Pistoia Alliance Debates: Sharing data with my co-petition 03-12-2015, 16.04

3 May 2023

Sharing data with my co-opetitionA Pistoia Alliance Debates webinar

Chaired by Richard Lingard, Dotmatics

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This webinar is being recorded


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33 May 2023

PanellistsRichard Lingard, Head of Commercial Operations, Dotmatics has a chemistry background and has been working in the life sciences industry for over two decades, focusing on the business side of drug discovery technology and services. Richard is responsible for the expansion and running of the commercial and customer-facing teams as well as building customer and partner relationships and executing company strategy at Dotmatics. Richard has enjoyed a number of international commercial roles at research supporting organisations: Thomson Reuters and Biovia, as well as research organisations like Argenta Discovery and Merck & Co Inc. As a Management & Chemical and Sciences graduate of UMIST he is still pleased to be using his degree in everyday working life.

Chris Waller received his Ph.D. in Medicinal Chemistry and Natural Products from the University of North Carolina in Chapel Hill in 1992. After a post-doctoral fellowship under the direction of Dr. Garland Marshall at Washington University in St. Louis, Dr. Waller began his career and has held a variety of positions in academic, government, biotech, and large pharmaceutical company sectors. In 2012, Dr. Waller joined Merck where he currently holds the position of Executive Director, MRLIT Modeling Platforms. Dr. Waller was a founding board member of the Pistoia Alliance, serves as an advisor to the Medicines for Malaria Venture and the Bill and Melinda Gates Foundation, serves on the Board of Visitors for the School of Pharmacy at the University of North Carolina-Chapel Hill where he has held an Adjunct Professor position since 2001, and is a frequent advisor to the FDA, NIH, NAS, IOM, and EPA. Dr. Waller has over 70 peer-reviewed publications and numerous chemistry and technology patents.

Martin Romacker is Principal Scientist in Data and Information Architecture and Terminologies at Roche Innovation Center Basel. Primary focus is definition and application of Data Standards to facilitate data federation and answering of complex scientific queries. Current activities include Terminology Management, Semantic Engineering, Scientific Data Integration/Curation, Text Mining and Information Retrieval/ Search Technologies. Previously a Senior Knowledge Engineering Consultant at Novartis. More than 20 years of practice in knowledge management. PhD in Computational Linguistics from University of Freiburg, Germany.


dotmaticsknowledge solutions

. . .

Sharing Data with my Co-opetitionRichard LingardSVP Global Commercial Operations

Dotmatics Ltd

[email protected]

Thanks to Rob Brown, VP Global Informatics

mailto:[email protected]


.. .

Budget - Reductions in Force in Science and IT

Portfolio - Changing ratio of Biologics to Small Molecules

Execution - Outsourcing and external collaboration

Methodology - Translational Medicine, Data Science, Omics…

The Changing Business of Research…

Patent cliffs and innovation deficit have driven life sciences to new business models


.. .

• Transformation has already happened– Top 10 Pharma dropped 30K employees between 2013 and 2014– 907 Biologics in Development in 2013 (out of approx 3400 total) – In 2014 total external R&D spend across top pharma was greater

than internal investment

• Is it working?– Strategically: More approvals? Bigger pipeline? Increased

profitability?– Tactically: Success in projects? ROI?

• How to increase the ROI and odds of success?– Organizational, cultural, scientific…

Changed Has Happened…Is it Working?

What can informatics do to help support successful change?


.. . What Can Informatics do to Increase Success?

Mordac the Preventer


.. .

• Support cost constrained research IT– Lower TCO– Increase agility (& customer satisfaction)

• Support resource constrained scientific teams– Integrated end-to-end workflows for efficiency– Self service decision support (data and science)

for innovation• Support the new research portfolio

– Biologics & mixed entities• Support the new scientific methods

– Translational science• Support the new organization

– Systems for collaboration

OR…informatics systems can…


.. .

• Demand from the business– Change has happened and now has to show

ROI• Enabling technology

– New technologies exist that weren’t available before

Why Now?


.. . Transformative Technology

https://www.gartner.com/doc/2049315/nexus-forces-social-mobile-cloud

Reduced TCOAgility, scalability and elasticity

Access anywhere

Real-time communication in any location

Informed Decisions


.. .

• Demand from the business– Change has happened and now has to show

ROI• Enabling technology

– New technologies exist that weren’t available before

• (Growing) acceptance from legal and management

• Scientific informatics vendor landscape is changing– COTS fully integrated suites are now available – Lab informatics, analytics and collaboration– Chemistry and biologics

Why Now?


