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 30-31 January, 2018
London, UK

Day One
Tuesday 30th January 2018

Day Two
Wednesday 31st January 2018

Chairman’s Opening Remarks

Opening Keynote Panel Discussion: Innovating the Application of Data Science in the Pharmaceutical Sector – Bridging the Gaps & Untapped Opportunities

  • Hao Zhang VP & Head of Global Data Generation, Knowledge & Performance Management; Global Medical Affairs Oncology, Merck KGaA
  • Simon Thornber R&D-IT Director of Analytics, Target Sciences, & InVitro-InVivo Translation, GlaxoSmithKline


  • Addressing the evolving landscape, growing interest and latest developments in the advanced analytics space in pharma
  • Analysing the continuing biopharma efforts to maximise the value retrieved from data and analytics
  • Outlining where we are and where we are going – important learnings to date and perspectives on future directions
  • Evaluating the promise of new tools and analysis methodologies vs. the continuing struggle to sort out the most elementary basis for data quality, data integration and data management
  • Looking at the future of data in pharma: talent recruitment, regulation, ethics

Global Oncology Big Data Alliance (GOBDA): Connecting & Empowering the Oncology Community

  • Hao Zhang VP & Head of Global Data Generation, Knowledge & Performance Management; Global Medical Affairs Oncology, Merck KGaA
  • Martin Murphy CEO, Project Data Sphere


  • Expanding open-access patient datasets and leveraging advanced analytics
  • Enabling and accelerating innovative drug development with a focus on rare tumors with high unmet need
  • Unleashing analytical power and big data to study and better manage rare but serious immune-related adverse events

Advancing Real-World Data & Real World Evidence Strategy – An Evolving Paradigm in Drug Development

Optimising Real World Data Strategy to Meet the Increased Evidence Needs of Pharma


  • Analysing the current state of affairs of Real World Data (RWD)
  • Discussing the influence of the external environment: What’s driving the use of RWD?
  • Using RWD across the drug’s life cycle

Morning Refreshments & Speed Networking Session

Protocol & Study Design Optimisation – De-Risking Clinical Trials Ahead with Better Evidence


  • Optimising criteria definition with RWE
  • Using evidence to better assess and model study feasibility
  • Enhancing patient and site selection – identifying relevant patient populations and understanding the impact of comorbidities and other design constructs
  • Using EMRs for historical controls – a more realistic assessment of the standard of care

Using Statistics & RWE Data Knowledge in Clinical Trial Planning & Execution

  • Steve Jones Director, Strategic Intelligence & Analytics, Covance


  • Optimising inclusion/exclusion criteria
  • Study feasibility through execution – using RWE to minimise non-recruiting sites and maintain timelines
  • Joining the dots between disparate data sources

Machine Learning for Late Stage Development: Realising the Potential

  • Andrew Cox Research Scientist, Real-World Evidence, Evidera


  • Analysing how machine Learning approaches are frequently used in early stage drug development, but rarely applied in later stage development
  • Detailing on the enormous potential of machine learning for late stage support – adapting to the changing data landscape where larger volumes of data, increasing numbers of variables and messier data is often encountered
  • Exploring the potential pitfalls and benefits of Machine learning in late stage applications and highlight best practices toward creation for industry guidelines

Lunch & Networking

Panel Discussion: Broadening Horizons for Real-World Evidence Analytics

  • Andrew Cox Research Scientist, Real-World Evidence, Evidera
  • Jeff Barrett VP, Translational Informatics, Sanofi


  • Clearly defining RWD and RWE – developing actionable insights from discovery to commercial
  • Analysing RWE’s impact and ROI: From clinical development and pharmacovigilance acceleration/optimisation, to better commercial decision making
  • Discussing the regulators perspective on RWE in drug development and drug life cycle management
  • Addressing the IT infrastructure, governance and data architecture necessary for effectively leveraging RWE – identifying, integrating and interpreting RWD

Supporting Drug Development with Better Data & Evidence

From Data Curation to Effective Exploration: A Quality Perspective – Looking at a Swedish Quality Registry as an Example


  • Outlining how data quality pervades all steps of data analytics – collection, transformation, storage, retrieval and analysis
  • Introducing the concept of conceptual ontologies and graph based information technology
  • Discussing how this new approach provides the backbone to consistently define and manage the relevant data in order to provide an unbiased source of quality data
  • Looking at a Swedish Quality Registry as an example of how this new approach can address the data quality challenges across all steps of the data exploration journey

Afternoon Refreshments & Networking

Improving the IT Infrastructure to Leverage Complex Datasets & Analytic Approaches – A Case-Study

  • Simon Thornber R&D-IT Director of Analytics, Target Sciences, & InVitro-InVivo Translation, GlaxoSmithKline


  • Analysing one of the world largest data registries
  • Understanding the need for new code development to tackle the problem of scale
  • Detailing on the technical challenges to tackle high volume datasets and complex analytical approaches

If We Build It, Why Should They Come?

  • Paul Metcalfe Senior Director & Head, Data Science Solutions, AstraZeneca


  • Defining data science within drug development
  • Detailing on the the process, tools and infrastructure to support this
  • Getting a balance between rigour and innovation

One Model to Rule Them All


  • Achieving your data-driven ambitions by transforming your data strategy
  • Post-Big Data awareness – how do we operationalise integration of diverse data sources?
  • How do we transform our approach to clinical development to usher in science and data-driven development?

Chairman’s Closing Remarks