Agenda

Pre-Conference Day | October 15th

Pre-conference workshops are taking place on October 15th, being delivered by representatives from AstraZeneca, Sanofi, BioInfi and former GSK.

To find out more details on topics, content, benefits of attending and the workshop leaders, click here.

Day 1 | October 16th

8.00     Registration and coffee

9.00     Opening Address from the Chairperson

Thomas Abbott, Head, Real World Evidence, Astellas

9.05     KEYNOTE: Translating Big Data Hype to Deliver Real Patient Hope

  • Human biology can be measured and quantified at unprecedented scale, speed and detail

  • How can this be leveraged to realize a new paradigm – across all sectors – to ensure these breakthroughs result in enhanced human healthcare?  

Nadeem Sarwar, President, Center for Genetics Guided Dementia discovery (G2D2), Eisai

9.30     KEYNOTE: Data as a Trustworthy and Novel Element to Drive Drug Discovery

  • Consider new insights into long established bioactivity data-based models

  • Explore multiple use cases for novel data types

Wendy Cornell, Global Lead, Drug Discovery Technologies, IBM Research

9.50     KEYNOTE: The Role and Impact of AI in Drug Development

  • Grasp AI & ML’s application in early drug discovery to preclinical work as demonstrated by evidence from use cases

  • See the measurable impact of AI in drug development

Bino John, Associate Director, AstraZeneca

10.10     SPEED NETWORKING

Meet other conference attendees in a fun session designed to break the ice and help you identify who you would most like to speak with later in the meeting. Bring plenty of business cards!

11.10     Morning break and coffee


Attendees Choose One of Two Tracks, Track One - Enabling Science and Track Two - Doing Science


TRACK ONE:
ENABLING SCIENCE

Track Chair: Thomas Abbott, Head, Real World Evidence, Astellas

PLANNING AND EXECUTING YOUR DATA-DRIVEN DRUG DEVELOPMENT STRATEGY  

11.30     CASE STUDY: Shaping Your Business Strategy with Advanced Analytics

  • Learn of the roles that big data and advanced analytics can play in forecasting demand to refocus your product pipeline

  • Delivering results via management of your pipeline along with the risk factors associated with using AI & ML

  • Understand the importance of end-to-end visibility and see how this can drive efficiency and operational excellence

Nuray Yurt, Executive Director, Enterprise Analytics & Data Science, Novartis

12.00     Building and Making the Business Case for Investment and Executive Attention on Better Processes and Infrastructure

  • Learn how to define the commercial costs and returns of data-driven infrastructure and processes

  • Using case-studies to examine when data-driven approaches will best deliver a return on investment

  • Discover a step-by-step process in building and delivering a business case towards data-driven drug development

Thomas Abbott, Head, Real World Evidence, Astellas

12.30     Lunch

1.30     CASE STUDY: Application of Advanced Analytics and Computational Platforms to Optimize Workflow

  • Discover, from case evidence, the effective application of advanced analytics and predictive IT platforms that optimize the drug R&D process

  • Understand the ROI in utilizing data-driven approaches to drug R&D, and grasp the opportunity to create and maximize the value of pre-existing data and materials

  • What are the questions and issues that a predictive IT platform should address? How to ensure IT solutions/ advance analytic approaches will bring value to drug discovery and development by meeting end users’ needs

Brian Kelley, Principal Scientist, Relay Therapeutics

PREPARING FOR PRECISION MEDICINE

2.00     Rediscovering Metadata - a Voyage on Scientific Data Ocean

Discover how to implement a strong metadata strategy for unstructured data to help you find data quickly and easily using content-based and policy-driven data classification & tagging. Hear how custom metadata simplifies management of massive unstructured data repositories, enabling groundbreaking biomedical research for organizations that are at the forefront of precision medicine.

Learn about key capabilities of the underlying reference architecture for high performance data and AI (HPDA) in healthcare and life sciences to help you manage the entire data lifecycle and optimize your AI and big data pipelines for analysis, leveraging containers and cloud.

Frank Lee, Global Healthcare and Life Sciences Industry Leader, IBM Systems

FROM INTEGRATIVE INFORMATICS FOR MULTI-OMICS AND FAIR DATA TO KNOWLEDGE GRAPHS

2.30     FAIR Data, Value to BioPharma and the Killer Use Case

  • Understand the meaning of FAIR beyond its 15 sub-principles, for relevance to our business model

  • Data stewardship, legacy data and data “born FAIR”

  • FAIR datasets as an enabler of rich knowledge graphs and border-less collaboration

Tom Plasterer, Director of Bioinformatics, Data Science & AI, Bio Pharmaceuticals R&D, AstraZeneca

3.00     CASE STUDY: Integrative Informatics, Unlocking the True Potential of Multi-Omics Data to Streamline Your Pipeline

