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Santa Clara, CA, United States

NextBio is a privately owned software company that provides a platform for drug companies and life science researchers to search, discover, and share knowledge across public and proprietary data. It was co-founded by Saeid Akhtari, Ilya Kupershmidt and Mostafa Ronaghi in 2004 and based in Cupertino, California, USA. The NextBio Platform is an ontology-based semantic framework that connects highly heterogeneous data and textual information. The semantic framework is based on gene, tissue, disease and compound ontologies. This framework contains information from different organisms, platforms, data types and research areas that is integrated into and correlated within a single searchable environment using proprietary algorithms. It provides a unified interface for researchers to formulate and test new hypotheses across vast collections of experimental data. According to the company, the enterprise version of the NextBio platform is being used in life science research and development and drug development by researchers and clinicians at: Merck Pharmaceutical, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Celgene, Genzyme, Eli Lilly and Company, and Regeneron Pharmaceuticals. This enterprise version allows internal, proprietary data to be uploaded and integrated into the NextBio database of publicly available data. According to the company, scientists are using NextBio to improve their ability to identify relevant prognostic and predictive molecular signatures which are significant in their research.NextBio was a receiver of the Frost & Sullivan North American Life Sciences Customer Value Enhancement Award in 2008. Wikipedia.


Cohen T.,Sanford Burnham Institute for Medical Research | Cohen T.,University of California at San Diego | Sundaresh S.,NextBio | Levine F.,Sanford Burnham Institute for Medical Research
Molecular Psychiatry | Year: 2013

Although effective in treating an array of neurological disorders, antipsychotics are associated with deleterious metabolic side effects. Through high-throughput screening, we previously identified phenothiazine antipsychotics as modulators of the human insulin promoter. Here, we extended our initial finding to structurally diverse typical and atypical antipsychotics. We then identified the transforming growth factor beta (TGFβ) pathway as being involved in the effect of antipsychotics on the insulin promoter, finding that antipsychotics activated SMAD3, a downstream effector of the TGFβ pathway, through a receptor distinct from the TGFβ receptor family and known neurotransmitter receptor targets of antipsychotics. Of note, antipsychotics that do not cause metabolic side effects did not activate SMAD3. In vivo relevance was demonstrated by reanalysis of gene expression data from human brains treated with antipsychotics, which showed altered expression of SMAD3 responsive genes. This work raises the possibility that antipsychotics could be designed that retain beneficial CNS activity while lacking deleterious metabolic side effects. © 2013 Macmillan Publishers Limited.


The present invention relates to methods, systems and apparatus for capturing, integrating, organizing, navigating and querying large-scale data from high-throughput biological and chemical assay platforms. It provides a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure.


Patent
NextBio | Date: 2010-06-08

According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.


Patent
NextBio | Date: 2010-06-08

According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 978.98K | Year: 2009

DESCRIPTION (provided by applicant): We propose to continue the development of our novel application that enables scientists to search and correlate information across studies in different model organisms. NextBio will develop methods that combine the power of genomic and proteomic studies in different organisms to perform exploratory science and generate novel hypothesis. As part of phase II NextBio will incorporate support for additional vertebrate organisms, such as Zebrafish, dog and rhesus monkey. Above organisms, in addition to currently supported human, mouse, rat, fly, worm and yeast will enable a powerful framework for using information from model organisms to better understand human physiology and disease. In addition, NextBio will develop infrastructure to support plant research by incorporating Arabidopsis, corn, rice and soy bean plant genomes into its cross-species framework. This will enable the use of collective knowledge across these species to help agricultural researchers in their research projects. As part of this proposal NextBio will develop gene and protein indexes, as well as translation methodology to link similar sets of genes and proteins from different organisms. Furthermore, it will develop advanced statistical and visualization methods to help correlate and interpret the data. PUBLIC HEALTH RELEVANCE: Project Narrative Large quantities of critical biological information pertaining to human and plant physiology is generated in studies from diverse model organisms. We propose to develop a platform in which researchers can use combined power of these studies to better understand disease development and compound effects on human and plant physiology. This will help scientific community design better diagnostic and treatment options for humans and develop superior agricultural products.

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