1. Scientific Excellence Innovation Collaboration
“Big Data and How It Is Being Used to Transform the Pharmaceutical Business Model”
Guna Rajagopal, VP & Global Head, Computational Sciences
Pharmacogenomics
Scientific Excellence Innovation Collaboration

2. Integrating the digital universe of data to deliver precision medicine
Eric Schadt, MSSM

3. Anon. The Big Data Challenge Big data is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it …
Anon.

4. Opportunities & Challenges
Growing confidence in ability to leverage ‘Big Data’ for healthcare despite critical challenges involving :
sustainability,
harmonization
Indemnification.
Increasing computational power and cheaper sequencing costs
Multiple academic, government and private company efforts initiated
Rich databases under development
Multi ‘omics and metadata
‘Normal’ and disease populations
Link genotype/phenotype
Key issues of privacy, confidentiality and sharing of data to advance R&D still unresolved.
Cost
depending on duration, # of individuals, type of data collected
Study subject retention
easier in clinical trials cf. prospective studies
Biobanking
quality, # of samples, heterogeneity
Sampling Errors
sample size, incomplete/noisy data
Bioinformatics
Signal/Noise issues
Experimental and/or study design
nature and scope of query, ease of acquiring high-quality samples and associated phenotype data

6. Example – Application to Pharmacogenomics
The Question: How do we identify new genetic markers and mechanisms for response/non- response for patients in ((anti-TNF treatment for RA)) using samples and phenotypic data collected in a clinical study?
This question addressed in 3 phases – we outline Phase 1
Collaboration: In partnership with R&D IT and Immunology colleagues and external collaborator (Nic Schork, JCVI), Amazon and SDSC experts.

7. Our Challenge – Integrating Big Data, Cloud, HPC platforms & Analytics
RA WGS
FASTQ, BAM,
VCF, GFF,
report
90 TB
Encryption
Factory
FASTQ/BAM
SDSC HPC
AWS
tranSMART
Amazon S3
FASTQ-> BAM-> VCF subset
BAM
Glacier (135 TB)
Clinical/biomarker data
VCF
Janssen
Scripps
Feinstein
Critical support from R&D IT and SDSC staff

8. Big Data Needs Big Compute!
257 TB Lustre scratch used at peak
5,000 cores (30% of Gordon) in use at once
Partnership with Intel/IMEC/SDSC to optimize code/HPC performance

9. Limitations - Meaningfulness of Answers
A risk with “Big Data Analytics” is that one can “discover” patterns that are meaningless => Check, Double Check & Verify!

10. REFINING THERAPEUTIC DECISIONS & PREDICTING DRUG EFFICACYNR R AE