3rd  Annual  

Big Data for Clinical Trials

Harnessing & Unlocking the Potential of Existing Data Sources 

April 24-25, 2017

The vast volumes of data collected across the clinical trials process offers pharma and biotech the opportunity to harness the information in these big data sets for improved clinical trial design, patient recruitment, site selection, monitoring insights and overall decision making. Harnessing and unlocking the potential in existing data sources, including biomarker, genomic, EHRs, claims data, real world data and clinical trial data, can ultimately lead to improved drug development. Cambridge Healthtech Institute’s Big Data for Clinical Trials conference gathers leaders across pharma, biotech and academia for discussions and case studies on harnessing existing clinical data to advance the clinical trials process.

Monday, April 24

7:25 am Conference Registration and Morning Coffee

NEW INSIGHTS FOR CLINICAL DEVELOPMENT FROM BIG DATA

8:25 Chairperson’s Opening Remarks

Balazs Flink, M.D., Clinical Trial Analytics Lead, R&D Business Insights and Analytics, Bristol-Myers Squibb

8:30 Implications of Big Data for Drug Development

Ray Liu, Ph.D., Senior Director & Head, Statistical Innovation & Consultation, Takeda

This presentation will describe how to maximize the impact of Big Data on drug development: its methodology, practical challenges and implications.

9:00 Big Data Analytics for Next-Generation Clinical Trials

Kevin Hua, Senior Manager, A.I./Machine Learning Development, Bayer LifeScience iHub

In pharmaceutical industry, data is largely available, such as historical clinical trials, drug databases, electronic medical records, sensors data, human genome, real life experience, scientific publications and social media data. We have been building a big data analytics platform and have defined a set of common analytics models that can be applied to many types of clinical trials. With big data and advanced analytics, we can help clinical scientists make data-driven decisions and reach conclusions faster and more accurately. Big data analytics can not only accelerate clinical trials, but also help reduce the risks and costs associated with clinical trials. Big data analytics plays a crucial role in future clinical trials.

9:30 Clinical Trials Innovations in the Age of Big Data and Advanced Analytics

Kaushik Raha, Ph.D., Associate Director & Head, Emerging Analytics and Advanced Visualizations, Janssen Pharmaceuticals

Clinical trials operations have historically been a domain rich in data as by nature clinical trials are heavily regulated processes that entail data collection. As a result, during the entire life cycle of a trial enormous amount of data is collected and stored in all phases, from selection of sites to monitoring and auditing to ensure quality and compliance. This has culminated in sponsors of trials having the unique opportunity to leverage big data and advanced analytics to optimize and improve clinical trials operations at a time when advanced analytics is coming of age. At Janssen, the data sciences group in partnership with global clinical operations has launched initiatives in site selection, risk based monitoring, and quality and compliance to bring innovations based on big data and advanced analytics to clinical trials operations. This has resulted in improved efficiencies in different aspects of operations during the life cycle of a trial. Additionally, we have pioneered the application of technologies such as machine learning, natural language processing, and artificial intelligence to create novel solutions which have resulted in data driven efficiencies realized from predictive and prescriptive analytics on clinical trials data. This presentation will delve on aspects of this work and present vignettes to highlight the challenges and successes.

10:00 Networking Coffee Break

10:30 Integrated Analytics: A Corporate Experiment

Balazs Flink, M.D., Clinical Trial Analytics Lead, R&D Business Insights and Analytics, Bristol-Myers Squibb

BMS decided to integrate all corporate analytics functions under one organization to drive enterprise level decision-making through data. The desired result is integrated, predictive analytics that help drive R&D strategy and execution, with clear ties to long term financial impacts. This presentation highlights the concept and the early results including the challenges and speaks about the cultural aspects of the change, which are much more complex hurdles than deriving insights from a wealth of data.

LEVERAGING EXISTING DATABASES FOR CLINICAL TRIALS

11:00 The VA Diuretic Comparison Project: A Large Scale Clinical Trial Embedded in a Healthcare System

Ryan Ferguson, Director, Cooperative Studies Program Coordinating Center, U.S. Department of Veterans Affairs

This presentation will discuss a pragmatic “Point-of-Care” clinical trial being conducted within the Department of Veterans Affairs. 13,000 patients will be enrolled at 50 sites using the EHR without any other trial management apparatus. Patients will be followed for outcomes and adverse events through the EHR. The trial is being done at a tiny fraction of the cost of a traditional clinical trial.

11:30 Centralizing Data to Address Imperatives in Clinical Development

Karim Damji, Senior Vice President, Product Management and Development, Saama Technologies

Benzi Mathew, Senior Director, Life Sciences Solutions, Saama Technologies

The clinical development data deluge is reaching critical mass for pharmaceuticals. Use of varied data for targeted outcomes remains difficult, despite studies that generate evidence of the risk-benefit profile of investigational products. New technologies are federating the ability to leverage analytic-ready data for innovations in clinical operations and clinical science. With the application of clinical data-as-a-service and meta-data core, centralized clinical data lakes have the power to improve data quality, evidence generation, and time-to-insights.

