Linear Solvent Strength Theory Research Articles

Linear solvent strength model on porous graphitic carbon stationary phase using high temperature liquid chromatographic method for allopurinol related substances analysis

A high-performance liquid chromatography (HPLC) method was developed for the analysis of Allopurinol and its Ph.Eur. impurities using a porous graphitic carbon (PGC) stationary phase. Retention behavior of solutes was studied across a wide temperature range (30–90 °C) and various gradient times (5–20 min). Analysis of the data revealed distinct retention mechanisms between reversed-phase and PGC phases. However, it was proved that the retention of Allopurinol and its Ph.Eur. impurities on PGC stationary phase can be effectively modeled using the linear solvent strength (LSS) theory. This allows for the utilization of LSS-based method development software to optimize methods under these conditions. By using commercial chromatographic modeling software, separation of Allopurinol and Ph.Eur. impurities was optimized within a large design space. At the optimized operating conditions (pH = 2.0, tG = 6 min, T = 60 °C), all solutes were separated within 6 min with baseline resolution. Comparison between predicted and experimentally measured chromatograms further confirmed the applicability of LSS theory in developing analytical methods for PGC-based HPLC systems. The presented approach offers a general framework for method development on PGC phases.

Determining gradient conditions for peptide purification in RPLC with machine-learning-based retention time predictions

A strategy for determining a suitable solvent gradient in silico in preparative peptide separations is presented. The strategy utilizes a machine-learning–based method, called ELUDE, for peptide retention time predictions based on the amino acid sequences of the peptides. A suitable gradient is calculated according to linear solvent strength theory by predicting the retention times of the peptides being purified at three different gradient slopes. The advantage of this strategy is that fewer experiments are needed to develop a purification method, making it useful for labs conducting many separations but with limited resources for method development.The preparative separation of met-enkephalin and leu-enkephalin was used as model solutes on two stationary phases: XBridge C18 and CSH C18. The ELUDE algorithm contains a support vector regression and is pre-trained, meaning that only 10–50 peptides are needed to calibrate a model for a certain stationary phase and gradient. The calibration is done once and the model can then be used for new peptides similar in size to those in the calibration set. We found that the accuracy of the retention time predictions is good enough to usefully estimate a suitable gradient and that it was possible to compare the selectivity on different stationary phases in silico. The absolute relative errors in retention time for the predicted gradients were 4.2% and 3.7% for met-enkephalin and leu-enkephalin, respectively, on the XBridge C18 column and 2.0% and 2.8% on the CSH C18 column. The predicted retention times were also used as initial values for adsorption isotherm parameter determination, facilitating the numerical calculation of overloaded elution profiles. Changing the trifluoroacetic acid (TFA) concentration from 0.05% to 0.15% in the eluent did not seriously affect the error in the retention time predictions for the XBridge C18 column, an increase of 1.0 min (in retention factor, 1.3). For the CSH C18 column the error was, on average, 2.6 times larger. This indicates that the model needs to be recalibrated when changing the TFA concentration for the CSH column.Studying possible scale-up complications from UHPLC to HPLC such as pressure, viscous heating (i.e., temperature gradients), and stationary-phase properties (e.g., packing heterogeneity and surface chemistry) revealed that all these factors were minor to negligible. The pressure effect had the largest effect on the retention, but increased retention by only 3%. In the presented case, method development can therefore proceed using UHPLC and then be robustly transferred to HPLC.

Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study.

The development of metabolomics based on ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) now allows hundreds to thousands of metabolites to be simultaneously monitored in biological matrices. In that context, bioinformatics and multivariate data analysis (MVA) play a crucial role in the detection of relevant alteration patterns. However, sound biological interpretations must necessarily be supported by metabolite identifications to be definitive or at least have a high degree of confidence. Each compound, should be characterised by unique molecular properties. Among them, the exact mass and the chromatographic retention time are recognised as major and complementary criteria for compound identification. While the former is easily derived from the molecular structure, building generic and accurate retention time open databases still constitutes a critical issue because of the vast diversity of instruments, stationary phases and operating conditions in UHPLC-HRMS. Because several hits matching a molecular formula obtained from an exact mass and an isotopic pattern are often generated for each analyte, this methodology rarely allows a unique and unambiguous molecular identity to be gained. This work aims to provide a flexible solution to facilitate reliable compound annotation based on retention time in reversed-phase liquid chromatography (RPLC). It proposes an innovative approach based on the chromatographic linear solvent strength (LSS) theory, allowing retention times under any gradient conditions at fixed temperature, stationary phase and mobile phase type to be predicted. Starting from a subset of the Human Metabolite Database (HMDB), a new dynamic database involving LSS parameters was developed. A real case study involving steroidogenesis alterations due to forskolin exposure was conducted using the adrenal H295R OECD reference cell model for endocrine disruptor screening. The prediction of retention times was successfully achieved, facilitating steroid identification. An automated procedure which implements the compound annotation levels encouraged by the Metabolite Standard Initiative (MSI) and the Coordination of Standards in Metabolomics (COSMOS) was also developed to speed up the process and enhance the data reusability.

