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TUESDAY, MARCH 24TH, 9:05 AM

Associating Live Analytical Data to Synthetic Chemistry Experiments-Applying FAIR Principles Across the Scientific Experimentation Lifecycle
Andrew Anderson, Michael Boruta

Division: CINF 100
Location: 115A

View Abstract

Associating Live Analytical Data to Synthetic Chemistry Experiments-Applying FAIR Principles Across the Scientific Experimentation Lifecycle
Andrew Anderson, Michael Boruta

Chemists have at their disposal a wealth of software applications which can effectively describe experiment material utilization, procedure descriptions, observational commentaries, and result summaries—all consistent with FAIR practices.

Traditional approaches to aggregating detailed summaries of synthetic chemistry experiments, however, requires the abstraction of reaction-characterizing analytical data. Rich analytical datasets are reduced to tabular numerical data fields and static graphical depictions.

This presentation will describe efforts to use newer digital data processing, interpretation, and storage functionalities which simultaneously allow for automated result analysis, while preserving full-fidelity datasets, in accordance with FAIR principles and practices. Select examples of HT synthesis experiments, coupled to automated analysis of chromatographic and spectral characterization, will be presented.

TUESDAY, MARCH 24TH, 6:00–8:30 PM

A CMC Development Decision Support Tool for Batch Genealogy Management
Matthew J. Binnington, Joe DiMartino

Session: COMP Poster Session
Location: Exhibit Hall A

View Abstract

A CMC Development Decision Support Tool for Batch Genealogy Management
Matthew J. Binnington, Joe DiMartino

Purpose
Successful completion of API process development is a tremendously challenging task, made even more difficult by the amount of analytical data collected. The numerical and non-numerical information compiled may be scattered amongst the project team: within email, Analytical ELN, and Chemistry ELN systems. While scientist have developed practices to gather this information, it remains a tedious manual process where simple questions often become difficult to answer.

A chemistry, manufacturing, and controls (CMC) decision support software tool should optimally provide users with the ability to consolidate analytical data, and construct corresponding maps (i.e. process routes, family tree, formulations map, etc.). These process maps would allow for visual comparison of molecular composition, enabling users to visualize all related spectroscopic and/or chromatographic data in a single environment.

This poster will provide an overview of a software application developed specifically to address these pharmaceutical development needs.

Methods
Analytical data collected for Agomelatine, synthesized by a six stage process route, was used in this work.

The analytical data was collected on an Agilent-1200-Series with an Agilent VWD-G1314B UV detector, acquiring spectra at 210nm and an Agilent 6110 Quadrupole API-ES Mass Spectrometer, collecting low resolution spectra in a mass range of 45-1000 Da. Column separation was done with an isocratic method using an ammonium formate/acetonitrile buffer combination (35:65) at pH 4.5. 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 5um - 4.6 x 150 mm.

The software application Luminata™ (v2019.1), based on the ACD/Spectrus Platform, was used to manage the analytical and chemical data for the process.

Results
Among currently available batch tracking systems, there exists no tool to connect the batch family tree of a project with the analytical data collected for each batch. Batch information is typically handled in static MS Excel worksheets, with all batch analytical data locked into chromatography data systems and/or various ELNs (Figure 1). This poster focuses on the new batch genealogy functionality within Luminata, a CMC project decision support tool, which offers dynamic visualization of project analytical data through the process scheme and batch genealogy family tree.

The typical current approach to managing batch schemes in process development. Static MS Excel spreadsheets are disconnected from supporting analytical data, leading to interpretation inefficiencies and possibly causing manual processing errors
Figure 1. The typical current approach to managing batch schemes in process development. Static MS Excel spreadsheets are disconnected from supporting analytical data, leading to interpretation inefficiencies and possibly causing manual processing errors.

Analytical data associated with several Agomelatine synthesis batches was collected and imported into Luminata. Each entity in the process route was connected to all corresponding chromatogram(s) and mass spectra, following data processing and structure verification (Figure 2).

Assembly of the Agomelatine synthesis process scheme following analytical data import
Figure 2. Assembly of the Agomelatine synthesis process scheme following analytical data import.

Further, each experimental batch was differentiated and automatically arranged into a comprehensive batch family tree, visualizing the relationships between batches (Figure 3). Agomelatine inter-batch comparisons were also performed to clarify any differences in the presence and concentrations of various reaction components: starting materials, intermediates, etc.

Automatic creation of a live, comprehensive batch family tree from all imported project analytical data
Figure 3. Automatic creation of a live, comprehensive batch family tree from all imported project analytical data.

Within Luminata, the onset, fate, and purge of impurities can also be tracked. For example, batch 302 in stage 2 (Figure 3) was found to possess a genotoxic impurity that was not present in another batch at the same stage. If the associated analytical data for batch 302 was not attached, this would have caused issues in the production environment.

Finally, Luminata can be further used to complete investigations of alternative catalysts and reagents for the same API endpoint. By providing real-time access to the LC/UV/MS data that supports these process scheme and batch genealogy frameworks, Luminata enables users to quickly verify their accuracy and also seamlessly manage complex multi-stage reaction projects.

Conclusions
By providing visualization of all batch analytical data into dynamic process scheme and batch family tree maps, Luminata facilitates rapid assessment and decision making for pharmaceutical product development teams. The software provides a single organizational source for process scheme and batch genealogy information, empowering teams to make the right process development decision the first time around.