January, 2018

London, UK

Day One
Tuesday 24th January 2017

Day Two
Wednesday 25th January 2017

08.35
Chair’s Opening Remarks

  • James Weatherall Executive Director & Head, Advanced Analytics Centre, AstraZeneca

The Bleeding Edge of Data Science in Healthcare & Pharmaceuticals

08.45
Internet Data as a Source of Healthcare Insight – What Search Engines, Forums & Social Media Can Tell Us About Patients, Populations & Pathology

Synopsis

  • Where these data are actually useful?
    -Activity online – seeking information, asking questions in forums etc
    -More sensitive sensor – a better detector than medical data – e.g. the flu
    -Patients who have sensitive or embarrassing issues are more likely to use internet than report to the doctor
  • Where can you get these data? How they should be matched to the problem… some data sources are more appropriate for certain questions – what to expect re truthfulness
  • The ethics of using these data. IRBs in the dark. Developing a system of ethics for big data
    research

09.30
Cognitive Real World Evidence

Synopsis

  • Understanding drug efficacy from data
  • Detecting adverse events by learning from data
  • Reducing trial-and-error to provide better clinical decision support
  • Addressing the causality challenge in retrospective studies

10.00
Morning Refreshments & Networking

Data Driven R&D and Translational Research

10.45
How Data Analytics Technology Helps NHS Healthcare Vision

  • Steven Collins Head Of Informatics & Analytics, Royal Brompton & Harefield NHS Foundation Trust

Synopsis

  • Driving service improvement by making sense of the complex, data rich records that make up clinical investigations
  • Enabling business improvement through better billing
  • Facilitating translational research through collaboration with external partners
  • Unlocking better care for the future from data assets

11.15
Case Study: Extracting & Exploiting Medicinal Chemistry ADMET Knowledge Automatically From Public & Large Pharma Data

Synopsis

  • Accelerating drug design with data mining
  • Enabling secure sharing of large scale ADMET knowledge while safeguarding IP security
  • Finding the synergy in collaboration to generate meaningful results
  • Extending the methodology to extract pharmacophores
  • Case studies and results from a successful multi-organisation collaboration

11.45
Case Study: Building the Infrastructure for Data Driven R&D

  • Simon Thornber Managing Director of Analytics, Acting Head of Architecture for Discovery, Development & Lab Systems IT, GSK

Synopsis

  • Mapping the landscape of our data sources
  • Powering up our IT infastructure
  • Enterprise scale secondary analytics

12.15
Ontology and graph database – the perfect combination for better data analytics

  • Peter Tormay Manager Business Development, Capish International

Synopsis

  • Outlining challenges in integrating and consolidating disparate data sources in clinical trials.
  • Introducing the concept of conceptual ontologies and graph based information technology.
  • Discussing how new graph based information technology can address the data integration challenges in clinical development.

12.45
Lunch

Advanced Analytics in Clinical Trials

13.45
Advanced Analytics for Clinical Data – the Transformation of Clinical Trials

  • James Weatherall Executive Director & Head, Advanced Analytics Centre, AstraZeneca

Synopsis

  • Enabling new trial designs with smart sensors, wearables and mobile technology
    -Overcoming the statistical and analytical challenges of continually collecting data from patients
  • Optimising trial operations with analytics – using data to drive decisions about sites, investigators, patient recruitment and patient retention
  • Applying secondary analytics to reveal new insights from trial data – data mining legacy trials

14.15
Advanced Analytic Techniques for Predictive Modelling Clinical Trials Operations

  • Vladimir Anisimov Statistical Consultant & Honorary Professor, University of Glasgow

Synopsis

  • Advanced analytic patient enrolment modelling
    -Optimal enrolment design accounting for time & cost constraints and PoS
    -Data-driven re-projection and optimal adjustment at different stages of clinical trial
  • Model-based risk monitoring interim trial performance
    -Detecting unusual data patterns, low/high enrolling sites/countries, sites with unusual number of screen failures, AE, etc.
  • Forecasting hierarchic operational processes
    -Predicting event’s counts in event-driven trials (oncology)
    -Follow-up patients, events (clinical/non-clinical), patients visits, operational costs

14.45
Afternoon Refreshments & Networking

Machine Learning and Artificial Intelligence – the Future of Pharma?

15.15
Deep Learning for In Silico & Ab Initio Drug Discovery & Actionable Biomarker Development

  • Polina Mamoshina Pharmaceutical Artificial Intelligence division, Insilico Medicine, Inc., , Department of Computer Science, University of Oxford

Synopsis

  • Applying deep learning and domain expertise to optimize drug discovery and biomarker development
  • Conducting intelligent analysis of high-throughput screening experiments and large repositories of biomedical data
  • Case study: creating multi-modal biomarkers and predictors of therapeutic use of the drugs for high-throughput screening, a deep-learned predictor of human age trained on human blood biochemistry and transcriptomics data

15.45
Panel Session: Forecasting a Roadmap & Timeline for Machine Learning & AI to Transform Pharma

  • James Weatherall Executive Director & Head, Advanced Analytics Centre, AstraZeneca
  • Simon Thornber Managing Director of Analytics, Acting Head of Architecture for Discovery, Development & Lab Systems IT, GSK
  • Polina Mamoshina Pharmaceutical Artificial Intelligence division, Insilico Medicine, Inc., , Department of Computer Science, University of Oxford

Synopsis

  • How will competition from tech firms (both silicon valley start-ups and global tech giants) change the industry?
  • Are the major roadblocks to using machine learning to drive discovery and development cultural or technical?
  • If the future of pharma – in research and commercial – is driven by analytics and algorithms, how does that change pharma companies? How can businesses plan and prepare for this?
  • What should businesses be doing

16.30
Chair’s Closing Remarks

  • James Weatherall Executive Director & Head, Advanced Analytics Centre, AstraZeneca

16.45
End of Day Two