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The Fundamentals of Good Data Stewardship

Posted on June 29th, 2016 by in Pharma R&D

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Bioactivity data is not in short supply. The days of limited availability of data on compounds and biological targets are long behind us. Now, drug developers get information from their own studies and in-house repositories, as well as from databases of published articles, patents and other literature.

However, all the data in the world means nothing if a researcher can’t find the relevant answers to their questions quickly. The point of having access to information is that it should accelerate research and improve the quality of decisions made.

Good stewardship is the key to unlocking the value of information collections and making the content useful for data-driven drug development. Uncovering meaningful relationships and patterns requires that scientists are able to quickly locate all of the information relevant to their projects and easily compare experimental results from many sources.

Access to high-quality data that are carefully curated with these goals in mind will enable scientists to derive the maximum benefit from the abundance of available information and make the best decisions possible for their projects.

What does this mean in practice?

The data must be comprehensive and accurate. Missing or incorrect information produces an incomplete picture, and decisions based on incomplete information are very risky. Information must also be curated with a high level of quality control, because poorly managed or inaccurate data are essentially useless.

Information must also be discoverable and accessible for quick relevance assessment. Every query must return all relevant search results and omit all non-relevant results. Excluding relevant data leads to uncertainty about the integrity of the repository, while including non-relevant data causes information overload.

Finally, data must be actionable: available for immediate use. Manually sorting through reference lists or reading full-text documents to extract relevant information is simply not feasible and is overly time-consuming. Therefore, data should be extracted from the original sources and provided in a clear and concise format so scientists can quickly assess the relevance and immediately use the information.

Data-driven drug development aims to use pharmacology, chemistry and biology data to establish the best possible understanding about a drug candidate. The idea is to gain insight into the compound’s mechanism of action, the target’s role in disease biology, and any potential safety concerns about their interaction, and then make well-informed decisions. Without insights, there can be no informed decisions, but without proper data stewardship, there can be no insights: just increasingly large databases.

Want to know more about data stewardship? This white paper deals in greater detail with how high-quality information and good data stewardship help to de-risk pharmaceutical development, ensuring that researchers are able to make informed decisions at every stage of their work.


All opinions shared in this post are the author’s own.

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