effectively Improved Oral Potentially Malignant Disorder in vitro and in vivo.

Biochem Pharmacol. 2019 Nov 13;:113713

Authors: Wang YY, Xiao LY, Wu PC, Chen YK, Lo S, Hu SCS, Chen YH, Chiu CCC, Yuan SSF

Abstract
Oral cancer is a prevalent cancer in male worldwide. Oral potentially malignant disorders (OMPDs) are the oral mucosa lesions that have high malignant transformation rate to oral cancer. The mainstay for OMPDs treatment includes carbon dioxide (CO2) laser and surgery, which may lead to the side effects of scarring and impaired function of oral cavity in the patients and reduced their willingness to receive curative therapy. Therefore, developing a non-invasive and function-preserving therapy is clinically important. Since development of a novel chemotherapeutic drug requires a lot of time and cost, we applied the high-throughput screening (HTS) approach to identify new bioactivities for FDA-approved drugs, known as drug repurposing. Through this drug repurposing approach, we discovered that gentian violet (GV), which is well known for its antibacterial, antifungal, antihelminthic, antitrypanosomal and antiviral activities, was able to induce significant cell death in DOK oral precancerous cells through ROS production. Moreover, decreased phosphorylation of p53(Ser15) and NFκB(Ser536) was required for GV-induced cell death. In vivo, 3% GV orabase effectively suppressed the progression of DMBA-induced oral precancerous lesions. In conclusion, this new formulation of GV through drug repurposing has the potential to be further developed as a therapeutic drug for OPMD clinically.

PMID: 31733192 [PubMed - as supplied by publisher]

Discovery of candesartan cilexetic as a novel neddylation inhibitor for suppressing tumor growth.

Eur J Med Chem. 2019 Nov 04;:111848

Authors: Ni S, Chen X, Yu Q, Xu Y, Hu Z, Zhang J, Zhang W, Li B, Yang X, Mao F, Huang J, Sun Y, Li J, Jia L

Abstract
Protein neddylation is a posttranslational modification of conjugating the neuronal precursor cell-expressed developmentally down-regulated protein 8 (Nedd8) to substrates. Our previous work revealed that neddylation pathway is overactivated in various human lung cancers and correlates with the disease progression, whereas pharmacologically targeting this pathway has emerged as an attractive therapeutic strategy. As a follow-up research, 1331 approved drugs were investigated the inhibitory activities of cullin1 neddylation for screening the hit compounds via an improved enzyme-based assay. An antihypertensive agent, candesartan cilexetic (CDC), was identified as a novel neddylation inhibitor that ATP-competitively suppressing Nedd8-activating enzyme (NAE, E1) in mechanism, which inhibited the cullins neddylation superior than two representative non-covalent NAE inhibitors, M22 and mitoxantrone. Following with the findings such as apoptotic induction and tumor growth suppression in human lung cancer A549 in vitro and in vivo, CDC represents a potential anticancer lead compound with promising neddylation inhibitory activity.

PMID: 31732254 [PubMed - as supplied by publisher]

Inhibition of the Formation In Vitro of Putatively Carcinogenic Metabolites Derived from S. haematobium and O. viverrini by Combination of Drugs with Antioxidants.

Molecules. 2019 Oct 25;24(21):

Authors: Gouveia MJ, Nogueira V, Araújo B, Gärtner F, Vale N

Abstract
Infections caused by Schistosoma haematobium and Opisthorchis viverrini are classified as carcinogenic. Although carcinogenesis might be a multifactorial process, it has been postulated that these helminth produce/excrete oxysterols and estrogen-like metabolites that might act as initiators of their infection-associated carcinogenesis. Current treatment and control of these infections rely on a single drug, praziquantel, that mainly targets the parasites and not the pathologies related to the infection including cancer. Thus, there is a need to search for novel therapeutic alternatives that might include combinations of drugs and drug repurposing. Based on these concepts, we propose a novel therapeutic strategy that combines drugs with molecule antioxidants. We evaluate the efficacy of a novel therapeutic strategy to prevent the formation of putative carcinogenic metabolites precursors and DNA adducts. Firstly, we used a methodology previously established to synthesize metabolites precursors and DNA adducts in the presence of CYP450. Then, we evaluated the inhibition of their formation induced by drugs and antioxidants alone or in combination. Drugs and resveratrol alone did not show a significant inhibitory effect while N-acetylcysteine inhibited the formation of most metabolite precursors and DNA adducts. Moreover, the combinations of classical drugs with antioxidants were more effective rather than compounds alone. This strategy might be a valuable tool to prevent the initiation of helminth infection-associated carcinogenesis.

PMID: 31731402 [PubMed - in process]

Old wine in new bottles: Drug repurposing in oncology.

