High-dose methotrexate with leucovorin rescue: For monumentally severe CNS inflammatory syndromes.

J Neurol Sci. 2017 Jan 15;372:187-195

Authors: Beh SC, Kildebeck E, Narayan R, Desena A, Schell D, Rowe ES, Rowe V, Burns D, Whitworth L, Frohman TC, Greenberg B, Frohman EM

Abstract
BACKGROUND: At sufficiently high doses, methotrexate (HDMTX) achieves substantial CNS penetration, whereas other tissues can be rescued from the effects of HDMTX by leucovorin rescue (LR), which does not penetrate the blood-brain barrier.
OBJECTIVES: To report on the efficacy and safety of HDMTX with LR (HDMTX-LR), in the treatment of acute demyelinating inflammatory CNS syndromes refractory to conventional immunotherapy.
METHODS: We performed a retrospective chart review of 12 patients treated (6 multiple sclerosis [MS], 4 neuromyelitis optica [NMO], and 2 Sjogren's syndrome myelopathy [SSM]) with HDMTX-LR after failing to improve, or exhibiting worsening following conventional immunotherapy. 11 patients were followed for a total of 6months following HDMTX-LR (one was lost to follow up after 1month); and clinical findings were documented at 1month, 3months, and 6months following HDMTX-LR therapy.
RESULTS: Ten patients demonstrated both clinical and radiologic evidence of near, if not complete, abolishment of disease activity, in conjunction with impressive reconstitution of neurologic function in the 6-month period following HDMTX-LR. Mean Kurtzke Expanded Disability Status Scale (EDSS) prior to HDMTX-LR was 8.1 (±1.4). Following HDMTX-LR, mean EDSS was 6.6 (±2.4) at 1month, 5.8 (±2.3) at 3months, and 5.7 (±2.3) at 6months.
CONCLUSIONS: In this retrospective assessment of treatment-recalcitrant fulminant inflammatory CNS syndromes, HDMTX-LR was observed to be a safe and highly effective treatment, producing the rapid and near complete cessation of disease activity, in conjunction with an important corresponding and 'durable remission' in the majority of our small treatment cohort.

PMID: 28017209 [PubMed - indexed for MEDLINE]

Repositioning FDA-Approved Drugs in Combination with Epigenetic Drugs to Reprogram Colon Cancer Epigenome.

Mol Cancer Ther. 2017 Feb;16(2):397-407

Authors: Raynal NJ, Da Costa EM, Lee JT, Gharibyan V, Ahmed S, Zhang H, Sato T, Malouf GG, Issa JJ

Abstract
Epigenetic drugs, such as DNA methylation inhibitors (DNMTi) or histone deacetylase inhibitors (HDACi), are approved in monotherapy for cancer treatment. These drugs reprogram gene expression profiles, reactivate tumor suppressor genes (TSG) producing cancer cell differentiation and apoptosis. Epigenetic drugs have been shown to synergize with other epigenetic drugs or various anticancer drugs. To discover new molecular entities that enhance epigenetic therapy, we performed a high-throughput screening using FDA-approved libraries in combination with DNMTi or HDACi. As a screening model, we used YB5 system, a human colon cancer cell line, which contains an epigenetically silenced CMV-GFP locus, mimicking TSG silencing in cancer. CMV-GFP reactivation is triggered by DNMTi or HDACi and responds synergistically to DNMTi/HDACi combination, which phenocopies TSG reactivation upon epigenetic therapy. GFP fluorescence was used as a quantitative readout for epigenetic activity. We discovered that 45 FDA-approved drugs (4% of all drugs tested) in our FDA-approved libraries enhanced DNMTi and HDACi activity, mainly belonging to anticancer and antiarrhythmic drug classes. Transcriptome analysis revealed that combination of decitabine (DNMTi) with the antiarrhythmic proscillaridin A produced profound gene expression reprogramming, which was associated with downregulation of 153 epigenetic regulators, including two known oncogenes in colon cancer (SYMD3 and KDM8). Also, we identified about 85 FDA-approved drugs that antagonized DNMTi and HDACi activity through cytotoxic mechanisms, suggesting detrimental drug interactions for patients undergoing epigenetic therapy. Overall, our drug screening identified new combinations of epigenetic and FDA-approved drugs, which can be rapidly implemented into clinical trials. Mol Cancer Ther; 16(2); 397-407. ©2016 AACR.

