Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development.

External content sources such as MEDLINE(®), National Institutes of Health (NIH) grants and conference websites provide access to the latest breaking biomedical information, which can inform pharmaceutical and biotechnology company pipeline decisions. The value of the sites for industry, however, is limited by the use of the public internet, the limited synonyms, the rarity of batch searching capability and the disconnected nature of the sites. Fortunately, many sites now offer their content for download and we have developed an automated internal workflow that uses text mining and tailored ontologies for programmatic search and knowledge extraction. We believe such an efficient and secure approach provides a competitive advantage to companies needing access to the latest information for a range of use cases and complements manually curated commercial sources.

[1]  Koichiro Ohmura.  [GWAS of Rheumatoid Arthritis and Drug Discovery]. , 2015, Rinsho byori. The Japanese journal of clinical pathology.

[2]  M. Ringel,et al.  Racing to define pharmaceutical R&D external innovation models. , 2015, Drug discovery today.

[3]  Jeffrey L Saver,et al.  Frequency and Determinants of Nonpublication of Research in the Stroke Literature , 2006, Stroke.

[4]  Despina Koletsi,et al.  Outcome Discrepancies and Selective Reporting: Impacting the Leading Journals? , 2015, PloS one.

[5]  Isabelle Boutron,et al.  Timing and Completeness of Trial Results Posted at ClinicalTrials.gov and Published in Journals , 2013, PLoS medicine.

[6]  John Hoey,et al.  Clinical Trial Registration: A Statement from the International Committee of Medical Journal Editors , 2004, Annals of Internal Medicine.

[7]  John Hoey,et al.  Clinical trial registration: a statement from the International Committee of Medical Journal Editors. , 2004, JAMA.

[8]  Carol Perez-Iratxeta,et al.  Text mining of biomedical literature: doing well, but we could be doing better. , 2015, Methods.

[9]  Raul Rodriguez-Esteban,et al.  Biomedical Text Mining and Its Applications , 2009, PLoS Comput. Biol..

[10]  M. Evers,et al.  The Role of Big Data and Advanced Analytics in Drug Discovery, Development, and Commercialization , 2014, Clinical pharmacology and therapeutics.

[11]  W. Alkema,et al.  Application of text mining in the biomedical domain. , 2015, Methods.

[12]  Khusru Asadullah,et al.  What makes a good drug target? , 2011, Drug discovery today.

[13]  Kalpana Raja,et al.  Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge , 2015, J. Biomed. Informatics.

[14]  Gary Oderda,et al.  Bringing Liraglutide to Market: A CER Case Study , 2012, Journal of managed care pharmacy : JMCP.

[15]  Christopher W. Jones,et al.  Non-publication of large randomized clinical trials: cross sectional analysis , 2013, BMJ.

[16]  Johannes M Freudenberg,et al.  Mining emerging biomedical literature for understanding disease associations in drug discovery. , 2014, Methods in molecular biology.

[17]  David Milward,et al.  Ontology-Based Interactive Information Extraction From Scientific Abstracts , 2005, Comparative and functional genomics.

[18]  J. Arrowsmith.  Trial watch: Phase II failures: 2008–2010 , 2011, Nature Reviews Drug Discovery.

[19]  David Milward,et al.  Developing timely insights into comparative effectiveness research with a text-mining pipeline. , 2016, Drug discovery today.

[20]  David B. Searls,et al.  Can literature analysis identify innovation drivers in drug discovery? , 2009, Nature Reviews Drug Discovery.

[21]  Thomas Klose,et al.  Leveraging text analytics in patent analysis to empower business decisions – A competitive differentiation of kinase assay technology platforms by I2E text mining software , 2014 .

[22]  David B. Searls,et al.  Literature mining in support of drug discovery , 2008, Briefings Bioinform..

[23]  John M. Lin,et al.  An Analysis of the Abstracts Presented at the Annual Meetings of the Society for Neuroscience from 2001 to 2006 , 2007, PloS one.

[24]  Michelle L. McGowan,et al.  Big data, open science and the brain: lessons learned from genomics , 2014, Front. Hum. Neurosci..