The proteasome inhibitor bortezomib attenuates renal fibrosis in mice via the suppression of TGF-β1.

Sci Rep. 2017 Oct 12;7(1):13086

Authors: Zeniya M, Mori T, Yui N, Nomura N, Mandai S, Isobe K, Chiga M, Sohara E, Rai T, Uchida S

Abstract
Kidney fibrosis and fibrogenesis significantly exacerbate chronic kidney disease (CKD) progression and are essential therapeutic targets. Bortezomib (BZM) is a proteasome inhibitor used for the treatment of multiple myeloma (MM). Several studies have demonstrated that BZM attenuates renal impairment in patients with MM, although this effect is generally considered to be the result of MM remission. Recently, several studies on BZM reported anti-fibrotic effects on liver and skin in experimental animal models. However, its effect on renal fibrosis has yet to be examined. Here, we investigated the anti-fibrotic effects of BZM in an experimental mouse model of fibrosis that uses aristolochic acid I (AA). Ten weeks of AA administration with BZM treatment twice a week significantly attenuated AA-induced renal dysfunction and albuminuria, reduced the expression of renal fibrosis-related proteins and kidney injury markers, such as αSMA, Kim1, and Ngal, and prevented renal fibrosis at the level of histopathology. Furthermore, pathological activation of TGFβ1-Smad3 signaling and apoptosis, essential pathophysiological causes of AA-induced nephropathy (AAN), were ameliorated by BZM, suggesting this mechanism may be involved in improving fibrosis in AAN. In conclusion, BZM directly inhibits renal fibrosis in CKD via suppression of TGFβ1-Smad3 signaling and is promising in terms of drug repositioning.

PMID: 29026167 [PubMed - in process]

Informed walks: whispering hints to gene hunters inside networks' jungle.

BMC Syst Biol. 2017 Oct 11;11(1):97

Authors: Bourdakou MM, Spyrou GM

Abstract
BACKGROUND: Systemic approaches offer a different point of view on the analysis of several types of molecular associations as well as on the identification of specific gene communities in several cancer types. However, due to lack of sufficient data needed to construct networks based on experimental evidence, statistical gene co-expression networks are widely used instead. Many efforts have been made to exploit the information hidden in these networks. However, these approaches still need to capitalize comprehensively the prior knowledge encrypted into molecular pathway associations and improve their efficiency regarding the discovery of both exclusive subnetworks as candidate biomarkers and conserved subnetworks that may uncover common origins of several cancer types.
METHODS: In this study we present the development of the Informed Walks model based on random walks that incorporate information from molecular pathways to mine candidate genes and gene-gene links. The proposed model has been applied to TCGA (The Cancer Genome Atlas) datasets from seven different cancer types, exploring the reconstructed co-expression networks of the whole set of genes and driving to highlighted sub-networks for each cancer type. In the sequel, we elucidated the impact of each subnetwork on the indication of underlying exclusive and common molecular mechanisms as well as on the short-listing of drugs that have the potential to suppress the corresponding cancer type through a drug-repurposing pipeline.
CONCLUSIONS: We have developed a method of gene subnetwork highlighting based on prior knowledge, capable to give fruitful insights regarding the underlying molecular mechanisms and valuable input to drug-repurposing pipelines for a variety of cancer types.

PMID: 29020948 [PubMed - in process]

Repurposing Zidovudine in combination with Tigecycline for treating carbapenem-resistant Enterobacteriaceae infections.

Eur J Clin Microbiol Infect Dis. 2017 Oct 10;:

Authors: Ng SMS, Sioson JSP, Yap JM, Ng FM, Ching HSV, Teo JWP, Jureen R, Hill J, Chia CSB

