Presentation is loading. Please wait.

Presentation is loading. Please wait.

William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 Biogen Idec Literature Informatics for Drug.

Similar presentations

Presentation on theme: "William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 Biogen Idec Literature Informatics for Drug."— Presentation transcript:

1 William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 Biogen Idec Literature Informatics for Drug Discovery

2 Mission Provide –access to literature and text resources –tools to access and manage literature and text resources –expert analyses of literature and text resources –the most advanced tools and analyses available

3 Agenda Value Proposition Literature Informatics Overview Projects Summary

4 Value Proposition A recent trend in the industry is to cut the library to a bare operational staff - to manage E-journals and document delivery To do so eliminates our ability to make knowledgeable decisions for drug development

5 The Scope of the Literature Problem – You cannot keep up! The annual worldwide production of information in publications is estimated as 8 TB in books, 25 TB in newspapers, 20 TB in magazines, and 2 TB in journals Every minute scientific knowledge increases by 2,000 pages It takes five years to read the new scientific material produced every 24 hours 80% of information is stored as unstructured text The number of papers associated with a pharma target: in 1990 = 100 in 2001 = 8

6 Library -> Literature Informatics Deliver information Requires variety of skill sets (Library science, operations, technical, informatics, domain expertise)

7 What is Literature Informatics ? Applying data mgmt and analytical technologies to extract and store knowledge from scientific/business literature Analytical technologies: –Information retrieval –Text mining –Semantic reasoning and inference Analytical objectives: –What protein interactions can be found in the corpus? –Which gene expressed in a particular pathway with respect to a special disease for a special genetic group –Which compounds inhibit a protein? –Which documents found are toxicology-related? –Show me all co-occurring genes and diseases

8 Literature Informatics Benefits Much more efficient overview of research areas –Save significant time for individual researchers/the company Ability to effectively extract information from hundreds to millions of documents Greater than 10X improvements in speed of analysis and recall More value captured from $Millions spent on literature content and research

9 External vs Internal Research Dollars US Total: $94.3B (2003) (JAMA. 2005;294:1333-1342) –Public 43% - NIH(28%), Other Federal (7%), State/local gov (5%), Charity (3%) –Private 57% - Pharma (29%), Biotech ~1500 companies (19%), Device (9%) Pfizer R&D (2004) –$8B (3.5X of Pfizer spend from one funding agency!) Biogen Idec - 3rd largest biotech –$684M (2004) R&D (0.7% of US Total)

10 Number of Papers Published (from Pubmed) 2002200320042005 Medline550538580725619626681899 Pfizer381444490460 Biogen Idec 70535152

11 Text Analytics Financial Analysis Given 1000 researchers 22% time searching and analyzing literature (Outsell survey 2002) 220 person-years per year analyzing literature – $22M / year Significant percentage of that time is retrievable using advanced text analytics and expert analysts

12 Front-loading Safety Concerns Lead optimization (LO) costs ~$126M (Tufts survey) LO projects take between 2-4 years ~50% LO projects undergo attrition due to safety concerns (Tufts survey results) ~50% of safety issues had literature indicators at beginning of project (anecdotal evidence) $25M per 4 LO projects can be recovered IF comprehensive literature analyses can speed up Safety analyses by 20%

13 Text Analytics Impact Case 1: start with an unknown protein, determine interaction network. No standard procedure without NLP tools – estimated 2-3 weeks of manual mining. With an NLP tool that extracts connectivity information w/ graph visualization from full-text journal articles – 1 hour Case 2: determine toxicity patterns for a compound, or determine toxicity side-effects of inhibiting a target. With manual OVID search – library scientists have already put in 3 months, a total of a year estimated. With NLP+ontologies (OBIIE) – 2-3 weeks. Case 3: An unknown protein is somehow linked to a known disease. There is a lot of disease literature, but only 4 papers on the protein. Establish a plausible connection of mechanism of action with this disease. Without NLP – indefinite. With OBIIE – 2-3 weeks.

14 The Analysts Role Understand questions asked, problems encountered –Too much information –Not enough information –Relevant information is buried Match resources to needs –Protein-centric versus pipeline? –Better clinical or chemistry coverage? Know search logic and available tools Pre-screen end-user tools

15 The Analysts Role Link disparate resources for improved coverage Repackage results to match question, user preferences Never lose sight of user experience –Alleviate tedium –Minimize error –Increase relevance –Make them look good Raise awareness of previously unanswerable questions

16 Drug Discovery & Due Diligence Information Requirements Set up alerts/RSS feeds on company, compound, clinical trial info, etc Whats in clinic for indication, trial info/protocols and stage of trial Safety issues Potential alternative indications Biomarkers Toxicities of compounds for indication Potential consultants, collaboration map More comprehensive searches for research, development, pharmacodynamics, clinical trials, adverse events, etc.

