A talk presented at the Big Data and Analytics conference in Boston on January 28, 2014. Emphasis on data and information sharing cultures in companies.
Presentation Alliance of European Life Sciences Law Firms
(Julian Hitchcock and Sofie van der Meulen) on legal aspects of big data in pharma. Topics: privacy, IP, medical devices and IVD.
Clinical Research Informatics World 2015Jaime Hodges
Complementing their exceptional series of informatics programming in Boston this spring, Cambridge Healthtech Institute and Clinical Informatics News are proud to launch Clinical Research Informatics World. The event brings together industry leaders, innovative thinkers and decision makers in the areas of clinical operations, clinical trial management, clinical innovation, data analysis, clinical trial informatics, data management, clinical research IT, and clinical information systems for two days of dynamic discussions, expert-led presentations and invaluable networking.
The 2015 program featuring a plenary keynote session and two concurrent conference tracks provides coverage on such topics as big data use and analytics for advancing clinical research, data visualization and analysis trends, new technologies in use for clinical trials (including mobile technology, wearables and social media), and cross-industry data sharing. Learn more at http://www.clinicalinformaticsworld.com
In the world of pharma precompetitive efforts are increasing. These developments have created a dynamic ecosystem with pharma as smaller nodes in a complex network, in which collaborations have become an important business model.
Catalysing Innovation in Pharma IT: Keeping AstraZeneca Ahead of Disruptive T...Nick Brown
Presentation by Rob Hernandez (senior data scientist) in my team at 14th Annual Pharmaceutical IT Congress in London on September 28th 2016 . Brief overview about how our world and more specifically the pharmaceutical industry is being disrupted by technology. We share some emerging technology areas that we are focusing on, demonstrating value to our R&D, Operations and Commercial teams but also how we catalyse innovation across our IT organisation. Examples include exploratory work on AI chatbots, video facial sentiment detection, scientific search and sensor technology.
Artificial Intelligence, Predictive Modelling and Chatbots: Applications in P...Nick Brown
Presentation by Hari Radhakrishnan (senior solution developer) and Josh Mesout (graduate developer), in my team at Deep Learning Summit in London on September 23rd 2016. Brief overview about how we have been exploring artificial intelligence and how predictive modelling has the potential to revolutionise what we do across the drug discovery and development process. Examples include recent exploratory work on AI chatbots and video facial sentiment detection.
Presentation Alliance of European Life Sciences Law Firms
(Julian Hitchcock and Sofie van der Meulen) on legal aspects of big data in pharma. Topics: privacy, IP, medical devices and IVD.
Clinical Research Informatics World 2015Jaime Hodges
Complementing their exceptional series of informatics programming in Boston this spring, Cambridge Healthtech Institute and Clinical Informatics News are proud to launch Clinical Research Informatics World. The event brings together industry leaders, innovative thinkers and decision makers in the areas of clinical operations, clinical trial management, clinical innovation, data analysis, clinical trial informatics, data management, clinical research IT, and clinical information systems for two days of dynamic discussions, expert-led presentations and invaluable networking.
The 2015 program featuring a plenary keynote session and two concurrent conference tracks provides coverage on such topics as big data use and analytics for advancing clinical research, data visualization and analysis trends, new technologies in use for clinical trials (including mobile technology, wearables and social media), and cross-industry data sharing. Learn more at http://www.clinicalinformaticsworld.com
In the world of pharma precompetitive efforts are increasing. These developments have created a dynamic ecosystem with pharma as smaller nodes in a complex network, in which collaborations have become an important business model.
Catalysing Innovation in Pharma IT: Keeping AstraZeneca Ahead of Disruptive T...Nick Brown
Presentation by Rob Hernandez (senior data scientist) in my team at 14th Annual Pharmaceutical IT Congress in London on September 28th 2016 . Brief overview about how our world and more specifically the pharmaceutical industry is being disrupted by technology. We share some emerging technology areas that we are focusing on, demonstrating value to our R&D, Operations and Commercial teams but also how we catalyse innovation across our IT organisation. Examples include exploratory work on AI chatbots, video facial sentiment detection, scientific search and sensor technology.
Artificial Intelligence, Predictive Modelling and Chatbots: Applications in P...Nick Brown
Presentation by Hari Radhakrishnan (senior solution developer) and Josh Mesout (graduate developer), in my team at Deep Learning Summit in London on September 23rd 2016. Brief overview about how we have been exploring artificial intelligence and how predictive modelling has the potential to revolutionise what we do across the drug discovery and development process. Examples include recent exploratory work on AI chatbots and video facial sentiment detection.