Optimization of non-linear gradient in hydrophobic interaction chromatography for the analytical characterization of antibody-drug conjugates

The goal of this work was to evaluate the potential of non-linear gradients in hydrophobic interaction chromatography (HIC), to improve the separation between the different homologous species (drug-to-antibody, DAR) of commercial antibody-drug conjugates (ADC). The selectivities between Brentuximab Vedotin species were measured using three different gradient profiles, namely linear, power function based and logarithmic ones. The logarithmic gradient provides the most equidistant retention distribution for the DAR species and offers the best overall separation of cysteine linked ADC in HIC. Another important advantage of the logarithmic gradient, is its peak focusing effect for the DAR0 species, which is particularly useful to improve the quantitation limit of DAR0.Finally, the logarithmic behavior of DAR species of ADC in HIC was modelled using two different approaches, based on i) the linear solvent strength theory (LSS) and two scouting linear gradients and ii) a new derived equation and two logarithmic scouting gradients. In both cases, the retention predictions were excellent and systematically below 3% compared to the experimental values.

Indirect quantitative structure-retention relationship for steroid identification: A chemometric challenge at “Chimiométrie 2016”

A chemometric challenge was proposed during the "Chimiométrie" congress 2016, held in Namur, Belgium, on 17–20 January. The aim of this contest was to challenge the ability of congress participants to build indirect Quantitative Structure-Retention Relationship models (QSRR) using the linear solvent strength (LSS) theory of reversed-phase liquid chromatography. QSRR is a very helpful method for the identification of unknown analytes, including the prediction of chromatographic retention time. Because of the potential presence of various isomeric compounds, accurate retention time prediction is particularly important in the context of steroid identification. In addition, the indirect prediction of retention time using the linear solvent strength (LSS) parameters S and log kW provides a great advantage for use in any gradient conditions. In the proposed dataset, the experimental values of S and log kW were estimated using Ultra High Pressure Liquid Chromatography separation with two linear gradients (5–95% ACN+0.1% FA) of 15 and 60min, respectively. The aim of the challenge was the accurate estimation of retention time for a 45min gradient by applying the LSS theory based on the predicted S and log kW values. Molecular descriptors were calculated from a series of reference steroid compounds using the VolSurf+ software. By these means, a collection of 128 variables related to molecular shape, volume, polarisability, polar surface area, hydrophobic surface area, lipophilicity, molecular diffusion, and solubility was generated automatically. The dataset (n=95) included 76 steroid compounds for calibration and 19 for validation. Experimental log kW, S and retention time values were provided for the calibration set only. The results were evaluated according to the smallest RMSEC obtained for the retention time predictions of the validation set with the 45min gradient using the LSS parameters. Moreover, each individual relative error should not exceed 5% of the experimental retention time for both the calibration and validation sets. This paper summarises the approaches proposed by the best three participants and the challenge organiser.

Generation of accurate peptide retention data for targeted and data independent quantitative LC-MS analysis: Chromatographic lessons in proteomics.

The emergence of data-independent quantitative LC-MS/MS analysis protocols further highlights the importance of high-quality reproducible chromatographic procedures. Knowing, controlling and being able to predict the effect of multiple factors that alter peptide RP-HPLC separation selectivity is critical for successful data collection for the construction of ion libraries. Proteomic researchers have often regarded RP-HPLC as a "black box", while vast amount of research on peptide separation is readily available. In addition to obvious parameters, such as the type of ion-pairing modifier, stationary phase and column temperature, we describe the "mysterious" effects of gradient slope, column size and flow rate on peptide separation selectivity. Retention time variations due to these parameters are governed by the linear solvent strength (LSS) theory on a peptide level by the value of its slope S in the basic LSS equation-a parameter that can be accurately predicted. Thus, the application of shallower gradients, higher flow rates, or smaller columns will each increases the relative retention of peptides with higher S-values (long species with multiple positively charged groups). Simultaneous changes to these parameters that each drive shifts in separation selectivity in the same direction should be avoided. The unification of terminology represents another pressing issue in this field of applied proteomics that should be addressed to facilitate further progress.

