D360: Unlock the value of your scientific data Solving Informatics Problems for Translational Research

Transcription

1 D360: Unlock the value of your scientific data Solving Informatics Problems for Translational Research Dr. Fabian Bös, Senior Application Scientist Certara Spain SL Martin-Kollar-Str. 17, Munich

2 Certara is the amalgamation of 3 companies covering different areas of drug discovery & development Tripos - Discovery Simcyp Preclinical Pharsight Clinical Building product portfolio through acquisition and organic growth to address scientific data needs for Translational approaches to life science research. 3 key areas of interest In Silico modeling and prediction across discovery/development Improved prediction of biological events from the molecular to the whole organism level Leverage of technologies from formerly disparate areas Data access within and between expertise silos Improved access to and thus use of data for all scientists throughout the process Providing an ability to generate answers to a very wide range of scientific questions Integration of data access with modeling Model creation for specific discovery and development end points Model deployment throughout the discovery and development process 2

3 Software Portfolio 3

4 Certara Consulting 4

5 Big Data, Complex Data, Many Sources More disciplines are generating more data than ever Combinatorial chemistry High throughput screening High content screening Proteomics Genomics The data is inherently complex Context a value only has meaning when you know how it was obtained Relationships meaning can only be derived when the right relationships between data are in place Data is stored in many sources Scientists often manually construct their datasets after using different applications Systems built to help scientists are custom to address a single bottleneck 5

6 Hard Coded Data Access Chemical data access Safety Data Access Research reporting Chemical Inventory Scientific literature search Discovery information portal Structure visualization Data manipulation & analysis Data analysis In Vitro data access Electronic notebook Research communication 6

7 A Data Network In Vitro Results Observations Patients Projects Batches Compounds Clinical Studies Treatments Assays Preclinical Studies Animals Treatments Observations 7

8 A Data Access Problem Discovery Discovery data available in multiple data sources Data is accessible, through an internally built application but Application was last updated ~2 years ago Requires a team of FTEs to support (just the application) Supports general user workflows for accessing project data in standard views But User requests for enhancements not satisfied for some time Many important data sources not integrated In vivo data Licensed commercial databases Very few analysis tools or integrations with other applications complex workflow 8

9 The Discovery Data Network in D360 Data Network for discovery data Data from chemistry and biology databases cast in terms of entities of interest Compounds Targets User can choose the data level Compound, Salt, Batch/Lot, Project, Assay Na + Salts Projects Assays Supports a wide variety of workflows: Standard Project data views Exploratory SAR data mining Assay QA/QC Batches Results Initial system deployed in ~ 4 weeks Currently in production at top 10 pharma I can get datasets and perform analyses that were difficult of not impossible before 9

10 A Data Access Problem Preclinical Safety Safety Study Data entered into a commercial system Data is accessible, but Only at a study level, one study at a time Easy to get all data for a project Monitoring and Study Report Study ABC-123 Study Groups Animals Very difficult for Pathologists to understand data across studies e.g. Historical control group behavior Can take days/weeks to answer project team questions on current study findings Can take days/weeks to answer regulatory body questions The situation gets worse when additional data is brought into the picture TK data is stored in different LIMS system Assay Data is stored in a different system Findings & Measurements 10

11 The Safety Data Network in D360 Data Network for preclinical safety Data from Safety database cast in terms of entities of interest User can choose the data level Study, Study Group, Animal, Observation, treatment Supports a wide variety of workflows: Cross study control group analysis Study Design from historical data Logistical Study monitoring System deployed in ~ 2 weeks Currently in production at top 10 pharma Cross study analysis of control groups can now be performed in minutes rather than weeks 11

12 Expanding this Data Network Additional Data Entities Chemical Structures TK parameters Discovery Assay Data From Additional Data sources Chemical Structure Cartridge LIMS system Assay database Adds additional workflows: Improved preclinical study design Structure-Safety relationships Exposure-Safety relationships Clinical finding Assay data relationships Current status Configuring TK data 12

13 What does the user of D360 see? Any user of the system can build a query Without knowledge of specific data location/format Without knowledge of Oracle, web services and other related technologies System is configured to show scientific entities of interest Presents data in the user s context 13

14 User Adoption The Google Effect: Everyone is used to Google very simple to use Not Every user wants to build a query D360 allows search capabilities to be Googleised Users who build data searches in D360 can widgetise them Leverages the intellectual assets of the team 70 14

15 Recent D360 Partnerships - Building the ecosystem Connectivity with the Cortellis web based data sources. Connectivity with MarvinSketch and JChem Cartridge. Connectivity with pre-clinical safety data from Pristima or customized data marts. Competitive intelligence decision support. 15

16 Summary Provides access to scientific data Accesses scientific data from structured storage Single databases, Multiple databases, non-database sources (web services) D360 transforms scientific data (mere access is not enough) Transforms from a complex structure into something a user actually finds useful Presents scientific data So a user can build a query themselves Spreadsheets, forms, grids, graphs and charts, correlation matrices, formatting, statistics, equations, etc. Whatever is most appropriate to the user s workflow Help the user derive meaning Shares scientific results and supports decision making Workspaces, annotations, notifications Integration with productivity tools and existing corporate systems 16