Business Intelligence & Technology_Pharmaceutical BIVikas Soni
An overview of business intelligence introduction, trends, components, approaches, functions, architecture and necessity to any successful business and applications of this BI approach for the better decision making in a pharmaceutical or health industry.
The report contains the following four chapters:
Chapter 1: Global Pharmaceutical Market
Chapter 2: Solutions to Challenges
Chapter 3: Global Players
Chapter 4: Overview of Industry Trends
You may follow my blog: biostrategyanalytics.wordpress.com for further posts related to financial and strategic issues in the Pharmaceutical / Biotechnology sector.
For any questions or recommendations do not hesitate to contact me.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
The Role of Community-Driven Data Curation for EnterprisesEdward Curry
With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of data and its fitness for use. Traditional approaches to curation are struggling with increased data volumes, and near real-time demands for curated data. In response, curation teams have turned to community crowd-sourcing and semi-automatedmetadata tools for assistance. This chapter provides an overview of data curation, discusses the business motivations for curating data and investigates the role of community-based data curation, focusing on internal communities and pre-competitive data collaborations. The chapter is supported by case studies from Wikipedia, The New York Times, Thomson Reuters, Protein Data Bank and ChemSpider upon which best practices for both social and technical aspects of community-driven data curation are described.
E. Curry, A. Freitas, and S. O’Riáin, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25-47.
How can you deliver real value with healthcare data analytics? Four things can help:
Tighten how you deliver information and insights.
Loosen the reins on who can be part of the conversation and contribute.
Create transparency into how data management and analytics works.
Paint a picture and tell a story with your insights.
...
And go do it! Don't just say you're going to do it.
Data democratization the key to future proofing data culturePolestarsolutions
Learn how to empower your organization with accessible data insights through democratizing your data. This guide offers tips for choosing the right tools and fostering a data-driven culture.
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
The third webcast in this series focuses on ways to meet your health system’s specific needs and achieve a 360-degree view of your patients, processes, physicians, and costs without purchasing multiple, disparate solutions, and creating information silos.
Our speakers discuss their collective experience in working with organizations to create tailored platforms that provide convenient access to data collected by, and stored in, disparate clinical information systems and enabling that data to be securely used by users throughout the broader healthcare community. Actionable data – available to all users when they need it – serves as a foundation for analysis and decision-making aimed at improving how care is delivered.
You can find it online at http://www.informationbuilders.com/webevents/online/24637#sthash.RnwoH27x.dpuf
A look at the key trends and challenges in applying Big Data to transform healthcare by supporting research, self care, providers and building ecosystems. Purchase the report here: https://gumroad.com/l/PlXP
Similar to How to Create a Big Data Culture in Pharma (20)
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
2. Overview
Pharmaceutical organizations are defining the road map for data
integration but how prepared are they to base their decisions and
practices on this data? Failure to truly encompass the attributes of a
data driven unit will hurt your ability to compete in the market. This
presentation will help business line executives and data professionals
to understand the steps needed to create a data driven organization,
by making the right decisions, while providing some real life examples
on companies who have done this successfully.
• Defining an information architecture framework for global research
and development processes
• Enlisting champions and creating an entrepreneurial spirit to
empower people to own new processes
• Key role players you cannot do without – creating a cohesive
strategy and building a winning team
3. Overview
Pharmaceutical organizations are defining the road map for data
integration but how prepared are they to base their decisions and
practices on this data? Failure to truly encompass the attributes of a
data driven unit will hurt your ability to compete in the market. This
presentation will help business line executives and data professionals
to understand the steps needed to create a data driven organization,
by making the right decisions, while providing some real life examples
on companies who have done this successfully.
• Defining an information architecture framework for global research
and development processes
• Enlisting champions and creating an entrepreneurial spirit to
empower people to own new processes
• Key role players you cannot do without – creating a cohesive
strategy and building a winning team
4. The Worldwide Healthcare Ecosystem
Providers
Regulators
Policy
Makers
Diagnostic
Services/
GCRCs
Accreditation
Entities
Employers
Consumers/
Patients
$
Care
Govt.
