Advancing clinical trials and translational research requires transforming biological insights and raw research data into clean, actionable data for integration, visualization, and analysis. The Clinical Research and Translational Informatics track
explores new and innovative tools and techniques—including big data analytics, machine learning, and artificial intelligence—and how they can be leveraged to address specific challenges faced across the drug discovery spectrum to accelerate
the translation of scientific discoveries from the bench to medical care. Gain practical recommendations and real-world insights from case studies across pharma and academia. Actionable insights require making the results of complex analysis readily
convertible into the common workflows of the clinician and researcher. How do you approach this problem?
Final Agenda
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Monday, April 20
9:00 am - 5:00 pm Hackathon*
*Pre-registration required.
Tuesday, April 21
7:30 am Workshop Registration Open and Morning Coffee
8:30 am - 3:30 pm Hackathon*
*Pre-registration required.
8:30 - 11:30 am Recommended Morning Pre-Conference Workshops*
W3. Introduction to Data Visualization for Biomedical Applications
Nils Gehlenborg, PhD, Assistant Professor, Department of Biomedical Informatics, Harvard Medical School
Alexander Lex, PhD, Assistant Professor, SCI Institute, School of Computing, University of Utah
12:30 - 3:30 pm Recommended Afternoon Pre-Conference Workshops*
W13. Structuring Data for Drug Development and Regulatory Submissions: The Role of Standards and Ontology
Lawrence Callahan, PhD, Chemist, Office of Health Informatics, Global Substance Registration System/Office of Health Informatics, Office of Chief Scientist, FDA
*Separate registration required.
2:00 - 6:30 Main Conference Registration Open
4:00 Welcome Remarks
Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute
4:05 Keynote Introduction
4:15 PLENARY KEYNOTE PRESENTATION: NIH’s Strategic Vision for Data Science
Susan K. Gregurick, PhD, Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of
Health
Rebecca Baker, PhD, Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health
5:00 - 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing (Sponsorship Opportunity Available)
Wednesday, April 22
7:30 am Registration Open and Morning Coffee
8:00 Welcome Remarks
Allison Proffitt, Editorial Director, Bio-IT World
8:05 Keynote Introduction
8:15 Toward Preventive Genomics: Lessons from MedSeq and BabySeq
Robert Green, MD, MPH, Professor of Medicine (Genetics) and Director, G2P Research Program/Preventive Genomics Clinic, Brigham & Women’s Hospital, Broad Institute, and Harvard Medical School
8:45 PANEL DISCUSSION: Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward
Pietro Michelucci, PhD, Director, Human Computation Institute
Additional Panelists to be Announced
9:45 Coffee Break in the Exhibit Hall with Poster Viewing
10:50 Organizer’s Welcome Remarks
Cambridge Healthtech Institute
10:55 Chairperson’s Remarks
11:00 Advancing Pharma R&D with Digital Health and AI-Enabled Insights
Ray Liu, Senior Director, Advanced Analytics and Statistical Consultation, Takeda Pharmaceuticals
Novel digital technology allows study subjects to be assessed phenotyped to reveal new patterns. Coupled with Big Data and new AI technologies, digital technology has great potential to make the drug more efficient and fulfill the promise of personalized
medicine. The presentation explores the status of digital technology implementation in clinical trials and impacts of AI/ML. Challenges and opportunities for analytical development will also be discussed.
11:30 Bridging Clinical Research and Real-World Data in a Patient-Centric Multiverse
Alexander Sherman, Director, Center for Innovation and Bioinformatics, Massachusetts General Hospital
Patient- and disease-related information resides in a multiverse of data silos, mostly institutional or modality-based databases. A patient centricity approach may help to bring such information together and bridge clinical trials data with RWD, such as data from EHRs, DNA sequences, image banks, biobanks, -omics, etc. We are introducing patient-centric approaches with a unique secure patient identification and aligning incentives for all players in a research continuum, including academia, industry, government, patient advocates, and patients.
