PharmSci 360 :: November 4-7, 2018 :: ACD/Labs
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PharmSci 360

November 4-7, 2018
Walter E. Washington Convention Center, Washington, DC, USA



Poster Schedule

TUESDAY, NOV. 6TH, 1:30–2:30 PM

An Impurity Control Strategy Using Impurity Mapping with Dynamic Purge Factor Determinations for Drug Substance Development
Andrew Anderson, Graham McGibbon, Sanjivanjit K. Bhal, Joe DiMartino, Anne Marie Smith
Poster Number: T1330-06-047

Purpose
API process development is tremendously challenging and needs to support Quality by Design (QbD) principles, to ensure that medicines are safe and efficacious. To achieve these goals, control strategies must be developed which comprehensively assess, classify, and report process route development. Ultimately controlling process inputs and materials, their attributes, the design spaces around unit operations, methods, variability, and final product specifications. Impurity mapping lies as the foundation of this. While scientist have developed practices to gather this information, it remains a tedious manual process.

Methods
Analytical data collected for Agomelatine, synthesized by a five 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 45-1000Da. Column separation was done with an isocratic method using an ammonium formate Buffer pH of 4.5/ACN (35:65). The flow rate was 1.2ml/min with a run time of 50min, the column used was a Zorbax Eclipse XDB C18 5um - 4.6 x 150mm.

The software application, Luminata™ (v2018.1) based on the ACD/Spectrus Platform was used to manage the analytical and chemical data for the process.

Results
In this paper, we will provide an overview of a software application developed specifically to address these challenges. Luminata was used to create an impurity map through input of the process scheme and related analytical data. Carryover calculations were automatically determined directly from the LC/UV/MS data by the software. Cumulative carryover was also calculated automatically, enabling direct comparison of batches and impurity determination of spiked samples. These complex calculations were able to be completed within minutes to determine a control strategy for Agomelatine.

Conclusions
Routine, tedious tasks such as the determination of purge factor can be automated through the use of software. Luminata enables project teams to develop control strategies more efficiently and support decision-making by quantitatively calculating purge factor from the process related impurities and associated analytical data.