Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning.

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

Functionalization of Alpha-Lactalbumin by Zinc Ions.

Alpha-lactalbumin (α-LA) and binding of zinc cations to protein were studied. Molecular characteristics of protein was determined by MALDI-TOF/MS and electrophoresis SDS-PAGE, and also, for complexes, it was determined by spectroscopic techniques (ATR-FT-IR and Raman) and microscopic techniques (SEM along with an EDX detector and also TEM). The pH dependence of zeta potential of α-LA was determined in saline solution. The zinc binding to the protein mechanism was investigated; zinc binding to protein kinetics, the molecular modeling by the DFT method, and electron microscopy (SEM and TEM) for microstructure observation were performed. The experiments performed indicate a quick binding process (equilibrium takes place after 2 min of incubation) which occurs onto the surface of α-LA. Zinc cations change the conformation of the protein and create spherical particles from the morphological point of view. DFT studies indicate the participation of acidic functional groups of the protein (aspartic acid and glutamic acid residues), and these have a decisive influence on the interaction with zinc cations. Application studies of general toxicity and cytotoxicity and bioavailability were conducted.

Study on silver ions binding to ß-lactoglobulin.

The drug-resistant pathogen phenomenon, resulting in infections and deaths that are increasingly difficult to treat, requires research into searching new potential antimicrobial agents. The presented study is focused on the investigation of impact of silver ions (Ag+ ions) to ß-lactoglobulin (ßLG) structure and mechanism formation of silver-ß-lactoglobulin nanocomposites, that could find potential applications in medicine. To determine the physicochemical characteristics of silver ion binding, kinetics and isothermal models were used. The presence of functional groups involved in the binding process was investigated by spectroscopic methods (FTIR-ATR, Raman spectroscopy). The binding ability and nanocomplexes formation was determined by instrumental analyses (SEM, TEM, EDX). Based on the obtained results, the binding of Ag+ ions to ßLG were heterogeneous in nature consisting of three main steps: rapid sorption of Ag+ ions on the ßLG surface, intramolecular diffusion of Ag+ ions, and chemical equilibrium. Microscopic studies showed a change in the surface morphology of ßLG and the appearance of silver nanoparticles. Spectroscopic studies indicated that acidic (Glu-, Asp-) and Lys, Tyr, Met amino groups play a key role in the formation of the AgßLG nanocomplex. Finally, molecular dynamics (MD) and density functional theory (DFT) calculated studies as a comparative and complementary method have proven contribution of respective amino acids in the binding process.

Three-Dimensional Quantitative Structure and Activity Relationship of Flavones on Their Hypochlorite Scavenging Capacity.

Flavonoids, a class of polyphenolic substances widely present in the plant realm, are considered as ideal hypochlorite scavengers. However, to our knowledge, little study has focused on the structure-activity relationship between flavonoids and hypochlorite scavenging capacity. Herein, we report for the first time the three-dimensional quantitative structure and activity relationship (3D-QSAR) combined with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Four models derived from CoMFA and CoMSIA with different combinations of descriptors were built and compared; the CoMFA model, which included both steric and electrostatic fields, showed great potential (R2 = 0.989; Q2 = 0.818) in predictive quality according to both internal and external validation criteria. Additionally, the average local ionization energy (ALIE), electrostatic potential (ESP), and orbital weighted dual descriptor (OWDD) were determined to identify the key structural moiety for scavenging capacity of flavonoids against hypochlorite. The computational results indicated that hypochlorous acid (HClO) serves as an electrophile undergoing electrophilic addition to the C6 carbon, which has the highest negative charge density, which are influenced by the functional groups on the flavones. The DFT calculated mechanism revealed the catalytic role of water of mono- and di-chlorination reactions, characterized by low activation barriers, and the involvement of neutral, instead of high-energy carbocation, intermediates.

The study of the molecular mechanism of Lactobacillus paracasei clumping via divalent metal ions by electrophoretic separation.