  • Discover a hybrid integrative informatic approach via semantic modelling in combination with traditional bioinformatics analytical pipelines

  • Understand the purpose of integrating genomics, molecular and clinical phenotypic data, and see how you can use this information to answer complex scientific questions

  • Learn how FAIR data principles and hybrid IT approaches can power data integration and provide return on investment

Jonathan Pryke, Senior Business Relationship Manager, AstraZeneca

STREAMLINING CLINICAL TRIALS WORKFLOW USING DATA

3.30     Data Driven Digital Protocol for Clinical Trials

The clinical study protocol is the start of the clinical trial process. Digitizing the protocol and creating metadata will drive efficiencies that will enable the automation of downstream outputs (i.e. eDC/eCRF, CSR, RAP). Using data to drive the design of the protocol will result in a more robust protocol with fewer costly and time-consuming amendments. Find out how.

Elizabeth George, Director, Digital Clinical Trials, GSK

TRACK TWO:
DOING SCIENCE

Track Chair: Varenka Rodriguez DiBlasi, Senior Scientist, Boehringer Ingelheim

DATA-POWERED DISCOVERY OF BIOMARKERS, DRUG TARGETS AND MECHANISMS OF ACTION

11.30     CASE STUDY: Target and Biomarkers Discovery via Transcriptome-Wide Studies of Alternative Splicing, Fueled by Advanced RNA-Sequencing Technologies and Computational Tools

  • Discover the possibility of powering alternative splicing using the latest RNA-sequencing technologies and analytical tools, and how this provides leads for drug discovery

  • Learn of the infrastructure and processes needed for efficient drug target and lead discovery via data-driven transcriptome-wide studies; and where the pitfalls are

  • Understand the start of the data life-cycle from RNA sequencing, how are these data stored for reusability and how do they maintain value in the long term?

Shanrong Zhao, Director, Computational Biology and Bioinformatics, Pfizer

12.00     Personalized Medicine from an Analytics Perspective: Using Biomarker Data to Model Disease Complexity

  • Construct impactful translational and clinical biomarker plans by focusing on key programmatic questions and context of use across phases of drug development

  • Evaluate optimal context for data-driven vs hypothesis-based approaches to biomarker data analysis and integrate fit-for-purpose hybrid strategies with a focus on enabling interpretation of results

  • Challenge status quo in clinical studies of univariate hypothesis testing and aversion to multiplicity, in favor of hypothesis generation and opportunistic replication more closely models the biological complexity

Tricia Thornton-Wells, Director, Translational Medicine, Alkermes

12.30     Lunch

PREDICTIVE MODELLING FOR TARGET IDENTIFICATION

1.30     CASE STUDY: Machine Learning for Holistic Drug Target Discovery

  • Discover how machine learning can be applied to the entire process of drug target identification

  • Learn about specific machine learning models for link inference on a knowledge graph, and 'omics models for target prediction

  • See how AI tools work hand-in-hand with researchers to make informed decisions about novel targets to test in the lab

Alix Lacoste, VP of Data Science, BenevolentAI

2.00     Computation-Led Drug Re-Purposing and Genetic Data-Driven Approaches to Guide Target Discovery

  • Discover how to initiate, enable, and land computational drug repurposing

  • The target features that translate into clinically successful drugs: an examination with AI and ML-based methods

  • You can do it, but who can help? Choosing the correct internal & external partners

Pankaj Agarwal, Senior Fellow, Computational Biology, GSK (former); Principal Computational Biologist, Biolnfi

USING DATA LAKE COMPRISED OF RWD AND CLINICAL DATA TO DRIVE DRUG DISCOVERY

2.30     CASE STUDY: Integration and Analysis of Clinical Trial and Real-World Data (RWD) for Drug Development: Challenges and Successes

  • What is the purpose of forming a data lake and how does that ultimately translate into progress for patients?

  • How can diverse internal and external data sources be accessed and integrated in a data lake?

  • What are key aspects of a data lake that improve its impact and uses?

  • How can data and metadata be discoverable and interpretable?

Philip C Ross, Director, Head of Translational Bioinformatics Data Science, Bristol-Myers Squibb

G. Celine Han, Research Investigator in Analytics Innovation, Oncology Translational Bioinformatics, Bristol-Myers Squibb

INTERPRETING MULTI-OMICS DATA

3.00     CASE STUDY: Interpreting “Higher-Dimension’’ Integrated Multi-Omics Data to Drive Research Translation

  • Understand what useful multi-omics data look like and how to access it

  • See the analytical tools and platforms involved along with the procedure for effective research translation

  • Where do the success and challenges lie for higher-dimension data?

