Changing the Way Diseases Are Cured
Biomedical knowledge is exploding including detailed clinical observations generated from high throughput assays, electronic medical records, public biomarker research, and electronic storage of pre-clinical or clinical trial experiments. But the odds of a potential compound making it through the development pipeline and becoming a drug are low. The new wealth of knowledge has the potential to speed the process, reduce the costs, and increase quality controls in drug discovery and development. Without the appropriate technology and experience to integrate and present the knowledge the information is not available or useful to answer questions for scientists and clinicians.
Pharmaceutical research and development organizations can leverage clinical data through a Recombinant By Deloitte Pharma Translational Research solution to:
- Reduce time and cost to new approved indications
- Define pharmacogenomic assays and personalization protocols
- Use pathway analysis and biomarkers to direct research investment decisions
- Execute the transition to bench to bedside translational research
Understanding how biomarkers can predict positive outcomes can help patients and clinical trial participants make increase the benefit of therapies by personalizing treatments to their biochemical and genetic profiles. Systems biology, the framework scientists need to execute personalized medicine, requires a new approach to data management and analysis. If a one size fits all blockbuster drug strategy is not working then the biological environment around therapeutics needs to be investigated to understand and increase the impact to potential patients. A clear picture in systems biology can only take form by taking into consideration the many components and working to look into the complexity of the systems. Experiments from across many areas are necessary to provide sufficient visibility to understand the mechanisms at work in the complex biological systems affected by drugs. The old approach of running experiments in silos is not proving effective at supporting this vision and many resources are being wasted creating results that can't be integrated.
Recombinant By Deloitte's Pharma Data Trust provides a data repository for viewing data sets and analyses from across the discovery and Research & Development (R&D) process to find and understand the impact of associations within gene variations, pathways, cell types, proteins, diseases and the network of effects of biologically active therapeutic compounds on them.
- Accelerate the drug discovery and development process by providing a scalable platform for biomarker analysis
- Provide translational data to support target qualification
- Identify biomarkers to interpret efficacy and toxicity throughout the drug development process
- Provide quantitative and qualitative information to inform indication selection decisions for compounds in the development process
- Enable pharmacogenomic discoveries to increase trial protocols in order to establish trials with the higher potential to achieve desired outcomes
- Integrate public trial data to enable enhanced trial design
- Deliver information and visualization of data to establish the need and requirements for standardization of coding for common information (e.g., clinical trials)
- Provide a platform for collaboration by sharing information about common concepts across groups (pre-clinical and clinical, biologists, clinicians and bioinformaticists)
The Recombinant By Deloitte pharma solution provides the technology including the Recombinant By Deloitte Data Trust and the experienced services needed to build and operate a data warehouse using clinical data sets.
- ETL design and loading of the data repository
- Data governance and management of consent
- Integration of front-end tools to analyze data like the i2b2 (Informatics for Integrating Biology and the Bedside) translational research application
Additional services are available to establish dictionaries, standards, ontologies, and operating procedures to align data collected across locations with source or public systems for the management and normalization of inbound data sets with regard to cell lines, lab samples, diseases, clinical trial observations, and molecular biology concepts.
Integrate multiple platforms
Leverage platforms including:
• Clinical trial management systems(CTMS) Electronic data capture (EDC)
• Tissue banks
• Analytics repositories
• Electronic laboratory notebooks
• Gene expression
• Single-Nucleotide Polymorphisms (SNPs)
• Copy Number Variation
• Rules Based Medicine
• Flow cytometry
• Entrez, EBI, Pubmed
• GEO (Gene Expression Omnibus)
Breaking Down the Pharma Data Silos
Traditional analysis in pharma has occurred in silos across many different groups all studying different angles of the same biological concepts but no centralized approach to studying the biomarkers themselves across groups. Results of pre-clinical experiments rarely reach the teams conducting clinical trials and the data can't be combined despite the common biomarkers and processes being studied. Trials conducted on the same therapeutic compound or indications rarely share results from common assays conducted across trials. Assays that relate to the same genes but in different states, e.g., mRNA vs. final proteins, are challenging to connect even within a single trial because they are locked in separate data sets not connected by the biological references that tie the data together. Publicly funded published research and data that would shed a light on disease functions like Entrez, GEO, clinicaltrials.gov, and PubMed are difficult to integrate with internal research because they are in too basic of a state, poorly curated, or are unavailable.