12:00 pm Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own

12:45 Session Break

1:25 Chairperson’s Opening Remarks

Ray Liu, Ph.D., Senior Director & Head, Statistical Innovation & Consultation, Takeda

1:30 Using Existing Databases to Inform Study Design and Data Quality: An Alzheimer’s Disease Trial Case Study

Chad Swanson, Ph.D., Director, Neuroscience Clinical Development, Neurology Business Group, Eisai, Inc.

The Alzheimer’s Disease Neuroimaging Initiative (ADNI; data funded by ADNI National Institutes of Health Grant U01 AG24904) is a large, natural history study spanning the spectrum of Alzheimer’s disease (AD), that provides an accessible data repository intended to further our understanding of the disease through the stimulation of new investigation. The present study used clinical and biomarker data from ADNI to help design a large Phase 2b study in Early AD with a monoclonal antibody targeted against soluble beta amyloid aggregates (Study BAN2401-G000-201). Moreover, longitudinal ADNI data were used in a series of analyses to develop a comprehensive clinical data monitoring approach in the ongoing Phase 2 study with methods that can be applied broadly across a variety of datasets and therapeutic areas.

2:00 Clinical Sample Management Enabling Precision Medicine Trials

Ron Bourque, Associate Director, R&D IS, Clinical Business Management & Analytics, MedImmune

We have developed a new and innovative sample management model combining Medimmune Clinical Operations with close alliance/partnership to a central lab. Together the technology we are employing is Labmatrix. This initiative is focused on accepting standardized data from all lab vendors. Discrepant data will be corrected at the source lab and reflected back into the tool. Labmatrix is also receiving consent data from our EDC. The result is a sample management tool that answers 3 fundamental questions: What samples do we have in inventory? What samples should we have and are there discrepancies? What consents do we have associated with each sample?

2:30 Refreshment Break in the Exhibit Hall

3:15 Interactive Breakout Discussion Groups

Concurrent breakout discussion groups are interactive, guided discussions hosted by a facilitator or set of co-facilitators to discuss some of the key issues presented earlier in the day’s sessions. Delegates will join a table of interest and become an active part of the discussion at hand. To get the most out of this interactive session and format, please come prepared to share examples from your work, vet some ideas with your peers, be a part of group interrogation and problem solving, and, most importantly, participate in active idea sharing.

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4:15 Welcome Reception in the Exhibit Hall

5:30 Close of Day

5:30 Dinner Short Course Registration

Tuesday, April 25

7:25 am Morning Coffee

DATA-DRIVEN PATIENT RECRUITMENT AND SITE SELECTION

7:55 Chairperson’s Opening Remarks

Robert Loll, Vice President, Business Development & Strategic Planning, Praxis

8:00 Data-Driven Patient Recruitment with Real World Data at Roche pRED

Liping Jin, Data-Driven Recruitment Lead, Pharmaceutical Research & Early Development, Roche Innovation Center New York

With the increasing use of Real World Data (RWD) in the pharma industry, the Data-Driven Recruitment (DDR) team at Roche Pharm Research & Early Development (pRED) would like to share our experience of integrating RWD (e.g. insurance claims, EMR) with trial metrics data to optimize study protocol design and target patient recruitment strategy. While the team has received positive feedback from our business partners (translational medicine, clinical program teams, and study leaders), we would like also to share the challenges to expanding the effort in broader US and international settings.

8:45 Site Selection and Trial Execution- Pearls and Pitfalls

Darin Curtiss, Pharm.D., Vice President, Clinical Development, Orphanos

9:00 Optimizing Clinical Research through Insight Generation and Data-Driven Approaches

Martine Lewi, Scientific Director, Quantitative Sciences, Real World Evidence, Medical Affairs, Established Products Statistics (RMEDS), Janssen

The presentation starts from a European perspective on health data re-use for optimizing clinical research, comparing the situation – from a data user perspective – with practices in other regions. Lessons learned from the public/privately funded Innovative Medicine Initiative EHR4CR will be shared and the objective of open collaboration with different stakeholders will be emphasized, aiming at the development of a sustainable ecosystem where new partnerships can emerge and clinical research can be optimized through early insight generation.

 9:30 Recruiting Beyond Traditional Patient Populations. A Risk-Free Approach

Ivor Clarke, CEO, SubjectWell

The statistics are familiar - only 4% of Americans have ever participated in a clinical trial and less than half can even recall seeing an ad for patient recruitment. Accelerating enrollment requires growing the total population of people participating. We’ll discuss a unique approach that leverages learnings from other industries.

9:45 Who’s Keeping Score? A Quantitative Approach to Trial Feasibility

Luke Stewart, MBA, Director, Product Management, Saama Technologies

With most trials failing to meet enrollment timelines, current approaches for feasibility fall short of identifying and minimizing risk. Sponsors must arm themselves with the right tools to own this analysis throughout the trial lifecycle. We will discuss a quantitative approach that operationalizes feasibility score tracking.

10:00 Coffee Break in the Exhibit Hall

1:20 Close of conference. Stay on to attend Data & Tech Driven Clinical Trials.