In this work, the molecular mechanism of Lactobacillus paracasei bio-colloid clumping under divalent metal ions treatment such as zinc, copper and magnesium at constant concentrations was studied. The work involved experimental (electrophoretic - capillary electrophoresis in pseudo-isotachophoresis mode, spectroscopic and spectrometric - FT-IR and MALDI-TOF-MS, microscopic - fluorescent microscopy, and flow cytometry) and theoretical (DFT calculations of model complex systems) characterization. Electrophoretic results have pointed out the formation of aggregates under the Zn2+ and Cu2+ modification, whereas the use of the Mg2+ allowed focusing the zone of L. paracasei biocolloid. According to the FT-IR analysis, the major functional groups involved in the aggregation are deprotonated carboxyl and amide groups derived from the bacterial surface structure. Nature of the divalent metal ions was shown to be one of the key factors influencing the bacterial aggregation process. Proteomic analysis showed that surface modification had a considerable impact on bacteria molecular profiles and protein expression, mainly linked to the activation of carbohydrate and nucleotides metabolism as well with the transcription regulation and membrane transport. Density-functional theory (DFT) calculations of modeled Cu2+, Mg2+ and Zn2+ coordination complexes support the interaction between the divalent metal ions and bacterial proteins. Consequently, the possible mechanism of the aggregation phenomenon was proposed. Therefore, this comprehensive study could be further applied in evaluation of biocolloid aggregation under different types of metal ions.

Affinity of Antifungal Isoxazolo[3,4-b]pyridine-3(1H)-Ones to Phospholipids in Immobilized Artificial Membrane (IAM) Chromatography.

Currently, rapid evaluation of the physicochemical parameters of drug candidates, such as lipophilicity, is in high demand owing to it enabling the approximation of the processes of absorption, distribution, metabolism, and elimination. Although the lipophilicity of drug candidates is determined using the shake flash method (n-octanol/water system) or reversed phase liquid chromatography (RP-LC), more biosimilar alternatives to classical lipophilicity measurement are currently available. One of the alternatives is immobilized artificial membrane (IAM) chromatography. The present study is a continuation of our research focused on physiochemical characterization of biologically active derivatives of isoxazolo[3,4-b]pyridine-3(1H)-ones. The main goal of this study was to assess the affinity of isoxazolones to phospholipids using IAM chromatography and compare it with the lipophilicity parameters established by reversed phase chromatography. Quantitative structure-retention relationship (QSRR) modeling of IAM retention using differential evolution coupled with partial least squares (DE-PLS) regression was performed. The results indicate that in the studied group of structurally related isoxazolone derivatives, discrepancies occur between the retention under IAM and RP-LC conditions. Although some correlation between these two chromatographic methods can be found, lipophilicity does not fully explain the affinities of the investigated molecules to phospholipids. QSRR analysis also shows common factors that contribute to retention under IAM and RP-LC conditions. In this context, the significant influences of WHIM and GETAWAY descriptors in all the obtained models should be highlighted.

Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization.

Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.

Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory.

This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical 'black box' approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

Lipophilicity Determination of Antifungal Isoxazolo[3,4-b]pyridin-3(1H)-ones and Their N1-Substituted Derivatives with Chromatographic and Computational Methods.

The lipophilicity of a molecule is a well-recognized as a crucial physicochemical factor that conditions the biological activity of a drug candidate. This study was aimed to evaluate the lipophilicity of isoxazolo[3,4-b]pyridine-3(1H)-ones and their N1-substituted derivatives, which demonstrated pronounced antifungal activities. Several methods, including reversed-phase thin layer chromatography (RP-TLC), reversed phase high-performance liquid chromatography (RP-HPLC), and micellar electrokinetic chromatography (MEKC), were employed. Furthermore, the calculated logP values were estimated using various freely and commercially available software packages and online platforms, as well as density functional theory computations (DFT). Similarities and dissimilarities between the determined lipophilicity indices were assessed using several chemometric approaches. Principal component analysis (PCA) indicated that other features beside lipophilicity affect antifungal activities of the investigated derivatives. Quantitative-structure-retention-relationship (QSRR) analysis by means of genetic algorithm-partial least squares (GA-PLS)-was implemented to rationalize the link between the physicochemical descriptors and lipophilicity. Among the studied compounds, structure 16 should be considered as the best starting structure for further studies, since it demonstrated the lowest lipophilic character within the series while retaining biological activity. Sum of ranking differences (SRD) analysis indicated that the chromatographic approach, regardless of the technique employed, should be considered as the best approach for lipophilicity assessment of isoxazolones.