PMID: 27980103 [PubMed - indexed for MEDLINE]

Drug-target interaction prediction by integrating multiview network data.

Comput Biol Chem. 2017 Mar 31;:

Authors: Zhang X, Li L, Ng MK, Zhang S

Abstract
Drug-target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug-target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results. In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug-target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.

PMID: 28648470 [PubMed - as supplied by publisher]

New antibacterial, non-genotoxic materials, derived from the functionalization of the anti-thyroid drug methimazole with silver ions.

J Inorg Biochem. 2016 Jul;160:114-24

Authors: Sainis I, Banti CN, Owczarzak AM, Kyros L, Kourkoumelis N, Kubicki M, Hadjikakou SK

Abstract
The new silver(I) compound {[AgBr(μ2-S-MMI)(TPP))]2} (1) and the known one [AgCl(TPP)2(MMI)] (2) were obtained by refluxing toluene solutions of silver(I) halide with triphenylphosphine (TPP) and the anti-thyroid drug 2-mercapto-1-methyl-imidazole or methimazole (MMI). The complexes were characterized by m.p., vibrational spectroscopy (mid-FT-IR), (1)H, (31)P-NMR, UV-Vis spectroscopic techniques and X-ray crystallography. The antibacterial effect of 1 and 2 against the bacterial species Pseudomonas aeruginosa (PAO) and Escherichia coli was evaluated. Compound 1 exhibits comparable activity to the corresponding one of the silver nitrate which is an antibacterial drug in use. The in vivo genotoxicity of 1-2 by the mean of Allium cepa test shows no alterations in the mitotic index values due to the absence of chromosomal aberrations. The mechanism of action of the title compounds is evaluated. The DNA binding tests indicate the ability of the complexes 1-2 to modify the activity of the bacteria. The binding constants of 1-2 towards CT-DNA indicate interaction through opening of the hydrogen bonds of DNA. Docking studies on DNA-complexes interactions confirm the binding of both complexes 1-2 in the major groove of the CT-DNA. In conclusion the silver complex 1 is an anti-bacterial and non-genotoxic material, which can be applied to antibacterial drug in the future.

PMID: 26765999 [PubMed - indexed for MEDLINE]

In vivo phenotypic screening: clinical proof of concept for a drug repositioning approach.

Drug Discov Today Technol. 2017 Mar;23:45-52

Authors: Ciallella JR, Reaume AG

Abstract
In vivo phenotypic screening and drug repositioning are strategies developed as alternatives to underperforming hypothesis-driven molecular target based drug discovery efforts. This article reviews examples of drugs identified by phenotypic observations and describes the use of the theraTRACE(®)in vivo screening platform for finding and developing new indications for discontinued clinical compounds. Clinical proof-of-concept for the platform is exemplified by MLR-1023, a repositioned compound that has recently shown significant clinical efficacy in Type 2 diabetes patients. These findings validate an in vivo screening approach for drug development and underscore the importance of alternatives to target and mechanism based strategies that have failed to produce adequate numbers of new medicines.

PMID: 28647085 [PubMed - in process]

Drug repurposing by simulating flow through protein-protein interaction networks.

Clin Pharmacol Ther. 2017 Jun 23;:

Authors: Manczinger M, Bodnár V, Papp BT, Bolla BS, Szabó K, Balázs B, Csányi E, Szél E, Erős G, Kemény L

Abstract
As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein-protein interaction networks, and uses Support Vector Machine to identify potentially effective drugs in our model disease, psoriasis. Using this method, we screened about 1500 marketed and investigational substances, identified fifty-one drugs that were potentially effective and selected three of them for experimental confirmation. All drugs inhibited TNF-induced NFκB activity in vitro, suggesting they might be effective for treating psoriasis in humans. Additionally, these drugs significantly inhibited imiquimod-induced ear thickening and inflammation in the mouse model of the disease. All results suggest high prediction performance for the algorithm. This article is protected by copyright. All rights reserved.

