big data and its impact on industry (example of the pharmaceutical industry)

Big Data Trends—A Basis for Personalized Medicine

Dr. Hellmuth Broda, Principal Technology Architect

eMedikation: Verordnung, Support Prozesse & Logistik

5. Juni, 2013, Inselspital Bern

Who is Infosys?

Over 150,000 Employees From 89 Nationalities

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but there is an innovation gap

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They know that the technology that built their success will not be relevant tomorrow

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Using every Infosys capability and IP, we help our clients exploit

opportunities for growth

By pursuing innovation alongside clients it is easier to help unlock the potential of their technology

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Photo by Infoscion Shailendra Tawade

Looks to the future for opportunities

We Are in the Midst of an Information Tsunami

• Mankind is generating more data in two days than we did from the dawn of man

until 2003 (Eric Schmidt, Google)

• Information is doubling every 18 months

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But this report did not include

genome sequencing:

Source: IDC White Paper: The Diverse and Exploding Digital Universe

Source: National Human Genome Research Institute

What is “Big Data”

• The global information explosion does not exclude the medical community

• In the past we used “Data Warehousing” to make data available for new quests

• But Data Warehouses are limited to one compute center and (often) one server

• Data Warehouses need structured tabular information

• But most of the information is free text

• Big Data approaches allow to tap the gold nuggets hidden in the haystacks of free text information

• Some believe medicine can become more of a science, rather than a practice, by relying more on technology

• By combining data from many current and future devices and from different records, healthcare providers can have greater insight into medicine

• This could generate thousands of data points about a person’s health and lead to faster and cheaper correct diagnoses

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Promises of Big Data

• More and more data can be gathered to identify patterns and

physiological interactions

• Machine learning software can point to abnormalities and predict health issues

• Smart phones and “iAnythings” will empower the patient to monitor his health

• Big Data analysis can help to move from corrective to preventive medicine

• Doctors will increasingly rely on technology and Big Data for triage, diagnosis and

decision-making

• Consequently, doctors will perform better, health care costs can decrease, and

patient care can improve

• Pharmaceutical companies when screening large clouds of Big Data can find and

corroborate seemingly weak correlations and target medicines to subgroups of

patients with similar genetic backgrounds

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Big Data Will Enable Smarter Healthcare

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Challenges Faced by Managing Big Data

• Explosion in Volume of Data Available for Research

– Expansion into genomics, proteomics and metabolomics research has

resulted in an explosion of data across the research organizations

– Advancements into Biomarkers and Imaging biomarkers have added to the existing data

volume and need for high throughput computing

– Availability of data from the public domains and collaborators for research have added to the

current data management issues

• Existence of Data Silos

– A large amount of this data resides in data silos across the global sites.

– In addition, translation and personalized medicine based drug development requires an

integration with clinical and real-world patient and physician data which are spread across

disparate silos across the globe

• Infrastructure challenges to meet the high throughput computing needs

– With the increase in computing and collaboration needs, IT infrastructure strategy needs to

adapt to keep up

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Leveraging Value from Big Data

• Integrate data siloes across the research organization to allow

scientists easy access to the wealth of information available

– Also the ability to capture and ingest data from multiple structured and unstructured data

sources

• Develop and place flexible and broad high throughput analytical layers on the

integrated data sets to mine the information

– Continue to allow the researchers the flexibility of choosing a broad array of analytical tools that

fit there research needs

– Advanced near real time analytics over Big Data combining structured and unstructured data

– Ability to process data on cloud architecture and cloud platforms

• Develop scalable and flexible infrastructure to support the increasing computing

needs.

– Bioinformatics is increasingly needing Tera-Scale computing in the areas of drug discovery and

drug repurposing

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Big Data in Life Sciences*

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Big Data Analytics

Sequencing and gene

expression data

Drug data (pathways,

structure etc.)

Clinical Trial & Hospital

Electronic Record data

In vitro diagnostics

& X-ray results

Patient self-reported and

social media data

Drug repurposing

New biomarkers

New drug targets

Personalized

healthcare

Adverse event

detection

Etc.

Telemedicine data

*This and the following nine slides contributed by Edward Currie, AVP Life Sciences, Infosys

Big Data in Life Sciences—Example Use Cases

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Drug

Repurposing

Treatment

Adherence

(Compliance)

Health record-

guided drug

development

Adverse Event

Detection

Next

Generation

Sequencing

Patient Pre-

Profiling

Personalized

Healthcare

Influencer

Profiling

Drug Repurposing

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Compound structures

Transcriptome signatures

Drug/protein interactions

Side-effect profiles

Mechanisms of action

Understand

which new

diseases are

most likely to

be treatable

with a drug

Drug Repurposing

BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

Healthcare Record Guided Drug Development

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+ Healthcare record data

Drug data as for DRP

Understand

outcomes of patients

who have been

treated with drugs of

similar profile to that

of the drug of

interest


Identify patients for clinical

trials

Predict “real-world” efficacy

& safety

Reiterate drug design to

optimize efficacy & safety

Next Generation Sequencing

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Outcome data

(clinical trial or hospital)

Sequencing data Understand

which gene

mutations are

associated with

which diseases


New drug targets

New biomarkers

Personalized Healthcare

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Understand

which patients

respond to

which drugs

Personalized Healthcare


Diagnostic test (Dx) data

Prescription (Rx) data

Sequencing data

Outcome data

(clinical trial or hospital)

Compliance (Treatment Adherence)

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Social media analytics


Understand

why patients

don’t take their

medication

correctly/fully

Treatment Adherence


Adverse Event Detection

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Identify

otherwise

unreported

drug side-

effects/

interactions

Drug safety


Patient Pre-Profiling

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Identify patients

eligible and willing to

take part in clinical

trials, and who have

elected to be gen-

etically profiled

Clinical Trial Subject

Pre-Profiling




Personal genomics data

Influencer Profiling

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Identify the key

influencers among

different customer

groups: key opinion

leaders; prescribers;

patients

Influencer Profiling




Medical literature

Infosys’ BigData Edge

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BigData-Edge

• Agility Actionable intelligence from new data in hours

• Speed Expedite your processing deployment across all data types by 10X or more

• Business Value Monetize your data through applied insights

Consumption

Full Featured Hub Management One-Click Cloud Deployment -Seamless Cluster Setup, Configuration

Industry leading Visualization techniques for deep insights

Integration with wide variety of industry solutions Industry

specific

Solutions

Enables building solutions though ETL like graphical tool—accelerating time to market while reducing cost

BigDataEdge “Intestines”

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Conclusion

1. Data in our world are growing exponentially

2. There is tons of hidden and useful information in those data

3. Clever methods to mine these data are necessary

4. Big Data analysis methodologies are instrumental in detecting new information

5. Infosys’ BigDataEdge is one of the most mature solutions on the market

6. Big Data will help to detect correlations between people and illnesses

7. Big Data will help in Drug Repurposing

8. Big Data will allow for patient pre-profiling

9. Big Data will help in adverse reaction monitoring and detection

10.Big Data will help progress in Personalized Medicine

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