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.

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.

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.

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.

Join us for a lively discussion among prominent pharma leaders, and learn:

Why, when & how to implement a public Cloud for your computing needs

Challenges and opportunities when setting and managing stakeholder expectations

Critical keys to success to realize the best outcomes

To learn more about RCH Solutions, visit our Virtual Booth

Hosted by Joe Donahue, Managing Director, Life Sciences, Accenture

Participants include: 

Andreas Matern, Head of Digital Translational Medicine, Sanofi

John Quackenbush, Professor of Computational Biology and Bioinformatics; Harvard T.H. Chan School of Public Health

Seungtaek Lee, VP, Strategic Partnerships and AI RWE Head of CoE; ConcertAI

Preston Keller, PhD, MBA, President & CCO, PercayAI

Philip Payne, PhD, Becker Professor and Chief Data Scientist, Washington University in St. Louis

Most large scale analysis of clinical trial data only leverages part of the picture, ignoring unstructured data and limiting findability across all the information collected throughout multiple disparate data sources.  This roundtable will discuss leveraging a cognitive platform to combine all data from multiple sources into one unified view using a single entry point to the data.

Evaluating, optimizing and benchmarking of next generation sequencing (NGS) methods are essential for clinical, commercial and academic NGS pipelines. Optimizations for speed and accuracy often require making trade-offs relative to other constraints. Join this roundtable to discuss benchmarking strategies, trade-offs, and the value of benchmarking genomics tools and applications. 

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.

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.

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.

Truly personalized healthcare is possible only if we have access to meaningful, diverse and integrated data at scale. We are working on an E2E Engine that will allow us to FAIRify our clinical and molecular data from planning, through ingestion to re-use. We are creating a solution to pool clinical data with molecular, genomics and digital biomarkers for diverse therapeutic areas to fully capitalize on Roche scientific data. To address the diversity of the scientific data we leverage a Data Commons approach to process data from source systems in order to pool and share them with scientists.

This session will discuss examples of how pharmaceutical organizations have leveraged a cognitive platform to combine all potential data, both structured and unstructured, pulled from multiple disparate sources, to offer knowledge workers a unified view using a single entry point to the data to increase findability and discoverability.

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.

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.

The application of advanced data science and artificial intelligence techniques is prevalent across drug development, from pre-clinical drug discovery, through Phase 1-3 trials, and beyond.  Whether analyzing images, digital devices with streaming data, predicting trial progress, or doing much more, opportunities to accelerate getting drugs to the market are numerous.  However, there are also challenges that are worth bearing in mind.

Researchers use biomarker and outcomes data to model and predict adverse events. However, access restrictions to safeguard patient privacy necessarily slow down the rate of discovery and increase research costs via IRB review. For these reasons, synthetic data that preserve patient-variable relationships have been an active area of research. We discuss current advances made by generative models in this area and the breakthrough AI technologies accelerating those advances.

How do you use data / digitization today to drive scientific discovery / product development?

What are you greatest scientific pain points / gaps that are not being met by digitization?

What kinds of outcomes do you believe digital tools could help you achieve?

Welcome to this discussion group on the growth of demand for HPC in scientific research. We are looking forward to a lively forum. We'll start by looking at three related topics:

- What events trigger demand in your organization? How has the current pandemic impacted resources?

- What could make scale and collaboration more accessible to more researchers?

- Share a recent experience of shifting workloads to manage HPC capacity.

In this session we’ll discuss how to provide researchers with performance and scale in genomics & research analytics, to drive results at a price point that’s economically viable on public & private cloud.

Storage solutions we’ve been using force bioinformaticists to make trade-offs between the capacity and low-cost of disk and the performance of flash. This results in complex tiering configurations that only deliver performance for a small slice of the data. In this session, we will review how advancements in technology enable VAST Data to revolutionize the cost of all-flash and allows bioinformatists faster analysis across larger datasets for deeper insights.