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June 2-6, 2019
Georgia World Congress Center, Atlanta, GA, USA

Related Materials

Related presentations, posters, and scientific talks from this event have been posted here for your reference. Please click the associated link to download.

New data container construct for automated processing of LC/UV/MS data to support high throughput chemistry, R. Lee and A. Paramonov

ACD/Labs Breakfast Seminar

Monday, June 3rd, 6:45–8:00 AM EST

Please join us at ASMS for our Breakfast Seminar in Room A313.

Time (AM) Details
6:45-7:00 Breakfast Buffet & Registration
7:00-7:10 General Introduction
Matt Binnington, Marketing & Communications Specialist (ACD/Labs)
7:10-7:25 Introducing New Tools to Support High Throughput LC/UV/MS Chemistry
Richard Lee,  Solutions Manager, Business Development (ACD/Labs)
7:25-7:40 Increasing Pyrolysis GC/MS Data Processing Efficiency using ACD/MS Workbook Suite
Krege Christison, Research Chemist (Chevron Energy Technology Company)
7:40-7:55 What’s New in MS and MetaSense
Anne Marie Smith, Application Scientist (ACD/Labs)
7:55-8:00 Closing Remarks
Matt Binnington, Marketing & Communications Specialist (ACD/Labs)

Oral and Poster Sessions

MONDAY, JUNE 3rd, 10:30 AM–2:30 PM
New data container construct for automated processing of LC/UV/MS data to support high throughput chemistry
Poster #: MP 424
Richard Lee and Andrey Paramonov
View Abstract

New data container construct for automated processing of LC/UV/MS data to support high throughput chemistry
Richard Lee and Andrey Paramonov

Introduction: High throughput chemistry workflows can often entail complex experimental designs and depending on the type of experiment that can range from screening to library synthesis, there can be up to 1536 unique reactions on a single plate. To analyze these experimental reactions, LC/UV/MS instruments are often the analytical platform of choice.

Due to the high volume of samples, not only does it place a burden on the LC/UV/MS system but the informatics support system must also manage with the high volume of data to be transferred and processed. Facile processing is a prerequisite for supporting such systems where chemists expect results to be reviewed quickly for real-time assessment of reactions prior to moving to the next steps.

Method: Here we present a new informatics framework to support high throughput chemistry data acquired on LC/UV/MS systems. Based on HDF5 technology, this data server allows for 1) multiplexed data storage and 2) facile data processing for an entire well plate worth of data. This serves to keep data relationships together within a complete high throughput experiment, ie all datafiles from an analysis plate can be stored together, retaining data continuity.

Preliminary Data: In high throughput chemistry environments, whether it is compound library synthesis, catalyst screening, or experimental condition optimization, the analytical platform of choice to analyze these reactions is LC/UV/MS. In a vast majority of these cases, the chromatographic method is typically 3-5 minutes in length, and although his may be short for a single sample, when analyzing a 96 well (or greater) plate, the number of samples to be run and analyzed can be significant, often requiring several hours for complete data acquisition. To mitigate the data processing challenges, it is often the case that data is processed as soon as the system has completed acquisition. This approach works well for initial data assessment and processing but when reprocessing/rework of the data is required (unanticipated targets, RT shift of target compounds, etc), the amount of time can be significant due to the multiple steps required to locate, pull, and process the entire raw data.

Here, we describe new a data container framework that addresses the data accessibility bottleneck based on the HDF5 format. By using our new data construct, the system allows for specific chromatographic information to be extracted from the data as opposed to lifting and loading the entire LC/UV/MS dataset, prior to data processing. Moreover, this new data structure allows LC/UV/MS data for an entire high throughput experiment (ranging from 12 to 1536 well plates) to be stored in a related fashion. This allows for the system to access a specific data container and reduces the time required for searching the individual datasets within a data repository.

By only extracting specific and useful information from the individual datasets, the automated data processing for large amounts of data (ie 96 well plate) is significantly reduced from hours to minutes.

Novel Aspect: A new LC/UV/MS informatics infrastructure to support high throughput chemistry workflows.

THURSDAY, JUNE 6TH, 10:30 AM–2:30 PM
Efficient identification and management of degradant data in process development
Poster #: ThP 348
Anne Marie Smith, Andrew Anderson, Sanjivanjit K. Bhal, and Joe DiMartino
View Abstract

Efficient identification and management of degradant data in process development
Anne Marie Smith, Andrew Anderson, Sanjivanjit K. Bhal, and Joe DiMartino

Introduction: During drug development, a large amount of time and effort is spent on chromatographic method design. Forced degradation studies (stress tests) are carried out to build a stability-indicating method (SIM) for the active pharmaceutical ingredient. Efficient SIM development 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.

Methods: Analytical data assembled for Agomelatine, synthesized by 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-ES 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™ (v2018.2), based on the ACD/Spectrus Platform, was utilized to manage analytical and chemical data for the process.

Preliminary Data: Luminata was developed specifically to address challenges associated with identifying impurities and degradants during pharmaceutical SIM development. However, applications of Luminata were previously focused on the formation and fate of process impurities, specifically detailing intended or unintended conversions of these compounds. The current investigation focuses on the software’s capability to further manage predicted degradant data, and store this alongside observed degradant information.

A full process map of the active pharmaceutical ingredient synthetic route, including intermediates and impurities, was created in the software for multiple batches of Agomelatine. Following import of the process route into Luminata, all associated LC/UV/MS data were connected to their corresponding entities and stages to consolidate synthesis information in a single repository.

One of the first steps in planning stress test conditions is attempting to predict possible degradants. ICH guidelines were used to identify common types of degradation studies (i.e., acid, base, peroxide, heat, UV) and these scenarios were applied in Luminata to visualize various degradant formation pathways. Thus, theoretical Agomelatine degradants were generated as an *.sdf list, while observed degradants were uploaded as an *.sk2 file and linked directly to corresponding LC/UV/MS data in the software. For any new impurities identified during forced degradation, associated chromatographic and method information was imported into Luminata to facilitate further improvement and optimization of the stability-indicating method.

Novel Aspect: Luminata enhances SIM development by directly linking observed and theoretical degradants to corresponding analytical data/structures in complete process maps.