Agenda

DISCOVERY INFORMATICS AND ANALYTICS SUMMIT 2016: DAY 1

8.30 Chair's Opening Remarks

Yike Guo, Director, Data Science Institute, Imperial College London

KEYNOTE: THE OPPORTUNITIES OF DISCOVERY INFORMATICS 

8.40 Assess Strategic Opportunities To Accelerate Drug Discovery: How Are Industry Leaders Already Capitalising On Next-Generation Informatics & Analytics?

Hear from this industry leader to understand how to accelerate drug discovery in the genomics era. The abundance of data and the increasingly sophisticated tools and techniques available to process it presents a tantalising opportunity to analyse R&D data through insight-focused applications.

Explore the possibilities of next-generation informatics, in order to:

  • Assess the key challenges and opportunities of advanced discovery informatics
  • Benchmark where your phase of R&D is headed against industry leaders
  • Chart what impact the strategic benefits of advanced informatics could have within your organisation

John P. Overington, Director of Biomedical Informatics and Design, Stratified Medical

PANEL: WHAT DO NEXT GENERATION TOOLS TRULY OFFER TO ACCELERATE DISCOVERY?

9.30 Will The Latest Modelling Applications Turn Data into Knowledge Better Than You Can Already?

Advances in data analytics are delivering scientific insights to reduce cycle time in Discovery R&D. There are multiple considerations however around the efficacy of new tools and applications versus tried and tested methods. What can the advent of new technology bring you?

Join this panel of industry experts to:

  • Cut through the hype and understand the true value of available technology to process data to deliver scientific insights
  • Identify how to overcome key challenges in terms of data integration using relevant tool
  • Develop a balanced view of solutions and how they might work in your own organisation

Yike Guo, Director, Data Science Institute, Imperial College London

John P. Overington, Director of Biomedical Informatics and Design, Stratified Medical

Hugo Cuelemans, Head of Discovery Data Sciences Unit, Janssen

10.20 Morning Refreshments In Exhibition Showcase Area

CASE STUDY: EFFECTIVE EARLY STAGE R&D

10.50 Machine Learning for Smarter Drug Discovery

Advances in machine learning are now having a real impact on how drug discovery is done. A deeper understanding of the quality of our predictions, together with the ability to integrate diverse sources of information to form accurate predictions, is allowing us to be more effective in early R&D at AstraZeneca.

In this session we will show how we use machine learning to:

  • Make better decision on which experiments to perform
  • Reduce costs by learning from our historical data
  • Discover new biology through analytics

Claus Bendtsen, Director and Head of Quantitative Biology, AstraZeneca

THEORY IN PRACTICE: EFFECTIVELY APPLY OMICS TO CUT THROUGH VALIDATION CYCLE TIME

11.30 Leverage Chemical Genomics to Transform Target Selection and Drug Discovery

The Chemical Genomics revolution is feeding into target validation in drug discovery. These petabytes of omics data - genomics, proteomics, metabolomics - facilitate patient stratification and predict toxicity, responsiveness and ultimately, the viability of the drug, in-vivo.

  • Learn from this practical use case on the end-to-end application of Chemical Genomics to:
  • Bring together chemical libraries and cellular assay data to develop meaningful data context for Target Validation
  • Understand what informatics and mining tools are necessary to store and analyse the data
  • Engage with the challenges and opportunities presented through this approach

Michael R. Barnes, Director of Bioinformatics, NIHR Cardiovascular BRU, Queen Mary, University of London

CASE STUDY: TRANSLATIONAL MEDICINE: A NEW ERA OF DATA COLLABORATION

12.10 Attain an Holistic View of Drug Performance Across R&D Through Data Sharing & Integration

One of the big challenges in the modelling, sharing, and using of data is collaboration. Traditionally, R&D functions have tended to work in silos, but bringing biologists, chemists and data scientists together, and applying Translational Medicine to drug discovery is driving efficiency and productivity into Discovery by unlocking valuable scientific insights.