PMID: 28643328 [PubMed - as supplied by publisher]

Potentiating the effects of radiotherapy in rectal cancer: the role of aspirin, statins and metformin as adjuncts to therapy.

Br J Cancer. 2017 Jun 22;:

Authors: Gash KJ, Chambers AC, Cotton DE, Williams AC, Thomas MG

Abstract
BACKGROUND: Complete tumour response (pCR) to neo-adjuvant chemo-radiotherapy for rectal cancer is associated with a reduction in local recurrence and improved disease-free and overall survival, but is achieved in only 20-30% of patients. Drug repurposing for anti-cancer treatments is gaining momentum, but the potential of such drugs as adjuncts, to increase tumour response to chemo-radiotherapy in rectal cancer, is only just beginning to be recognised.
METHODS: A systematic literature search was conducted and all studies investigating the use of drugs to enhance response to neo-adjuvant radiation in rectal cancer were included. 2137 studies were identified and following review 12 studies were extracted for full text review, 9 studies were included in the final analysis.
RESULTS: The use of statins or aspirin during neo-adjuvant therapy was associated with a significantly higher rate of tumour downstaging. Statins were identified as a significant predictor of pCR and aspirin users had a greater 5-year progression-free survival and overall survival. Metformin use was associated with a significantly higher overall and disease-free survival, in a subset of diabetic patients.
CONCLUSIONS: Aspirin, metformin and statins are associated with increased downstaging of rectal tumours and thus may have a role as adjuncts to neoadjuvant treatment, highlighting a clear need for prospective randomised controlled trials to determine their true impact on tumour response and overall survival.British Journal of Cancer advance online publication, 22 June 2017; doi:10.1038/bjc.2017.175 www.bjcancer.com.

PMID: 28641310 [PubMed - as supplied by publisher]

Drug-Repositioning Screening for Keap1-Nrf2 Binding Inhibitors using Fluorescence Correlation Spectroscopy.

Sci Rep. 2017 Jun 21;7(1):3945

Authors: Yoshizaki Y, Mori T, Ishigami-Yuasa M, Kikuchi E, Takahashi D, Zeniya M, Nomura N, Mori Y, Araki Y, Ando F, Mandai S, Kasagi Y, Arai Y, Sasaki E, Yoshida S, Kagechika H, Rai T, Uchida S, Sohara E

Abstract
The Kelch-like ECH-associating protein 1 (Keap1)-nuclear factor erythroid 2-related factor 2 (Nrf2)-antioxidant response element (ARE) signaling pathway is the major regulator of cytoprotective responses to oxidative and electrophilic stress. The Cul3/Keap1 E3 ubiquitin ligase complex interacts with Nrf2, leading to Nrf2 ubiquitination and degradation. In this study, we focused on the disruption of the Keap1-Nrf2 interaction to upregulate Nrf2 expression and the transcription of ARE-controlled cytoprotective oxidative stress response enzymes, such as HO-1. We completed a drug-repositioning screening for inhibitors of Keap1-Nrf2 protein-protein interactions using a newly established fluorescence correlation spectroscopy (FCS) screening system. The binding reaction between Nrf2 and Keap1 was successfully detected with a KD of 2.6 μM using our FCS system. The initial screening of 1,633 drugs resulted in 12 candidate drugs. Among them, 2 drugs significantly increased Nrf2 protein levels in HepG2 cells. These two promising drugs also upregulated ARE gene promoter activity and increased HO-1 mRNA expression, which confirms their ability to dissociate Nrf2 and Keap1. Thus, drug-repositioning screening for Keap1-Nrf2 binding inhibitors using FCS enabled us to find two promising known drugs that can induce the activation of the Nrf2-ARE pathway.