Quantitative Structure-Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order.

In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(tR) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.

Non-Linear Quantitative Structure⁻Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants.

In this work, we developed quantitative structure-activity relationships (QSAR) models for prediction of oxygen radical absorbance capacity (ORAC) of flavonoids. Both linear (partial least squares-PLS) and non-linear models (artificial neural networks-ANNs) were built using parameters of two well-established antioxidant activity mechanisms, namely, the hydrogen atom transfer (HAT) mechanism defined with the minimum bond dissociation enthalpy, and the sequential proton-loss electron transfer (SPLET) mechanism defined with proton affinity and electron transfer enthalpy. Due to pronounced solvent effects within the ORAC assay, the hydration energy was also considered. The four-parameter PLS-QSAR model yielded relatively high root mean square errors (RMSECV = 0.783, RMSEE = 0.668, RMSEP = 0.900). Conversely, the ANN-QSAR model yielded considerably lower errors (RMSEE = 0.180 ± 0.059, RMSEP1 = 0.164 ± 0.128, and RMSEP2 = 0.151 ± 0.114) due to the inherent non-linear relationships between molecular structures of flavonoids and ORAC values. Five-fold cross-validation was found to be unsuitable for the internal validation of the ANN-QSAR model with a high RMSECV of 0.999 ± 0.253; which is due to limited sample size where resampling with replacement is a considerably better alternative. Chemical domains of applicability were defined for both models confirming their reliability and robustness. Based on the PLS coefficients and partial derivatives, both models were interpreted in terms of the HAT and SPLET mechanisms. Theoretical computations based on density functional theory at ωb97XD/6-311++G(d,p) level of theory were also carried out to further shed light on the plausible mechanism of anti-peroxy radical activity. Calculated energetics for simplified models (genistein and quercetin) with peroxyl radical derived from 2,2'-azobis (2-amidino-propane) dihydrochloride suggested that both SPLET and single electron transfer followed by proton loss (SETPL) mechanisms are competitive and more favorable than HAT in aqueous medium. The finding is in good accord with the ANN-based QSAR modelling results. Finally, the strongly predictive ANN-QSAR model was used to predict antioxidant activities for a series of 115 flavonoids designed combinatorially with flavone as a template. Structural trends were analyzed, and general guidelines for synthesis of new flavonoid derivatives with potentially potent antioxidant activities were given.

Fiber-Optic Raman Spectroscopy with Nature-Inspired Genetic Algorithms Enhances Real-Time in Vivo Detection and Diagnosis of Nasopharyngeal Carcinoma.

Raman spectroscopy is an optical vibrational spectroscopic technique capable of probing specific biochemical structures and conformation of tissue and cells in biomedical systems. This work aims to assess the clinical utility of a fiber-optic Raman spectroscopy with nature-inspired genetic algorithms for enhancing in vivo detection and diagnosis of nasopharyngeal carcinoma (NPC) patients. The Raman diagnostic platform is developed based on simultaneous fingerprint (FP) and high-wavenumber (HW) fiber-optic Raman endoscopy associated with genetic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA). A total of 2126 in vivo FP/HW Raman spectra (598 NPC, 1528 normal) acquired from 113 tissue sites of 14 NPC patients and 48 healthy subjects during nasopharyngeal endoscopic examinations. Distinct Raman peaks have been identified (853 cm-1 - proteins, 1209 cm-1 - phenylalanine, 1265 cm-1 - proteins, 1335 cm-1 - proteins and nucleic acids, 1554 cm-1 - tryptophan, porphyrin, 2885 cm-1 - lipids, 2940 cm-1 - proteins, 3009 cm-1 - lipids, and 3250 cm-1 - water) that are related to the significant biochemical changes ( p < 1 × 10-5) in NPC compared to normal tissue. Raman diagnostic performance is evaluated through the leave-one-object (tissue site)-out cross-validation (LOOCV) method. A statistically significant GA-PLS-LDA model ( p < 1 × 10-5) on FP/HW Raman yields a CV diagnostic accuracy of 98.23% (111/113), sensitivity of 93.33% (28/30), and specificity of 100% (83/83) for NPC classification. This work demonstrates that the fiber-optic FP/HW Raman diagnostic platform developed has great promise for improving real-time in vivo detection and diagnosis of NPC at the molecular level during clinical nasopharyngeal endoscopy.

