Using Technology to Increase R&D Productivity in the Life Sciences Sector
Successful drug discovery and development relies upon access to accurate, trusted and reliable data at every stage of the research process. This is particularly important in the early discovery and pre-clinical stages. In the early phases of life sciences research, data is at the very crux of R&D – as researchers seek to better understand both the biology of diseases. However, while access to data is crucial, the sheer volume of data now available to researchers can actually hamper their productivity and make research less efficient. Thus the challenge for CIOs of life sciences organizations is to provide the researchers with technology solutions that enable them to remain productive in the face of the 21st Century data deluge.
Technology to Understand Data Better
With over 1 million new articles published every year and more than 25 million references added into PubMed, theUnited States National Library of Medicine’s search engine, finding the relevant pieces of information hidden in the big data haystack of scientific literature is overwhelming researchers. The amount of data that researchers must sift through can lead to many months of work just to summarise the current state of knowledge in a particular area. Additionally, the data used in the literature is often key to validating the researcher’s interpretation and the data is often not in an easy to use format, or not shared within the article. Further, that picture may actually be incomplete and may also have data in it that is not accurate or reproducible.
In the life sciences sector, making sense of data is fundamental to the creation of new treatments as it enables researchers to improve understanding of a disease development
In the life sciences sector, making sense of data is fundamental to the creation of new treatments as it enables researchers to improve understanding of a disease development by determining the complex molecular relationships that underlie it. Armed with this knowledge, scientists can more accurately design drugs, model other possible side effects that treatment could cause, and in the case of genetic variation predict how or if a patient will respond. However, for big data to fulfill its potential and not hamper productivity, scientists must be able to filter through the ‘noise’.
Ultimately, the ability of researchers to use technology tools that help them harness the data and information available is the primary variable in the overall success of their research. CIOs must therefore explore technology solutions that are designed with the life sciences in mind to ensure productivity remains high. For example, being able to quickly and accurately ‘text mine’ articles for relevant information, and also examine the underlying datasets used in the scientific studies, is critical in furthering research.
Evolving Technology Tools in the Life Sciences
In recent years, broader market challenges have forced a change in how the tools used in scientific research are designed and applied. Increasingly competitive markets and spiralling R&D costs have increased the pressure on life science companies to improve their R&D productivity. Because of this,research tools are becoming particularly astute at screening, aggregating and integrating large data sets into scientists’ workflows, to improve their productivity and ability to draw valuable insights.
In addition to improving technologies, the ease and relative low cost of cloud computing and supercomputing has lowered the barriers to access; the time and cost associated with crunching many terabytes of data had notably reduced. Sharing data is another growing trend that aims to support researchers make the most of the time available to them – for example, sharing proprietary, unpublished precompetitive data between large pharmaceutical companies, via the tranSMART platform and other initiatives.
To take advantage of developing technologies and the benefits of productivity tools, CIOs of life science companies must look to invest more in solutions that support the early and preclinical phases of the drug discovery process, to ensure successful R&D that reduces the likelihood of late stage failures. This approach – the combination of a focus on early research and the adoption of technology solutions – will enable life science companies to increase both their R&D productivity and their return on investment.
The Balance of Science and Technology
Structure, quality, actionability and standardization are all issues with scientific data. Further, over 85 percent of medical data is unstructured, yet still clinically relevant. Unstructured data will continue to proliferate in the future, thanks to the pervasiveness of social media, the increased use of ‘wearables’, and the growing number of devices connected to the Internet of Things. As a result, using this data productively requires a combination of scientific and technical expertise. For CIOs of life sciences companies, they must take into account that technology solutions by themselves are not effective in improving productivity, there must also be a scientific understanding of how data is categorized.
In particular, the use of relevant vocabularies and taxonomies are essential. Without taxonomies, the only way to find comparable data points is to compute the distance of this point to every other point in the space, which is a huge number of computations. Taxonomies combined with semantic technology and text-mining tools are the most efficient way to discover and extract key data from disparate data sources. Thiscareful balance of scientific knowledge and technological know-how results in the creation of tools that will help researchers become more productive.
Achieving Increased Productivity with Data driven Decision-making
Across all fields, as the amount of information we produce increases, productivity is at risk of being negatively impacted as we struggle to make sense of the data available to us. However, if organisations are able to use technology solutions that allow data to be investigated in a standard, repeatable and structured manner, they can reduce this risk.
In the life sciences, this translates into giving researchers the ability to mine and analyse vastly different literature and data sources,to help them make relevant associations in the search for new drugs and treatments.With such a wealth of data available to organizations today, it’s possible that answers to important, long sought queries could finally be reached within a fraction of the time. Life science CIOs must therefore ensure that researchers are not daunted by the deluge of data at their fingertips, but have the tools at their disposal that enable them to embrace the benefits it brings.
See Also: Life Science Review