Programs
Regulations
Applications
and Approvals
Licenses
Licensed
Health
Prof’nals
Payers
Coverage
Premium $
Health Plans
Health
Delivery
Systems/
Facilities
Nursing and
Home Health
$
“orders”
$
Pharma.
(including
Biotech)
Medical
Products
Generic
Mfgs.
$
Products
Pharmacies
Distributors
Producers
Info
Companies
Carve
Outs
(PBMs,
others)
Contract
Services
Global
Insurers
5. Healthcare Trends and Technologies
Producers
Developmen
t
Research
Providers
Commercial
Medical
EMR/PHR
HIE
Payers
NHIN
Products
Services
Regulators
Precision Medicine
Expansion into
Emerging Markets
Clinical Trial Design
and Execution
Next Generation Sequencing
-omics
Big Data Analytcis
Patient Care and Outcomes
Comparative Effectiveness
Pharmacovigilance
Mobility
Digital Marketing
Social Sentiment Analysis
Patient Stratification
ePlacebo
Virtual Trials
Remote Monitoring
Trends
Social Media Mining
Big Data Analytics (Text)
Clinical Decision Support Aids
Care Augmentation Provision
Tele-health (mHealth, eHealth…)
Technologies
Big Data Analytics
Behavioral Modification Tools
-etics
6. A week in the lab can save an hour of data mining.
Today’s real problem – how to use what we already know!
Experiments
Data
Yesterday
Data
Data
Data
True/False
Data
Data Mining
Tomorrow
Massive Databases
7. Cheminformatics Platform at Merck
PCC
Lead
Identification
Get Me The Data
Preclinical
Phase 3
Lead
Lead First in Human
Candidate
to
Optimization
Optimization to to
First in Human
Phase 2B File
What Do I Make Next?
Now, Help Me Make It
End User Interface, Analytics Tools, Chemist
Sharepoint (one.merck.com/cheminfo)
WorkBench
Sharepoint
Integration and(one.merck.com/cheminfo) Services
Model/Workflow
Sharepoint (one.merck.com/cheminfo)
Core Merck Data Repositories
Sharepoint (one.merck.com/cheminfo)
Transactional IT Applications
Sharepoint (one.merck.com/cheminfo)
Local (Project Team) QSAR
Models
Sharepoint (one.merck.com/cheminfo)
Ligand-based Design
Support
-
Sharepoint (one.merck.com/cheminfo)
Structure-based Design
Support
8. Need to converge activities to gain
the most value and leverage
Today
Chemistry
Independent
Pairwise Processes
SAR
Chemistry
Screening
Chemical
Genomics
Genomics
Future State
Converged Processes
Build Systems To Find
Correlation In The Data
Screening
Pathways
Genomics
The Greatest Information
Content & Value Is In The
Intersection Of The Data
“Chemical-Biology”
Chemistry
Screening
9. Translational Research Platform at Merck
PCC
Pre-Lead
Optimization
Chemical Biology
(chemical probes
predict targets)
Lead
Identification
Phase IIb
Preclinical
Phase 3
Lead
Lead First in Human
Candidate
to
Optimization
Optimization to to
First in Human
Phase 2B File
Early
Development
Clinical Trials
(ADMET predictions)
Get Me The Data
What Do I Make Next?
Now, Help Me Make It
End User Interface, Analytics Tools, Chemist
Sharepoint (one.merck.com/cheminfo)
WorkBench
Integration and Sharepoint (one.merck.com/cheminfo)
Model/Workflow Services
Sharepoint (one.merck.com/cheminfo)
Core Merck Data Repositories
Sharepoint (one.merck.com/cheminfo)
Transactional IT Applications
Systems Biology
(off target activity
prediction)
Chemical Pharmacology
(toxicity predictions)
-
10. From Two Crows Consulting in
1999
(1) What Are The Questions
(2) Agile Process First – Find All The Data and Layers
(3) Then Build The Solution on SOA Framework
13. Overview
Pharmaceutical organizations are defining the road map for data
integration but how prepared are they to base their decisions and
practices on this data? Failure to truly encompass the attributes of a
data driven unit will hurt your ability to compete in the market. This
presentation will help business line executives and data professionals
to understand the steps needed to create a data driven organization,
by making the right decisions, while providing some real life examples
on companies who have done this successfully.
