Bio-IT World Announces 2020 Innovative Practices Award Winners

July 6, 2020 | Bio-IT World has announced the winners of the 2020 Innovative Practices Awards. Entries from Roche, Eli Lilly, Bristol-Myers Squibb, the University of Chicago, Massachusetts General Hospital, Mission: Cure, and the Pistoia Alliance were honored.

Since 2003, Bio-IT World has hosted an elite awards program, highlighting outstanding examples of how technology innovations and strategic initiatives can be powerful forces for change in the life sciences, from basic biomedical research to drug development and beyond.

“The Innovative Practices Awards consistently recognize efforts in our community to push our technological boundaries further to advance life sciences research. Sharing these collaborations offers opportunities for the whole community to emulate best practices and leapfrog current hurdles for future growth,” said Allison Proffitt, Editorial Director of Bio-IT World. “Although the 2020 Bio-IT World Conference & Expo has been moved to October, we couldn’t wait to share these well-deserving projects with our community.”

A panel of expert judges joined the Bio-IT World editors and Cambridge Healthtech Institute executives in reviewing detailed submissions from pharmaceutical companies, academic centers, government agencies, and technology providers. Five outstanding entries were named winners.

2020 Bio-IT World Innovative Practices Awards Winners

Bristol-Myers Squibb and the University of Chicago, Center for Translational Data Science nominated by BioTeam

The ability for researchers to find, access and share genomics data in order to maximize the potential of data-driven science is still a large challenge for pharmaceutical organizations. In late 2018, the Center for Translational Data Science at the University of Chicago released “Gen3”: a free, open source framework for creating a data commons. In 2019, IT for Translational Medicine at Bristol-Myers Squibb created “SiloBreaker,” a program with the mission to break down the barriers that exist between genomics data and scientists and create the conditions necessary to foster a collaborative genomics discovery ecosystem. BMS is the first pharmaceutical organization to leverage Gen3 to build its own internal data commons (“BMS Genomics Discovery Hub”).

Center for Innovation and Bioinformatics, Neurological Clinical Research Institute, Massachusetts General Hospital

The SigNET Platform enables scientific collaboration, integration, analyses, and distribution of research data across studies and repositories. It generates Unique Clinical Research Identifiers (UCRIs) per Patient per Study per Disease, thus permitting collaboration and sharing in rare diseases, in which utilization of “standard” GUIDs may not be enough to protect identities of patients participating in multiple research projects. UCRIs allow aggregation and prevent duplication of patients’ records, enable integration/sharing of patient information originating in clinical/research silos, and allow automation of searches across multiple repositories. Such innovative approach and technology offer standardized, reliable, and secure way to collaborate across research continuum accelerating discoveries across academia, foundations, and the industry. Several pharma/biotech companies, government agencies, academic consortia, and disease foundations utilize SigNET in clinical trials/studies.

Eli Lilly and Company nominated by EPAM Systems

As a 143-year-old company, Lilly has access to myriad high value data. The challenge Lilly faced was the ability to derive value from data in record time and in a cost-efficient manner. Lilly’s Research.Data Program is a solution that has helped the company overcome that challenge. By enabling the FAIR principles for information in a deep ecosystem of data products, discovery researchers and data scientists can now gain insights for portfolio decisions more quickly and efficiently than ever before: deliver all research data in a consistent, managed and sustainable environment where researchers find, access, interoperate and reuse data (FAIR); deploy integrated catalog, data dictionary, data lake, data pipelines, and secure application programmable interfaces that enable cost efficient delivery of data products; expose more than 100 data products to eight production applications used by hundreds of scientists worldwide; and accelerate science in discovery chemistry, biology and molecular innovation in key therapeutic areas including Diabetes, Immunology, Neuroscience and Oncology.

Pistoia Alliance and Mission: Cure nominated by Elsevier

Elsevier and Pistoia Alliance organized a drug-repurposing datathon, with Cures Within Reach and Mission: Cure being the consulting organizations. The objective was to identify repurposable drug candidates for chronic pancreatitis—a rare disease that affects about 1 million people globally, and currently doesn’t have an approved treatment. Datathon participants used Entellect, an Elsevier data integration platform, to access Elsevier supplied datasets such as Reaxys Medicinal Chemistry, ResNet (aka PathwayStudio) and PharmaPendium. In addition, participants were able to incorporate third party datasets into Entellect to create a single, harmonized and linked dataset against which they applied machine learning and statistical techniques, from exploratory data analysis and data pre-processing, to feature engineering, model building, validation and comparison, and finally result visualization and model deployment. As a result, 4 drug candidates were identified in 30-60 days, then reviewed and approved by the expert panel, pending further clinical trials by Mission: Cure.

Roche nominated by Linguamatics

The focus of the Roche research was to discover if social media, particularly patient blogs and forum, can provide a good substrate to develop clinical endpoints relevant to Parkinson’s disease patients. Being able to understand what matters most to patients and find unexpected insights into patient problems shapes clinical trials, e.g. by influencing design and outcome measures. Highly trusted social media sources (i.e. patient-focused communities) provided a robust substrate to analyze. From 24k verbatims downloaded and processed with Linguamatics NLP (Natural Language Processing), the Roche team extracted valuable data from ~450 posts. Ensuring that privacy issues were addressed, they were able to categorize the comments into symptom or impact categories. The study identified symptoms confirmatory of the clinical trial endpoints, and also added additional ones; and these specific recommendations have been added to the clinical disease model used in the clinical trial. Roche’s approach is an example of patient-centered drug development (an FDA initiative) and ensures that clinical disease models encompass patient needs.