Simulation of elution profiles in liquid chromatography⿿I: Gradient elution conditions, and with mismatched injection and mobile phase solvents

High-performance liquid chromatography (HPLC) simulators are effective method development tools. The goal of the present work was to design and implement a simple algorithm for simulation of liquid chromatographic separations that allows for characterization of the effect of injection solvent mismatch and injection solvent volume overload. The simulations yield full analyte profiles during solute migration and at elution, which enable a thorough physical understanding of the effects of method variables on chromatographic performance. The Craig counter-current distribution model (the plate model) is used as the basis for simulation, where a local retention factor is assigned for each spatial and temporal element within the simulation. The algorithm, which is an adaptation of an approach originally described by Czok and Guiochon (Ref. [10]), is sufficiently flexible to allow the use of either linear (e.g., Linear Solvent Strength Theory) or non-linear models of solute retention (e.g., Neue-Kuss (Ref. [36])). In this study, both types of models were used, one for simulating separations of a homologous series of alkylbenzenes, and the other for separations of selected amphetamines. The simulation program was validated first by comparison of simulated retention times and peak widths for five amphetamines to predictions obtained using linear solvent strength (LSS) theory, and to results from experimental separations of these compounds. The simulated retention times for the amphetamines agreed within 0.02% and 2.5% compared to theory and experiment, respectively. Secondly, the program was evaluated for simulating the case where there is a compositional mismatch between the mobile phase at the column inlet and the injection solvent (i.e., the sample matrix). This work involved alkylbenzenes, and retention time and peak width predictions from simulations were within 1.5 and 6.0% of experimental values, respectively, even without correction for extra-column dispersion. The issues of sample/eluent solvent mismatch and solvent volume overload are especially important when considering the challenges of transferring eluent from the first to the second dimension in comprehensive two-dimensional liquid chromatography.

Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification

The untargeted profiling of steroids constitutes a growing research field because of their importance as biomarkers of endocrine disruption. New technologies in analytical chemistry, such as ultra high-pressure liquid chromatography coupled with mass spectrometry (MS), offer the possibility of a fast and sensitive analysis. Nevertheless, difficulties regarding steroid identification are encountered when considering isotopomeric steroids. Thus, the use of retention times is of great help for the unambiguous identification of steroids. In this context, starting from the linear solvent strength (LSS) theory, quantitative structure retention relationship (QSRR) models, based on a dataset composed of 91 endogenous steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were developed to predict retention times of steroid structures in any gradient mode conditions. Satisfactory performance was obtained during nested cross-validation with a predictive ability (Q2) of 0.92. The generalisation ability of the model was further confirmed by an average error of 4.4% in external prediction. This allowed the list of candidates associated with identical monoisotopic masses to be strongly reduced, facilitating definitive steroid identification.

Model-based description of peptide retention on doped reversed-phase media

Reversed-phase (RP) chromatography is one of the main tools for the preparative purification of therapeutic peptides. In previous works [1,2], a new type of RP chromatography, doped reversed-phase chromatography (DRP) was presented. By adding small amounts (up to 15% of the surface ligands) of repulsive ion exchange ligands to a traditional RP material, significant improvements in peptide purification performance were observed, at the same or in similar operating conditions. These improvements included increases in selectivity in diluted conditions (up to twice as high), increases in yield in preparative conditions (up to 20% higher) and in productivity in preparative conditions (up to twice as high), when compared to RP materials [2]. A proper physical model is developed in this work to quantitatively explain and rationalize this behavior. The developed model is then used to correctly fit the retention data of several peptides in different buffering conditions. The increase in selectivity is related to a controlled decrease in free surface area available for adsorption due to the ionic ligands creating a repulsive sphere the analytes cannot enter. This decrease in adsorption surface is calculated using Debye–Hückel theory, and in combination with linear solvent strength theory, allows for the quantitative description of peptide retention on DRP media.

