Empowering industrial research with shared biomedical vocabularies

Abstract

The life science industries (including pharmaceuticals, agrochemicals and consumer goods) are exploring new business models for research and development that focus on external partnerships. In parallel, there is a desire to make better use of data obtained from sources such as human clinical samples to inform and support early research programmes. Success in both areas depends upon the successful integration of heterogeneous data from multiple providers and scientific domains, something that is already a major challenge within the industry. This issue is exacerbated by the absence of agreed standards that unambiguously identify the entities, processes and observations within experimental results. In this article we highlight the risks to future productivity that are associated with incomplete biological and chemical vocabularies and suggest a new model to address this long-standing issue.

Challenges and solutions for personalizing medicines

“…Efforts to reprofile established therapies for new indications include the need to protect off-patent molecules for new indications. What is also becoming clear is that visualizing the drug-target landscape [49] is more than just finding the right words [50]. To the researcher with an interest in the modern words for formulation, nanomedicine [51] and theranostics have become a means to innovate from established drugs [52].…”

Finding the Right Approach to Big Data-Driven Medicinal Chemistry

“…Identifying relevant data from the huge number of disparate publicly available data sources is addressed by Open PHACTS [7], an initiative developing an open information environment to integrate multiple data sources. These types of open public-private initiatives are crucial to managing the diversity and complexity of public data and unlocking its huge potential value [8][9][10].…”

Toward interoperable bioscience data

“…Fragmented datasets can only be reassembled by those equipped to navigate the various reporting guidelines, terminologies and formats involved 17 . Cross-cutting, topic-specific reference datasets have been assembled, but predominantly by large initiatives (such as Sage Commons) and programs (such as ENCODE or the US National Institutes of Health–National Institute of Allergy and Infectious Diseases’ Bioinformatics Resource Centers (BRCs)).…”

“…It is widely acknowledged that unlocking shared data promises to accelerate discovery, but this process requires new models for the way we collaborate 1–3,5,6,17,18,26 . But reporting standards often have different levels of maturity, and inevitably, duplication of effort.…”