Column Characterization and Selection Systems in Reversed-Phase High-Performance Liquid Chromatography.

Reversed-phase high-performance liquid chromatography (RP-HPLC) is the most popular chromatographic mode, accounting for more than 90% of all separations. HPLC itself owes its immense popularity to it being relatively simple and inexpensive, with the equipment being reliable and easy to operate. Due to extensive automation, it can be run virtually unattended with multiple samples at various separation conditions, even by relatively low-skilled personnel. Currently, there are >600 RP-HPLC columns available to end users for purchase, some of which exhibit very large differences in selectivity and production quality. Often, two similar RP-HPLC columns are not equally suitable for the requisite separation, and to date, there is no universal RP-HPLC column covering a variety of analytes. This forces analytical laboratories to keep a multitude of diverse columns. Therefore, column selection is a crucial segment of RP-HPLC method development, especially since sample complexity is constantly increasing. Rationally choosing an appropriate column is complicated. In addition to the differences in the primary intermolecular interactions with analytes of the dispersive (London) type, individual columns can also exhibit a unique character owing to specific polar, hydrogen bond, and electron pair donor-acceptor interactions. They can also vary depending on the type of packing, amount and type of residual silanols, "end-capping", bonding density of ligands, and pore size, among others. Consequently, the chromatographic performance of RP-HPLC systems is often considerably altered depending on the selected column. Although a wide spectrum of knowledge is available on this important subject, there is still a lack of a comprehensive review for an objective comparison and/or selection of chromatographic columns. We aim for this review to be a comprehensive, authoritative, critical, and easily readable monograph of the most relevant publications regarding column selection and characterization in RP-HPLC covering the past four decades. Future perspectives, which involve the integration of state-of-the-art molecular simulations (molecular dynamics or Monte Carlo) with minimal experiments, aimed at nearly "experiment-free" column selection methodology, are proposed.

Target-based drug discovery through inversion of quantitative structure-drug-property relationships and molecular simulation: CA IX-sulphonamide complexes.

In this work, a target-based drug screening method is proposed exploiting the synergy effect of ligand-based and structure-based computer-assisted drug design. The new method provides great flexibility in drug design and drug candidates with considerably lower risk in an efficient manner. As a model system, 45 sulphonamides (33 training, 12 testing ligands) in complex with carbonic anhydrase IX were used for development of quantitative structure-activity-lipophilicity (property)-relationships (QSPRs). For each ligand, nearly 5,000 molecular descriptors were calculated, while lipophilicity (logkw) and inhibitory activity (logKi) were used as drug properties. Genetic algorithm-partial least squares (GA-PLS) provided a QSPR model with high prediction capability employing only seven molecular descriptors. As a proof-of-concept, optimal drug structure was obtained by inverting the model with respect to reference drug properties. 3509 ligands were ranked accordingly. Top 10 ligands were further validated through molecular docking. Large-scale MD simulations were performed to test the stability of structures of selected ligands obtained through docking complemented with biophysical experiments.

Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids.

Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD2 , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD2 and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc.

Non-target analysis of phospholipid and sphingolipid species in egg yolk using liquid chromatography/triple quadrupole tandem mass spectrometry.

