2018 Archived Content
Clinical Research and Translational Informatics

At the cusp of advancing clinical trials and translational research lies the need to transform biological insights and research data using innovative techniques for the integration, visualization and analysis of biological and clinical research data. Track 8 explores new approaches to the integration, visualization, analysis, and application of biological and clinical trial data, including big data analytics, machine learning, artificial intelligence, and additional technologies with case studies from across pharma and academia.

Tuesday, May 15

7:00 am Workshop Registration Open (Commonwealth Hall) and Morning Coffee (Foyer)

8:0011:30 Recommended Morning Pre-Conference Workshops*

W2. Common Statistical Mistakes to Avoid for Data Scientists

W6. An Intro to Blockchain in Life Sciences

12:304:00 pm Recommended Afternoon Pre-Conference Workshops*

W11. Data Science Driving Better Informed Decisions

* Separate registration required.

2:006:30 Main Conference Registration Open (Commonwealth Hall)

4:00 PLENARY KEYNOTE SESSION (Amphitheater & Harborview 2)

5:007:00 Welcome Reception in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

Wednesday, May 16

7:00 am Registration Open (Commonwealth Hall) and Morning Coffee (Foyer)

8:00 PLENARY KEYNOTE SESSION (Amphitheater & Harborview 2)


9:45 Coffee Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

Beacon Hill

10:50 Chairperson’s Remarks

David John, Director, Translational Solutions, PerkinElmer

11:00 PANEL DISCUSSION: Real World Data in Drug Discovery and Development: Opportunities and Challenges

Michael N. Liebman, PhD, IPQ Analytics, LLC and Strategic Medicine, Inc. (Moderator)

Jonathan Morris, MD, Vice President, Provider Solutions; Chief Medical Informatics Officer, Real World Insights, IQVIA

Charles Barr, MD, MPH, Group Medical Director and Head, RWE Strategy and External Relationships, Genentech

Elizabeth Baker, Esq., Senior Science Policy Specialist, Toxicology and Regulatory Testing, Physicians Committee for Responsible Medicine

Significant efforts are underway to use real world data in support of regulatory decision making in drug development involving both pharma and the FDA. Real world data and real world evidence provide both additional opportunities and challenges that should also be explored. Quality, quantity and transparency of data and annotation can greatly impact its value and utility. Opportunities also exist to use such data much earlier in the drug development process, to improve its efficiency and effectiveness. Understanding the complexity of real world patients and clinical practice is critical for the development of appropriate study cohorts through drug discovery and development. The panel will discuss these issues and how to optimize both collection and use of real world data.

Molecular Health NEW12:00 pm MH Effect® – Deriving Deeper Insights from Integrating Real World with Molecular Data

Armin Schneider, MD, PhD, Vice President, Medical & Scientific Affairs, Business Development Pharma & Biotech, Molecular Health

Enriching clinical level RWD with molecular level data enables exploration of why and how a drug-related event happened to a patient. Powered by Dataome® technology, where clinical and molecular data are interconnected on a pathway level, MH Effect® enables deeper signal detection of drug outcomes beyond clinical level.

12:30 Session Break

 InterSystems_HBG 12:40 Luncheon Presentation I: Leverage NLP Technology to Accelerate Clinical Trial/Research Recruitment 

Kedar Radhakrishna, Clinical Provider Technology Solution Strategist , Northwell Health

Looking for the right patient for clinical trials and research programs require extensive review of the patient chart. Even though the information is in the EMR, the researcher has to search through multiple records before contacting the provider and patient to enroll the patient for the clinical study. This process is tedious as the information resides in multiple locations.

1:10 Luncheon Presentation II (Sponsorship Opportunity Available)

1:40 Session Break

Beacon Hill

1:50 Chairperson’s Remarks

Alexander Sherman, Director, Center for Innovation and BioInformatics, Neurological Clinical Research Institute, Massachusetts General Hospital; Principal Associate, Neurology, Harvard Medical School

1:55 Patient Centricity and Big Data in Clinical Research: Realities and Incentives

Alexander Sherman, Director, Center for Innovation and BioInformatics, Neurological Clinical Research Institute, Massachusetts General Hospital; Principal Associate, Neurology, Harvard Medical School

Patient-centric research is encouraged and almost required by sponsoring and regulatory organizations. An implemented system for secure unique patient identification allows to aggregate information and data for people with diseases across studies and venues, thus creating a clinical and translational research ecosystem, in which clinical and phenotypical data are connected to biobanks, image banks, whole-genome sequences, -omics, patient-reported outcomes and mobile apps. Obstacles and incentives for data sharing and best practices in technological platforms and approaches will be discussed and illustrated.

