Trial News & Events

Bio-IT World Announces 2018 Best Practices Awards Winners

Bio-IT World announced the winners of the 2018 Best Practices Awards this morning at the Bio-IT World Conference and Expo. Entries from Alexion Pharmaceuticals, Takeda Pharmaceuticals, AstraZeneca, Celgene, and Massachusetts General Hospital were honored.

Since 2003, the Bio-IT World Best Practices Awards has honored excellence in bioinformatics, basic and clinical research, and IT frameworks for biology and drug discovery. Winners were chosen in four categories, and one discretionary award was given.

“I continue to be inspired by the work done in our field,” said Bio-IT World Editorial Director Allison Proffitt. “The Bio-IT World Community is increasingly open, and the partnerships and projects showcased here prove our dedication to collaborative excellence.”

Bio-IT World debuted the Best Practices Awards at the second Bio-IT World Conference & Expo in 2003, hoping to not only elevate the critical role of information technology in modern biomedical research, but also to highlight platforms and strategies that could be widely shared across the industry to improve the quality, pace, and reach of science. In the years since, hundreds of projects have been entered in the annual competition, and over 80 prizes have been given out to the most outstanding entries.

This year, a panel of expert judges joined the Bio-IT World editors in reviewing detailed submissions from pharmaceutical companies, academic centers, government agencies, and technology providers.

The awards ceremony was held at the Seaport World Trade Center in Boston, where the winning teams received their prizes from Proffitt and Philips Kuhl, president of conference organizer Cambridge Healthtech Institute.

2018 Bio-IT World Best Practices Award Winners:

Clinical & Health-IT:

Takeda Pharmaceuticals nominated by Deloitte

Project Platypus

Takeda has developed a Data and Analytics Hub platform conceived, designed, and built to address issues of data transparency, trust, and accessibility to support the efficient generation of data insights for functions across R&D. For this project, Takeda focused on a critical use case of this platform, clinical data review/medical monitoring: the Data Hub platform was configured to deliver a fit-for-purpose solution for medical reviewers to make the review process more efficient and incisive. The robust visualization tool and workflows allow for tool scalability and provide an efficient, intuitive interface for comprehensive data review and oversight across multiple disease indications. It will also reduce time constraints on internal staff and support various outsourcing strategies. The Data Hub architecture with support for standardized formats allows management and oversight of multiple vendors performing different functions and supplying different subsets of trial data e.g. lab data, PROs, adherence assessments.

Informatics & Knowledge Management:

AstraZeneca, Discovery Sciences, IMED Biotech Unit nominated by Genedata

Deep Learning for Automated Phenotypic Image Analysis

AstraZeneca’s project presents a new Deep Learning for Phenotypic Imaging software and corresponding workflows based on convolutional neural networks. It yields improvements in automated image analysis for high content screens (HCS) including the ability to: rapidly detect and define all cellular phenotypes in an HCS; efficiently generate training data and on these train Deep Learning networks for subsequent classification of HCS image sets in production assays; and precisely quantify the relevant pharmacology.

IT Infrastructure:

Celgene Co.

Celgene Laboratory Instrument Mobile Alert

The Celgene Lab Instrument Mobile Alert (LabAlert) system allows a user to get warning or error notifications generated from laboratory instruments delivered instantly to user’s mobile devices.

For the first time within the company's R&D organization, there is a system that combines digital technologies such as Amazon Web Services (AWS) Cloud-native application framework and corporate mobile application platform to deliver digital experience that empower the scientists to better plan and execute scientific experiments.  In just six weeks the team built and delivered the first version of the system utilizing Agile methodology.

Personalized & Translational Medicine:

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

NeuroBANK Patient-Centric Platform for Clinical Research

Currently, there are no effective treatments in 95% of 7,000+ rare diseases. Establishing clinical trial readiness for rare neurological conditions by identifying patient populations, discovering and validating outcome measures and biomarkers, developing disease progression models and disease phenotypes is essential.  Collaborations between multiple stakeholders in a clinical research continuum are vital.

NeuroBANK patient-centric platform allows researchers to capture and link patients’ data from multiple observational clinical research and natural history studies to medical images, genetic information. tissue repositories, and patient reported outcomes.

After data from a particular study are analyzed and the results are published, the entire study data set is de‐identified and released into a central pool of disease‐specific information available to anyone studying those conditions.

NeuroBANK platform helps to accelerate the discovery, development, and delivery of future treatments, providing new hope to patients and their families.

Judges’ Prize:

Alexion Pharmaceuticals nominated by EPAM Systems

SmartPanel: A Rare Genetic Disease Diagnosis Algorithm Competition Platform

Alexion in collaboration with EPAM has developed a software competition platform to allow the objective comparison of contributed algorithms for automated rare disease diagnosis. Each algorithm installed on the platform is given as input the same set of observations about real or simulated patients that have previously been positively diagnosed. The platform runs each algorithm and ranks it as to its ability to correctly diagnose the disease. It is an open platform wherein algorithms can be contributed to it by independent developers. The development team also developed and contributed a set of novel algorithms to increase the diversity of algorithms present on the platform.

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