PMID: 28638054 [PubMed - in process]

Update on the genetic architecture of rheumatoid arthritis.

Nat Rev Rheumatol. 2017 Jan;13(1):13-24

Authors: Kim K, Bang SY, Lee HS, Bae SC

Abstract
Human genetic studies into rheumatoid arthritis (RA) have uncovered more than 100 genetic loci associated with susceptibility to RA and have refined the RA-association model for HLA variants. The majority of RA-risk variants are highly shared across multiple ancestral populations and are located in noncoding elements that might have allele-specific regulatory effects in relevant tissues. Emerging multi-omics data, high-density genotype data and bioinformatic approaches are enabling researchers to use RA-risk variants to identify functionally relevant cell types and biological pathways that are involved in impaired immune processes and disease phenotypes. This Review summarizes reported RA-risk loci and the latest insights from human genetic studies into RA pathogenesis, including how genetic data has helped to identify currently available drugs that could be repurposed for patients with RA and the role of genetics in guiding the development of new drugs.

PMID: 27811914 [PubMed - indexed for MEDLINE]

Repositioning FDA Drugs as Potential Cruzain Inhibitors from Trypanosoma cruzi: Virtual Screening, In Vitro and In Vivo Studies.

Molecules. 2017 Jun 18;22(6):

Authors: Palos I, Lara-Ramirez EE, Lopez-Cedillo JC, Garcia-Perez C, Kashif M, Bocanegra-Garcia V, Nogueda-Torres B, Rivera G

Abstract
Chagas disease (CD) is a neglected disease caused by the parasite Trypanosoma cruzi, which affects underdeveloped countries. The current drugs of choice are nifurtimox and benznidazole, but both have severe adverse effects and less effectivity in chronic infections; therefore, the need to discover new drugs is essential. A computer-guided drug repositioning method was applied to identify potential FDA drugs (approved and withdrawn) as cruzain (Cz) inhibitors and trypanocidal effects were confirmed by in vitro and in vivo studies. 3180 FDA drugs were virtually screened using a structure-based approach. From a first molecular docking analysis, a set of 33 compounds with the best binding energies were selected. Subsequent consensus affinity binding, ligand amino acid contact clustering analysis, and ranked position were used to choose four known pharmacological compounds to be tested in vitro. Mouse blood samples infected with trypomastigotes from INC-5 and NINOA strains were used to test the trypanocidal effect of four selected compounds. Among these drugs, one fibrate antilipemic (etofyllin clofibrate) and three β-lactam antibiotics (piperacillin, cefoperazone, and flucloxacillin) showed better trypanocidal effects (LC50 range 15.8-26.1 μg/mL) in comparison with benznidazole and nifurtimox (LC50 range 33.1-46.7 μg/mL). A short-term in vivo evaluation of these compounds showed a reduction of parasitemia in infected mice (range 90-60%) at 6 h, but this was low compared to benznidazole (50%). This work suggests that four known FDA drugs could be used to design and obtain new trypanocidal agents.

PMID: 28629155 [PubMed - in process]

Innovative approaches to treat Staphylococcus aureus biofilm-related infections.

Essays Biochem. 2017 Feb 28;61(1):61-70

Authors: Richter K, Van den Driessche F, Coenye T

Abstract
Many bacterial infections in humans and animals are caused by bacteria residing in biofilms, complex communities of attached organisms embedded in an extracellular matrix. One of the key properties of microorganisms residing in a biofilm is decreased susceptibility towards antimicrobial agents. This decreased susceptibility, together with conventional mechanisms leading to antimicrobial resistance, makes biofilm-related infections increasingly difficult to treat and alternative antibiofilm strategies are urgently required. In this review, we present three such strategies to combat biofilm-related infections with the important human pathogen Staphylococcus aureus: (i) targeting the bacterial communication system with quorum sensing (QS) inhibitors, (ii) a 'Trojan Horse' strategy to disturb iron metabolism by using gallium-based therapeutics and (iii) the use of 'non-antibiotics' with antibiofilm activity identified through screening of repurposing libraries.