17 Text Processing Stemming, Stop-word filters, Pattern filters, Lexicon matching, Ontologies, NLP parsing etc,.. Feature Extraction Statistical: Word Counts, Pattern Extraction & Counts, etc Domain-specific Gene Name counts, etc NLP-specific Phrase counts, etc Data Mining Classification, Clustering, Association, Statistical Analysis, Visual Analysis, etc … Text documents Text docs Numerical Feature Vectors Retrieval/ Storage Indexing Access Drivers Storage Text docs Pre-process documents to enhance the ease of feature extraction Features are summarized into vector forms which are suitable for data mining Results can be document characterization or hidden relationship extraction Retrieve and organize relevant documents Using workflow technologies to build text mining applications using finer grain components/services Typical Text Mining Workflow

18 Overview Collect –Quosa –Medline Explore –Biovista Extract –Linguamatics I2E Infrastructure –KDE

19 Quosa Federated search/alerts Localize full-text papers Find information not found in abstracts (kinetic parameters, experimental protocols, etc) Manage literature Collaborate Analyze literature sets Develop corpora for other applications to analyze

20 Biovista Interactive Co-occurrence Analysis Basic Research –Target expansion and off-target effects –Experimental design –Going fishing –Finding connections between known facts –Comprehensive summary of a research area –Collaboration Clinical Development –Drug-Drug interactions –Timeline studies –Side effects to worry about Intellectual Property –Analyze issued patents Competitive Intelligence

21 Linguamatics I2E Fact search engine Uses semantic entity types coupled with syntactic search criteria for relationship extraction Agile NLP application


23 Inforsense KDE Text Mining Infrastructure Text/Data workflow environment

24 Use case 1: Where are early licensing opportunities in academia? Goal: identify areas of research that could yield potential therapeutics Criteria: some efficacy is established in the form of testing in animal models Pre-IND filing Approach: Survey the literature for papers that describe in vivo testing of reagents that affect a particular biology (eg immunity, neurology or tumor growth)

25 Paint a picture of the desired target Use internal projects to develop search criteria Four early-stage projects each have 5-10 papers describing neutralizing antibodies The papers mention an indication only half the time The papers always mention tissues and cell types Antibodies are described in a limited number of ways The target of the antibody is almost always in the same sentence as the antibody term The ability of an antibody to block function is described in a limited number of ways

26 Use the desired features to construct a search Antibody and protein terms in the same sentence Block/neutralize and variations somewhere in the abstract Nervous tissues somewhere in the abstract a neutralizing monoclonal antibody against IL-1 beta was infused into the wound immediately following the injury a neutralizing monoclonal antibody directed against MMP-9 was administered intravenously anti-rat neutralizing IL-1 beta antibody (anti-IL-1 beta) or control immunoglobulin G antibody (IgG) was microinjected potent blocking of p75 binding occurs only with MAb 909 an antibody that blocks erbB2/neu-mediated signaling inhibited vestibular ganglion neuron viability

27 Search Results

28 Use Case 2: the Gene List Official Name BIIB name Itga4 Tysabri Itgb1 Tysabri Tnfsf13b BAFF Tdgf1 Cripto Cd80 Galiximab Fcer2a Lumiliximab –Generated by biomarker studies, toxicity studies, central to translational medicine –Often hundreds of genes –Official names are obscure –Finding all the names, the most common name is hard –On average, one a week

29 Find Relevant Genes from Online Databases Find Associations between Frequent Terms Gene Expression Analysis A Literature Analytics Workflow

30 Visualizing search results and information within yields new insights Paging through abstracts one by one doesnt show the big picture: –Whos collaborating with whom? –Whos patenting their work? –When did the field develop and mature? –Who are the opinion leaders?

31 1934 Author/Affiliations 8893 relations Blue = Aurora Kinases Green = Cancer lit Red = Patents lit


33 Where do we need to be? Spend less time acquiring, more time assimilating Provide domain experts with powerful literature analytics Mix/match best of breed applications for combining text/data mining Need knowledge discovery/exploitation environment that supports rapid construction of integrated text/data results for researchers

34 Acknowledgements Connie Matsui June Ivey Pam Gollis Harry Bochner Adrean Andreas Cindy Shamel Steve French Research Informatics

Download ppt "William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 William Hayes, PhD Phoebe Roberts, PhD March 19, 2007 Biogen Idec Literature Informatics for Drug."

Similar presentations

Ads by Google