NEW YORK—Late 2018 saw Elsevier, an information analytics business specializing in science and health, announce the launch of Entellect, a new cloud-based data platform designed to help life-sciences companies overcome the challenges of modern R&D by “enriching and harmonizing proprietary and external data and delivering it in an AI-ready environment.”
As Elsevier noted in its announcement of the launch, nearly a fifth of pharmaceutical spending goes to research and development, and the average cost of bringing a new drug to market is estimated at $4 billion; therefore, life-sciences companies urgently need more efficient ways to analyze data.
The Entellect platform is intended to help ease some of that financial (and time) stress by using artificial intelligence and machine learning tools to remove data from a “silo” style of organization, as well as to contextualize and connect drug, target and disease data to deliver normalized, discoverable and model-ready information.
“The life sciences are perhaps the most demanding field in which to undertake data science,” said Cameron Ross, managing director of the Life Science Solutions operations at Elsevier. “The complexity involved is the reason so many companies are looking to AI, and machine and deep learning, to solve their biggest challenges. Too often their efforts are frustrated because of multiple data management barriers, and they’re not yet seeing the insights from AI they expected.
“Entellect provides a sophisticated platform, enabling them to get far more value out of their data. We designed Entellect to handle the sorts of data challenges that life-sciences companies encounter—from handling huge volumes of existing data stored in individual electronic lab notebooks, to finding the desired piece of information in scientific literature.”
Entellect reportedly allows researchers to produce far more accurate predictive models across a range of pre- and post-market activities, including drug efficacy studies, risk-benefit analyses and pharmacovigilance activities. Some of the key attributes of Entellect include:
- Aligns and integrates with Elsevier’s data and ontologies
- Engineered for flexibility and scale
- Incorporates a full-data scientist development stack, so data scientists can work on solving problems, not manipulating the data
- Operates on an open platform for collaboration, data and application sharing
- Provides “frictionless ingestion” of disparate content from multiple sources—like websites, LIMS, archives and applications—along with text and data mining of that content.
Entellect is already being used by Elsevier clients to gather, cleanse and connect more than 10,000 different unstructured medical documents in various formats to make them standardized and searchable. Entellect’s taxonomy capabilities allow users to search across various fields including drug names, targets and diseases.
“At Elsevier we’ve spent decades creating solutions that allow individual researchers to find answers, and we’ve amassed a wealth of knowledge and practical experience in data science,” remarked Tim Miller, vice president of the Life Sciences Platform Solutions business at Elsevier. “To develop Entellect, we took that data science knowledge and experience and applied it to prepare machine learning solutions.”