.. .“Next Generation”

Integrated Informatics?Capability Legacy (Today) Next Generation

End User

Lab Workflow Multiple applications, custom integration, manual processes, ad-hoc requests

Fully integrated workflow for individual and across groups

Data access Multiple sources, multiple applications, “exact” searches

All relevant data in one interface (internal and external), new methods of search

Collaboration Manual, ad-hoc, work-arounds Team collaboration apps , team publishing on data, objects, analytics, KPIs

IT

Deployment Thick clients – high TCOOn-premise installations

Interactive web clients lowers TCOSimple deployment Enables hosted environments

Development environment

Proprietary scripting languagesCoding by IT or vendor servicesCustom versions

Configuration not customizationRapid application extension Webservice integrationSingle code base for ease of upgrade


.. . The Effect of Data Silos

Com

poun

d #

TimeThis result

On thiscompoun

d

Cannot influence the design

of these compounds

Data silos


.. . Federated Data Access


.. . Self Service Analytics and Visualization

Progressive Disclosure

Aggregated Data

All Data Types


.. . Workflow - EXAMPLE: Sample Manager

WorkflowExperimentRequestMonitorAutomate


.. . Integrated Informatics Solutions

Design

ReportAnalyze

Test

Make


.. .

• Submit single or multiple tasks to be performed on one or multiple samples

• Manage workflow, timing, resource allocation

• Request and schedule in-house or CRO tasks

• Track compound or sample progression

Request and Track


.. . Dashboards and KPIs


.. . Now Add External Partners….

Provision Spin Down

Design

ReportAnalyze

Test

Make

How to exchange scientific data?

How to communicate across the project

members?

How to spin up new

projects or partners quickly?

How to safeguard IP

and distribute it to the

partners?

How to track project status and work schedules?

How to maximize the efficiency of the virtual research team?

How to make project decisions collaboratively across

partners?


.. . The Maturity Curve Disconnect

*The De-Evolution of Informatics – Scientific Computing Oct 2012 – Michael Elliott

IT is struggling to catch up with the business• Only 2 of top 20 pharma

reported that they have a comprehensive externalization data management strategy

30% of biopharma R&D spend beyond company boundaries• 20% YoY growth on externalized

research c.f. overall spending is flat• 30-50% of discovery work through

partner alliances• Payment on milestones not on work

units (molecules, assays)


.. . Collaboration – State of the Art?

Cambridge Healthtech Media Group – 2012 – 310 Qualified Respondants


.. . Characteristics of a Collaboration Solution

Secure Scientific Data Storage and Exchange• project oriented• granular access control

Cloud Implementation• no local installation• little infrastructure required at

partners – fast to spin up/down• no VPN issues

Comprehensive Application Suite• highly configurable to required

workflows• small molecule and/or biologics

research projects

Real Time • communication and

collaboration• project management and

tracking, CRO metrics

Full Audit Trail• management/distribution of IP


.. .

Scientific Project Data RepositoryScientific Search & Browse

Molecules, Biologics, Experiments, Assays, Samples, Reagents, Images

Scientific CollaborationDocument Exchange

Scientific Document IndexingSocial Commentary

Project Scheduling and StatusRequesting

Request Status TrackingProject Progress Tracking

Cloud Collaboration Platform

AdministrationUsers & Groups

Projects, Data Types


.. . Cloud Collaboration Solution

Provision Spin Down

Design

ReportAnalyze

Test

Make


.. . Role, row and data type security

Example: Logged in as an sponsor scientist, 2369 records available to query and browse

Logged in as a CRO/collaborative scientist, 131 records available to query and browse


.. . Social Communication

(Non-Scientific) Trello Board Scientific Collaboration Board


.. .

Modern informatics systems can help increase the success of changes occurring in today’s research environments• Do more with less in science and IT• Support collaboration• Support new research methodologies

and portfolios

Summary

Sharing Data with my Co-opetition

Value Driven from “Horizontal” Capabilities Is A Core Differentiator of the MRL IT Strategy

TM IT PCD IT Clinical IT GRA ITCORE IT

Scientific Modeling Platform(Provide Cross-domain Modeling Capabilities)

Scientific Information Management Platform(Provide Cross-domain Data Integration and Delivery)

R&D Labs Platform

Registration Management

Platform

Integrated Development

PlatformGenomics Platform

Real World Evidence Platform

Domain Specific Applications





Collaborative User Experience(Provide Cross-domain Workflow Support)

Open Cell Line Registry for Public Cell LinesSharing and Jointly Maintaining a Registration SystemMartin Romacker, Principal ScientistData and Information Architecture and TerminologiesPharma Research and Early Development InformaticsRoche Innovation Center BaselPistoia Alliance Webinar “Sharing Data with my Co-opetition”, 3rd December 2015

Cell Line CurationA typical setting

• Roche has a domain master for Cell Lines called RNCB (Roche Non-Clinical Biorepository)

• Genentech has a domain master for Cell Lines called gCELL• Loading of cell based exploratory studies from ArrayExpress into the

Roche tranSMART equivalend called UDIS (Understanding Disease Informatics Systems) – Data in ArrayExpress were provided by Genentech

• Collaboration between Roche and Genentech – Genentech offering a superset of the ArrayExpress data to Roche (all public cell lines)

• Inconsistencies between data sets at all levels: cell line names and annotations such as disease, tissue type etc

Curation of the same cell lines many times No synchronization between registration systemNeed for a shared and open Cell Line Registry covering all public cell lines

