Gradient design for liquid chromatography using multi-scale optimization

A comprehensive method for modeling and simulating ion exchange chromatography of complex mixtures

In recent years, many biological-based products have been developed, representing a significant fraction of income in the pharmaceutical market. Ion exchange chromatography is an important downstream step for the purification of target recombinant proteins present in clarified cell extracts, together with many other unknown impurities. This work develops a robust approach to model and simulate the purification of untagged heterologous proteins, so that the improved conditions to carry out an ion exchange chromatography are identified in a rational basis prior to the real purification run itself. Purification of the pneumococcal surface protein A (PspA4Pro) was used as a case study. This protein is produced by recombinant Escherichia coli and is a candidate for the manufacture of improved pneumococcal vaccines. The developed method combined experimental and computational procedures. Different anion exchange operating conditions were mapped in order to gather a broad range of representative experimental data. The equilibrium dispersive and the steric mass action equations were used to model and simulate the process. A training strategy to fit the model and separately describe the elution profiles of PspA4Pro and other proteins of the cell extract was applied. Based on the simulation results, a reduced ionic strength was applied for PspA4Pro elution, leading to increases of 14.9% and 11.5% for PspA4Pro recovery and purity, respectively, compared to the original elution profile. These results showed the potential of this method, which could be further applied to improve the performance of ion exchange chromatography in the purification of other target proteins under real process conditions.

Procedure to explore a ternary mixture diagram to find the appropriate gradient profile in liquid chromatography with fluorescence detector. Application to determine four primary aromatic amines in napkins

The purpose of this work is to develop a tool to search for a gradient profile with ternary or binary mixtures in liquid chromatography, that can provide well-resolved chromatograms in the shortest time for multianalyte analysis. This approach is based exclusively on experimental data and does not require a retention time model of the compounds to be separated. The methodology has been applied for the quantification of four primary aromatic amines (PAAs) using HPLC with fluorescence detector (FLD). Aniline (ANL), 2,4-diaminotoluene (TDA), 4,4'-methylenedianiline (MDA) and 2-aminobiphenyl (ABP) have been selected since their importance in food contact materials (FCM). In order to achieve that, partial least squares (PLS) models have been fitted to relate CMP (control method parameters) and CQA (critical quality attributes). Specifically, PLS models have been fitted using 30 experiments for each one of the four CQA (resolution between peaks and total elution time), considering 33 predictor variables (the composition of the methanol and acetonitrile in the mobile phase and the time of each one of the 11 isocratic segments of the gradient). These models have been used to predict new candidate gradients, and then, some of those predictions (the ones with resolutions above 1.5, in absolute value, and final time lower than 20 min) have been experimentally validated. Detection capability of the method has been evaluated obtaining 1.8, 189.4, 28.8 and 3.0 µg L^-1 for ANL, TDA, MDA and ABP, respectively. Finally, the application of chemometric tools like PARAFAC2 allowed the accurate quantification of ANL, TDA, MDA and ABP in paper napkins in the presence of other interfering substances coextracted in the sample preparation process. ANL has been detected in the three napkins analysed in quantities between 33.5 and 619.3 µg L^-1, while TDA is present in only two napkins in quantities between 725.9 and 1908 µg L^-1. In every case, the amount of PAAs found, exceeded the migration limits established in European regulations.

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.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Multi-scale optimisation vs. genetic algorithms in the gradient separation of diuretics by reversed-phase liquid chromatography

Multi-linear gradients are a convenient solution to get separation of complex samples by modulating carefully the gradient slope, in order to accomplish the local selectivity needs for each particular solute cluster. These gradients can be designed by trial-and-error according to the chromatographer experience, but this strategy becomes quickly inappropriate for complex separations. More evolved solutions imply the sequential construction of multi-segmented gradients. However, this strategy discards part of the search space in each step of the construction and, again, cannot deal properly with very complex samples. When the complexity is too large, the only valid alternative for finding the best gradient is the use of global search methods, such as genetic algorithms (GAs). Recently, a new global approach where the level of detail is increased along the search has been proposed, namely Multi-scale optimisation (MSO). In this strategy, cubic splines are applied to build intermediate curves to define any arbitrary solvent variation function. Subdivision schemes are used to generate the cubic splines and control their level of detail. The search was subjected to a number of restrictions, such as avoiding long elution and favouring a balanced peak distribution. The aim of this work is evaluating and comparing the results of GAs and MSO. Both approaches were tested with a set of 14 diuretics and probenecid, eluted with acetonitrile-water mixtures using a C18 column. Satisfactory baseline resolution was obtained with an analysis time of 15-16 min. We found that GAs optimisation offered results equivalent to those provided by MSO, when the penalisation parameters were included in the cost function.