Hear how an industry leader has taken steps to:

  • Benefit from improved linkage between Bioinformatics & Cheminformatics
  • Harness Systems Pharmacology to develop an holistic view of how compounds perform in both pre-clincial and clinical environments
  • Close the loop on vital scientific insights through an integrated and collaborative approach

Hugo Cuelemans, Head of Discovery Data Sciences Unit, Janssen

12.50 Lunch In Exhibition Showcase Area

1.40 SOLUTIONS SPOTLIGHT SESSIONS

CASE STUDY: SYSTEMS PHARMACOLOGY: IMPROVE TARGETING IN DRUG DISCOVERY

2.00 How Data Mining Can Increase Efficiency in Target Identification

Data mining has led to a significant increase in target identification. But which approach will lead to the most accurate results in the best possible time? What approach will help you to select and prioritise potential disease targets most effectively?

  • With a resurgence in phenotypic screening, join this panel session to:
  • Share insight on the range of models available to accelerate and focus target identification:
  • Including: mNRA/protein level exploration, genetic association & phenotypic impact from mutation
  • Develop your understanding of the available data sets and how to select these effectively
  • Hear practical examples from leading pharmacologists on how they have applied this innovative medicinal approach

David Ruau, Head of Scientific Computing Solutions, Advanced Analytics Centre, AstraZeneca

DRIVING VALIDITY AND VERACITY: MODELS TO IMPROVE CONSISTENCY IN TARGET VALIDATION

2.40 Consistently and Accurately Identify Targets at Both the Compound and Molecular Levels

Valid outcomes and veracity in insights at Discovery phase are critical if Pharma is to reduce the 97% failure rate across R&D. The challenge is not in the volume of data available, but how to model and match datasets in a relevant way to drive accuracy into target validation.

Discover how strategic application of automation and analytics can unlock the potential to:

  • Leverage all legacy information on compounds and molecular modulation effectively
  • Improve prediction of which compounds are inhibitors to advance your projects
  • Reduce the time taken to identify active targets to accelerate drug discovery

Philippe Sanseau, Head of Computational Biology, GSK

3.20 Afternoon Refreshments In The Exhibition Area

COMPLEX APPLICATIONS: DYNAMIC PHENOTYPE NETWORKS

3.50 Target Dynamic Networks Using Bioinformatics Approaches to Broad Data Sets

Pre-competitive data pooling creates a unique opportunity to model individual compounds and their outcomes. What is also now possible is the potential to apply next generation bioinformatics to this data to extract insights across a medicinal network.

Focus on this next-level approach to understand how you can:

  • Unlock the synergistic effects of medicine through modelling of diverse data sets against diverse proteins
  • De-convolute targets from phenotypic screening to aid prioritisation of set, pathway and network identification
  • Identify differentiators in molecular signatures or genetic backgrounds to predict effective targeting for non-responding molecules, as well as those in focus

Francesco Iorio, Senior Bioinformatician, European Molecular Biology Laboratory - European Bioinformatics Institute

CASE STUDY: THE ART OF THE POSSIBLE - ENABLE DISCOVERY THROUGH AI AND DEEP LEARNING

4.25 Successfully Applying Deep Learning to Drug Discovery and Repurposing

The advent of artificial intelligence and deep learning is widely anticipated to transform the pharmaceutical industry. While for some its application still lies in the future, this session offers the opportunity to hear from those leading the field who are already deriving real benefits.

