8. Science at the Bench and the Bedside: Less of a Tightrope, More of a Super Highway
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Transcript of 8. Science at the Bench and the Bedside: Less of a Tightrope, More of a Super Highway
Enabling Science
IDBS and Translational Medicine
Enabling ScienceSlide 2 © IDBS 2012
Enabling ScienceSlide 3 © IDBS 2012Right Treatment to the Right Group at the Right Time
Drug toxic but beneficial
Drug NOT toxic and NOT beneficial
Same diagnosis, same prescription
Patient group
Drug NOT toxic and beneficial
Drug toxic but NOT beneficial
Enabling ScienceSlide 4 © IDBS 2012
Health Science Ecosystem: Driven by Data
Medical Research
Centre
Pharma and Biotech
Healthcare Provider Healthcare
Payer
Diagnostic company
Patient dataand samples
High qualityPatient dataand samples Clinical Trial
Candidates and Outcomes data
CompanionDiagnostics
Molecular assaysfor disease and
safety predictions
Predictive modelsand Comparative
Effectiveness
New drugEffectiveness
Data
Outcomes analysis
Outcomes data
Enabling ScienceSlide 5 © IDBS 2012
Advanced Clinical
ResearchDiagnostics
Pharma and Biotech
We are privileged to work with:
Enabling ScienceSlide 6 © IDBS 2012
Stratification: Some Common Problems Cohort selection is complex and
users are unable to interactively explore and define cohorts
Clinical / Patient data are not easily connected to research data
Multiple sources of clinical information from EHR, patient history, pathology, HL7, CDISC, EDC, CTMS
Clinical data can be of poor quality
Need to support regulatory and clinical compliance e.g. Pseudonymisation
Enabling ScienceSlide 7 © IDBS 2012
IC50
Enabling ScienceSlide 8 © IDBS 2012
It is possible to solve these problems
10 years clinical
trials data
Omics Data
ResearchData
NormalisationQuality scoreLongitudinalDisease
Outcomes analysis
BiomarkerAnalysis
Comp.Effect.
Trial Simulation
Enabling ScienceSlide 9 © IDBS 2012
Data Landscape NOT Data Warehouse
Data from disparate stores can be located and analysed in situ
New data types easily indexed for ‘future-proofing and flexibility’
Can be readily mirrored for ‘up-time’ security
Demands all data to be moved and stored centrally
New data types demand new data models
Becomes huge very quickly
NOT
Enabling ScienceSlide 10 © IDBS 2012
Read and count the Fs – 5 secs
FINISHED FILES ARE THE RESULTOF MANY YEARS OF SCIENTIFIC STUDY COMBINED WITH THE EXPERIENCE OF MANY YEARS
Enabling ScienceSlide 11 © IDBS 2012
How many F’s
Enabling ScienceSlide 12 © IDBS 2012
FINISHED FILES ARE THE RESULTOF MANY YEARS OF SCIENTIFIC STUDY COMBINED WITH THE EXPERIENCE OF MANY YEARS
Enabling ScienceSlide 13 © IDBS 2012
Computers don’t tend to miss stuff
They can help with analysis They can remove the human factor where
neededBut not replace it where it is needed
Decision Support not Decision Making
Enabling ScienceSlide 14 © IDBS 2012
Surfing the Omics wave
Enabling ScienceSlide 15 © IDBS 2012
What Happens in a NGS Study
Study Design
Dosing & Sample
Collection
NGS and Analysis
Reporting
Enabling ScienceSlide 16 © IDBS 2012
NGS Workflow leveraging the Biomolecular Hub
Single end (SE) readsPaired end(PE) readsDirectional SE reads
Raw Reads
IDBS Biomolecular Hub
Raw Readscsfasta, fastq, fasta
Reference sequence
Alignment(Bowtie, BWA, SOAP)
SNP
In/Del
SAM/BAM
SNP report
In/Del report
Genomeviewer
Downstream analysis
Feature filesAnnotation files
RNA-seq
ChIP-seq
Exome-seq
Whole Genome
Enabling ScienceSlide 17 © IDBS 2012
Ethics and Consent Management, Clinical Data Collection, Sample Collection, Banking, Omics Processing and Analysis
Sample Collection (from patient)
Aliquots and storage
... ... End Repair Concentration
estimation (pico green)
Not just data harmonisation – but process harmonisation
Enabling ScienceSlide 18 © IDBS 2012
How very similar to ...