In this work, phospholipids extracted from egg yolk (control group, experimental group) were identified using high performance liquid chromatography coupled with electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS). Combinations of fatty acyls occurring in 11 classes of phospholipids from egg yolk were investigated. Differences between the profile of fatty acyls from hens fed traditionally and the ones that received special diet supplementation were observed. Experimental findings were complemented with multivariate chemometric analysis. Multiple reaction monitoring mass spectrometry mode was utilized and 123 distinct combinations of fatty acyls occurring in phospholipids were identified. From these, large portions are polyunsaturated fatty acyls from the omega-3 and omega-6 family. HPLC MS/MS analysis allows for quick, accurate and precise determination of biologically active compounds, found in low concentrations within the tested material.

Silver-Lactoferrin Nanocomplexes as a Potent Antimicrobial Agent.

The process of silver immobilization onto and/or into bovine lactoferrin (LTF), the physicochemical properties of bovine lactoferrin and obtained silver-lactoferrin complexes, as well as antibacterial activity of silver-lactoferrin complexes were investigated in this work. Kinetic study of the silver immobilization into lactoferrin was carried out using batch sorption techniques. Spectrometric (MALDI-TOF/TOF-MS, ICP-MS), spectroscopic (FTIR, SERS), electron microscopic (TEM) and electrophoretic (I-DE) techniques, as well as zeta potential measurements, were applied for characterization of LTF and binding nature of silver in Ag-LTF complexes. On the basis of the results of the kinetics study, it was established that the silver binding to LTF is a heterogeneous process involving two main stages: (i) internal diffusion and sorption onto external surface of lactoferrin globules; and (ii) internal diffusion and binding into lactoferrin globule structure. Spectroscopic techniques combined with TEM analysis confirmed the binding process. Molecular dynamics (MD) analysis was carried out in order to simulate the mechanism of the binding process, and locate potential binding sites, as well as complement the experimental findings. Quantum mechanics (QM) simulations were performed utilizing density functional theory (DFT) in order to support the reduction mechanism of silver ions to elemental silver. Antimicrobial activity of synthesized lactoferrin complexes against selected clinical bacteria was confirmed using flow cytometry and antibiograms.

Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches.

Peptides' retention time prediction is gaining increasing popularity in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics. This is a promising approach for improving successful proteome mapping, useful both in identification and quantification workflows. In this work, a quantitative structure-retention relationships (QSRR) model for its direct prediction from the molecular structure of 185 peptides originating from 8 well-characterized proteins and two Bacillus subtilis proteomes has been developed. Genetic Algorithm (GA) was used for selection of a subset of molecular descriptors coupled with three machine learning methods: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and kernel Partial Least Squares (kPLS) for regression. Final GA-SVR, GA-ANN, and GA-kPLS models were validated through an external validation set of 95 peptides originating from the human epithelial HeLa cells proteomes. Robustness and stability was ensured by defining their applicability domain. The descriptors of the developed models were interpreted confirming a causal relationship between parameters of molecular structure and retention time. GA-SVR model has shown to be superior over the others in terms of both predictive ability, and interpretation of the selected descriptors.

Assessment of column selection systems using Partial Least Squares.

Column selection systems based on calculation of a scalar measure based on Euclidean distance between chromatographic columns, suffer from the same issue. For diverse values of their parameters, identical or near-identical values can be calculated. Proper use of chemometric methods can not only provide a remedy, but also reveal underlying correlation between them. In this work, parameters of a well-established column selection system (CSS) developed at Katholieke Universiteit Leuven (KUL CSS) have been directly correlated to parameters of selectivity (retention time, resolution, and peak/valley ratio) toward pharmaceuticals, by employing Partial Least Squares (PLS). Two case studies were evaluated, separation of alfuzosin, lamotrigine, and their impurities, respectively. Within them, comprehensive correlation structure was revealed, which was thoroughly interpreted, confirming a causal relationship between KUL parameters and parameters of column performance. Furthermore, it was shown that the developed methodology can be applied to any distance-based column selection system.