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The Value of Live Data to Support Decision-Making in R&D

By Sanji Bhal, ACD/Labs

We often speak about the many benefits of standardizing data in analytical chemistry, particularly around how it ensures data quality, supports data integrity, improves shareability, and helps to characterize the interpretation of data by enabling comparison of data generated on different instruments.

As with many R&D processes, implementing certain strategies and software solutions can provide decision support to the organization throughout the entire product development lifecycle – increasing overall productivity and addressing key challenges along the way.

Andrew Anderson, VP of Innovation & Informatics Strategy, recently spoke with Scientific Computing World about some of the challenges associated with data standardization and how the value of data can be realized. Here’s an overview of what he covered:

Data assembly as a critical and difficult first step

In discussing how data standardization enables scientists and managers to make strategic decisions, limit risks, and effectively share data within their organization and beyond, Andrew notes that it also causes two major challenges.

“The first challenge is that scientists are relying on data transcription between systems to bring relevant data together for decision making, which introduces the risk of errors – something to be avoided, especially with the growing emphasis on data integrity and the ALCO principles for data, that it should be attributable, legible, contemporaneous, original, and accurate.”

 “The second challenge is that scientists are often relying on abstracted data, effectively complex spectral and chromatographic data simplified to images, numbers, and text, without chemical context and meta-data.”

Many different analytical techniques are necessary to characterize substances and make decisions around identity and composition, which is information often required by regulatory authorities before substances can be approved for use.

ACD/Spectrus Platform was created to offer a commercially available environment that allows homogenization of data from all major analytical techniques and from the broadest number of instrument vendor formats. Essentially Spectrus offers an analytical data standardization platform. Not only that with an array of data processing and interpretation tools scientists can turn data into answers and finally, the storage of interpreted data in context offers knowledge management. Users are also able to use a variety of chemically intelligent parameters to search internally created and commercially available databases to assist with spectral analysis and interpretation, knowledge sharing and knowledge retention.

Data-Support-for-Decision-Making-1

Data assembly for decision support

Going beyond data standardization, significant bottlenecks still exist in the area of decision support.

“Take the example of GMP drug manufacture for clinical trials. Each lot is rigorously tested, but if a purity issue arises and batches don’t pass quality control, there will be an investigation, part of which will be comparative, and the results of that investigation will inform next steps, and potentially help to prevent future issues.”

The ultimate goal for analytical data assembly is not only to standardize its format, but to consider the places where the data resides and the scope of the data that will inform the issue. Andrew notes that while it may be possible to put the majority of data in standardized formats so that current and historical data and metadata can be cross-referenced, the application of standards around characterizing the interpretation of that data is also of utmost importance.

“Chromatographic data analysis will aim to understand the composition of impurities in your substance. In addition to the absolute peaks in the chromatogram, you will ideally include another layer of data relevant for interpretation of the analytical data set.”

At ACD/Labs, we have been working with customers to develop a decision support application that facilitates increased productivity by giving users access to live data across data sources. Through this process we have connected different data-generating systems to the decision support systems used by project teams in various workflows. As one example Luminata (software for CMC decision support) consolidates all critical product development information in one location collating all relevant development information in one repository for easy access.

To read more about the value of data to support decision-making in R&D, read Scientific Computing World’s articles here and here.

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