Instrument parameters controlling retention precision in gradient elution reversed-phase liquid chromatography

The precision of retention time in RPLC is important for compound identification, for setting peak integration time windows and in fundamental studies of retention. In this work, we studied the effect of temperature (T), initial (ϕo) and final mobile phase (ϕf) composition, gradient time (tG), and flow rate (F) on the retention time precision under gradient elution conditions for various types of low MW solutes. We determined the retention factor in pure water (k′w) and the solute-dependent solvent strength (S) parameters of Snyder's linear solvent strength theory (LSST) as a function of temperature for three different groups of solutes. The effect of small changes in the chromatographic variables (T, ϕo, ϕf, tG and F) by use of the LSST gradient retention equation were estimated. Peaks at different positions in the chromatogram have different sensitivities to changes in these instrument parameters. In general, absolute fluctuations in retention time are larger at longer gradient times. Drugs showed less sensitivity to changes in temperature compared to relatively less polar solutes, non-ionogenic solutes. Surprisingly we observed that fluctuations in temperature, mobile phase composition and flow rate had less effect on retention time under gradient conditions as compared to isocratic conditions. Overall temperature and the initial mobile phase composition are the most important variables affecting retention reproducibility in gradient elution chromatography.

Investigating retention characteristics of a mixed-mode stationary phase and the enhancement of monolith selectivity for high-performance liquid chromatography.

The synthesis and chromatographic behavior of an analytical size mixed-mode bonded silica monolith was investigated. The monolith was functionalized by an in situ modification process of a bare silica rod with chloro(3-cyanopropyl)dimethyl silane and chlorodimethyl propyl phenyl silane solutions. These ligands were selected in order to combine both resonance and nonresonance π-type bonding within a single separation environment. Selectivity studies were undertaken using n-alkyl benzenes and polycyclic aromatic hydrocarbons in aqueous methanol and acetonitrile mobile phases to assess the methylene and aromatic selectivities of the column. The results fit with the linear solvent strength theory suggesting excellent selectivity of the column was achieved. Comparison studies were performed on monolithic columns that were functionalized separately with cyano and phenyl ligands, suggesting highly conjugated molecules were able to successfully exploit both of the π-type selectivities afforded by the two different ligands on the mixed-mode column.

Simultaneous Determination of Some Analgesic Drugs Using Mixed Mode Stationary Phase

A new HPLC method based on a mixed mode stationary phase Hypersil Duet C18/SAX was developed and applied for the simultaneous determination of acetaminophen, acetylsalicylic acid and codeine. Parameters, such as the composition of the mobile phase, the nature of the organic modifier, the buffer type and the flow rate were investigated to optimize the separation. The results obtained show that the new HPLC method is rapid, highly efficient and selective. The studied compounds are separated in 10 min, by means of a mobile phase containing phosphate buffer (pH 7.50) and methanol (65:35 v v−1). The retention mechanisms of each analyte were investigated using both the linear solvent strength theory and stoichiometric displacement model. The method was fully validated and showed good linearity for each compound for a concentration ranging between 2.0 and 40 μg mL−1. The limits of detection and quantification were determined and they are lower than 0.1 μg mL−1. The precision (RSD) of the method does not exceed 2 % for all studied compounds. The method was successfully applied for the assay of acetaminophen, acetylsalicylic acid and codeine in pharmaceutical formulations.

Predicting Retention Time Shifts Associated with Variation of the Gradient Slope in Peptide RP-HPLC