• Defining an information architecture framework for global research
and development processes
• Enlisting champions and creating an entrepreneurial spirit to
empower people to own new processes
• Key role players you cannot do without – creating a cohesive
strategy and building a winning team
14. Information Silos
An information silo is a management system incapable of reciprocal operation with other,
related management systems.
15. Information Silo Causes
• Technology
– Enterprise data systems are too rigid, slow, prone to
outages, hard to use…
• Process
– Legacy processes don’t factor in the need for
information sharing (the technologies didn’t exist)…
• People
– People are not properly incentivized for collaborative
work and lack trust…
16. Information Silo Effects
•
•
•
•
•
Limits productivity
Stifles creativity
Hampers innovation
Inhibits collaboration
<Fill in the blank with your favorite pejorative
expression>
17. Information Silo Solutions
• Provide technologies that support information
sharing processes and reward collaborative
behaviors (people).
18. Information Integration Technologies
(Life Sciences)
•
•
•
•
•
Standard Data Models (CDISC, etc.)
Standard RDB Platforms (Oracle, etc.)
Standard Ontologies (W3C, etc.)
Semantic Platforms (IOInformatics, etc.)
All of the above (Open PHACTS)
20. Collaborative Business Culture
Why Don’t People Collaborate (Share Information)?
•
•
•
•
•
•
•
•
Not knowing the answer.
Unclear or uncomfortable roles.
Too much talking, not enough doing.
Information (over)sharing.
Fear of fighting.
More work.
More hugs than decisions.
It's hard to know who to praise and who to blame.
http://blogs.hbr.org/cs/2011/12/eight_dangers_of_collaboration.html
21. Collaborative Business Culture
• 10% of Senior HR Execs and 39% of Employees
Believe that their Companies Effectively
Encourage Collaboration
• Mutual Trust (Lack of) is a Significant Barrier
to Collaboration
– 31% of Developed Market R&D Staff Trust
Emerging Market Colleagues
– 22% of Emerging Market R&D Staff Trust
Developed Market Colleagues
Source: Research and Technology Executive Council Research
22. Stimulating Information Sharing (NIH/FDA)
Reports > Harnessing the Potential of Data Mining and Inform ation Sharing
12/ 9/ 11 10:17 AM
Home > About FDA > Reports, Manuals, & Forms > Reports
About FDA
With the establishment of NCATS in the
fall of 2011, NIH aims to reengineer the
translation process by bringing together
expertise from the public and private
sectors in an atmosphere of collaboration
and precompetitive transparency.
Through partnerships that capitalize on
our respective strengths, NIH, academia,
philanthropy, patient advocates, and the
private sector can take full advantage of
the promise of translational science to
deliver solutions to the millions of people
who await new and better ways to detect,
treat, and prevent disease.
Harnessing the Potential of Data Mining and Information Sharing
Previous Section: Expedited Drug Development Pathway 1
FDA currently houses the largest known
repository of clinical data (all of which is deidentified to protect patients’ privacy),
including all the safety, efficacy, and
performance information that has been
submitted to the Agency for new products, as
well a an increasing volume of post-market
safety surveillance data. The ability to
integrate and analyze these data could
revolutionize the development of new
patient treatments and allow us to address
fundamental scientific questions about how
different types of patients respond to
therapy.
As noted in PCAST’s Report to the President on Health Information Technology, IT has the potential to transform healthcare and—
through innovative capabilities—improve safety and efficiency in the development of new tools for medicine, support new clinical
studies for particular interventions that work for different patients, and transform the sharing of health and research data.
FDA currently houses the largest known repository of clinical data (all of which is de-identified to protect patients’ privacy),
including all the safety, efficacy, and performance information that has been submitted to the Agency for new products, as well as
an increasing volume of post-market safety surveillance data. The ability to integrate and analyze these data could revolutionize
the development of new patient treatments and allow us to address fundamental scientific questions about how different types of
patients respond to therapy. It would also provide an enhanced knowledge of disease parameters— such as meaningful measures
of disease progression and biomarkers of safety and drug responses that can only be gained by analyses of large, pooled data sets
— and would allow a determination of ineffective products earlier in the development process.
Additionally, the ability to share information in a public forum about why products fail, without compromising proprietary
information, presents the potential to save companies millions of dollars by preventing duplication of failure. FDA sometimes sees
applications from multiple companies for the same or similar products. Although we may have reason to believe that such a
product is likely to fail or that trial design endpoints will not provide necessary information based on a previous application from
another company, we are currently unable to share this information. As a result, companies may pour resources into the
development of products that FDA knows could be dead ends.
