News Article | April 20, 2017
Scientists have identified two small molecules that could be pursued as potential treatments for chronic inflammatory diseases. According to a paper published in PLOS Computational Biology, the researchers singled out the molecules using a new drug screening approach they developed. Both molecules, known as T23 and T8, inhibit the function of a protein called tumor necrosis factor (TNF), which is involved in inflammation in diseases such as rheumatoid arthritis, Crohn's disease, psoriasis, multiple sclerosis, and more. Drugs that inhibit TNF's function are considered the most effective way to combat such diseases. However, not all patients respond to them, and their effectiveness can wear off over time. To aid discovery of better TNF inhibitor drugs, Georgia Melagraki and colleagues from Greece and Cyprus developed a new computer-based drug screening platform. The platform incorporates proprietary molecular properties shared between TNF and another protein called RANKL, which is also involved in chronic inflammatory diseases. The researchers developed the platform based on a combination of advanced computational tools. The platform was then used to virtually screen nearly 15,000 small molecules with unknown activity and to predict their interactions with the TNF and RANKL proteins; specifically, how well the small molecules might disrupt the protein-protein interactions (PPIs) leading to the trimerization and activation of these crucial proteins. "This virtual experiment identified nine promising molecules out of thousands of candidates," says study co-corresponding author Antreas Afantitis of NovaMechanics Ltd, Cyprus. To further evaluate their potential, the scientists studied how the nine small molecules interacted with TNF and RANKL in real-world laboratory experiments. Of the nine molecules, T23 and T8 surfaced as particularly strong TNF inhibitors. Both molecules bind to TNF and RANKL, preventing them from interacting properly with other proteins. Both also show low potential for causing toxic side effects in humans. With further research, T23 and T8 could be "further optimized to develop improved treatments for a range of inflammatory, autoimmune, and bone loss diseases," says study co-corresponding author George Kollias of the Biomedical Sciences Research Center 'Alexander Fleming', Greece. Meanwhile, the new virtual drug screening approach could enable discovery of other promising TNF inhibitors, and could be modified to search for potential treatments for additional diseases. In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals. Citation: Melagraki G, Ntougkos E, Rinotas V, Papaneophytou C, Leonis G, Mavromoustakos T, et al. (2017) Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL). PLoS Comput Biol 13(4): e1005372. https:/ Funding: This work was funded by Greek "Cooperation" Action project TheRAlead (09SYN-21-784) co-financed by the European Regional Development Fund and NSRF 2007-2013, the Innovative Medicines Initiative (IMI) funded project BTCure (No 115142) and Advanced European Research Council (ERC) grant MCs-inTEST (No 340217) to GKol. AA would like to acknowledge funding from Cyprus Research Promotion Foundation, DESMI 2008, ΕΠΙΧΕΙΡΗΣΕΙΣ/ΕΦΑΡΜ /0308/20 http://www. . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Georgia Melagraki, Georgios Leonis and Antreas Afantitis are employed by Novamechanics Ltd, a drug design company. Other authors declare that there are no conflicts of interest.
Agency: European Commission | Branch: H2020 | Program: MSCA-RISE | Phase: MSCA-RISE-2015 | Award Amount: 706.50K | Year: 2016
NanoMaterials safety is of great societal concern and raises many questions for the general public, governments, industry, scientists and regulators. Identifying and controlling the hazards associated with NMs is required to ensure the safety in parallel to exploiting the technological benefits. NANOGENTOOLS answers this challenge by creating a collaborative excellence-based knowledge exchange network that will: i) push forward knowledge via method development and pre-validation, ii) train scientists in new methodologies to assess long term nanosafety, and iii) support their inclusion in standardization and EU regulations. NANOGENTOOLS combines toxicogenomics, proteomics, biophysics, molecular modeling, chemistry, bio/chemoinformatics to develop fast in vitro high throughput (HTS) assays, with molecular based computational models for nanotoxicity. Its objectives are to: Provide solutions for faster, more reliable assessment of NM toxicity and propose HTS and omics tools for predicting toxicological properties of NMs. Develop new bioinformatics methodologies for analyzing -omics data and create an open database in collaboration with the EU Nanosafety Cluster. Conduct research and training on biophysical techniques and mathematical models for accurate and fast nanotoxicity prediction. Build/improve the safe by design concept, demonstrated using carbon-NMs and nanosensors. Place our new knowledge in the context of regulations and EU roadmaps. NANOGENTOOLS brings together cutting edge research, innovative knowledge-transfer and co-development, and cross-sectoral and cross-disciplinary secondments linking EU academic institutes/networks with industry and policy makers across 8 countries. Expected impacts include pre-validated tools for efficient cost-effective nanosafety assessment applicable to SMEs for incorporation into regulatory frameworks, and translation of knowledge via development of a CNT-based nanosensor based on safe-by-design principles.