Listen to this in-depth & pioneering case study to:

  • Glimpse the possible: See first hand the insights being gleaned from deep learning and it's impact on the industry
  • Develop a clear understanding of the foundations required to access deep learning
  • Grapple with the challenges of standardising data sufficiently to apply AI

Leonardo Rodrigues, Associate Director, Advanced Analytics, BERG Health

5.00 Chair's Closing Remarks

5.30-6.30 Networking Drinks Reception In Exhibition Showcase Area

DISCOVERY INFORMATICS AND ANALYTICS SUMMIT 2016: DAY 2

8.45 Chair's Opening Remarks

KEYNOTE: DRUG COMBINATION PREDICTION

9.00 Improving Approvals of Drug Combinations Through Pharmacogenomics

FDA approval of pre-existing drugs as a combined medicine are relatively straightforward. However, when presented with a back catalogue of compounds that have previously failed, predicting the synergistic outcomes in this case is challenging.

Hear from this industry leader on how improved computational biology and chemistry supported them to:

  • Identify optimal drug combinations from large sets of drugs and compounds
  • Apply machine-learning methodology to classify drugs based on molecular and pharmacological terms
  • Remove the requirement for additional experimental testing to validate throughout the prediction process

Bissan Al-Lazikani, Head of Data Science, Institute of Cancer Research

CASE STUDY: NEXT GENERATION DRUG REPURPOSING

9.40 Extract Value from Previous Projects and Find New Life in Your Compound Back Catalogue

Have you wondered what to do with the large quantities of data from discontinued research projects and trials?  New analytics tools can unlock the potential in your legacy data to uncover new applications for compounds.

Open your eyes to this example of a successful drug repurposing project, and understand how they were able to:

  • Identify alternative indications of existing drugs/compounds through predictive modelling against new data sets
  • Reveal non-obvious drug-target-disease relationships for discovery and repurposing
  • Increase clinical starts & strengthen the drug development pipeline

Andreas Bender, Reader for Molecular Informatics, Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge

10.10 Morning Refreshments In Exhibition Showcase Area

MODELLING IN PRACTICE

Join the discussion most relevant to your role, to deep-dive into how to apply predictive modelling, data standardisation methodologies andinformatics tools to aid discovery in these specific areas.

STREAM ONE

10.45 Gene Expression and Genetic Sequencing

Drive speed and efficiency into DNA and RNA sequencing by discussing with peers how to:

  • Explore Illumina, Roche, Ion Torrent & SOLiD approaches and their complementary potential
  • Understand the value of modern technology based approaches for your organisation to achieve Deep Genomics
  • Assess which sequencing tools best fit your scale of study

Nicholas Kelley, Research Investigator, Genomics, Novartis

11.30 PKPD Modelling

Considerations for the regulatory evaluation of in-silico cardiac safety assessment models

Key points:

  • Increasing emphasis moves towards the implementation of in-silico methods for offering an improved translation between in-vitro, in-vivo and clinical data.
  • The focus for these in-silico cardiac models however is not just to predict single cell outcomes (e.g. action potential) or whole-body physiological outcomes (e.g. ECG), but instead to population-level outcomes (e.g. incidence of pro-arrhythmia).
  • Careful consideration of what the ideal data set should be is therefore needed to ensure that those critical model validation exercises will result in the most predictive model for cardiac risk assessment.

Mark Davies, Modelling and Simulation Scientist, Roche

12.15 Maturing Image Analysis

Address key challenges in unlocking the value of small, image-based data sets:

  • Share insights on how to improve systems recognition and classification of patterns to drive consistent analysis
  • Consider the value of multi-channel image analysis and how this can power Deep Learning
  • Refine strategy to deliver qualitative analysis consistently, alongside quantitative data

This session is reserved for a leading provider to share insight on how their tools & methodology are supporting Pharma to reach their business objectives.
For more information please contact info@lbcg.com

STREAM TWO

10.45 Compound Profiling And Predicted Assay Response

Expand your predictive potential across key areas such as resistance, responsiveness and toxicology by learning how to:

  • Expand compound libraries for more effective modelling
  • Interpret multiple readouts and their significance
  • Assess the difference between next-generation and current High Throughput Screening techniques