Cell Line CurationA typical issue

• Excellent and comprehensive work• No central repository to store results• No central repository for annotation• No central repository for maintenance

Cell Line RegistryBenefits• Saving Time and Money

– Each cell line needs to be curated only once– Region specific biohazard levels for each cell streamlining safety approval process

• Improving Quality– Data curation quality is known and standardized– Curation rules and tools implemented– Cell line miss-idenfication/ contamination can be minimized (STR profiles)

• Supporting Research– Coverage of use cases provided by business– Easy identification and selection of cell lines streamlining experimental design

process– Open access for everybody (including tools, API etc)

• Supporting Cell Line Suppliers– Easier access for customers to cell line catalogues– Market transparency can support business strategy of suppliers– Suppliers have full access to tools and annotations

Source: Cell Line Registry WG

Cell Line RegistryFoundation for Data Science

• Use cases collected from the Working Group – hands on approach (selection)

– What is the biohazard classification of a cell lines X in a given geographic region?– Retrieve all cell lines with chromosomal rearrangement on long arm of chromosome

16– Retrieve all cell lines which can be grown on medium X and which derive from liver– Retrieve all cell lines that are derived from a patient with a mutation in the NGLY1

gene– Retrieve all cell lines with a given gene fusion– Retrieve correct cell line for a misspelled entry– All cell lines expressing gene X including the parental cell line– Histologic and molecular characterization of given tumor cell – Retrieve cell lines stably transfected to express gene X under an inducible promoter– Retrieve cell lines that have relatively normal karyotypes– Find all cell lines whose creation was funded by NIAID grants– Find cell lines derived from male homo sapiens over the age of 75

Prioritization of use cases to determine required Metadata elements

Cell Line RegistryDeveloping Metadata Model

• Collect all metadata descriptors currently in use at the WG partners(this is clearly pre-competitive)

• Align metadata descriptors (which descriptors are shared possibly having different names) and enumerable value domains (code lists, terms lists)

• Provide a list of all cell lines vendors and collect all metadata descriptors, cross check these descriptors with the ones of the consortium partners

• Identify gaps in the merged metadata model (WG partners and vendors) based on the prioritized use cases defined by the WG partners

• Compare harmonized metadata model with existing terminological and ontological resources (Cell Line Ontology, OBI, CCONT etc.)

• Include new metadata descriptors for cell line registration if required to finalize metadata model

Cell Line RegistryInvestigation of Ontologies for Metadata Definition and Capture

Cell Line RegistryCovering Use Cases

• What is the biohazard classification of a cell lines X in a given geographic region?

Source: Work done by Angeli for the WG

Cell Line RegistryCuration Platform and Knowledge Representation• Curation Process

– Open cell line registry - single point of reference for all public cell lines

– Cell line vendors should also be part of the registry platform by synchronizing and linking their catalogues

– Information architecture capable of linking different processes• Curation process of the cell line registry itself• Maintenance and update of procurement catalogues of vendors• Synchronization with cell line registry system of all project partners

• Semantic Technologies for Knowledge Representation– Provision of standards for knowledge representation– Facilitating integration of existing resources for data capture/

annotation– Simple extension of model if proprietary metadata elements

needed– Simple extension of content for proprietary cell lines

Cell Line RegistryConclusions

• Public cell lines constitute a finite and still tractable universe• No standards for cell line naming, no consistency across

repositories (both internal and external repositories)

• Permanent re-curation of same entities, lack of a single point of truth to capture evolving science (eg STR analysis, gene fusions)

• Opportunity for creation of a shared open Cell Line Registry• Pre-Work done by the Working Group, results will be exposed

to a larger community in 2016 to get more feedback and to extend the WG

• Final objective: establishing a sustainable approach to data sharing very likely using semantic technologies and collaborative co-opetitive maintenance of a public Cell Line Registry

Acknowledgements(alphabetic order)

• Andreas Thielemann, Veit Uelshofer (Merck KGaA)• Angeli Moeller (Thomson Reuters)• Melissa Haendel, Matthew Brush (Oregon Health & Science

University)• Nicole Washington (Lawrence Berkeley National Laboratory)• Philippe Rocca-Serra (University of Oxford e-Research Centre)• Stephanie Kueng, Said Aktas, Satu Nahkuri, Joachim Rupp

(Roche Innovation Center Basel)• Tom Quaiser, Jan Kuentzer (Roche Innovation Center

Penzberg)

In Italics: Members of the Cell Line Registry WG

Doing now what patients need next

Panellist discussion


Audience Q&APlease use the Questions function in GoToWebinar


Clinical trials and wearablesThursday 21st January 2016 @ 10am-11am EST

Register at: https://attendee.gotowebinar.com/register/1918293019175946497


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[email protected] @pistoiaalliance www.pistoiaalliance.org

Thanks for attending – see you next time!


Pistoia Alliance Debates: Sharing data with my co-petition 03-12-2015, 16.04

Science

Transcript of Pistoia Alliance Debates: Sharing data with my co-petition 03-12-2015, 16.04