Abstract
The global emergence of carbapenem-resistant Enterobacteriaceae (CRE) presents a significant clinical concern, prompting the WHO to prioritize CRE as a top priority pathogen in their 2017 global antibiotic-resistant bacteria priority list. Due to the fast-depleting antibiotic arsenal, clinicians are now resorting to using once-abandoned, highly toxic antibiotics such as the polymyxins and aminoglycosides, creating an urgent need for new antibiotics. Drug repurposing, the application of an approved drug for a new therapeutic indication, is deemed a plausible solution to this problem. A total of 1,163 FDA-approved drugs were screened for activity against a clinical carbapenem- and multidrug-resistant E. coli isolate using a single-point 10 μM assay. Hit compounds were then assessed for their suitability for repurposing. The lead candidate was then tested against a panel of clinical CREs, a bactericidal/static determination assay, a time-kill assay and a checkerboard assay to evaluate its suitability for use in combination with Tigecycline against CRE infections. Three drugs were identified. The lead candidate was determined to be Zidovudine (azidothymidine/AZT), an oral anti-viral drug used for HIV treatment. Zidovudine was shown to be the most promising candidate for use in combination with Tigecycline to treat systemic CRE infections. Further experiments should involve the use of animal infection models.

PMID: 29019016 [PubMed - as supplied by publisher]

Formalizing drug indications on the road to therapeutic intent.

J Am Med Inform Assoc. 2017 Nov 01;24(6):1169-1172

Authors: Nelson SJ, Oprea TI, Ursu O, Bologa CG, Zaveri A, Holmes J, Yang JJ, Mathias SL, Mani S, Tuttle MS, Dumontier M

Abstract
Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.

PMID: 29016968 [PubMed - in process]

Phenytoin repositioned in wound healing: clinical experience spanning 60 years.

Drug Discov Today. 2017 Oct 06;:

Authors: Keppel Hesselink JM

Abstract
Drug repositioning is hot, and much development time and money can be spared if one selects an old drug and explores the efficacy and safety in a new indication. Phenytoin is studied and repositioned in many disorders after the initial indication epilepsy (from 1937). Its repositioning in depression was put in the spotlight by the Wall Street icon Jack Dreyfus, already in the 1970s. Innovations in the field of phenytoin still appear to be possible for a number of indications such as wound healing, bipolar disorder and aggression, and via a topical formulation for neuropathic pain. We will discuss wound healing and identified a number of critical issues related to its repositioning in this indication.

PMID: 28993152 [PubMed - as supplied by publisher]

Kinase Inhibitor Screening in Myeloid Malignancies.

Hematol Oncol Clin North Am. 2017 Aug;31(4):693-704

Authors: Tyner JW

Abstract
Kinase pathways are primary effectors of many targeted therapy approaches for cancer. Kinase pathways can be dysregulated by mechanisms far more diverse than chromosomal rearrangements or point mutations, which drove the initial application of kinase inhibitors to cancer. Functional screening with kinase inhibitors is one tool by which we can understand the diversity of target kinases and candidate drugs for patients before fully understanding the mechanistic rationale for kinase pathway dysregulation. By combining functional screening with genomic data, it is also possible to accelerate understanding of these mechanistic underpinnings.

PMID: 28673396 [PubMed - indexed for MEDLINE]

Repurposing of Human Kinase Inhibitors in Neglected Protozoan Diseases.

ChemMedChem. 2017 Aug 22;12(16):1235-1253

Authors: Dichiara M, Marrazzo A, Prezzavento O, Collina S, Rescifina A, Amata E

Abstract
Human African trypanosomiasis (HAT), Chagas disease, and leishmaniasis belong to a group of infectious diseases known as neglected tropical diseases and are induced by infection with protozoan parasites named trypanosomatids. Drugs in current use have several limitations, and therefore new candidate drugs are required. The majority of current therapeutic trypanosomatid targets are enzymes or cell-surface receptors. Among these, eukaryotic protein kinases are a major group of protein targets whose modulation may be beneficial for the treatment of neglected tropical protozoan diseases. This review summarizes the finding of new hit compounds for neglected tropical protozoan diseases, by repurposing known human kinase inhibitors on trypanosomatids. Kinase inhibitors are grouped by human kinase family and discussed according to the screening (target-based or phenotypic) reported for these compounds on trypanosomatids. This collection aims to provide insight into repurposed human kinase inhibitors and their importance in the development of new chemical entities with potential beneficial effects on the diseases caused by trypanosomatids.

PMID: 28590590 [PubMed - indexed for MEDLINE]

Exposure Matching of Pediatric Anti-infective Drugs: Review of Drugs Submitted to the Food and Drug Administration for Pediatric Approval.

