Oligonucleotides are commonly purified using either ion exchange chromatography (IEX) or ion-pair reversedphase liquid chromatography (IP-RPLC). This study compares the purification of a crude 20-mer oligonucleotide (ON) using both methods under preparative conditions. Two variables were investigated during the separation: column load and gradient slope. Although the IEX purifications using agarose-based resins had longer cycle times, this was compensated by the high loadability compared to the silica-based IP-RPLC media. This resulted in both higher productivity and lower solvent consumption at all evaluated purities, ranging from 95 % to 99 %, at optimal productivity levels. At 95 % purity, IEX achieved more than twice the productivity, and at 99 % purity, the productivity was seven times higher. Additionally, solvent consumption was significantly reduced, with IEX consuming only one-third to one-tenth of the solvents compared to IP-RPLC at purities from 95 % to 99 %.

In liquid chromatography (LC), adsorption heterogeneity arises from the distribution of adsorption sites on stationary phases with varying interaction energies, affecting retention and separation performance. This heterogeneity can cause peak tailing, reduced resolution, and unpredictable retention times in analytical chromatography, as well as broad, asymmetric elution profiles in preparative systems. Adsorption heterogeneity depends on the combined effects of the stationary phase, the mobile phase composition, the analyte properties, and the chromatographic conditions. Traditional adsorption isotherms often fail to fully describe these complex interactions because they assume uniform adsorption energies. The Adsorption Energy Distribution (AED) framework offers a powerful alternative by modelling adsorption as a sum of independent homogeneous sites, each with a specific energy, offering a realistic representation of heterogeneous adsorption. This review introduces the theoretical foundations of AED, including its mathematical formulation and computational approaches, and discusses its application in interpreting retention mechanisms in LC. AED analysis is illustrated through its use in both chiral and achiral separations, as well as its ability to explain peak tailing and surface heterogeneity. Practical considerations, such as the range of concentration data in the adsorption isotherm, the selection of a suitable kernel function, and the number of iterations and grid points in AED analysis, are discussed. Special emphasis is given on how to visualize and interpret the AED. This review aims to provide chromatographers with a comprehensive understanding of AED, emphasizing its practical value in characterizing the chromatographic system and elucidating retention mechanisms in liquid chromatography.

Poor data quality limits the advantageous power of Machine Learning (ML) and weakens high-performing ML software systems. Nowadays, data are more prone to the risk of poor quality due to their increasing volume and complexity. Therefore, tedious and time-consuming work goes into data preparation and improvement before moving further in the ML pipeline. To address this challenge, we propose an intelligent data-centric evaluation framework that can identify high-quality data and improve the performance of an ML system. The proposed framework combines the curation of quality measurements and unsupervised learning to distinguish high- and low-quality data. The framework is designed to integrate flexible and general-purpose methods so that it is deployed in various domains and applications. To validate the outcomes of the designed framework, we implemented it in a real-world use case from the field of analytical chemistry, where it is tested on three datasets of anti-sense oligonucleotides. A domain expert is consulted to identify the relevant quality measurements and evaluate the outcomes of the framework. The results show that the quality-centric data evaluation framework identifies the characteristics of high-quality data that guide the conduct of efficient laboratory experiments and consequently improve the performance of the ML system. 

This study presents an improved workflow to support the development of machine learning models to predict oligonucleotide retention times, peak widths and thus peak resolutions, from larger datasets where manual processing is not feasible. We explored diverse oligonucleotide forms, ranging from native to fully phosphorothioated, using three different gradient slopes. Both native and phosphorothioated oligonucleotides were separated, using a chromatographic C18 system with tributylaminium ion as the ion-pair reagent in the eluent, resulting in retention time data for approximately 900 sequences per gradient. For managing the large and extensive datasets, we developed a semi-automatic rule-based approach for retention time determination, peak decomposition, peak width assessment, signal-to-noise ratio, and skewness analysis. Probability density functions (PDFs) were fitted to elution profiles, with PDF selection based on an Ftest. Co-eluting peaks were addressed using a multiple Gaussian PDF. The encoded sequence data underwent modeling using support vector regression (SVR), gradient boosting (GB), random forest (RF), and decision tree (DT) models. GB and SVR showed promise for retention predictions, while RT and DT were faster but demonstrated limited generalization capabilities. The machine learning models exhibited larger errors for the shallowest gradient and lower predictability for P=O sequences, potentially due to signal intensity and sequence heterogeneity. Improvements in signal-to-noise ratios were considered, including mass spectrometry in selected ion monitoring mode. The best model for this data sets were GB, closely followed by the SVR model. With established models for retention and peak width, chromatograms can now be predicted for various gradient slopes, offering prediction of impurity peak resolution for arbitrary sequences and gradient slopes.

