Automatic Optimization of Gradient Conditions by AI Algorithm for Impurity Analysis

User Benefits

・The AI algorithm of LabSolutions MD can automatically optimize gradient conditions to greatly reduce labor of LC method development. ・Anyone can optimize gradient conditions, regardless of their experience in chromatography. ・Gradient conditions that meet the resolution criteria for specified peaks are automatically searched (e.g., principal component and its related impurities).

Introduction

In the typical LC method development, the process begins with “preparation” which includes mobile phase preparation, column installation, and creation of analysis schedules, then the analysis is started. After that, the acquired data is analyzed and “preparation” for the subsequent analysis is carried out, followed by starting the next analysis again. The method development progresses by repeating these processes, but in addition to the significant time required to repeatedly create analysis schedules, expertise in chromatography is necessary to explore optimal conditions based on data analysis. In other words, typical method development requires “human intervention”. Therefore, eliminating human involvement and automating such method development processes would be desirable to improve labor efficiency. This article introduces an example of automatic optimization of gradient conditions to separate montelukast (a small molecule drug) and its related impurities using LabSolutions MD (Technical Report C190-E309), a dedicated software for supporting method development.

September 5, 2024 GMT