Clin Ther. 2016 Sep;38(9):1995-2005

Authors: Zimmerman K, Putera M, Hornik CP, Brian Smith P, Benjamin DK, Mulugeta Y, Burckart GJ, Cohen-Wolkowiez M, Gonzalez D

Abstract
PURPOSE: Over the last decade, few new antibiotics have been approved by the Food and Drug Administration (FDA) for pediatric use. For most anti-infective agents, including antibiotics, extrapolation of efficacy from adults to children is possible if the disease and therapeutic exposures are similar between the 2 populations. This approach reduces the number of studies required in children, but relies heavily on exposure matching between children and adults. Failures in exposure matching can lead to delays in pediatric approvals of new anti-infective agents. We sought to determine the extent of exposure matching, defined by a comparison of area under the concentration-time curve, between children and adults, for anti-infective drug products submitted to the FDA for approval.
METHODS: We reviewed anti-infective submissions to the FDA (2002-2014) for pediatric indication. We included drug products administered via oral, intravenous, or intramuscular administration routes, and those with AUC estimates for children in available FDA reports. Our main outcome of interest was the proportion of drugs with median (or mean) pediatric AUC within 20% of the median (or mean) reported adult value.
FINDINGS: We identified 29 drug products that met inclusion criteria, 14 (48%) of which had mean (or median) AUCs of all submitted age groups within 20% of that in adults. Only route of administration and drug class were associated with pediatric AUC within 20% of adult AUC.
IMPLICATIONS: Future research is needed to define criteria for and predictors of successful exposure matching of anti-infectives between children and adults.

PMID: 27364807 [PubMed - indexed for MEDLINE]

A systematic analysis of FDA-approved anticancer drugs.

BMC Syst Biol. 2017 Oct 03;11(Suppl 5):87

Authors: Sun J, Wei Q, Zhou Y, Wang J, Liu Q, Xu H

Abstract
BACKGROUND: The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.
RESULTS: In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.
CONCLUSIONS: In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

PMID: 28984210 [PubMed - in process]

MD-Miner: a network-based approach for personalized drug repositioning.

BMC Syst Biol. 2017 Oct 03;11(Suppl 5):86

Authors: Wu H, Miller E, Wijegunawardana D, Regan K, Payne PRO, Li F

Abstract
BACKGROUND: Due to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients.
RESULTS: In this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action.
CONCLUSIONS: This work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.

PMID: 28984195 [PubMed - in process]

Drug repurposing in idiopathic pulmonary fibrosis filtered by a bioinformatics-derived composite score.

Sci Rep. 2017 Oct 03;7(1):12569

Authors: Karatzas E, Bourdakou MM, Kolios G, Spyrou GM

Abstract
Idiopathic Pulmonary Fibrosis (IPF) is a rare disease of the respiratory system in which the lungs stiffen and get scarred, resulting in breathing weakness and eventually leading to death. Drug repurposing is a process that provides evidence for existing drugs that may also be effective in different diseases. In this study, we present a computational pipeline having as input a number of gene expression datasets from early and advanced stages of IPF and as output lists of repurposed drugs ranked with a novel composite score. We have devised and used a scoring formula in order to rank the repurposed drugs, consolidating the standard repurposing score with structural, functional and side effects' scores for each drug per stage of IPF. The whole pipeline involves the selection of proper gene expression datasets, data preprocessing and statistical analysis, selection of the most important genes related to the disease, analysis of biological pathways, investigation of related molecular mechanisms, identification of fibrosis-related microRNAs, drug repurposing, structural and literature-based analysis of the repurposed drugs.

PMID: 28974751 [PubMed - in process]

Propranolol for Off-Label Treatment of Patients With Melanoma: Results From a Cohort Study.

