Identifying Unknown Impurity Formation

Background:

Our approach uses advanced computational modeling and predictive analysis to identify potential unknown impurities that may form during a chemical synthesis process. By anticipating the formation of these impurities, we can proactively modify reaction conditions or purification steps to prevent their creation, thereby ensuring the final product's purity and safety. This is a critical step in drug development and manufacturing, as even trace amounts of certain impurities can be harmful or affect a drug's efficacy.

Case Study: Prediction of Impurities in a Synthesis Route

This case study demonstrates our ability to predict the formation of three unknown impurities (Imp. B, Imp. C, and Imp. F) during a synthesis route.

The Challenge:

During the development of a new synthesis route, analytical testing often reveals the presence of minor, unidentified peaks. These peaks correspond to unknown impurities. Traditional methods of identifying these impurities can be a time-consuming and labor-intensive process, involving complex spectroscopic analysis and additional synthesis. Our goal was to use predictive modeling to rapidly identify the likely structures of these impurities and their potential formation pathways.

Our Solution: Predictive Impurity Analysis

Using our platform, we analyzed the reported synthesis route and the known starting materials, intermediates, and reaction conditions. We then predicted the most likely structures of the unknown impurities and their formation mechanisms.

• Impurity B: Our platform predicted that Imp. B might be generated due to the presence of trace amounts of benzoic acid during the reaction of Intermediate-6 (Int-6) to Y101. This side reaction could occur if benzoic acid, a common reagent or byproduct, is present in the reaction mixture. The AMES mutagenicity test for this impurity was predicted to be negative.
• Impurity C: The model predicted that Imp. C was likely introduced by a reaction of Impurity B with methanol (MeOH), which was used for purification in the final step. This type of reaction, a transesterification, is a common source of impurities when using alcohol solvents for purification. The AMES mutagenicity test for Imp. C was also predicted to be negative.
• Impurity F: The platform predicted that Imp. F could be formed by the reaction of the final product, Y101, with small amounts of Impurity B. This suggests a side reaction between the main product and a minor impurity, leading to the formation of a new, larger impurity. The AMES mutagenicity test for this impurity was predicted to be negative.

Outcome:

By using predictive modeling, we were able to successfully identify the likely structures and formation pathways of three unknown impurities. This approach provided crucial insights that enabled us to adjust the synthesis and purification processes to prevent the formation of these impurities. The negative AMES mutagenicity predictions provided a preliminary safety assessment, allowing for a more focused and efficient experimental validation. This case study highlights the value of proactive, in-silico impurity prediction in ensuring the quality and safety of pharmaceutical products.