This work introduces the Adsorption Energy Distribution (AED) calculation using competitive adsorption isotherm data, enabling investigation of the simultaneous AED of two components for the first time. The AED provides crucial insights by visualizing competitive adsorption processes, offering an alternative adsorption isotherm model without prior assuming adsorption heterogeneity, and assisting in model selection for more accurate retention mechanistic modeling. The competitive AED enhances our understanding of multicomponent interactions on stationary phases, which is crucial for understanding how analytes compete on the stationary phase surface and for selecting adsorption models for numerical optimization of preparative chromatography. Here, the two-component AED was tested on both synthetic and experimental data, and a very successful outcome was achieved.

Oligonucleotides (ONs) play a vital role in diagnostics and therapeutics, with ion-pair reversed-phase liquid chromatography (IP-RPLC) being a key method for their purification. This study presents a numerical approach to optimize ON purification using both single-objective (productivity) and multi-objective (productivity and yield) optimization. A transport–dispersive column model incorporating a gradient-modified Langmuir kinetic adsorption–desorption isotherm was employed to describe the separation dynamics. The model was validated against experimental elution profiles of a 20-mer ON and its six most abundant shortmer impurities, and then used to optimize injection volume and gradient slope under varying purity constraints. A hybrid optimization strategy combining simulated annealing (stochastic) with the simplex algorithm (deterministic) was applied. The results showed that productivity was maximized at the highest injection volume, while higher purity constraints required shallower gradient slopes. Multi-objective optimization yielded Pareto fronts with unexpected shapes, including inflection points or local minima. We hypothesize that slow adsorption–desorption kinetics limited the model’s ability to accurately describe separations across a broad range of gradient slopes. To test this, two new models were calibrated using only shallow or steep gradient data. The resulting Pareto fronts displayed no inflection points or local minima, supporting our hypothesis. 

In indirect detection, sample components lacking detectable properties are detected by adding a detectable component to the eluent, a so-called probe that interacts with the analytes to be detected. This study focuses on modeling indirect detection in two principally different cases. In case (1), the analyte component has the same charge as the probe component, so the probe acts as a co-ion of the analyte. In case (2), the analyte component has the opposite charge to the probe, so the probe acts as a counter-ion of the analyte. In the co-ion case (1), the analytes are alkyl sulfonates, and a competitive bi-Langmuir isotherm model was used. In the counter-ion case (2), the analytes are amines, and a modified bi-Langmuir isotherm model, incorporating ion-pairing on the stationary phase surface, was derived and applied for simulating the elution profiles. The chromatographic system comprised an XBridge Phenyl column as the stationary phase and an acetonitrile/phosphate buffer mixture with varying concentrations of sodium 2-naphthalenesulfonate as the eluent. In both cases, the detectable probe component was sodium 2-naphthalenesulfonate. The applied isotherm models successfully predicted system peaks with high agreement in both model cases, with calculated relative errors in retention times typically below 4.72 % and often below 1 %. The models were employed to predict the sensitivity of analytical methods, demonstrating excellent agreement between experimental and calculated sensitivities. These findings confirm the validity of the new adsorption isotherm model under these experimental conditions. 

