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Rhrissorrakrai K.,IBM | Belcastro V.,Philip Morris Products S.A. | Belcastro V.,Telethon Institute of Genetics and Medicine | Bilal E.,IBM | And 12 more authors.
Bioinformatics | Year: 2015

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and 'translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. © 2014 The Author. Source


Melas I.N.,U.S. Food and Drug Administration | Sakellaropoulos T.,National Technical University of Athens | Iorio F.,European Bioinformatics Institute | Alexopoulos L.G.,National Technical University of Athens | And 5 more authors.
Integrative Biology (United Kingdom) | Year: 2015

Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury. © The Royal Society of Chemistry. Source


Melas I.N.,National Technical University of Athens | Melas I.N.,ProtATonce Ltd. | Lauffenburger D.A.,Massachusetts Institute of Technology | Lauffenburger D.A.,Harvard University | And 2 more authors.
13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013 | Year: 2013

Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some promising clinical outcomes [1]. This is amongst the first studies where the mode of action of some of the compounds extensively used in clinical trials is interrogated on the phosphoproteomic level, in an attempt to build predictive models for clinical efficacy. Signaling data are combined with previously published gene expression and clinical data within a consistent framework that identifies drug effects on the phosphoproteomic level and translates them to the gene expression level. The interrogated drugs are then correlated with genes differentially expressed in normal versus tumor tissue, and genes predictive of patient survival. Although the number of clinical trial results considered is small, our approach shows potential for discerning signaling activities that may help predict drug efficacy for HCC. © 2013 IEEE. Source


Melas I.N.,National Technical University of Athens | Melas I.N.,ProtATonce Ltd. | Kretsos K.,UCB Pharma | Alexopoulos L.G.,National Technical University of Athens | Alexopoulos L.G.,ProtATonce Ltd.
Biopharmaceutics and Drug Disposition | Year: 2013

Computational modeling has been adopted in all aspects of drug research and development, from the early phases of target identification and drug discovery to the late-stage clinical trials. The different questions addressed during each stage of drug R&D has led to the emergence of different modeling methodologies. In the research phase, systems biology couples experimental data with elaborate computational modeling techniques to capture lifecycle and effector cellular functions (e.g. metabolism, signaling, transcription regulation, protein synthesis and interaction) and integrates them in quantitative models. These models are subsequently used in various ways, i.e. to identify new targets, generate testable hypotheses, gain insights on the drug's mode of action (MOA), translate preclinical findings, and assess the potential of clinical drug efficacy and toxicity. In the development phase, pharmacokinetic/pharmacodynamic (PK/PD) modeling is the established way to determine safe and efficacious doses for testing at increasingly larger, and more pertinent to the target indication, cohorts of subjects. First, the relationship between drug input and its concentration in plasma is established. Second, the relationship between this concentration and desired or undesired PD responses is ascertained. Recognizing that the interface of systems biology with PK/PD will facilitate drug development, systems pharmacology came into existence, combining methods from PK/PD modeling and systems engineering explicitly to account for the implicated mechanisms of the target system in the study of drug-target interactions. Herein, a number of popular system biology methodologies are discussed, which could be leveraged within a systems pharmacology framework to address major issues in drug development. © 2013 The Authors. Biopharmaceutics & Drug Disposition published by John Wiley & Sons, Ltd. © 2013 The Authors. Biopharmaceutics & Drug Disposition published by John Wiley & Sons, Ltd. Source


Morris M.K.,Merck And Co. | Chi A.,Merck And Co. | Melas I.N.,ProtATonce Ltd. | Melas I.N.,National Technical University of Athens | And 2 more authors.
Drug Discovery Today | Year: 2014

Several important aspects of the drug discovery process, including target identification, mechanism of action determination and biomarker identification as well as drug repositioning, require complete understanding of the effects of drugs on protein phosphorylation in relevant biological systems. Novel high-throughput phosphoproteomic technologies can be employed to measure these phosphorylation events. In this review, we describe the advantages and limitations of state-of-the-art phosphoproteomic approaches such as mass spectrometry and antibody-based technologies in terms of sample and data throughput as well as data quality. We then discuss how datasets from each technology can be analyzed and how the results can be and have been applied to advance different aspects of the drug discovery process. © 2013 Elsevier Ltd. Source

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