John Quackenbush, Chair of the Biostatistics Department, Harvard T.H. Chan School of Public Health

3.30     CASE STUDY: Computational Biology Approaches Towards Interrogating the Tumor Microenvironment

  • Quantifying tumour immune and stroma contexture with transcriptome and chromatin landscapes

  • Cell signatures redefined by multi-omics approaches

  • TME Spatial Features

Varenka Rodriguez DiBlasi, Senior Scientist, Boehringer Ingelheim


4.00     Afternoon break and coffee

Plenary Chair: Varenka Rodriguez DiBlasi, Senior Scientist, Boehringer Ingelheim

INNOVATION SHOWCASE

4.30     INNOVATION SHOWCASE: 4 x 10 minute presentations.

This is an interactive session presenting the most innovative breakthrough technologies affecting data-driven drug development today (not tomorrow). At the end of the session, the audience vote for the technology/presentation that they think will have the greatest impact.

4.35     TALK 1: A Solution for the “Big Data” Challenge in Biomarker-Guided Drug Development

Renee Deehan-Kenney, VP of Computational Biology, QuartzBio

4.45     TALK 2: From Point Solutions to a Next Generation Enterprise Science Platform

Vasu Rangadass, PhD, CEO, L7 Informatics

4.55     TALK 3: The RWE System of Intelligence: How End-to-End R&D Models With AI and Data Science are Breaking Down Barriers to Deliver Highly Influential Sources of Clinical Evidence

Jeff Elton, CEO, Concerto HealthAI

5.05     TALK 4: Leveraging Large Data in Digital Pathology

Thomas Westerling-Bui, Senior Scientist, Regional Business Development, Aiforia

5.15     INNOVATION SHOWCASE: Voting and winner announcement

5.20     PANEL SESSION: “How would you build a pharma company from scratch, five years from now? What would be the difference, from talent to drug discovery to commercialization?“ Bertrand Bodson, Chief Digital Officer, Novartis. This panel will seek to provide enlightenment on that important question.

·         What would be the difference, from talent to drug discovery to commercialization?” Bertrand Bodson, Chief Digital Officer, Novartis (former)

·         Do we need a new, overall model for data/information in medicine, if it’s use/housing is not going to be limited to pharma anymore?

Ping Liu, Expert Principal, Bain & Company

Bino John, Associate Director, AstraZeneca

Alix Lacoste, VP of Data Science, BenevolentAI

Nuray Yurt, Executive Director, Enterprise Analytics & Data Science, Novartis

5.55     Chairperson’s Closing Remarks

Varenka Rodriguez DiBlasi, Senior Scientist, Boehringer Ingelheim

6.30     Optional Dinner

A relaxed, informal meal at a local restaurant. Sign-up on the day. The meal is not included in your ticket price.


Day 2 | October 17th

8.00     Registration and coffee

9.00     Opening Address from the Chairperson

Alix Lacoste, VP of Data Science, BenevolentAI

9.05     KEYNOTE: Why and How Knowledge Graphs will Transform (and Rescue) Drug Discovery

  • Data-driven processes generally have the goal of finding key information and converting it into knowledge.  With human’s cognitive limitations to manage large data sets, knowledge graphs can provide framework for finding, visualizing, extracting, and predicting relationships

  • This talk will introduce the knowledge graphs and their key underlying technologies

Peter V Henstock, Machine Learning & AI Technical Lead: Combine AI, Software Engineering, Statistics & Visualization, Pfizer

9.35     Supporting Widespread Adoption of AI/ML-Driven Approaches. What’s Required? What’s New?

  • Discover the latest innovations of AI/ML in complex trait genetics    

  • Organizational approaches to aligning incentives and enabling value creation across the ecosystem

  • The next frontier: quantifying measurable benefit relative to investment and ongoing costs

Danielle Ciofani, Senior Director, Data Sciences Platform, Broad Institute

10.05     Morning break and coffee


Attendees Choose One of Two Tracks, Track One - Enabling Science and Track Two - Doing Science


TRACK ONE:
ENABLING SCIENCE

Track Chair: Elizabeth George, Director, Digital Clinical Trials, GSK

AI & ML’S CURRENT DEVELOPMENTS AND THEIR APPLICATION IN DRUG DEVELOPMENT

10.45     CASE STUDY: Real-World Effective Application of Machine Learning Solutions in Clinical Settings to Drive Research Translation

  • Discover the current cutting-edge developments of ML and their impact in healthcare 

  • Understand how to effectively apply and implement this technology to drive research translation

  • What kinds of data are ideal to power these advanced analytical techniques and how to access these data   

Mark Michalski, Executive Director, MGH & BWH Center for Clinical Data Science

DOWNSTREAM PROSPECTS OF DATA-CENTRIC DRUG DEVELOPMENT

11.15     PANEL DISCUSSION: Pharma Ecosystem Alignment in a New Data-Driven World: Speed Versus Regulation, Privacy and Consent

  • With the increasing trend of ‘‘taking drug development out of the lab’’, what does this imply for the quality and safety of drug ADMET data, and how do you validate this data efficiently?