Agency: European Commission | Branch: FP7 | Program: CP-FP-SICA | Phase: HEALTH.2010.2.1.2-3 | Award Amount: 3.88M | Year: 2010
SYSPATHO focuses on the development of novel and generally applicable mathematical methods and algorithms for systems biology. These methods and algorithms will be applied to study the complex interactions of hepatitis C virus (HCV), a human-pathogenic virus of high medical relevance, with its host at the systems level. Using a multidisciplinary, integrative approach, PATHOSYS will (a) develop methods to analyze and integrate a wide variety of data from wet lab experiments, databases and biological literature, (b) develop and apply machine learning tools to reconstruct and study intracellular interaction networks from experimental data, (c) develop new and improve existing algorithms and mathematical methods for bottom-up modelling, to fit models to data, and to analyze the dynamic behaviour of models (d) generate new experimental data to gain novel insights into hepatitis C virus host interactions, and (e) use the newly developed methods and data to model and analyze HCV-host interactions at the systems level. Guided by biological data, PATHOSYS focuses on the design of novel algorithms and mathematical methods for systems biology, with the aim to provide generally applicable tools to elucidate biological processes. Based on developed models and using systems analysis, PATHOSYS will elucidate virus host interactions of Hepatitis C virus at an unprecedented level. As a direct spin-off, models and analysis methods developed in PATHOSYS will lead to the identification of new candidate host cell target genes applicable for the design of novel anti-viral drugs against hepatitis C. Targeting of host cell factors will reduce the likelihood for the development of therapy resistance and increase the chance for broad-spectrum antivirals. Inclusion of two SME partners will ensure exploitation of results generated in PATHOSYS and their transfer into industrial and pharmaceutical applications, thus strengthening economy and health care system in Europe.
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: NMP.2012.1.3-1 | Award Amount: 12.95M | Year: 2013
The NanoMILE project is conceived and led by an international elite of scientists from the EU and US with the aim to establish a fundamental understanding of the mechanisms of nanomaterial interactions with living systems and the environment, and uniquely to do so across the entire life cycle of nanomaterials and in a wide range of target species. Identification of critical properties (physico-chemical descriptors) that confer the ability to induce harm in biological systems is key to allowing these features to be avoided in nanomaterial production (safety by design). Major shortfalls in the risk analysis process for nanomaterials are the fundamental lack of data on exposure levels and the environmental fate and transformation of nanomaterials, key issues that this proposal will address, including through the development of novel modelling approaches. A major deliverable of the project will be a framework for classification of nanomaterials according to their impacts, whether biological or environmental, by linking nanomaterial-biomolecule interactions across scales (sub-cellular to ecosystem) and establishing the specific biochemical mechanisms of interference (toxicity pathway).
Vrontaki E.,NovaMechanics Ltd. |
Vrontaki E.,National and Kapodistrian University of Athens |
Melagraki G.,NovaMechanics Ltd. |
Mavromoustakos T.,National and Kapodistrian University of Athens |
Afantitis A.,NovaMechanics Ltd.