Antisense oligonucleotides (ASOs) are nucleic acid molecules with transformative therapeutic potential, especially for diseases that are untreatable by traditional drugs. However, the production and purification of ASOs remain challenging due to the presence of unwanted impurities. One tool successfully used to separate an ASO compound from the impurities is ion pair liquid chromatography (IPC). It is a critical step in separation, where each compound is identified by its retention time (tR) in the IPC. Due to the complex sequence-dependent behavior of ASOs and variability in chromatographic conditions, the accurate prediction of tR is a difficult task. This study addresses this challenge by applying machine learning (ML) to predict tR based on the sequence characteristics of ASOs. Four ML models—Gradient Boosting, Random Forest, Decision Tree, and Support Vector Regression — were evaluated on three large ASOs datasets with different gradient times. Through feature engineering and grid search optimization, key predictors were identified and compared for model accuracy using root mean square error, coefficient of determination R-squared, and run time. The results showed that Gradient Boost performance competes with the Support Vector Machine in two of the three datasets, but is 3.94 times faster to tune. Additionally, newly proposed features representing the sulfur count and the nucleotides residing at the first and last positions of a sequence found to improve the predictive power of the models. This study demonstrates the advantages of ML-based tR prediction at scale and provides insights into interpretable and efficient utilization of ML in chromatographic applications.

Short interfering RNA (siRNA) represents a rapidly expanding class of marketed oligonucleotide therapeutics. Due to its double-stranded nature, the characterization of siRNA is twofold: (i) at the single-strand (denaturing) level for impurity profiling and (ii) at the intact (nondenaturing) level to confirm duplex formation and quantify excess single strands (including single strand-derived impurities). While denaturing analysis can be carried out using conventional ion-pair reversed-phase liquid chromatography (IP-RPLC), nondenaturing characterization of siRNA is a significantly less straightforward task. Typical IP-RPLC conditions have an intrinsic denaturing effect on siRNA, thereby limiting the development of viable approaches for the intact duplex analysis. In this study, we demonstrate, through the design of experiments of siRNA melting temperatures and chromatography analyses, that the simple addition of salts, such as phosphate-buffered saline and ammonium acetate, to eluents enhances the suitability of IP-RPLC for the nondenaturing analysis of siRNA during both UV- and mass spectrometry-based analysis. This work represents a milestone in overcoming the challenges associated with nondenaturing analysis of siRNAs by IP-RPLC and offers a fresh angle for exploring IP-RPLC of siRNAs.

The retention of three peptides was studied under analytical and overloaded conditions at different concentrations of trifluoroacetic acid (TFA) and water added to the co-solvent methanol (MeOH). Four columns with different stationary phase properties, i.e., silica, diol, 2-ethylpyridine and cyanopropyl (CN) columns, were evaluated in this investigation. The overall aim was to get a deeper understanding on how column chemistry as well as water and TFA in the co-solvent affect the analytical and overloaded elution profiles using multivariate design of experiments and adsorption measurements of co-solvent components. Multivariate experimental design modeling indicated that water had on average around five times higher effect on the retention than the addition of TFA. The results also showed that the retention increases with the addition of TFA and water to the co-solvent on all columns except the CN column, on which the retention decreased. When examining the effect of adding water to the co-solvent, evidence of a hydrophilic interaction liquid chromatography (HILIC)-like retention mechanism was found on the three other columns with more polar stationary phases. However, on the CN column water acted as an additive, decreasing the retention due to competition with the peptide for available adsorption surface. Adsorption isotherm measurements of the polar solvent MeOH showed that MeOH adsorbs much weaker on the CN column than on the other columns. Addition of TFA and water to the co-solvent substantially sharpened the elution profiles under both overloaded and analytical conditions. Adding a small amount of TFA (from 0 % to 0.05 %) to the co-solvent substantially improved the peak shape of the elution profiles, while further addition (from 0.05 % to 0.15 %) had only a minor effect on the elution profile shape. The reduced retention on the CN column could not be explained by TFA adsorption, which was very weak on all studied columns (retention factor, 0.05–0.15). One could therefore speculate that the ion-pairing complex formed between the peptide and TFA in the mobile phase, reduce the retention due to its reduced polarity. On the other columns displaying HILIC-like properties, the TFA probably just decreased the pH of the mobile phase, thereby promoting the partitioning of the peptide into the water-rich layer. Finally, peak deformation due to diluent–eluent mismatch was observed under overloaded conditions. This was most severe in the cases where MeOH adsorption to the stationary phase was strong and the peptides were only mildly retained. Adding 1,4-dioxan to the diluent resolved this issue.