JAMA Oncol. 2017 Sep 28;:

Authors: De Giorgi V, Grazzini M, Benemei S, Marchionni N, Botteri E, Pennacchioli E, Geppetti P, Gandini S

Abstract
Importance: Preclinical and retrospective studies showed that β-blockers inhibit angiogenesis and disrupt migration of melanoma cells via inhibition of noradrenaline-dependent responses.
Objective: To study the clinical effectiveness of β-blocker therapy in patients with melanoma and to determine whether propranolol improves progression-free survival in patients with melanoma.
Design, Setting, and Participants: We conducted a prospective study in patients treated for melanoma in our center with propranolol for off-label use. Patients with histologically confirmed stage IB to IIIA cutaneous melanoma and no evidence of metastasis were eligible for the study. At the time of diagnosis, they were asked to take propranolol (80 mg daily) as an off-label adjuvant treatment. If they accepted the treatment, they were considered part of the propranolol cohort (PROP). If they refused treatment but agreed to participate in the study control group, they were considered part of the nonpropranolol cohort (No-PROP).
Main Outcomes and Measures: The primary outcome was progression-free survival. Disease progression was assessed by evaluating the presence of lymphatic, in-transit, or visceral metastases, and the cause of death was recorded.
Results: Among the 53 patients (median [interquartile range] age, 63 [48-75] years; 33 men) included in the study, 19 were eligible for the PROP cohort. Thirty-four patients otherwise eligible but unwilling to take propranolol were enrolled in the No-PROP cohort. The 2 cohorts were comparable in terms of demographic characteristics and primary prognostic factors at baseline. After adjusting for known prognostic factors, Cox models confirmed that use of propranolol at the time of diagnosis was significantly inversely associated with recurrence of melanoma with approximately an 80% risk reduction for propranolol users (hazard ratio, 0.18; 95% CI, 0.04-0.89; P = .03).
Conclusions and Relevance: In the absence of randomized, double-blind, placebo-controlled clinical trials, this study is the first off-label study of which we are aware of propranolol for melanoma treatment. These results confirm recent observation that β-blockers protect patients with thick cutaneous melanoma from disease recurrence. This study is in accordance with the present policy of "drug repurposing" in oncology. Repurposing the vast arsenal of approved drugs with a nononcology primary purpose may prove an attractive and inexpensive strategy for offering more effective treatment options to patients with cancer.

PMID: 28973254 [PubMed - as supplied by publisher]

Pharmacological use of a novel scaffold, anomeric N,N-diarylamino tetrahydropyran: molecular similarity search, chemocentric target profiling, and experimental evidence.

Sci Rep. 2017 Oct 02;7(1):12535

Authors: Venkanna A, Kwon OW, Afzal S, Jang C, Cho KH, Yadav DK, Kim K, Park HG, Chun KH, Kim SY, Kim MH

Abstract
Rational drug design against a determined target (disease, pathway, or protein) is the main strategy in drug discovery. However, regardless of the main strategy, chemists really wonder how to maximize the utility of their new compounds by drug repositioning them as clinical drug candidates in drug discovery. In this study, we started our drug discovery "from curiosity in the chemical structure of a drug scaffold itself" rather than "for a specific target". As a new drug scaffold, anomeric diarylamino cyclic aminal scaffold 1, was designed by combining two known drug scaffolds (diphenylamine and the most popular cyclic ether, tetrahydropyran/tetrahydrofuran) and synthesized through conventional Brønsted acid catalysis and metal-free α-C(sp(3))-H functionalized oxidative cyclization. To identify the utility of the new scaffold 1, it was investigated through 2D and 3D similarity screening and chemocentric target prediction. The predicted proteins were investigated by an experimental assay. The scaffold 1 was reported to have an antineuroinflammatory agent to reduce NO production, and compound 10 concentration-dependently regulated the expression level of IL-6, PGE-2, TNF-α, ER-β, VDR, CTSD, and iNOS, thus exhibiting neuroprotective activity.

PMID: 28970544 [PubMed - in process]

Recent Advances in the Development of Pharmaceutical Agents for Metabolic Disorders: A Computational Perspective.