2:25 CO-PRESENTATION: How Well Do Toxicology Studies Predict Clinical Safety Outcome – A Translational Safety Big Data Analysis

Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG

Matthew Clark, Scientific Services, R&D Solutions, Elsevier

We present the results of a statistical analysis of concordance between animal toxicities and human adverse events based on data available for 3290 compounds from the database Pharmapendium. Our work will provide answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human, is dependent on the animal species.


2:55 Co-Presentation: Supporting Exploratory Biomarker Research with a Cross-Trial Search and Analytics Platform

Darin Benner, IT Manager, Bristol-Myers Squibb

David John, Director, Translational Solutions, PerkinElmer

Translational scientists face challenges of finding and utilizing data hidden within their organizations to support exploratory biomarker research. Most of their time is spent collecting and curating data with little time left for analyzing the information in new and meaningful ways. We have launched a platform to support scientists more effectively in this endeavor and are starting to see the results.

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

4:00 Exploration of Clinical and Biomarker Data across the Immuno-Oncology Portfolio: Integration, Data Lakes and Visualizations

Philip Ross, Director, Data Science, Bristol-Meyers Squibb

We continue to expand capabilities to explore clinical and biomarker data across multiple cancer types and therapeutic programs. The goal is to detect and better understand the value of biomarkers to distinguish the likely responses of diverse cancer patients to an expanding portfolio of alternative and combined treatments. Data lakes and integration and semantic layers are critical to support analyses and visual exploration of the data spanning disparate programs.

4:30 Using Genomics to Match Patients to Cancer Clinical Trial

Catherine Del Vecchio Fitz, Senior Research Scientist, Clinical Genomics, Dana-Farber Cancer Institute

Molecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Currently, physicians struggle to efficiently and accurately match patients to relevant clinical trials using the patient's genomic profile and manual trial pre-screening, which can result in missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials. To meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI).

5:00 Savings Lives and Reducing Healthcare Costs Using AI-Assisted Rapid Whole Genome Sequencing for Neonatal and Pediatric Intensive Care Units

Ray Veeraraghavan, PhD, Director, IT & Informatics, Rady Children’s Institute for Genomic Medicine

Benefits of genomics have been successfully demonstrated in clinical practise, specifically for undiagnosed “odyssey” cases. However, from a healthcare perspective, the intensive care units (ICUs) account for bulk of the resource consumption, yet remains underserved in terms of leveraging the benefits of genome-based precision medicine. To address this critical need, we have developed a rapid whole genome sequencing (rWGS) based and CAP/CLIA compliant precision medicine enterprise, to address the specific needs of children admitted to neonatal and pediatric intensive care units. Employing artificial intelligence methods, EHR-integration, hardware acceleration, cutting edge bioinformatics and decision support tools, a cloud-based hybrid high-performance infrastructure, extensive systems integration coupled with timely customer support to neonatologists and intensivists, we have established a scalable framework that is saving lives and reducing healthcare costs. This framework is also being used to offer clinical rWGS services to pediatric hospitals around the country. We will discuss our experiences, challenges and on-going efforts to permeate genomics as an integral part of medical practice.

5:30 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing (Commonwealth Hall)


7:0010:00 Bio-IT World After Hours @Lawn on D

Thursday, May 17

7:30 am Registration Open (Commonwealth Hall) and Morning Coffee (Foyer)

8:00 PLENARY KEYNOTE SESSION & AWARDS PROGRAM (Amphitheater & Harborview 2)

9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced (Commonwealth Hall)

Beacon Hill

10:30 Chairperson’s Remarks

Piyush Bansal, Senior Industry Analyst, Transformational Health, Frost & Sullivan

10:40 Machine Learning Approaches Applied to Biomedical Data for Patient Stratification and Decision Support

Kimberly Robasky, PhD, Senior Translational Scientist, Renaissance Computing Institute, University of North Carolina at Chapel Hill (CC)

