Nikolsky Y.,Genego, Inc.
Methods in molecular biology (Clifton, N.J.) | Year: 2010
Atherogenic lipids and chronic inflammation drive the development of cardiovascular disorders such as atherosclerosis. Many cardiovascular drugs target the liver which is involved in the formation of lipid and inflammatory risk factors. With robust systems biology tools and comprehensive bioinformatical packages becoming available and affordable, the effect of novel treatment strategies can be analyzed more comprehensively and with higher sensitivity. For example, beneficial as well as adverse effects of drugs can already be detected on the gene and metabolite level, and prior to their macroscopic manifestation. This chapter describes a systems approach for a prototype CV drug with established beneficial and adverse effects. All relevant steps, for example, experimental design, tissue collection and high quality RNA preparation, bioinformatical analysis of functional processes, and pathways (targeted and untargeted) are addressed. Source
Szalma S.,Centocor R and D Inc. |
Koka V.,Centocor R and D Inc. |
Khasanova T.,Genego, Inc. |
Perakslis E.D.,Centocor R and D Inc.
Journal of Translational Medicine | Year: 2010
Background: The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health.Methods: The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern.Results: The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface.Conclusions: The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs. © 2010 Szalma et al; licensee BioMed Central Ltd. Source
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 292.06K | Year: 2009
DESCRIPTION (provided by applicant): The goal of this project is to develop a database and functional genomic analysis platform specifically designed for the study of the biological consequences of mutations and sequence heterogeneity in human genes and their controlling regions. The product will comprise i) a comprehensive database of known single nucleotide polymorphisms (SNPs), splice variants, translocations, copy number changes, and other alterations in genetic sequence that change the sequence, transcription or translation of human, rat or mouse proteins; ii) a comprehensive knowledgebase of literature-reported associations between human, rat and mouse sequence heterogeneities and the functional consequences of those differences to protein levels, protein stability and half life, enzymatic activity, substrate specificity, protein complex formation, protein-protein and protein-compound interactions, transcription factor activity or any other characteristic of biological consequence; iii) a suite of software tools, building on the GeneGo MetaDiscovery platform. This product, by combining knowledge on the functional consequences of sequence variations with a best- in-class systems biology software platform, will be unique in the marketplace. The product will allow the user to search for known sequence variations and their functional effects, identify metabolic and signaling networks within which the altered proteins are acting, and to categorize diseases and adverse drug effects associated with sequence variations. The user will be able to quickly identify potential strategies and targets for therapeutic interventions to combat increased vulnerability to disease or toxicity, find alternative therapeutic approaches to avoid sequence-related altered pharmacology or absorption, distribution, metabolism, elimination or toxicity parameters (ADMET), identify biomarkers of adverse effects associated with sequence variations. The product will smooth the progress personalized medicine by facilitating the application of personal genetic profiles to identify optimal therapeutic strategies for illness and disease. This will be an invaluable tool for disease research, pharmaceutical discovery, toxicology and clinical medicine. It will market and sold by GeneGo as part of the MetaDiscovery suite of products.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 336.19K | Year: 2010
DESCRIPTION (provided by applicant): In this project we propose to develop predictive regulation signatures which should overcome shortcomings of existing methods of molecular diagnostics. As a proof of concept, in Phase I we will develop regulation signatures for the sensitivity of cancer cell lines to the inhibitors of EGF receptor and will test their accuracy using publicly available datasets. This goal will be accomplished in two stages. First, we will build and optimize focused network model for global Erb family signaling using well-defined training data and knowledge content. In the second step we will test performance of the developed Erb signaling network model on publicly available sets of gene expression profiles from the variety of non-small cell lung carcinoma cell lines with variable drug sensitivity. Analysis will be performed to identify sets of key signaling proteins associated with drug resistance. Using these proteins predictive regulation signatures will be developed and their performance will be tested. If successful, the methodology could be replicated for developing predictive models for sensitivity to a broad range of targeted therapies, leading to a number of diagnostic applications such as specialized molecular tests, systems for formulating combination therapies and procedures for selecting patient cohorts for clinical trials. PUBLIC HEALTH RELEVANCE: In this project we will develop novel regulation signatures to predict sensitivity to the inhibitors of EGF receptor and identify mechanisms of drug resistance. Project will utilize public gene expression data in combination with knowledge base on protein interactions and our recently developed network analysis algorithm. If successful, the methodology could lead to a number of diagnostic applications.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 119.17K | Year: 2010
DESCRIPTION (provided by applicant): The goal of this project is to develop a database and data analysis platform specifically designed for the study of the causes and biological consequences of drug abuse. The product will comprise: i) a comprehensive disease ontology and database of associations between genes, their protein products and predisposition to drug abuse, development of dependency and pathologies associated with chronic drug abuse; ii) a collection of publicly-available systems biology datasets (gene-expression, proteomics, metabolomics) relating to drug abuse and it's affects on multiple organ systems; iii) a set of prebuilt maps and networks relating to drug dependency, and software tools that will enable the analysis of extant and newly-generated systems biology datasets, independently and in combination, to identify metabolic and signaling networks, pathways, and mechanisms of disease development, and potential strategies and targets for therapeutic interventions; iv) computational tools for the analysis of novel compounds to predict their potential for abuse and dependency. The product will be unique in the marketplace and of great interest to academic researchers, foundations and pharmaceutical companies performing research into the causes and treatment of alcoholism and alcohol- related disease and will be marketed and sold by GeneGo as part of the MetaDiscovery suite of products. PUBLIC HEALTH RELEVANCE: The aim of this grant is to develop a software database and analysis platform to enable a systems-level approach to understanding, preventing and treating drug abuse. The platform will provide a comprehensive knowledge-base, data analysis tools and predictive models to support research into the causes and treatment of drug abuse.