Curr Med Chem. 2017 Oct 02;:

Authors: Janardhan S, Konova V, Lagunin A, Rao V, Sastry N, Poroikov V

Abstract
BACKGROUND: Metabolic disorders comprise a set of different disorders varying from epidemic diseases such as diabetes mellitus to inborn metabolic orphan diseases such as phenylketonuria. Despite considerable evidence showing the importance of the computational methods in discovery and development of new pharmaceuticals, there are no systematic reviews outlining how they are utilized in the field of metabolic disorders. This review aims to discuss the necessity of the development of web-based tools and databases by integration of available information for solving Big Data problems in network pharmacology of metabolic disorders.
METHODS: We undertook a structured search of bibliographic databases for peer-reviewed research literature using a focused review question and inclusion/exclusion criteria. The quality of retrieved papers was appraised using standard tools.
RESULTS: The alterations in metabolic pathways cause various cardiovascular, hematological, neurological, gastrointestinal, immune disorders and cancer. In this regard, informatics, Big Data and modeling techniques aid in the design of novel therapeutic agents for metabolic diseases by addressing various Big Data problems in the network polypharmacology (drugs/pharmaceutical agents, proteins, genes, diseases, bioassays, ADMET and metabolic pathways), identification of privileged scaffolds, developing new diagnostic biomarkers, understanding the pathophysiology of disease and progress in personalized medicine.
CONCLUSION: The recent advances of developing pharmaceutical agents for various metabolic disorders by considering their pathogenesis, mechanisms of action, therapeutic and adverse effects have been summarized. We have highlighted the role of computational techniques, drug repurposing, and network-based polypharmacological approaches in the identification of new/existing medicines with improved drug-likeness properties for the rare metabolic disorders.

PMID: 28969540 [PubMed - as supplied by publisher]

A systems-level analysis of drug-target-disease associations for drug repositioning.

Brief Funct Genomics. 2017 Aug 17;:

Authors: Rutherford KD, Mazandu GK, Mulder NJ

Abstract
Drug repositioning is the process of finding new therapeutic uses for existing, approved drugs-a process thathas value when considering the exorbitant costs of novel drug development. Several computational strategies exist as a way to predict these alternative applications. In this study, we used datasets on: (1) human biological drug targets and (2) disease-associated genes and, based on a direct functional interaction between them, searched for potential opportunities for drug repositioning. From the set of 1125 unique drug targets and their 88 490 interactions with disease-associated genes, 30 drug targets were analyzed and (3) discussed in detail for the purpose of this article. The current indications of the drugs thattarget them were validated through the interactions, and new opportunities for repositioning were predicted. Among the set of drugs for potential repositioning werebenzodiazepines for the treatment of autism spectrum disorders; nortriptyline for the treatment of melanoma, glioma and other cancers; and vitamin B6 in prevention of spontaneous abortions and cleft palate birth defects. Special emphasis was also placed on those new potential indications that pertained to orphan diseases-these are diseases whose rarity means that development of novel treatment is not financially viable. This computational drug repositioning approach uses existing information on drugs and drug targets, and insights into the genetic basis of disease, as a means to systematically generate the most probable new uses for the drugs on offer, and in this way harness their true therapeutic power.

PMID: 28968683 [PubMed - as supplied by publisher]

Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.

Oncogene. 2017 Oct 02;:

Authors: Nagaraj AB, Wang QQ, Joseph P, Zheng C, Chen Y, Kovalenko O, Singh S, Armstrong A, Resnick K, Zanotti K, Waggoner S, Xu R, DiFeo A

Abstract
Computation-based drug-repurposing/repositioning approaches can greatly speed up the traditional drug discovery process. To date, systematic and comprehensive computation-based approaches to identify and validate drug-repositioning candidates for epithelial ovarian cancer (EOC) have not been undertaken. Here, we present a novel drug discovery strategy that combines a computational drug-repositioning system (DrugPredict) with biological testing in cell lines in order to rapidly identify novel drug candidates for EOC. DrugPredict exploited unique repositioning opportunities rendered by a vast amount of disease genomics, phenomics, drug treatment, and genetic pathway and uniquely revealed that non-steroidal anti-inflammatories (NSAIDs) rank just as high as currently used ovarian cancer drugs. As epidemiological studies have reported decreased incidence of ovarian cancer associated with regular intake of NSAIDs, we assessed whether NSAIDs could have chemoadjuvant applications in EOC and found that (i) NSAID Indomethacin induces robust cell death in primary patient-derived platinum-sensitive and platinum- resistant ovarian cancer cells and ovarian cancer stem cells and (ii) downregulation of β-catenin is partially driving effects of Indomethacin in cisplatin-resistant cells. In summary, we demonstrate that DrugPredict represents an innovative computational drug- discovery strategy to uncover drugs that are routinely used for other indications that could be effective in treating various cancers, thus introducing a potentially rapid and cost-effective translational opportunity. As NSAIDs are already in routine use in gynecological treatment regimens and have acceptable safety profile, our results will provide with a rationale for testing NSAIDs as potential chemoadjuvants in EOC patient trials.Oncogene advance online publication, 2 October 2017; doi:10.1038/onc.2017.328.

