Design and application of an expert system for mobile phase optimisation in reversed-phase liquid chromatography

Deep Q-learning for the selection of optimal isocratic scouting runs in liquid chromatography

An important challenge in chromatography is the development of adequate separation methods. Accurate retention models can significantly simplify and expedite the development of adequate separation methods for complex mixtures. The purpose of this study was to introduce reinforcement learning to chromatographic method development, by training a double deep Q-learning algorithm to select optimal isocratic scouting runs to generate accurate retention models. These scouting runs were fit to the Neue-Kuss retention model, which was then used to predict retention factors both under isocratic and gradient conditions. The quality of these predictions was compared to experimental data points, by computing a mean relative percentage error (MRPE) between the predicted and actual retention factors. By providing the reinforcement learning algorithm with a reward whenever the scouting runs led to accurate retention models and a penalty when the analysis time of a selected scouting run was too high (> 1h); it was hypothesized that the reinforcement learning algorithm should by time learn to select good scouting runs for compounds displaying a variety of characteristics. The reinforcement learning algorithm developed in this work was first trained on simulated data, and then evaluated on experimental data for 57 small molecules - each run at 10 different fractions of organic modifier (0.05 to 0.90) and four different linear gradients. The results showed that the MRPE of these retention models (3.77% for isocratic runs and 1.93% for gradient runs), mostly obtained via 3 isocratic scouting runs for each compound, were comparable in performance to retention models obtained by fitting the Neue-Kuss model to all (10) available isocratic datapoints (3.26% for isocratic runs and 4.97% for gradient runs) and retention models obtained via a "chromatographer's selection" of three scouting runs (3.86% for isocratic runs and 6.66% for gradient runs). It was therefore concluded that the reinforcement learning algorithm learned to select optimal scouting runs for retention modeling, by selecting 3 (out of 10) isocratic scouting runs per compound, that were informative enough to successfully capture the retention behavior of each compound.

Strategies for peak-purity assessment in liquid chromatography

One of the key requirements for the validation of chromatographic methods is to determine the purity of a chromatographic peak. Statistical modelling of the chromatographic process suggests that overlapping components are highly probable in a chromatogram. Hence extensive efforts have been directed at the development of sensitive, reliable and robust methods to assess peak purity. This is especially the case in the pharmaceutical industry, where liquid chromatography (LC) is widely utilized and the demands on method validation are justifiably high. On-line multiwavelength absorptiometric detection is often used to generate the additional data required to facilitate peak-purity assessment in LC. This approach, using photodiode-array technology, is directly compatible with the aqueous-based reversed-phase LC solvents used extensively in drug analysis. Consequently, this work highlights many of the peak-purity algorithms, which may be applied using LC with diode-array detection. The relative merits of the individual techniques are discussed, and a rationale is developed for their application.

Advances in automation of pharmaceutical analysis

The development of a new drug substance and its dosage forms demands the establishment and implementation of suitable analytical methods. Through the application of chemometrics, "intelligent" laboratory systems and integrated data handling systems, considerable progress is being made in automating both the development and routine use of analytical methods in pharmaceutical analysis.