Pistoia alliance debates analytics 15-09-2015 16.00

2 May 2023

Analytics in pharma R&D

a Pistoia Alliance Debates webinarchaired by Matt Jones

Partner logo if required

This webinar is being recorded


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Agenda


• Introductions• Analytics growth and recent trends: Matt

Jones, Tessella Analytics• Insights of how big pharma is responding:

Simon Thornber, GSK R&D IT• How a platform supplier is reacting - use

cases and examples: Eduardo Gonzalez, Perkin Elmer BioInformatics Products Manager & Strategist

• Q&A• Close

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Panellists


Matt Jones has 16 years' experience of working in R&D IT groups within the pharmaceutical industry. He joined GSK (Glaxo Wellcome) following completion of his PhD in synthetic organic chemistry and worked through various groups and domains, from cheminformatics through to enterprise R&D IT delivery, before leaving to join Tessella last year. Matt now works as part of Tessella Analytics, working with our partners to leverage analytics and bear down upon the challenges modern R&D faces today.

Eduardo Gonzalez received his PhD in Molecular Genetics from the University of Geneva in 1997 then joined GSK (Glaxo Wellcome) in Geneva. He has extensive experience working with a number of leading pharmaceutical and biotechnology companies across Europe. Eduardo joined PerkinElmer Informatics as Bioinformatics Product Manager in 2014, having previously been Chief Technology Officer then Chief Strategy Officer atIntegromics.

Simon Thornber has been at GSK for almost twenty years, during which time he was worked in Bioinformatics, Cheminformatics, and Research IT. His current role is 'director of analytics' in R&D-IT, with responsibilities for driving innovation in analytics and scientific computing.

Analytics and

Pharma R&D

Matt Jones

Tessella AnalyticsDomain

Knowledge

Data Handling

Skills

Math & Statistics

Knowledge

Machine Learning

Self Service Traditional

Research

250 staff Over 30 years of

experience

Delivery of 1000s of data

analytics projects

Platform

independent Tessella Analytics

Analytics - growth

Analytics – Trends

• Lots of (big) data– Combination of external

and internal data– And the diversity of

sources• Visualisation and explore

relationships– Dashboards and

interactive plots• New insights from old

data

• Internet of Things • … and wearables

• Rise of the data scientist role

• Analytics as a service• “No IT” solutions

• Cloud analytics• …

Oversight Hindsight Insight Foresight

Not all Analytics are the Same

Predictive AnalyticsLet’s see if we can guess what will happen next?

Prescriptive AnalyticsWhat should we do now to optimise our business?

Descriptive AnalyticsLet’s see if we can show what happened?

Diagnostic AnalyticsLet’s understand why it happened

visual explorationpattern finding

predictive modelsoptimal choice

manual automatic

Analytics Essentials

Speed Trust

R&D and the need for speed

• R&D is a unique environment• Speed, Trust and Agility needed• People need answer to questions

immediately– Have rapidly changing priorities across a

number of scientific domains– Direct and govern the next phase of work– Can’t wait for months or even years

In R&D the right technology can start the analytics opportunity. The right people with the right skills are needed to deliver quick solutions and keep the research cadence going.

Analytics presents many opportunities and challenges…

How is R&D responding?

Data Management Strategy.• Data retention strategy.

– When storage costs can outstrip generation costs, we need a very clear data retention strategy.

• Data Blue Printing.– How is data flowing through your processes, and

how is it informing decisions?

• Clear Data Standards.– Which ones are you adopting, and how will you

translate between leading standards? (Batch, Lot, Sample, Batch record, etc..)

• Roles like Chief Data Officer becoming important.– Company wide standards, sponsored from the

top.

Secondary Data Use

Public Data Published DataIn House Data Real World Data

Data Translation (Re-Formatting, Text Analytics, Normalisation) to produce

ANALYTICS READY DATAData Analysis

Partner Data

Bringing our software to their data. Challenge:

•Data Sets can be too large to mirror.•Data may not be permitted to leave a Partner organisation.

One Solution:

• Embassy Approach:• Secured computing environment in the Partner organisation.• GSK has set up one of these at the EBI.

• Firewall• Intrusion detection• Antivirus• 2FA• Encryption at rest• VM management• Software licensing• Back-ups• User Account management• Support• etc….

Complex calculations.

http://aws.amazon.com/solutions/case-studies/novartis/

One of the most extreme use cases is from Novartis who run complex insilico screening on the cloud - the linked example shows how they ran a 10 million compound screen over 87,000 compute cores for 9 hours (39 CPU years of calculations), for a cost of $4,232

Need for a clear Information protection strategy

http://aws.amazon.com/solutions/case-studies/novartis/

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Agenda

• Data Science examples

• The biomedical industry vision

• Data re-use examples in the biomedical sector◦ Gene Expression Omnibus (GEO) – Children’s Tumor Foundation

◦ SciDB - Novartis

◦ The Cancer Genome Atlas (TCGA) - Roche

• Devices re-use new trend

• Potentiating a new role

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Data Science examples

• 2015◦ To - Predicting novel therapeutic targets, novel biomarkers

- Genome sequencing(genomic variants, gene expression patterns, etc…)

- Medical, pre-clinical & pathology imaging

- Electronic Health Records, Sensors…

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The biomedical industry vision

• Is it possible to predict new drug targets and patient responses

?

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Agenda








• Targeting Data Scientists

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• Translational Medicine is already transforminghow new therapies are discovered and developed

Patient Population Segregated Patient PopulationIHC

FISH

Multiplex ELISA

NGS

GEA

CNV and Translocations

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• Translational Medicine is already transforminghow new therapies are discovered and developed whilehigh-content omics technologies accelerate this trend

Patient Population Segregated Patient PopulationIHC

FISH

Multiplex ELISA

NGS

GEA

CNV and Translocations

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Data re-use examples in the biomedical sector

• Gene Expression Omnibus

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• “In database” computing

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• The Cancer Genome Atlas (TCGA)◦ Predicting novel therapeutic targets, novel biomarkers…

◦ Example of public genomic data mining & leveraging

◦ TCGA- Contains the main

genomic changes in cancer

- >30 cancer types- >45000 archives- Size ~75 TB

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• The Cancer Genome Atlas (TCGA)◦ Predicting novel therapeutic targets, novel biomarkers

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• Targeting Data Scientists

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Devices re-use new trend• Sensor analysis for improved trial designs with Kinect

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Devices re-use new trend• At the interface

between diagnostics and telemedicine

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Devices re-use new trend• At the interface

between diagnostics and telemedicine

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Potentiating a new role

• Producing the data is not where the value resides◦ Mining the data provides the valuable findings◦ Algorithms are at the core of analytic capabilities

• Analytics potential value◦ Find patterns/markers associated with a patient response◦ Extract quantitative and discriminative information (faster) from sensors

• Technologies involved◦ Computing closer to the data source◦ “Translational” databases

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Potentiating a new role

• Challenge◦ The complexity and size of the data, coupled to complex technologies limit the

opportunity for life science experts to explore and interpret the data

• A solution – Data Science◦ Integrate the tools allowing to process the data and visualize the results◦ Data Science combines strong scientific and disease domain expertise with

analytics capabilities to generate answers rather than information

• Educate “Data Scientists” to be able to use such integrated tools,enabling them to perform advanced results exploration and queries

◦ For instance finding patients with similar patterns of mutationsin large genome-wide association studies databases

Audience Q&APlease use the chat / question functions in GoToWebinar


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