PMID: 28967908 [PubMed - as supplied by publisher]

New Role for FDA-Approved Drugs in Combating Antibiotic-Resistant Bacteria.

Antimicrob Agents Chemother. 2016 Jun;60(6):3717-29

Authors: Andersson JA, Fitts EC, Kirtley ML, Ponnusamy D, Peniche AG, Dann SM, Motin VL, Chauhan S, Rosenzweig JA, Sha J, Chopra AK

Abstract
Antibiotic resistance in medically relevant bacterial pathogens, coupled with a paucity of novel antimicrobial discoveries, represents a pressing global crisis. Traditional drug discovery is an inefficient and costly process; however, systematic screening of Food and Drug Administration (FDA)-approved therapeutics for other indications in humans offers a rapid alternative approach. In this study, we screened a library of 780 FDA-approved drugs to identify molecules that rendered RAW 264.7 murine macrophages resistant to cytotoxicity induced by the highly virulent Yersinia pestis CO92 strain. Of these compounds, we identified 94 not classified as antibiotics as being effective at preventing Y. pestis-induced cytotoxicity. A total of 17 prioritized drugs, based on efficacy in in vitro screens, were chosen for further evaluation in a murine model of pneumonic plague to delineate if in vitro efficacy could be translated in vivo Three drugs, doxapram (DXP), amoxapine (AXPN), and trifluoperazine (TFP), increased animal survivability despite not exhibiting any direct bacteriostatic or bactericidal effect on Y. pestis and having no modulating effect on crucial Y. pestis virulence factors. These findings suggested that DXP, AXPN, and TFP may modulate host cell pathways necessary for disease pathogenesis. Finally, to further assess the broad applicability of drugs identified from in vitro screens, the therapeutic potential of TFP, the most efficacious drug in vivo, was evaluated in murine models of Salmonella enterica serovar Typhimurium and Clostridium difficile infections. In both models, TFP treatment resulted in increased survivability of infected animals. Taken together, these results demonstrate the broad applicability and potential use of nonantibiotic FDA-approved drugs to combat respiratory and gastrointestinal bacterial pathogens.

PMID: 27067323 [PubMed - indexed for MEDLINE]

Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach.

Sci Rep. 2017 Sep 29;7(1):12460

Authors: Gaspar HA, Breen G

Abstract
Using successful genome-wide association results in psychiatry for drug repurposing is an ongoing challenge. Databases collecting drug targets and gene annotations are growing and can be harnessed to shed a new light on psychiatric disorders. We used genome-wide association study (GWAS) summary statistics from the Psychiatric Genetics Consortium (PGC) Schizophrenia working group to build a drug repositioning model for schizophrenia. As sample size increases, schizophrenia GWAS results show increasing enrichment for known antipsychotic drugs, selective calcium channel blockers, and antiepileptics. Each of these therapeutical classes targets different gene subnetworks. We identify 123 Bonferroni-significant druggable genes outside the MHC, and 128 FDR-significant biological pathways related to neurons, synapses, genic intolerance, membrane transport, epilepsy, and mental disorders. These results suggest that, in schizophrenia, current well-powered GWAS results can reliably detect known schizophrenia drugs and thus may hold considerable potential for the identification of new therapeutic leads. Moreover, antiepileptics and calcium channel blockers may provide repurposing opportunities. This study also reveals significant pathways in schizophrenia that were not identified previously, and provides a workflow for pathway analysis and drug repurposing using GWAS results.

PMID: 28963561 [PubMed - in process]

Statistically controlled identification of differentially expressed genes in one-to-one cell line comparisons of the CMAP database for drug repositioning.