To harness the potential of information sharing and data mining, FDA is rebuilding its IT and data analytic capabilities and
establishing science enclaves that will allow for the analysis of large, complex datasets while maintaining proprietary data
protections and protecting patients’ information.
Scientific Computing and the Science Enclaves at FDA
Historically, the vast majority of FDA de-identified clinical trial data has gone un-mined because of the inability to combine data
from disparate sources and the lack of computing power and tools to perform such complex analyses. However the advent of new
technologies, such as the ability to convert data from flat files or other formats like paper into data that can be placed in flexible
relational database models, dramatic increases in supercomputing power, and the development of new mathematical tools and
approaches for analyzing large integrated data sets, has radically changed this situation. Furthermore, innovations in
computational methods, including many available as open-source, have created an explosion of statistical and mathematical
models that can be exploited to mine data in numerous ways to enable scientists to analyze large complex biological and clinical
data sets.
The FDA scientific computing model provides an environment where communities of scientists, known as enclaves, can come
together to analyze large, integrated data sets and address important questions confronting clinical medicine. These communities
will be project-based and driven by a specific set of questions that will be asked of a dataset. Each enclave is defined by its
participants, datasets, and sets of interrogations to be performed on the data. Enclaves may be comprised of internal FDA
scientists and reviewers working together or outside collaborators working with FDA scientists under an appropriate set of security
controls to protect the sensitive and proprietary data of patients and sponsors, respectively. Engagement of industry sponsors as
part of community building will be vigorously pursued, leveraging expertise from the companies that submitted the data in a
public-private partnership model.
The scientific computing environment will also provide a dedicated infrastructure for application development and software testing
for FDA scientists and reviewers. This will allow FDA staff to develop new applications to improve review, monitoring, and business
processes in an environment separate from where regulatory review data is assessed. Additionally, the scientific computing
environment will be used to evaluate novel software developed outside of FDA and to rapidly incorporate innovative developments
in support of FDA regulatory reviews. This ability to “test drive” new applications outside the regulatory review environment has
the potential to shorten traditional FDA development cycles and facilitate the adoption of new software that can enhance quality,
efficiency, and accuracy of FDA regulatory reviews, as well as streamline the adaptation of new higher-powered analytical tools
into FDA review and research efforts.
http:/ / www.fda.gov/ AboutFDA/ ReportsManualsForm s/ Reports/ ucm 274442.htm
Page 1 of 3
23. Stimulating Information Sharing (NHS, EU)
Prime minister David Cameron has
announced a package of measures
designed to boost the UK's life sciences
industry. These include a £180 million fund
to support innovation and plans to allow
healthcare companies access to NHS
patient records to support research.
Horizon 2020 is the financial instrument
implementing the Innovation Union, a
Europe 2020 flagship initiative aimed at
securing Europe's global competitiveness.
This conference will explore how EU
funding can promote economically and
socially sustainable innovation models with
the aim of more openness, easier
accessibility and higher result-oriented
efficiency.
24. Caveats
A well-constructed system can
enable scientist to test but also
generate new hypotheses using wellcurated, high-content translational
medicine data leading to deeper
understanding of various biological
processes and eventually helping to
develop better treatment options.
Active curation and enterprise data
governance have proven to be
critical aspects of success.