12:00 pm Optimising Site Feasibility Using AI and Predictive Insights
Nicola Marlin, Chief Product Officer, Pharma Intelligence
Co-Presenter to be Announced
12:15 Sponsored Presentation (Opportunity Available)
12:30 Session Break
12:40 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own
1:40 Session Break
1:50 Chairperson’s Remarks
Alexander Sherman, Director, Center for Innovation and Bioinformatics, Massachusetts General Hospital
1:55 Enabling the Connection between Preclinical and Clinical Data
Michael Lange, ML/AI Lead, R&D Informatics, Small Molecule Discovery Informatics, Roche
Preclinical Project Data Hub is going clinical. Over the past years, it was established in Small Molecule Discovery and recently expanded into Large Molecule Research. Next stop: Clinical Data. We plan to provide our users with an application that allows the connection between preclinical and clinical metadata.
2:15 A Comprehensive Platform for Innovation with Data
Ajay Shah, PhD, MBA, Executive Director & Head of IT for Translational Medicine, Bristol-Myers Squibb
Sage is a comprehensive platform that enables FAIR data, for data ranging from discovery, clinical research, and real-world. This talk will focus on the overview of Sage and solutions developed in Sage ecosystem for biomarker analytics, including
an overview of essential components of the platform, such as uniform high-quality data ingestion, data lake enhancement with semantic integration conformance of data, and a reproducible research framework.
2:35 Maximizing Real-World Assets through a Comprehensive Patient Data Platform
Albert Wang, MS, Director, IT for Translational Research & Technologies, Bristol-Myers Squibb
Sage ecosystem is a cross-functional cohesive platform for finding, accessing, integrating, and analyzing patient-centric data. This talk will focus on real-world data (RWD). It will highlight how Sage catalogs, models, integrates, conforms, and presents
patient-level metadata across all RWD assets to facilitate downstream cross-dataset analysis within an integrated managed analytics environment. This talk will touch on the business drivers for this initiative, our current progress, as well as
some lessons learned.
2:55 Sponsored Presentation (Opportunity Available)
3:25 Refreshment Break in the Exhibit Hall with Poster Viewing
4:00 Chairperson’s Remarks
Lawrence Callahan, PhD, Chemist, Office of Health Informatics, Global Substance Registration System/Office of Health Informatics, Office of Chief Scientist, FDA
4:05 Clinical Data Visualizations to Drive Clinical and Biomarker Exploration Using Both Clinical Trial and Real-World Data
Philip Ross, PhD, Director, Translational Bioinformatics Data Science, Bristol-Myers Squibb
Exploratory visualizations generated from clinical trials and real-world data sources provide important insights into safety, efficacy and biomarker responses to novel and standard-of-care treatments. Automation of data updates in near-real time increases
the impact of this information on decision-making.
4:25 The Global Substance Registration System (GSRS): An Essential Tool for Structuring Translational Clinical and Regulatory Data
Lawrence Callahan, PhD, Chemist, Office of Health Informatics, Global Substance Registration System/Office of Health Informatics, Office of Chief Scientist, FDA
The ISO IDMP is a set of standards developed by regulators and industry to structure medicinal product information in a consistent manner. The GSRS is freely distributed software developed in collaboration with NIH/NCATS that implements the substance
standard. The GSRS defines substances in medicinal products and related substances such as targets, metabolites, and impurities and links these substances to products, clinical trials, applications, and adverse events.
4:45 Offshoot Applications from the G-SRS Moonshot
Daniel Katzel, Senior Software Engineer National Center for Advancing Translational Science, NIH
While developing G-SRS, the NIH/NCATS team developed useful standalone tools to help streamline registration, curation and regulation of medicinal ingredients. This presentation will cover three such projects: an image to chemical structure recognition
program (molvec), an abstraction layer allowing cheminformatics software to switch between underlying informatics frameworks at runtime (molwitch) and Inxight:Drugs, which incorporates and unifies marketing, regulatory status, rigorous drug ingredient
definitions, biological activity and clinical use.
