Impurity formation during the synthesis of Active Pharmaceutical Ingredients (APIs) can affect their pharmacological and toxicological properties. Therefore, the monitoring and removal of all impurities is paramount to ensure that manufactured APIs are safe for human consumption. One of the most powerful techniques for impurity detection is High Performance Liquid Chromatography (HPLC), which is used extensively for quality control, reaction monitoring and quantitative analysis. Impurities can then be isolated using preparative HPLC for characterisation. Finding suitable HPLC methods for separation, as well as reaction conditions for maximising yield of impurities, are necessary for efficient characterisation but can both be difficult and time consuming to explore.<br/>Optimisation algorithms that autonomously find suitable HPLC method conditions, giving baseline resolution of the starting materials, products, by-products and impurities of a reaction run in flow, were investigated. The same algorithms were then used to automatically change flow reaction conditions to maximise the yield of different impurities detected in the HPLC chromatogram. Both aspects tied together create an automated impurity scouting system. A multi-objective Bayesian optimisation algorithm was used to optimise three continuous HPLC method variables. The number of peaks detected, the resolution between peaks, and the method length were selected as the objectives to maximise the chromatogram quality. For impurity synthesis in flow a single objective Bayesian optimisation algorithm was used to optimise three continuous variables. The yield was selected as the objective to be maximised and was evaluated using HPLC. This was repeated for each unknown peak detected in the chromatogram.<br/>This work offers a fully automated and novel methodology for optimising HPLC method conditions when compared to other modern techniques such as Design of Experiments (DOE) or Quantitative Structure Retention Relationships (QSSR). The automated HPLC method development was able to find conditions that gave rise to baseline separation for seven and eleven analyte mixtures in 40 experiments or less when run overnight. A fully automated methodology that maximises the yield of different impurities in flow is also in development, which hopes to aid impurity detection and characterisation for industrially relevant pharmaceutical reactions.

Artificially intelligent flow platform for automated impurity scouting

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