  • How does the growing traction of patient-centricity for data affect consent and access, is this a positive step for both the industry and patients?

Sridevi Ponduru, Associated Medical Director, Takeda

Danielle Ciofani, Director of Data Strategy and Alliances, Broad Institute

NATURAL LANGUAGE PROCESSING IN DRUG R&D

11.45     CASE STUDY: Natural Language Processing, The Latest Advances and Where Do The Challenges Lie

With many current NLP solutions base their cataloguing on educated guesses instead of real-observed data, what does this mean for the reliability of collected data and the cost-effectiveness of implement NLP?

  • Do current NLP solutions meet expectation?

  • Discover the effective applications of NLP from case evidence

  • Where are the challenges and what are the possible solutions

  • How do we proceed with NLP going forward?

Armaghan Naik, AVP R&D Scientific & Digital Innovation, Sanofi

12.15     Lunch

TRACK TWO:
DOING SCIENCE

Track Chair: Alix Lacoste, VP of Data Science, BenevolentAI

OPTIMIZING CLINICAL TRIAL PATIENT STRATIFICATION AND DRUG LEADS

10.45     CASE STUDY: Combining Big Clinical Data with Multi-omics Approaches to Define Endotypes in Respiratory Disease

  • Learn about the impact a well-defined endotype can have on drug development through the lens of Th2 asthma

  • See the opportunity for defining new endotypes in respiratory diseases like asthma and chronic obstructive pulmonary diseases

  • Explore the impact of applying learnings from clinical data of large patient cohorts to molecular profiling datasets

Melody Morris, Lead Computational Biologist, Novartis Institutes for BioMedical Research

UTILIZING AI IN PREDICTING CLINICAL OUTCOMES

11.15     CASE STUDY: Leveraging Pathology Images and Artificial Intelligence to Predict Immuno-Therapy Treatment Outcomes

  • What can we glean from computational analysis of pathology images?

  • Discussion of the challenges associated with quantitative pathology

George Lee, Digital Pathology Informatics Lead, Bristol-Myers Squibb     

PATIENT DATA CENTRICITY AND DRUG DEVELOPMENT

11.45     CASE STUDY: Application of Knowledge Graphs Built From Pharmacy Data to Contrast The Drug Effectiveness of Opioid and Opioid Alternatives in a Healthcare Delivery System

  • Understand the process to building knowledge graphs from pharmacy data with consideration to data discovery and access

  • Discover the ontologies and mapping of data to scientific context for the construction of the knowledge graph’s neural networks

  • See the real-life application of knowledge graphs in evaluating the drug effectiveness between opioid and opioid alternatives in a healthcare delivery system

  • Grasp the possibility of translating this approach to clinical trials

Phil Lanzafame, Director of Knowledge Solutions, Sentara Healthcare

Ashlee Hamel, System Coordinator, Pharmacy Clinical Programs, Sentara Healthcare

12.15     Lunch


Plenary Chair: Vibhor Gupta, Director, Pangaea Data

1.30     CASE STUDY: Applying AI/ML and Analytical Approaches to Enhance Clinical Trial Success Rates

  • Presentation of a project where AI/ML have been developed for the prediction of outcomes with live trial patients based on their baseline and trial data

  • Evaluate the meaningful values of applying advanced analytics to clinical trials

  • Where are the successes and pitfalls of AI/ML guided approaches

Yan Ge, Director, Data Analytics, Takeda

Wenlong Tang, Data Sciences Solutions Manager, Takeda

2.00    CASE STUDY: “Biological Data Representations: Formulating Your Problem to Best Leverage Machine Learning’s Strengths”

  • Get more out of Convolutional Neural Networks to solve genomics problems

  • Graphs as a general-purpose data representation tool for machine learning

Gerald Sun, Senior Data Scientist, AstraZeneca

2.30     PANEL DISCUSSION: The Now and Future of Drug Discovery and Development

  • Driven by Slido (interactive audience participation), a summary of the day’s discussion and main audience takeaways

  • What the ‘enablers’ and ‘doers’ can learn from each other

  • What needs to happen now? Important focus areas, outstanding questions and priorities

  • Building a report/plan for your team to benefit from this meeting

Justin H. Johnson, Associate Director and Principal Translational Genomic Scientist, AstraZeneca

Philip C Ross, Director, Head of Translational Bioinformatics Data Science, Bristol-Myers Squibb

Phil Lanzafame, Director of Knowledge Solutions, Sentara Healthcare

Ashlee Hamel, System Coordinator, Pharmacy Clinical Programs, Sentara Healthcare

Vibhor Gupta, Director, Pangaea Data

3.30     Chairperson’s Closing Remarks and Close of Conference

Vibhor Gupta, Director, Pangaea Data