Pharmacology, BA, PK, Formulations, QA, QC, ...
The interesting aspect here is it is a language or “meta data” and process issue not a technology issue
If you have the right technology and data management – “the case is solved”
Enabling ScienceSlide 19 © IDBS 2012
The same is true in most areas
Allow data normalisation against many different ontologies science, disease, research, clinical (ontologies/taxonomies)
Allow data exploration in any number of tools (standards and openness)
Streamlines analytical workflows – empowering scientists
Psedonymisation of patient data and importantly re-identification of patients based on security
Enabling ScienceSlide 20 © IDBS 2012
Collaboration
I cant see CRO and collaborators data next to my internal results
All my data is in Excel, I cant find anything or search it
I cant share my cohorts with colleagues easily
I need to share data securely with my partners
I want to generate and validate my reports more quickly
Enabling ScienceSlide 21 © IDBS 2012
Case Study Advanced Clinical Research
Improved speed, quality and volume of oncology research will deliver enhanced patient outcomes
Supports partner collaboration across the organisation, external collaborators and industry
High quality data sets and cohort tools will make King’s an attractive partner for clinical trials
King’s Health Partners is a £3 billion academic health science centre comprising multiple hospitals and research groups. As part of a major business initiative King’s and IDBS have developed an enterprise-scale translational research platform to deliver increased levels of highly innovative research through an improved ability to integrate and analyse clinical, image and ‘omics data across the partners.
King’s Health Partners is a pioneering collaboration between King’s College London and Guy’s and St Thomas’, King’s College Hospital and South London and Maudsley NHS Foundation Trusts.
Enabling ScienceSlide 22 © IDBS 2012
ACROPOLIS – an example health science ecosystem powered by IDBSLead by IDBSHosted UK National infrastructure for translational
medicine and collaboration in Cancer Initially - Consortium of Kings Health Partners,
Manchester AHSC, Roche Pharmaceuticals, PA Consulting, CRUK, NIHR
Once delivered - Roll out to all other cancer centres in UK
Based on the IDBS translational medicine platform
Enabling ScienceSlide 23 © IDBS 2012
Acorns and Oak trees
Enabling ScienceSlide 24 © IDBS 2012
Some IDBS Translational Science / Research / Medicine Case Studies Medimmune
◦ Biomarker discovery and qualification Daiichi Sankyo
◦ Population PK / PD simulation and biomarker analysis of historical trial and omics data Large Pharma
◦ Analysis of historical trial and omics data Large Pharma
◦ Omics Data indexing and analysis Windber Research Institute
◦ Biomarker discovery and clinical risk assessment in Cancer Barts and the London Hospital
◦ Adaptive Clinical Trial Design, Patient recruitment and Execution using Translational Medicine knowledge in CV treatments
Dana Farber Cancer Institute◦ Translational Medicine Research data capture and collaboration for multiple myeloma and lung
cancer Imperial NHS Trust
◦ All of the above and more over the next 7 years for all diseases◦ Biomarker Discovery for use in Clinical treatment of CV disease
Kings Health Partners ◦ All of the above over the next few years◦ Starting in Cancer moving to all disease areas
Enabling ScienceSlide 25 © IDBS 2012
Forward Looking
Clinical decision support◦ Take all the data
landscape◦ Learning models◦ Deliver tools to help
make decisions
◦ No longer chasing a dream
Enabling ScienceSlide 26 © IDBS 2012
Ambition is becoming reality
Science at the Bench and the Bedside: making it less of a tightrope and more of a super highway
Enabling ScienceSlide 27 © IDBS 2012
Translational Medicine – a long road aheadSomething I am very used to - St Malo to Biarritz
in 6 days then the Tourmalet !
Enabling ScienceSlide 28 © IDBS 2012
Enabling ScienceSlide 29 © IDBS 2012
IDBS Brings Together the Pieces of the Translational Puzzle
Clinical Data
TranslationalScience
Sample
‘omics
Study /Research
Public and Proprietary
Clinical
Enabling ScienceSlide 30 © IDBS 2012
Special Thanks to name but a few
WindberDana Faber MedimmuneCardiff UniversityBartsKings Health Partners Imperial NHS TrustGenomic HealthMayo ClinicRocheCelera …
CustodixOracleOrion HealthGoogle Images !
IDBS◦ Everyone