PMID: 28258230 [PubMed - indexed for MEDLINE]

A high-throughput phenotypic screen identifies clofazimine as a potential treatment for cryptosporidiosis.

PLoS Negl Trop Dis. 2017 Feb;11(2):e0005373

Authors: Love MS, Beasley FC, Jumani RS, Wright TM, Chatterjee AK, Huston CD, Schultz PG, McNamara CW

Abstract
Cryptosporidiosis has emerged as a leading cause of non-viral diarrhea in children under five years of age in the developing world, yet the current standard of care to treat Cryptosporidium infections, nitazoxanide, demonstrates limited and immune-dependent efficacy. Given the lack of treatments with universal efficacy, drug discovery efforts against cryptosporidiosis are necessary to find therapeutics more efficacious than the standard of care. To date, cryptosporidiosis drug discovery efforts have been limited to a few targeted mechanisms in the parasite and whole cell phenotypic screens against small, focused collections of compounds. Using a previous screen as a basis, we initiated the largest known drug discovery effort to identify novel anticryptosporidial agents. A high-content imaging assay for inhibitors of Cryptosporidium parvum proliferation within a human intestinal epithelial cell line was miniaturized and automated to enable high-throughput phenotypic screening against a large, diverse library of small molecules. A screen of 78,942 compounds identified 12 anticryptosporidial hits with sub-micromolar activity, including clofazimine, an FDA-approved drug for the treatment of leprosy, which demonstrated potent and selective in vitro activity (EC50 = 15 nM) against C. parvum. Clofazimine also displayed activity against C. hominis-the other most clinically-relevant species of Cryptosporidium. Importantly, clofazimine is known to accumulate within epithelial cells of the small intestine, the primary site of Cryptosporidium infection. In a mouse model of acute cryptosporidiosis, a once daily dosage regimen for three consecutive days or a single high dose resulted in reduction of oocyst shedding below the limit detectable by flow cytometry. Recently, a target product profile (TPP) for an anticryptosporidial compound was proposed by Huston et al. and highlights the need for a short dosing regimen (< 7 days) and formulations for children < 2 years. Clofazimine has a long history of use and has demonstrated a good safety profile for a disease that requires chronic dosing for a period of time ranging 3-36 months. These results, taken with clofazimine's status as an FDA-approved drug with over four decades of use for the treatment of leprosy, support the continued investigation of clofazimine both as a new chemical tool for understanding cryptosporidium biology and a potential new treatment of cryptosporidiosis.

PMID: 28158186 [PubMed - indexed for MEDLINE]

Drug repurposing for aging research using model organisms.

Aging Cell. 2017 Jun 16;:

Authors: Ziehm M, Kaur S, Ivanov DK, Ballester PJ, Marcus D, Partridge L, Thornton JM

Abstract
Many increasingly prevalent diseases share a common risk factor: age. However, little is known about pharmaceutical interventions against aging, despite many genes and pathways shown to be important in the aging process and numerous studies demonstrating that genetic interventions can lead to a healthier aging phenotype. An important challenge is to assess the potential to repurpose existing drugs for initial testing on model organisms, where such experiments are possible. To this end, we present a new approach to rank drug-like compounds with known mammalian targets according to their likelihood to modulate aging in the invertebrates Caenorhabditis elegans and Drosophila. Our approach combines information on genetic effects on aging, orthology relationships and sequence conservation, 3D protein structures, drug binding and bioavailability. Overall, we rank 743 different drug-like compounds for their likelihood to modulate aging. We provide various lines of evidence for the successful enrichment of our ranking for compounds modulating aging, despite sparse public data suitable for validation. The top ranked compounds are thus prime candidates for in vivo testing of their effects on lifespan in C. elegans or Drosophila. As such, these compounds are promising as research tools and ultimately a step towards identifying drugs for a healthier human aging.

PMID: 28620943 [PubMed - as supplied by publisher]

Predicting anatomic therapeutic chemical classification codes using tiered learning.