5:05 Sponsored Presentation (Opportunity Available)
5:35 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing
6:45 End of Day
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Thursday, April 23
7:30 am Registration Open and Morning Coffee
8:00 Organizer’s Remarks
Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute
8:05 Awards Program Introduction
8:10 Benjamin Franklin Award and Laureate Presentation
J.W. Bizzaro, Managing Director, Bioinformatics.org
8:35 Bio-IT World Innovative Practices Awards
Allison Proffitt, Editorial Director, Bio-IT World
9:00 AI in Pharma: Where We Are Today and How We Will Succeed in the Future
Natalija Jovanovic, PhD, Chief Digital Officer, Sanofi Pasteur
9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced at 10:00
10:30 Organizer’s Remarks
Cambridge Healthtech Institute
10:35 Chairperson’s Remarks
10:40 CO-PRESENTATION: How a Knowledge-Base Analytics Platform Has Empowered Data-Driven Decision Making and Is Transforming Translational Research
Yan Ge, Director, Data Analytics, Data Science Institute, Takeda
Erik Koenig, Principal Scientist, Translational Sciences, Takeda
Takeda’s R&D Data Hub has been established to maximize the value of data, make them FAIR, increase access for efficient analysis and to drive data-driven decision making. The Strategic Translational Oncology Research Knowledge-base (STORK)
platform is a mission-critical strategic application leveraging both the R&D Data Hub and leading-edge Big Data technologies to harmonize the increasing data density of Immuno-Oncology Research and Development. STORK provides better catalogued
and enriched biomarker assays data, allows researchers to intuitively and easily query internal preclinical data, clinical trials data, and external data like full-text literature and clinicaltrials.gov sources using NLP. Furthermore, STORK’s
self-service visualizations enable more efficient benchmarking, cross comparisons, forward and reverse translational insights to support key decision-making throughout the therapeutic lifecycle.
11:10 A Grassroots Translational Research Data Commons: Lessons from a Patchwork Data Lake Implementation
Jay Bergeron, Director, Translational Research Business Technologies, Pfizer
Managing and exposing clinical biomarker information using traditional data warehouse platforms promotes efficient exploratory analysis. However, performing analyses across clinical study collections available in data warehouses has proven challenging
due to a high degree of data heterogeneity coupled with the high cost of manually conforming study data to consistent standards. An alternative data management philosophy (i.e. data lake) promotes aggregating clinical datasets in native formats
and delaying costly dataset transformation until specific analytical needs arise. Effective content search capabilities are required to identify scientific datasets of interest in order to attain the cost advantage of purpose-driven analytical
data preparation. A data lake implementation based on toolsets commonly available to pharmaceutical informaticians, including Elastic Search, scientific terminology services, Jupyter Hub and R, will be presented as an option for building useable
large-scale clinical data collections while limiting the necessity of comprehensive dataset transformations.
11:40 Sponsored Presentation (Opportunity Available)
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 Last Chance Poster Viewing
1:55 Chairperson’s Remarks
Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC
2:00 PANEL DISCUSSION: Big Data Meets RWE: There Are Elephants in the Room
Moderator:
Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC
Panelists:
Anastasia Christianson, Vice President, R&D Operations and Oncology IT, Janssen
Michael Montgomery, MD, former Global Head Medical Affairs, Incyte Pharma
Jonathan Morris, MD, Vice President, Provider Solutions; Chief Medical Informatics Officer, Real World Insights, IQVIA
We are applying Big Data to solve complex health-related problems but do not always acknowledge that there are Elephants in the Room: 1) It’s the Diagnosis, Stupid!; and 2) Clinical Trials: Data, Data Everywhere but Not without Bias. This panel
will address issues of diagnostic quality, mis- and missed diagnosis, diagnosis vs stratification in complex disorders and syndromes and how these impact healthcare decisions and clinical development.
3:00 Digital Process Automation for Clinical Trial Management
Alan Rogerson, BSc, MSc, Principal Scientist, pRED Informatics – Early Development Informatics, Roche
Early phase clinical trials are governed by complex, concurrent and largely manual GxP processes. We have developed a solution to digitalize and automate these processes, to help study management teams to drive and manage their studies to completion
with improved efficiency. The solution could help to transform the way that study teams work in the future.
3:30 Data Science in Predictive Trial Enrollment: Increasing Success and Improving Risk Mitigation Strategies
Gina Rothenberger, Global Feasibility Therapeutic Area (TA) Head – Oncology, Janssen
Janssen is utilizing machine learning to compare planned vs. actual enrollment performance, showing success in early detection and intervention to improve strategies after study start-up and initial site selection. The strength and opportunity of
utilizing tools and technologies to show meaningful correlations and visibility into tracking to plan will be demonstrated. Case studies utilizing data science algorithms will be shared to highlight how insights were derived and applied to increase
success and improve risk mitigation strategies.
4:00 Close of Conference
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