Eur J Pharmacol. 2019 Nov 12;:172784

Authors: Antoszczak M, Markowska A, Markowska J, Huczyński A

Abstract
Increasing costs, much time consumption and high risk of failure associated with the process of de novo development of new anticancer drugs have prompted the pharmaceutical industry to seek alternative strategies that may facilitate and accelerate the whole process. In particular, the repurposing strategy, known also as repositioning or reprofiling strategy, is a potential source of new treatment options for cancer patients with high unmet medical needs. However, it should be noted that the repurposing strategy, being still a new trend in drug development, should only complement the process of discovering new anticancer drugs, and should not be its alternative. The best repurposable oncological drug candidates are the agents whose original patent protection has already expired, and for which there is a possibility to create a formulation enabling, together with a new therapeutic indication, new patent protection. In this review article we discuss the advantages of the repurposing strategy, and provide an overview of a number of promising candidates, such as aspirin, artesunate, cimetidine, doxycycline, ivermectin, metformin, rapamycin (sirolimus), and thalidomide, that have the potential to be repurposed as anticancer drugs both in cancer prevention and therapy. In addition, we highlight some of the studies regarding the signalling pathways and molecular targets altered by these drugs, and describe the biological mechanisms underlying their anticancer effects.

PMID: 31730760 [PubMed - as supplied by publisher]

A Novel Drug Repositioning Approach Based on Integrative Multiple Similarity Measures.

Curr Mol Med. 2019 Nov 14;:

Authors: Yan C, Feng L, Wang W, Wang J, Zhang G, Luo J

Abstract
BACKGROUND: Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development.
METHODS: In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology similarity for drug and disease. Third, a similarity integration and adjusting process are further conducted to obtain more comprehensive drug and disease similarity measure,respectively.
RESULTS: On this basis, an Bi-random walk algorithm is implemented in the constructed heterogeneous network to rank diseases for each drug. Compared with other approaches, the proposed DR_IMSM can achieve superior performance in terms of AUC on the gold standard datasets. Case studies further confirm the practical significance of DR_IMSM.

PMID: 31729291 [PubMed - as supplied by publisher]

The assessment of efficient representation of drug features using deep learning for drug repositioning.

BMC Bioinformatics. 2019 Nov 14;20(1):577

Authors: Moridi M, Ghadirinia M, Sharifi-Zarchi A, Zare-Mirakabad F

Abstract
BACKGROUND: De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited number of candidate pairs of drugs and diseases. In other words, they are not scalable to a large number of drugs and diseases. Most of the in-silico methods mainly focus on linear approaches while non-linear models are still scarce for new indication predictions. Therefore, applying non-linear computational approaches can offer an opportunity to predict possible drug repositioning candidates.
RESULTS: In this study, we present a non-linear method for drug repositioning. We extract four drug features and two disease features to find the semantic relations between drugs and diseases. We utilize deep learning to extract an efficient representation for each feature. These representations reduce the dimension and heterogeneity of biological data. Then, we assess the performance of different combinations of drug features to introduce a pipeline for drug repositioning. In the available database, there are different numbers of known drug-disease associations corresponding to each combination of drug features. Our assessment shows that as the numbers of drug features increase, the numbers of available drugs decrease. Thus, the proposed method with large numbers of drug features is as accurate as small numbers.
CONCLUSION: Our pipeline predicts new indications for existing drugs systematically, in a more cost-effective way and shorter timeline. We assess the pipeline to discover the potential drug-disease associations based on cross-validation experiments and some clinical trial studies.

PMID: 31726977 [PubMed - in process]

Decoding the similarities and specific differences between latent and active tuberculosis infections based on consistently differential expression networks.

Brief Bioinform. 2019 Nov 13;:

Authors: Sun J, Shi Q, Chen X, Liu R

Abstract
Although intensive efforts have been devoted to investigating latent tuberculosis (LTB) and active tuberculosis (PTB) infections, the similarities and differences in the host responses to these two closely associated stages remain elusive, probably due to the difficulty in identifying informative genes related to LTB using traditional methods. Herein, we developed a framework known as the consistently differential expression network to identify tuberculosis (TB)-related gene pairs by combining microarray profiles and protein-protein interactions. We thus obtained 774 and 693 pairs corresponding to the PTB and LTB stages, respectively. The PTB-specific genes showed higher expression values and fold-changes than the LTB-specific genes. Furthermore, the PTB-related pairs generally had higher expression correlations and would be more activated compared to their LTB-related counterparts. The module analysis implied that the detected gene pairs tended to cluster in the topological and functional modules. Functional analysis indicated that the LTB- and PTB-specific genes were enriched in different pathways and had remarkably different locations in the NF-κB signaling pathway. Finally, we showed that the identified genes and gene pairs had the potential to distinguish TB patients in different disease stages and could be considered as drug targets for the specific treatment of patients with LTB or PTB.

PMID: 31724702 [PubMed - as supplied by publisher]