BMC Bioinformatics. 2017 Jun 07;18(Suppl 8):266

Authors: Olson T, Singh R

Abstract
BACKGROUND: The low success rate and high cost of drug discovery requires the development of new paradigms to identify molecules of therapeutic value. The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed classification that assigns multi-level codes to compounds based on their therapeutic, pharmacological and chemical characteristics as well as the in-vivo sites(s) of activity. The ability to predict ATC codes of compounds can assist in creation of high-quality chemical libraries for drug screening and in applications such as drug repositioning. We propose a machine learning architecture called tiered learning for prediction of ATC codes that relies on the prediction results of the higher levels of the ATC code to simplify the predictions of the lower levels.
RESULTS: The proposed approach was validated using a number of compounds in both cross-validation and test setting. The validation experiments compared chemical descriptors, initialization methods and classification algorithms. The prediction accuracy obtained with tiered learning was found to be either comparable or better than that of established methods. Additionally, the experiments demonstrated the generalizability of the tiered learning architecture, in that its use was found to improve prediction rates for a majority of machine learning algorithms when compared to their stand-alone application.
CONCLUSION: The basis of our approach lies in the observation that anatomical-therapeutic biological activity of certain types typically precludes activities of many other types. Thus, there exists a characteristic distribution of the ATC codes, which can be leveraged to limit the search-space of possible codes that can be ascribed at a particular level once the codes at the preceding levels are known. Tiered learning utilizes this observation to constrain the learning space for ATC codes at a particular level based on the ATC code at higher levels. This simplifies the prediction and allows for improved accuracy.

PMID: 28617230 [PubMed - in process]

Drug repositioning for enzyme modulator based on human metabolite-likeness.

BMC Bioinformatics. 2017 May 31;18(Suppl 7):226

Authors: Lee YH, Choi H, Park S, Lee B, Yi GS

Abstract
BACKGROUND: Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs.
METHODS: We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness.
RESULTS: In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence.
CONCLUSIONS: This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

PMID: 28617219 [PubMed - in process]

Neighbours of cancer-related proteins have key influence on pathogenesis and could increase the drug target space for anticancer therapies.

NPJ Syst Biol Appl. 2017 Jan 24;3:2

Authors: Módos D, Bulusu KC, Fazekas D, Kubisch J, Brooks J, Marczell I, Szabó PM, Vellai T, Csermely P, Lenti K, Bender A, Korcsmáros T

Abstract
Even targeted chemotherapies against solid cancers show a moderate success increasing the need to novel targeting strategies. To address this problem, we designed a systems-level approach investigating the neighbourhood of mutated or differentially expressed cancer-related proteins in four major solid cancers (colon, breast, liver and lung). Using signalling and protein-protein interaction network resources integrated with mutational and expression datasets, we analysed the properties of the direct and indirect interactors (first and second neighbours) of cancer-related proteins, not found previously related to the given cancer type. We found that first neighbours have at least as high degree, betweenness centrality and clustering coefficient as cancer-related proteins themselves, indicating a previously unknown central network position. We identified a complementary strategy for mutated and differentially expressed proteins, where the affect of differentially expressed proteins having smaller network centrality is compensated with high centrality first neighbours. These first neighbours can be considered as key, so far hidden, components in cancer rewiring, with similar importance as mutated proteins. These observations strikingly suggest targeting first neighbours as a novel strategy for disrupting cancer-specific networks. Remarkably, our survey revealed 223 marketed drugs already targeting first neighbour proteins but applied mostly outside oncology, providing a potential list for drug repurposing against solid cancers. For the very central first neighbours, whose direct targeting would cause several side effects, we suggest a cancer-mimicking strategy by targeting their interactors (second neighbours of cancer-related proteins, having a central protein affecting position, similarly to the cancer-related proteins). Hence, we propose to include first neighbours to network medicine based approaches for (but not limited to) anticancer therapies.

PMID: 28603644 [PubMed - in process]

Mendelian randomisation in cardiovascular research: an introduction for clinicians.

