dispatches from Pittcon 2017

ACD/Labs uses informatics to manage impurity information

By Joseph James Whitworth

- Last updated on GMT

Andrew Anderson at Pittcon 2017
Andrew Anderson at Pittcon 2017

Related tags Chemistry

ACD/Labs has introduced an informatics system that enables organizations to establish effective impurity control strategies at Pittcon 2017.

The informatics company that supports chemical and pharmaceutical R&D sectors said it enhances collaboration between process chemistry, analytical, regulatory and quality departments.

Luminata offers data standardization and visualization for substance impurity characterization and the formation and fate of impurities, with chromatographic and spectral data.

Built on ACD/Labs’ vendor-agnostic ACD/Spectrus Platform it is a single system for organizing analytical data for impurities within the context of a substance and the process by which it was made.

Impurity analytical data

Andrew Anderson, VP, innovation and informatics strategy at ACD Labs, said Luminata is its platform for impurity characterization and management for food and beverage.

“It intends to capture analytical characterisation data straight from a customer’s instruments in the existing proprietary data format, process that information, conduct analysis for each substance or lot of material that is going through a product lifecycle and be able to relay that information in a database across an enterprise,”​ he told FoodQualityNews at Pittcon.

“We have a fairly comprehensive list of data file formats we support, all of the major vendors from LC-MS, spectroscopic vendors, Ramon, FTIR, NMR, most of the techniques involved in substance characterization, the instruments that supports that type of technique we support those formats.”

Anderson said if you have characterization data for a substance it can be in three components.

“You’ll have a spectral signal component of a particular compound, there will be spectral features, so storing that collection of features that allows you to relate that set of spectral features to other substances to see if there are matches between them or similarities,” ​he said.

“Quantitative figures and values so measuring the amount of a material observed in a substance through chromatography, being able to measure an area percent value, percent of a total.

“Another example would be additional qualitative information like retention time, it gives you an indication of the degree of lipophilicity of a molecule. Also, the chemical structure and the relationship between the chemical structure itself and specific spectral features.

“Things like in mass spec how a molecule fragments and being able to relate that to structural fragments, so the structural fragment to spectral fragment association, we have digital ways of handling that. If you think about historically how analytical chemists would ‘annotate spectra’ we have a way of digitally annotating analytical information that all is stored and related in a single interface.”

There are many different routes to storing and managing analytical data, said Anderson.

“It is large volumes and so historically people would use the data and they would have a report and that report would somehow get stored in a file system​,” he said.

“When I first started in the labs in the late 90s you would have these draft boards full of large, high resolution plots that became your evidence, your traceability from a decision to the supporting data for that decision. In the digital era, being able to take information out of the drawing board and into a relational database has still proven to be difficult.

“Lets also remember that a lot of decisions are made from multiple data types coming together, whether that is LC-MS and NMR and those are different data sources so being able to assemble analytical information in a single digital representation including all those annotations and assignments is really where the world is heading.”

Licencing and difference from LIMS

ACD Labs offers licencing options depending on the size and need of a customer.

“There is an enterprise version and a personal edition version that allows for smaller companies to take advantage of some of the decision making capabilities when maybe they don’t have all of the collaborative requirements of a very large company that has to have many groups collaborating on a single version of data in a highly scalable fashion,” ​said Anderson.

“Whereas smaller companies need one place where people can put data and maybe there is a need to still make decisions on it but not on the same scale.

“Usually in our beta-testing we cover functional and non-functional requirements, so we’ll go through what user requirements there are and we’ll ensure during testing that we have the appropriate user experience based on those requirements.

“There is also non-functional performance specifications, if for example during a beta-test you have users in the US and other users in the UK for the same company, can you have appropriate fidelity when it comes to collaboration.”

ACD Labs collaborates with Agilent and Shimadzu but looks at a layer outside of the LIMS.

“From my perspective LIMS is a lot about transaction management and operational efficiency, going from request to fulfil and then there is data in-between,”​ said Anderson.

“Where ACD Labs fits into that paradigm is to be able to make the decisions, lets assume there is enough infrastructure in a company to manage the flow of request to fulfil effectively. Where ACD Labs fits in is a layer above that, which allows you to take your data sources after fulfilment and use that data in a live and rich fashion to make decisions."

Anderson said it can help with data becoming white noise or ‘abstracted information’.

“An example would be if I am looking at quantitative values for five components in a mixture I could report that as a list with numbers but the supporting information is that white noise you are talking about. We have ways to compress and reduce data while at the same time not compromising on a major loss on fidelity.

“There are ways to reduce data down what we would call zero out that data without losing all the richness that it has when it is generated and we have some mechanisms to store and maintain that data in a relational fashion.”

Related topics Food Safety & Quality

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