We have developed a sequence-specific model for predicting slopes (S) in the fundamental equation of linear solvent strength theory for the reversed-phase HPLC separation of tryptic peptides detected in a typical bottom-up-proteomics experiment. These slopes control the variation in the separation selectivity observed when the physical parameters of chromatographic separation, such as gradient slope, flow rate, and column size are altered. For example, with the use of an arbitrarily chosen set of tryptic peptides with a 4-times difference in the gradient slope between two experiments, the R(2)-value of correlation between the observed retention times of identical species decreases to ~0.993-0.996 (compared to a theoretical value of ~1.00). The observed retention time shifts associated with variations of the gradient slope can be predicted a priori using the approach described here. The proposed model is based on our findings for a set of synthetic species (Vu, H.; Spicer, V.; Gotfrid, A.; Krokhin, O. V. J. Chromatogr., A, 2010, 1217, 489-497), which postulate that slopes S can be predicted taking into account simultaneously peptide length, charge, and hydrophobicity. Here we extend this approach using an extensive set of real tryptic peptides. We developed the procedure to accurately measure S-values in nano-RP HPLC MS experiments and introduced sequence-specific corrections for a more accurate prediction of the slopes S. A correlation of ~0.95 R(2)-value between the predicted and experimental S-values was demonstrated. Predicting S-values and calculating the expected retention time shifts when the physical parameters of separation like gradient slope are altered will facilitate a more accurate application of peptide retention prediction protocols, aid in the transfer of scheduled MRM (SRM) procedures between LC systems, and increase the efficiency of interlaboratory data collection and comparison.

Rapid HPLC Method Development of Polynuclear Aromatic Hydrocarbons Separation Based on Quantitative Structure Retention Relationships

Previous studies demonstrated that quantitative structure retention relationships (QSRR) combined with the linear solvent strength (LSS) model allow for prediction of gradient reversed phase high performance liquid chromatography (HPLC) retention time for any analyte of a known molecular structure under defined HPLC conditions. The QSRR model derived at the selected gradient time was tested at the same gradient time. Presently, in the first step, experimental retention data for model sets of just 5 analytes were used to derive appropriate QSRR models at two gradient times. Additionally, a new molecular modeling approach based on a more accurate ab initio method was here proposed. Those QSRR models were used to further predict gradient retention times for sets of 16 test analytes belonging to polynuclear aromatic hydrocarbons (PAHs), at two selected gradient times. Then, applying linear solvent strength (LSS) theory, only predicted retention times for PAHs were used to find the optimal gradient conditions to separate them. Satisfactory predictions of gradient retention times for PAHs were obtained. Contrary to the previous achievements, the proposed QSRR provides the chance to predict retention of PAHs with the appropriate selectivity achieving the same sequence of analytes eluted in the experiment and during the simulation performed on the computer screen.

Predictions of Reversed-Phase Gradient Elution LC Separations Supported by QSRR

Previous studies demonstrated that quantitative structure-retention relationships (QSRR) combined with the linear solvent strength (LSS) model allow for prediction of gradient reversed-phase liquid chromatography retention time for any analyte of a known molecular structure under defined LC conditions. A QSRR model derived at the selected gradient time and at the same gradient time was tested. The aim the present study was to evaluate the accuracy of QSRR predictions used during the predictions of LC gradient retention times with variable gradient times. For this purpose, predictions of retention times at two gradient times were used to find the optimal, different gradient times. In the first step, experimental retention data for the model set of analytes were used to derive appropriate QSRR models at two gradient times. These QSRR models were further used to predict gradient retention times for another set of testing analytes at the two selected above gradient times. Then, applying linear solvent-strength (LSS) theory, the predicted retention times for test analytes were used to find other optimal gradient times for those analytes. Satisfactory predictions of gradient retention times for test analytes were obtained at gradient times different from those applied for model analytes.

Aligning LC peaks by converting gradient retention times to retention index of peptides in proteomic experiments

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful tool in proteomics studies, but when peptide retention information is used for identification purposes, it remains challenging to compare multiple LC-MS/MS runs or to match observed and predicted retention times, because small changes of LC conditions unavoidably lead to variability in retention times. In addition, non-contiguous retention data obtained with different LC-MS instruments or in different laboratories must be aligned to confirm and utilize rapidly accumulating published proteomics data. We have developed a new alignment method for peptide retention times based on linear solvent strength (LSS) theory. We found that log k(0) (logarithm of retention factor for a given organic solvent) in the LSS theory can be utilized as a 'universal' retention index of peptides (RIP) that is independent of LC gradients, and depends solely on the constituents of the mobile phase and the stationary phases. We introduced a machine learning-based scheme to optimize the conversion function of gradient retention times (t(g)) to log k(0). Using the optimized function, t(g) values obtained with different LC-MS systems can be directly compared with each other on the RIP scale. In an examination of Arabidopsis proteomic data, the vast majority of retention time variability was removed, and five datasets obtained with various LC-MS systems were successfully aligned on the RIP scale.