Stephen Pickett, Group Leader, Computational Chemistry and Informatics, GSK

11.30 Small Molecule Applications

Accelerate lead discovery and optimisation in small molecule drug development by joinig your peers to:

  • Discuss modelling tools that can be applied across a range of small molecules
  • Assess the value of linking transcriptomics data and small molecules
  • Consider the benefits of virtual screening options

Roman Affentranger, Head of Small Molecule Discovery Workflows, Roche

12.15 Text Mining

Understand how Text Mining can enable you to pursue new discovery projects in a quicker and more effective way:

  • Overcome the lexical challenges of scientists and how to standardise terminology
  • Consider how automation delivers efficiency and improves on natural language processes
  • Identify the value and quality of the text you are mining

This session is reserved for a leading provider to share insight on how their tools & methodology are supporting Pharma to reach their business objectives.
For more information please contact info@lbcg.com

1.00 Lunch In Exhibition Showcase Area

CASE STUDY: DATA STANDARDISATION: DRIVING INTEROPERABILITY ACROSS DATASETS

2.00 Standardise Diverse Datasets to Optimise Actionable Insights

Standardising varied and broad datasets to make them easier to interpret and integrate into machine learning efforts is the key challenge in applying predictive modelling effectively. Data must be organised and the industry can develop this through agreeing set parameters for classification and identification.

Keeping this front of mind, this strategic session will uncover the tips and tricks to:

  • Combine datasets such as chemical structure information, activity information, phenotypic information, and clinical information, and feeding them all into one single model
  • Apply open standards for data management, ensuring it is usable in the case of a merger or collaboration
  • Normalise annotations, meta-data, and ontologies to drive clarity across data-sets

Aldo Schepers, Head, Clinical Data Stewardship and CDISC Standards, GSK

CASE STUDY: RATIONALISING DATASETS

2.40 Build and Understand Relevant Datasets To Curate a Discovery Gold Mine

Recent proliferation of available data in a range of formats has, as discussed, delivered significant opportunity. The devil, however, is in the detail of how to make insights extractable from relevant data sets.

Tackle these challenges with this data expert to:

  • Develop clear classification parameters and identifying features to make sense of structured and unstructured data
  • Assess data gaps and data sourcing strategies to ensure solid hypothesis generation and comprehensive modelling
  • Consider the preliminary experimental requirements to understand how to assess data relevancy against specific use cases

Bhushan Bonde, Associate Director, Data Integration Lead, New Medicines IT, UCB

3.20 Afternoon Refreshments In The Exhibition Area

LOOKING FURTHER DOWN THE MEDICINAL PATHWAY

4.00 Translational Bioinformatics: from molecular data to therapeutic targets and biomarkers

An overview of collaborative working models, bioinformatics methodologies and their applications to different therapeutic areas, towards the identification of novel targets and biomarkers for patients stratification.

  • Bioinformatics in a large translational research environment: needs, expectations, challenges
  • Working Models and Survival Strategies
  • Big Data and Translational Research: Operational, Analytical and IT Infrastructure
  • Hunting for disease causing genes and molecular biomarkers

Emanuele de Rinaldis, Head of Translational Bioinformatics, NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust

INTERDISCIPLINARY DATA STRATEGIES

4.30 How Lessons Learned through Investment Banking Can Accelerate Drug Discovery

Share parallel insights with this leading Investment Bank to understand how repurpose tools to drive performance into Pharma data integration and predictive modelling. With similar regulatory focus and significant financial pressures within a highly competitive landscape, the applications are closer than you might expect.

Turn your preconceptions on their head to:

  • Clarify the need for pace and how to harness performance based tools to drive down cost and time in Pharmaceutical R&D
  • Consider the benefits of alternative solutions geared to deliver competitive advantage within milliseconds
  • Take heed from a more advanced industry on the pitfalls to avoid and lessons they have learned in driving advanced analytics

5.00 Chair's Closing Remarks And End Of Summit

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