Methods | Year: 2015
Molecular docking, 3D-QSAR CoMSIA and similarity search were combined in a multi-step framework with the ultimate goal to identify potent indole analogs, in the ChEMBL database, as inhibitors of HCV replication. The crystal structure of HCV RNA-dependent RNA polymerase (NS5B GT1b) was utilized and 41 known inhibitors were docked into the enzyme "Palm II" active site. In a second step, the docking pose of each compound was used in a receptor-based alignment for the generation of the CoMSIA fields. A validated 3D-QSAR CoMSIA model was subsequently built to accurately estimate the activity values. The proposed framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogs on HCV polymerase. The obtained in silico model was used to predict the activity of novel compounds prior to their synthesis and biological testing, within a Virtual Screening framework. The ChEMBL database was mined to afford compounds containing the indole scaffold that are predicted to possess high activity and thus can be prioritized for biological screening. © 2014 Elsevier Inc.
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2012.1.2-1 | Award Amount: 6.68M | Year: 2012
THALAMOSS is aimed at development of universal sets of markers and techniques for stratification of -thalassaemia patients into treatment subgroups for (a) onset and frequency of blood transfusions, (b) choice of iron chelation, (c) induction of fetal hemoglobin, (d) prospective efficacy of gene-therapy. At present, no framework exists to guide therapeutic decisions and personalised treatment of -thalassaemia. THALAMOSS Workpackages: WP1. Recruitment, patient characterization and development of erythroid precursor cells cultures; WP2. Omics analyses; WP3. Novel therapeutic approaches; WP4. Data analysis; WP5. Dissemination and exploitation; WP6. Regulatory and ethical issues; WP7. Management. The impact of THALAMOSS is the provision of novel biomarkers for distinct treatment subgroups in -thalassaemia (500-1000 samples from four European medical centres), identified by combined genomics, proteomics, transcriptomics and tissue culture assays, and establishment of routine techniques for detection of these markers. Translation of these activities into the product portfolio and R&D methodology of participating SMEs will be a major issue. THALAMOSS tools and technologies will (a) facilitate identification of novel diagnostic tests, drugs and treatments specific to patient subgroups and (b) guide conventional and novel therapeutical approaches for -thalassaemia, including personalised medical treatments. Key researchers of THALAMOSS are R.Gambari (Ferrara University, Italy), M. Kleanthous (The Cyprus Foundation for Muscular Dystrophy Research, Cyprus), S.Philipsen (Erasmus Universitair Medisch Centrum Rotterdam, The Netherlands), E.Katsantoni (Biomedical Research Foundation, Academy of Athens, Greece), S.Rivella (Weill Cornell Medical College, NY, USA), P.Holub (Masaryk University, Czech Republic), R.Galanello (Cagliari University, Italy), SL.Thein (Kings College Hospital, UK), E.Voskaridou (Laiko General Hospital, Greece). Participating SMEs are Biocep (Israel), NovaMechanics Ltd. (Cyprus) and IRBM (Italy). Industrial activities are also provided by Harbour Antibodies (The Netherlands).
Afantitis A.,NovaMechanics Ltd |
Melagraki G.,University of Cyprus |
Koutentis P.A.,University of Cyprus |
Sarimveis H.,National Technical University of Athens |
Kollias G.,Biomedical science Research Center Alexander Fleming
European Journal of Medicinal Chemistry | Year: 2011
In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence. © 2010 Elsevier Masson SAS. All rights reserved.
Melagraki G.,Novamechanics Ltd |
Afantitis A.,Novamechanics Ltd
RSC Advances | Year: 2014
Engineered nanoparticles (ENPs) are being extensively used in a great variety of applications with a pace that is increasingly growing. The evaluation of the biological effects of ENPs is of utmost importance and for that experimental and most recently computational methods have been suggested. In an effort to computationally explore available datasets that will lead to ready-to-use applications we have developed and validated a QNAR model for the prediction of the cellular uptake of nanoparticles in pancreatic cancer cells. Our insilico workflow was made available online through the Enalos InSilicoNano platform (http://enalos.insilicotox.com/QNAR-PaCa2/), a web service based solely on open source and freely available software that was developed with the purpose of making our model available to the interested user wishing to generate evidence on potential biological effects in the decision making framework. This web service will facilitate the computer aided nanoparticle design as it can serve as a source of activity prediction for novel nano-structures. To demonstrate the usefulness of the web service we have exploited the whole PubChem database within a virtual screening framework and then used the Enalos InSilicoNano platform to identify novel potent nanoparticles from a prioritized list of compounds. © 2014 the Partner Organisations.