Some estimates place the cost of bringing a drug to market at one billion U.S dollars and hence reducing the cost and length of clinical trials can indirectly lower healthcare costs. One source of clinical trial failure, lack of efficacy and safety, could be mitigated through decision support for patient stratification. As part of the NIH-funded Biomedical Data Translator project, we are integrating multiple, previously disparate datasets, which is empowering investigators with new tools for data-driving patient subtyping. For example, through the Data Translator project, we can combine clinical records with exposure data in support of powerful models for classification. We have implemented supervised and unsupervised machine learning models on these data to create decision trees both for predicting patient outcomes and for clustering patients by phenotype and exposure to better understand patient disease and response. Here we will present the results from supervised and unsupervised machine learning models trained to real world evidence (RWE) from asthma phenotypes curated in the Carolina Data Warehouse (CDW-H), combined with publicly available exposome data (e.g., PM2.5, ozone).

11:10 Respect Individual Differences – Analytical Innovation in Personalized Medicine via Big Data

Ray Liu, PhD, Senior Director & Head, Statistical Innovation & Consultation, Takeda

Patients are not a single, homogenous group. Instead, patients are heterogeneous and respond differently even to the same drug treatment. Big data has the potential to fulfill the promise of personalized medicine. This presentation will focus on recent analytical innovation using big data to match the right patient to the right drug at the right dose at the right time.

11:40 Machine Learning: An Essential Tool for Building Digital Biomarkers

Shyamal Patel, Senior Manager, Digital Medicine, Pfizer, Inc.

We live in an ecosystem of connected devices (e.g. wearables, smartphones and IoT systems). Sensors embedded in these devices, which we interact with on a daily basis, capture rich real-world information about our health and well-being. In this talk, I will use case studies to illustrate how machine learning can be used as a powerful tool for tapping into these data streams to develop and deploy digital biomarkers at scale.

12:10 pm Session Break

12:20 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)

Beacon Hill

1:55 Chairperson’s Remarks

Nils Gehlenborg, PhD, Assistant Professor, Department of Biomedical Informatics, Harvard Medical School

2:00 CO-PRESENTATION: Assessment of Disease and Relapse Using Remote Monitoring Technology

Maximilian Kerz, PhD, BRC Software Developer, Biostatistics & Health Informatics, King’s College London

Nikolay Manyakov, PhD, Principal Scientist, Janssen

Typically, disease progression is monitored during infrequent clinical visits, generating a sparse and subjective clinotype derived during periods of sickness. This subsequently leads to late intervention with modest outcomes. Continuous monitoring could help to generate a more objective, pervasive phenotype throughout the disease continuum. The EU Innovative Medicines initiative €25m major programme, RADAR-CNS (https://www.radar-cns.org/) is exploring the use of remote measurement technologies, utilizing smartphone sensors, consumer wearables, information about smartphone usage, and experience sampling method to predict and avert negative outcomes through monitoring of current clinical states and assessment of future deterioration.

2:30 Developing Digital Biomarkers through Crowdsourcing

Larsson Omberg, Vice President, Systems Biology, Sage Bionetworks

The high quality sensors embedded in the typical smartphone coupled with the ease of gathering high frequency data is opening up new ways of tracking disease and performing participant centered research. Building biomarkers from this data is a non-trivial task however. In this talk I will present our experience in collecting data from 20,000 participants to build disease biomarkers and how we engaged 400 researchers across the globe to enhance them.

Harborview 2


3:00 CO-PRESENTATION: Leveraging a R&D Data Hub Platform for Next Generation of Clinical Data Review

Krista McKee, Director, Data Analytics, Takeda

Raveen Sharma, Specialist Leader, Deloitte Consulting LLP

*Contributed work: Ramin Daron, Senior Director, Data Architecture, Takeda and Sunny Shahdadpuri, Senior Consultant, Deloitte Consulting LLP

The Data and Analytics Hub platform was conceived, designed, and built to address issues of data transparency, trust, and accessibility. This will support the efficient generation of insights for functions across R&D. In this presentation, we will focus on a critical use case of the platform, clinical data review/medical monitoring that will ultimately allow for efficient cross-study and cross-compound analysis which will advance our ability to interact with the data.

4:00 Conference Adjourns

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