Estimating Partition Coefficients for Fuel−Water Systems: Developing Linear Solvation Energy Relationships Using Linear Solvent Strength Theory To Handle Mixtures

In many environmental transport problems, organic solutes partition between immiscible phases that consist of liquid mixtures. To estimate the corresponding partition coefficients, we evaluated the efficacy of combining linear solvation energy relationships (LSERs) developed for pure 1:1 systems via application of linear solvent strength theory. In this way, existing LSERs could be extended to treat solute partitioning from gasoline, diesel fuel, and similar mixtures into contacting aqueous mixtures. Unlike other approaches, this method allowed prediction of liquid-liquid partition coefficients in a variety of fuel-water systems for a broad range of dilute solutes. When applied to 37 polar and nonpolar solutes partitioning between an aqueous mixture and 12 different fuel-like mixtures (many including oxygenates), the root-mean-squared error was a factor of approximately 2.5 in the partition coefficient. This was considerably more accurate than application of Raoult's law for the same set of systems. Regulators and scientists could use this method to estimate fuel-water partition coefficients of novel additives in future fuel formulations and thereby provide key inputs for environmental transport assessments of these compounds.

Estimation of significant solvent concentration ranges and its application to the enhancement of the accuracy of gradient predictions

The solvent concentration range actually useful for gradient predictions is significantly narrower than the total range scanned in a gradient run. This range, called “solvent informative range” (SIR), if known with the highest accuracy, allows to predict gradient retention times ( t g) with minimal error. The small size of the SIR supports the application of the linear solvent strength theory (LSST). Furthermore, LSST allows a closed-form solution to the integral required to predict gradient retention times, which eliminates numerical integration, needed with other retention models. A methodology that calculates the SIR by applying error analysis, and uses it to improve the accuracy in the prediction of t g from isocratic experiments, is proposed. The importance of those mobile-phase compositions that do not contribute significantly to the prediction of t g is selectively attenuated within the prediction algorithm, relying the predictions more heavily on the SIR. As a result, t g was found to be predicted with similar accuracy using isocratic training data with regard to predictions based on gradient training data. The approach is useful for all situations where the chromatographer is able to provide predictions of retention at constant solvent concentration, and wish to predict the retention in gradient mode.

Possibility of Calculating System Maps Using Gradient Elution Reversed-Phase Liquid Chromatography

Gradient elution is explored as a rapid method for calculating system constants of the solvation parameter model for reversed-phase liquid chromatography. Linear solvent-strength theory is used to relate gradient retention times to system properties and to predict isocratic retention factors for the calculation of system maps. The combination of linear solvent strength theory and the solvation parameter model provides only a poor model for gradient retention times. A significant source of uncertainty in these models is the variation of the solute-dependent ratio (column hold-up time / gradient steepness parameter), and to a lesser extent, non-linearity in the plots of retention factors (log k) against mobile phase composition. Reasonable agreement between gradient predicted and experimental isocaratic retention factors was obtained when plots of the isocratic retention factors (log k) against mobile phase composition are linear. But even in this case, small systematic differences in the calculated system constants with increasing volume fraction of the strong solvent were observed. When plots of the isocratic retention factors against mobile phase composition are non-linear, the retention factors estimated by linear solvent strength theory yield system constants that contain significant error, producing fictitious system maps. Consequently, none of the methods studied is recommended for the fast and reliable calculation of system maps or solute descriptors by gradient elution reversed-phase liquid chromatography.

Predictive approaches to gradient retention based on analyte structural descriptors from calculation chemistry

Quantitative structure retention relationships (QSRRs) were applied to predict reversed-phase HPLC gradient retention. The performance of the recently recommended QSRR models was compared. One tested model is based on structural descriptors from molecular modeling. To quantitatively characterize the structure of analytes the following three structural descriptors are employed: total dipole moment, electron excess charge of the most negatively charged atom and water-accessible molecular surface area. Reliability of the resulting gradient retention time predictions was compared to that provided by the models relating retention to the theoretically calculated logarithm of n-octanol–water partition coefficient, log P. The requested values of log P were obtained using three commercially available softwares. The predicted retention parameters were compared for a series of structurally diversified small molecular mass analytes. It has been demonstrated that the retention predictions from both the molecular modeling descriptors-based and the log P-based QSRR are characterized by similar errors. It has been hypothesized that the optimization of separation based on QSRRs and the linear solvent strength theory might be of practical analytical value.