25. The Future: Virtual Life Sciences
• Forrester has identified three themes driving the
future of collaboration and information sharing
technology
– The global, mobile workforce
• 62% of workforce works outside an office at some point (this
number is growing)
– Mobility driven consumerization
• Cloud-based collaboration solutions are being used in
conjunction with numerous devices
– The principle of “any”
• Need to connect anybody, anytime, anywhere on any device
26. Life Science Information Landscape
A rapidly evolving ecosystem
Yesterday
Today
Tomorrow
Big Life
Science
Company
Yesterday
Today
Tomorrow
Innovation
Model
Innovation inside
Searching for Innovation
Heterogeneity of collaborations. Part of the
wider ecosystem
IT
Internal apps & data
Struggling with change
Security and Trust
Cloud/Services
Data
Mostly inside
In and Out
Distributed
Portfolio
Internally driven and owned
Partially shared
Shared portfolio
26
27. The Evolving Life Sciences Ecosystem
Evolving paradigm for the discovery of medicines (Collaborative)
A vision that points towards open innovation and collaborations
Open research model to collectively share scientific expertise
Enhance speed of drug discovery beyond individual resource capabilities (Speed)
Limited research budgets and capabilities driving greater shared resources
Goal to see all partners succeed by accelerating the SCIENCE
Synergize Pfizer’s strengths with Research Partners (Knowledge)
Pair Pfizer’s design, cutting edge tools, synthetic excellence with research partners (academics, not-for-profits,
venture capitalists, or biotechs) to develop break through science, novel targets, and indications of unmet medical
need
Current example of academic and not-for-profits partners (Discover and Publish)
Drive to publish in top journal with science receiving high visibility and interest
Body clock mouse study suggests new drug potential
Mon, Aug 23 2010
By Kate Kelland
LONDON (Reuters) - Scientists have used experimental drugs being developed
by Pfizer to reset and restart the body clock of mice in a lab and say their work
may offer clues on a range of human disorders, from jetlag to bipolar disorder.
a few months ago we entered into a collaboration with
the giant pharmaceutical industry Pfizer to test some of
their leading molecules for potential relevance to HD.
Contacts:
Travis Wager (travis.t.wager@pfizer.com)
Paul Galatsis (paul.galatsis@pfizer.com)
28. Overview
Pharmaceutical organizations are defining the road map for data
integration but how prepared are they to base their decisions and
practices on this data? Failure to truly encompass the attributes of a
data driven unit will hurt your ability to compete in the market. This
presentation will help business line executives and data professionals
to understand the steps needed to create a data driven organization,
by making the right decisions, while providing some real life examples
on companies who have done this successfully.
• Defining an information architecture framework for global research
and development processes
• Enlisting champions and creating an entrepreneurial spirit to
empower people to own new processes
• Key role players you cannot do without – creating a cohesive
strategy and building a winning team
29. Collaboration and Information Sharing
Barometer
• Does your company..
– …motivate and link innovation efforts by
identifying and routinely communicating key areas
for innovation activity?
– …have a strategy that allows for geographically
dispersed staff to access the resources necessary
to collaborate and share information?
– …have tools that support rapid collaboration, such
as data sharing and analysis or crowdsourcing
platforms?
30. People: Some Questions to Ask
• What is the staff structure as it relates to data
reporting?
• Do staff members have the training they need
to understand relevant data?
• Do staff members understand how to glean
insights and actionable steps from data?
• Do staff members have good working
relationships with data analysts?
http://wholewhale.com/data-culture-building/
31. Process: Some Questions to Ask
• Are staff accessing and communicating data
across teams well?
• Do staff act on data or regularly share
learnings from experiments?
• Are goals set in a way that can be tracked
through metrics?
• Does the organization use a
Gather<Analyze<Insight method?
• How often do staff receive data feedback?
http://wholewhale.com/data-culture-building/
32. Technology: Some Questions to Ask
• Are tools in place to analyze large data sets
(beyond Excel)?
• Are consistent naming and storage conventions in
place across databases?
• Are dashboards and metrics updated as
automatically as possible?
• Is data stored in a way that reporting can be done
across the organization?
• Are semi-annual security audits and passwords
changed?
http://wholewhale.com/data-culture-building/
33. Overview
Pharmaceutical organizations are defining the road map for data
integration but how prepared are they to base their decisions and
practices on this data? Failure to truly encompass the attributes of a
data driven unit will hurt your ability to compete in the market. This
presentation will help business line executives and data professionals
to understand the steps needed to create a data driven organization,
by making the right decisions, while providing some real life examples
on companies who have done this successfully.
• Defining an information architecture framework for global research
and development processes
• Enlisting champions and creating an entrepreneurial spirit to
empower people to own new processes
• Key role players you cannot do without – creating a cohesive
strategy and building a winning team
34. Thank You
• Chris L. Waller, Ph.D.
• chris.waller@merck.com
• http://www.linkedin.com/in/wallerc
Editor's Notes
The worldwide healthcare ecosystem is a complex network of interactions between providers, producers, payers, and regulators of/for products and services aimed at improving/maintaining the health and wellness of patients/consumers. A massive amounts of information/data circulates through this ecosystem. Health information technology is the umbrella term used to characterize the creation, collection, storage, retrieval, exchange, and analysis of the information in the healthcare ecosystem.