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ASMS Online

June 1-12, 2020

FRIDAY, JUNE 5TH, 1:00–5:00 PM EDT

Join ACD/Labs in our Virtual Booth on Friday, June 5th, to chat with Joe DiMartino and Richard Lee of ACD/Labs.

Meeting ID: 983 7437 8402
Password: 018463


TP 235: New dataflow model for rapid processing of large volumes of LC/UV/MS data
Richard Lee, Eugene Volopianov, Vladislav Solomatin

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New dataflow model for rapid processing of large volumes of LC/UV/MS data
Richard Lee, Eugene Volopianov, Vladislav Solomatin

There is enormous pressure on the high throughput chemists to increase productivity and select successful reactions to carry forward based on analytical data assessment. A significant challenge in high throughput chemistry (HTP) is supporting the large amount of analytical data that is generated from these types of experiments, primarily, LC/UV/MS. However, its often the case, that the analytical data processing and review is a bottleneck, including reprocessing large amounts of LC/UV/MS data.

We present a new dataflow architecture and processing unit that will address large volumes of LC/UV/MS data, to support workflows such as HTP chemistry, within a reduced timeframe. The new informatics server acts as a data container connected to processing units which allows for users to batch process large plates (24 or greater number of wells) worth of data. Users can quickly review and assess the processing results via a web interface and execute, if needed, reprocessing directly from the web application.

Preliminary Data:
For HTP chemistry groups, analysis of LC/UV/MS data is often the rate limiting step to assess how successful the reaction has been. In these groups, multiplexed reaction plates are often used for library generation or screening experiments, that can vary from 24 – 1536 well plates. In these experiments, users will run corresponding analysis plates of the same number of wells, typically, on their open access systems, using predefined chromatographic methods that normally range from 2-4 min in length.

Data can be processed as they are acquired, can reduce the initial review time. However, the results may be unsuitable as 1) retention times may have shifted, 2) unexpected by-products could be generated, leaving them unlabeled, 3) open access chromatographic systems are not optimized and components often overlap. These reasons can often lead to reprocessing up to 80% of the data. This is compounded by dataflow challenges to store, locate, and push raw data to the processing framework.

In this work, we describe a new dataflow flow model and data processing framework that alleviates these issues to support a web based experimental design application. This new data framework allows for efficient storage of hyphenated data for facile recall of the raw data. In conjunction, the configured system allows for two separate data processing routines 1) targeted analysis based on chemical formula and/or RT and 2) non-targeted analysis based on a componentization algorithm.

Using this new data flow architecture, we have been able to reduce the processing time from several dozen minutes to sub-3 min for a 96 well plate of low resolution LC/UV/MS data for either targeted or non-targeted data processing. The user interface is a web based application where LC/UV/MS data be can visualize including chromatograms and mass spectra, review numerical results, and execute the reprocessing of the analytical plate.

Novel Aspect:
A new dataflow architecture to support rapid data processing and reprocessing of large volumes of LC/UV/MS data on demand.


TP 323: Comprehensive Degradant Identification and Management of Analytical Data in Drug Product Development
Joe DiMartino

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Comprehensive Degradant Identification and Management of Analytical Data in Drug Product Development
Joe DiMartino

During drug product development, a large amount of time and effort is spent on chromatographic method development. Forced degradation studies (or stress tests) are carried out to build the stability indicating method. Efficient development of this method requires a complete understanding of the manufacturing process and open communication between various interrelated departments—from process chemistry through to analytical R&D.

Theoretical degradants are used by scientists for targeted analysis in forced degradation studies. Often this vital chemical information is disconnected from observed degradant data making the identification of impurities and degradants challenging and thereby slowing down the process.

Analytical data assembled for Agomelatine, synthesized following a five-stage process route, was used in this work. The data was collected on an Agilent-1200-Series with an Agilent VWD-G1314B UV detector, acquiring spectra at 210 nm, and an Agilent 6110 Quadrupole API-ESI Mass Spectrometer, collecting low resolution spectra in a 45-1000 Da mass range. Column separation was performed via an isocratic method using an ammonium formate Buffer H of 4.5/ACN (35:65). The flow rate was 1.2 mL/min with a run time of 50 min, and the column used was a Zorbax Eclipse XDB C18 5 µm – 4.6 x 150 mm. The software application Luminata® (v2019.2), based on the ACD/Spectrus Platform, was utilized to manage analytical and chemical data for the process.

Preliminary Data
In this poster we will present software tools that manage structural, analytical, and all process related data into a structured and searchable manner to facilitate inter- and intra- departmental communications. Luminata® intelligently links chemical information with related live analytical data. The tools take a theoretical degradant targeted list and batch process the mass spectrometry data automatically. Once completed, a degradant map is generated, and all related mass spectra are associated to each degradant in the map. The software also captures both observed and theoretical degradants (structures and meta data from predicted third party software) in the same interface.

Novel Aspect
Luminata enhances stability-indicating method development by analyzing stress test data in a single application and identifying degradants more efficiently.