Heart. 2017 Jun 08;:

Authors: Bennett DA, Holmes MV

Abstract
Understanding the causal role of biomarkers in cardiovascular and other diseases is crucial in order to find effective approaches (including pharmacological therapies) for disease treatment and prevention. Classical observational studies provide naïve estimates of the likely role of biomarkers in disease development; however, such studies are prone to bias. This has direct relevance for drug development as if drug targets track to non-causal biomarkers, this can lead to expensive failure of these drugs in phase III randomised controlled trials. In an effort to provide a more reliable indication of the likely causal role of a biomarker in the development of disease, Mendelian randomisation studies are increasingly used, and this is facilitated by the availability of large-scale genetic data. We conducted a narrative review in order to provide a description of the utility of Mendelian randomisation for clinicians engaged in cardiovascular research. We describe the rationale and provide a basic description of the methods and potential limitations of Mendelian randomisation. We give examples from the literature where Mendelian randomisation has provided pivotal information for drug discovery including predicting efficacy, informing on target-mediated adverse effects and providing potential new evidence for drug repurposing. The variety of the examples presented illustrates the importance of Mendelian randomisation in order to prioritise drug targets for cardiovascular research.

PMID: 28596306 [PubMed - as supplied by publisher]

Repurposed drugs as potential therapeutic candidates for management of Alzheimer's disease.

Curr Drug Metab. 2017 Jun 06;:

Authors: Shoaib M, Kamal MA, Rizvi SMD

Abstract
Drug repurposing is an innovative approach as it provides fresh implications to previously approved and established drug compounds. Due to high failure rates and cost involved in the drug development process, many pharmaceutical companies are primarily focusing on drug repurposing strategy. In Alzheimer disease, existing therapeutic agents only provide symptomatic benefits and does not get involved in disease modification, therefore, the alternative approach of repurposing could be applied to inhibit neurodegeneracy process and other pathological complications. In our review, we have mentioned current treatment strategies, including therapies based on nanotechnology and their limitations. Moreover, we have presented several classes of licensed drugs having beneficial effects in Alzheimer's pathology based on in-vitro studies, epidemiological data, and clinical trials. In addition, the application of bioinformatics and in-silico drug repurposing strategy is crucial for drug research and identification of potential repurposed drugs. Thus, we also have mentioned several drugs repurposing computational tools that are robust and can predict reliable results based on available gene expression data.

PMID: 28595531 [PubMed - as supplied by publisher]

High-content drug screening for rare diseases.

J Inherit Metab Dis. 2017 Jun 07;:

Authors: Bellomo F, Medina DL, De Leo E, Panarella A, Emma F

Abstract
Per definition, rare diseases affect only a small number of subjects within a given population. Taken together however, they represent a considerable medical burden, which remains poorly addressed in terms of treatment. Compared to other diseases, obstacles to the development of therapies for rare diseases include less extensive physiopathology knowledge, limited number of patients to test treatments, and poor commercial interest from the industry. Recently, advances in high-throughput and high-content screening (HTS and HCS) have been fostered by the development of specific routines that use robot- and computer-assisted technologies to automatize tasks, allowing screening of a large number of compounds in a short period of time, using experimental model of diseases. These approaches are particularly relevant for drug repositioning in rare disease, which restricts the search to compounds that have already been tested in humans, thereby reducing the need for extensive preclinical tests. In the future, these same tools, combined with computational modeling and artificial neural network analyses, may also be used to predict individual clinical responses to drugs in a personalized medicine approach.

PMID: 28593466 [PubMed - as supplied by publisher]

A disease similarity matrix based on the uniqueness of shared genes.

BMC Med Genomics. 2017 May 24;10(Suppl 1):26

Authors: Carson MB, Liu C, Lu Y, Jia C, Lu H

Abstract
BACKGROUND: Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing.
METHODS: Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation.
RESULTS: Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study.
CONCLUSIONS: We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases.

PMID: 28589854 [PubMed - in process]