J Transl Med. 2017 Sep 29;15(1):198

Authors: He J, Yan H, Cai H, Li X, Guan Q, Zheng W, Chen R, Liu H, Song K, Guo Z, Wang X

Abstract
BACKGROUND: The Connectivity Map (CMAP) database, an important public data source for drug repositioning, archives gene expression profiles from cancer cell lines treated with and without bioactive small molecules. However, there are only one or two technical replicates for each cell line under one treatment condition. For such small-scale data, current fold-changes-based methods lack statistical control in identifying differentially expressed genes (DEGs) in treated cells. Especially, one-to-one comparison may result in too many drug-irrelevant DEGs due to random experimental factors. To tackle this problem, CMAP adopts a pattern-matching strategy to build "connection" between disease signatures and gene expression changes associated with drug treatments. However, many drug-irrelevant genes may blur the "connection" if all the genes are used instead of pre-selected DEGs induced by drug treatments.
METHODS: We applied OneComp, a customized version of RankComp, to identify DEGs in such small-scale cell line datasets. For a cell line, a list of gene pairs with stable relative expression orderings (REOs) were identified in a large collection of control cell samples measured in different experiments and they formed the background stable REOs. When applying OneComp to a small-scale cell line dataset, the background stable REOs were customized by filtering out the gene pairs with reversal REOs in the control samples of the analyzed dataset.
RESULTS: In simulated data, the consistency scores of overlapping genes between DEGs identified by OneComp and SAM were all higher than 99%, while the consistency score of the DEGs solely identified by OneComp was 96.85% according to the observed expression difference method. The usefulness of OneComp was exemplified in drug repositioning by identifying phenformin and metformin related genes using small-scale cell line datasets which helped to support them as a potential anti-tumor drug for non-small-cell lung carcinoma, while the pattern-matching strategy adopted by CMAP missed the two connections. The implementation of OneComp is available at https://github.com/pathint/reoa .
CONCLUSIONS: OneComp performed well in both the simulated and real data. It is useful in drug repositioning studies by helping to find hidden "connections" between drugs and diseases.

PMID: 28962576 [PubMed - in process]

Propofol enhances BCR-ABL TKIs' inhibitory effects in chronic myeloid leukemia through Akt/mTOR suppression.

BMC Anesthesiol. 2017 Sep 29;17(1):132

Authors: Tan Z, Peng A, Xu J, Ouyang M

Abstract
BACKGROUND: The anti-cancer activities of intravenous anesthetic drug propofol have been demonstrated in various types of cancers but not in chronic myeloid leukemia (CML).
METHODS: We systematically examined the effect of propofol and its combination with BCR-ABL tyrosine kinase inhibitors (TKIs) in CML cell lines, patient progenitor cells and mouse xenograft model. We analyzed propofol's underlying mechanism focusing on survival pathway in CML cells.
RESULTS: We show that propofol alone is active in inhibiting proliferation and inducing apoptosis in KBM-7, KU812 and K562 cells, and acts synergistically with imatinib or dasatinib, in in vitro cell culture system and in vivo xenograft model. In addition, propofol is more effective in inducing apoptosis and inhibiting colony formation in CML CD34 progenitor cells than normal bone marrow (NBM) counterparts. Combination of propofol and dasatinib significantly eliminates CML CD34 without affecting NBM CD34 cells. We further demonstrate that propofol suppresses phosphorylation of Akt, mTOR, S6 and 4EBP1 in K562. Overexpression of constitutively active Akt significantly reverses the inhibitory effects of propofol in K562, confirm that propofol acts on CML cells via inhibition of Akt/mTOR. Interestingly, the levels of p-Akt, p-mTOR and p-S6 are lower in cells treated with combination of propofol and imatinib than cells treated with propofol or imatinib alone, suggesting that propofol augments BCR-ABL TKI's inhibitory effect via suppressing Akt/mTOR pathway.
CONCLUSION: Our work shows that propofol can be repurposed to for CML treatment. Our findings highlight the therapeutic value of Akt/mTOR in overcoming resistance to BCR-ABL TKI treatment in CML.

PMID: 28962554 [PubMed - in process]