Mechanistic multi-objective optimization of ion-pair reversed-phase liquid chromatography for oligonucleotide
Marek Leśko1, Martin Enmark1, Krzysztof Kaczmarski2
1Department of Engineering and Chemical Sciences, Karlstad University, SE-651 88 Karlstad, Sweden.
Journal of Chromatography. A
|June 24, 2025
Summary
This study optimizes oligonucleotide (ON) purification using numerical modeling for improved diagnostics and therapeutics. Results show optimal injection volume and gradient slope enhance productivity and yield, with model refinement improving accuracy.
Area of Science:
- Biochemistry
- Chemical Engineering
- Analytical Chemistry
Background:
- Oligonucleotides (ONs) are crucial for diagnostics and therapeutics.
- Ion-pair reversed-phase liquid chromatography (IP-RPLC) is a primary method for ON purification.
- Optimizing IP-RPLC is essential for efficient ON production.
Purpose of the Study:
- To develop and apply a numerical approach for optimizing oligonucleotide purification via IP-RPLC.
- To investigate single-objective (productivity) and multi-objective (productivity and yield) optimization strategies.
- To enhance the understanding of separation dynamics and kinetic limitations in ON purification.
Main Methods:
- Utilized a transport-dispersive column model with a gradient-modified Langmuir kinetic adsorption-desorption isotherm.
- Validated the model against experimental elution profiles of a 20-mer ON and its impurities.
- Employed a hybrid optimization strategy combining simulated annealing and the simplex algorithm.
Main Results:
- Maximized productivity with the highest injection volume; higher purity required shallower gradient slopes.
- Multi-objective optimization revealed complex Pareto fronts with unexpected shapes (inflection points, local minima).
- Refined models based on gradient-specific data eliminated Pareto front anomalies, supporting the kinetic hypothesis.
Conclusions:
- Numerical optimization effectively balances productivity and yield in ON purification.
- Adsorption-desorption kinetics significantly influence separation dynamics and model accuracy across varying gradient slopes.
- Model refinement based on experimental conditions is crucial for accurate prediction and optimization of IP-RPLC separations.
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