Comparative study of the linear solvation energy relationship, linear solvent strength theory, and typical-conditions model for retention prediction in reversed-phase liquid chromatography

Improved hydrophobic subtraction model of reversed-phase liquid chromatography selectivity based on a large dataset with a focus on isomer selectivity

Reversed-phase (RP) liquid chromatography is an important tool for the characterization of materials and products in the pharmaceutical industry. Method development is still challenging in this application space, particularly when dealing with closely-related compounds. Models of chromatographic selectivity are useful for predicting which columns out of the hundreds that are available are likely to have very similar, or different, selectivity for the application at hand. The hydrophobic subtraction model (HSM1) has been widely employed for this purpose; the column database for this model currently stands at 750 columns. In previous work we explored a refinement of the original HSM1 (HSM2) and found that increasing the size of the dataset used to train the model dramatically reduced the number of gross errors in predictions of selectivity made using the model. In this paper we describe further work in this direction (HSM3), this time based on a much larger solute set (1014 solute/stationary phase combinations) containing selectivities for compounds covering a broader range of physicochemical properties compared to HSM1. The molecular weight range was doubled, and the range of the logarithm of the octanol/water partition coefficients was increased slightly. The number of active pharmaceutical ingredients and related synthetic intermediates and impurities was increased from four to 28, and ten pairs of closely related structures (e.g., geometric and cis-/trans- isomers) were included. The HSM3 model is based on retention measurements for 75 compounds using 13 RP stationary phases and a mobile phase of 40/60 acetonitrile/25 mM ammonium formate buffer at pH 3.2. This data-driven model produced predictions of ln α (chromatographic selectivity using ethylbenzene as the reference compound) with average absolute errors of approximately 0.033, which corresponds to errors in α of about 3 %. In some cases, the prediction of the trans-/cis- selectivities for positional and geometric isomers was relatively accurate, and the driving forces for the observed selectivity could be inferred by examination of the relative magnitudes of the terms in the HSM3 model. For some geometric isomer pairs the interactions mainly responsible for the observed selectivities could not be rationalized due to large uncertainties for particular terms in the model. This suggests that more work is needed in the future to explore other HSM-type models and continue expanding the training dataset in order to continue improving the predictive accuracy of these models. Additionally, we release with this paper a much larger data set (43,329 total retention measurements) at multiple mobile phase compositions, to enable other researchers to pursue their own lines of inquiry related to RP selectivity.

Perspective on the Future Approaches to Predict Retention in Liquid Chromatography

The demand for rapid column screening, computer-assisted method development and method transfer, and unambiguous compound identification by LC/MS analyses has pushed analysts to adopt experimental protocols and software for the accurate prediction of the retention time in liquid chromatography (LC). This Perspective discusses the classical approaches used to predict retention times in LC over the last three decades and proposes future requirements to increase their accuracy. First, inverse methods for retention prediction are essentially applied during screening and gradient method optimization: a minimum number of experiments or design of experiments (DoE) is run to train and calibrate a model (either purely statistical or based on the principles and fundamentals of liquid chromatography) by a mere fitting process. They do not require the accurate knowledge of the true column hold-up volume V₀, system dwell volume V_dwell (in gradient elution), and the retention behavior (k versus the content of strong solvent φ, temperature T, pH, and ionic strength I) of the analytes. Their relative accuracy is often excellent below a few percent. Statistical methods are expected to be the most attractive to handle very complex retention behavior such as in mixed-mode chromatography (MMC). Fundamentally correct retention models accounting for the simultaneous impact of φ, I, pH, and T in MMC are needed for method development based on chromatography principles. Second, direct methods for retention prediction are ideally suited for accurate method transfer from one column/system configuration to another: these quality by design (QbD) methods are based on the fundamentals and principles of solid-liquid adsorption and gradient chromatography. No model calibration is necessary; however, they require universal conventions for the accurate determination of true retention factors (for 1 < k < 30) as a function of the experimental variables (φ, T, pH, and I) and of the true column/system parameters (V₀, V_dwell, dispersion volume, σ, and relaxation volume, τ, of the programmed gradient profile at the column inlet and gradient distortion at the column outlet). Finally, when the molecular structure of the analytes is either known or assumed, retention prediction has essentially been made on the basis of statistical approaches such as the linear solvation energy relationships (LSERs) and the quantitative structure retention relationships (QSRRs): their ability to accurately predict the retention remains limited within 10-30%. They have been combined with molecular similarity approaches (where the retention model is calibrated with compounds having structures similar to that of the targeted analytes) and artificial intelligence algorithms to further improve their accuracy below 10%. In this Perspective, it is proposed to adopt a more rigorous and fundamental approach by considering the very details of the solid-liquid adsorption process: Monte Carlo (MC) or molecular dynamics (MD) simulations are promising tools to explain and interpret retention data that are too complex to be described by either empirical or statistical retention models.

Development of a simple liquid chromatography method for dissolution testing of low dose desogestrel and ethinylestradiol tablets

Accurate quantitation of low dose, multi-active dissolution samples poses unique challenges in the pharmaceutical industry, often resulting in separate HPLC methods for each active or the use of multiple detectors for increased sensitivity. In this study, we report a fast, isocratic HPLC method utilizing only UV detection for dissolution testing of low dose desogestrel and ethinylestradiol tablets. Rapid separation is completed in 5 min using isocratic elution at a flow rate of 0.45 mL/min, with a column temperature at 30 °C, an injection volume of 50 μL and the detection wavelength at 200 nm. After extensive method development and optimization, the cyano stationary phase was used to overcome the large difference in hydrophobicity for desogestrel and ethinylestradiol, providing balanced retention for both analytes under isocratic elution. Chromatography modeling software was used to provide a rapid analysis of multiple columns and chromatography conditions. The optimized method boasts fast and efficient separation through use of a short, small I.D. column and a large injection volume of dissolution solution to achieve high sensitivity. The stable baseline from an isocratic separation allows low detection wavelengths to be used, resulting in accurate and precise quantitation of both desogestrel and ethinylestradiol. The method has been successfully validated for specificity, linearity, accuracy and precision in the range of 75 - 600 ng/mL for desogestrel and 10 - 80 ng/mL for ethinylestradiol using both HPLC and UHPLC systems. The method robustness was characterized using a design of experiment approach, and the operational design region of the method was established.

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.

Improved high sensitivity analysis of polyphenols and their metabolites by nano-liquid chromatography-mass spectrometry

This study was conducted to assess the value of a high resolution, high mass accuracy time-of-flight analyzer in combination with nanoliquid chromatography for the analysis of polyphenols and their metabolites. The goal was to create a method that utilizes small volumes of biological fluids and provides a significant improvement in sensitivity compared with existing methods. Accordingly, nanoLC-MS and nanoLC-pseudo-multiple reaction monitoring (MRM) methods were developed that had a lower limit of quantification of 0.5 nM for several polyphenols and were linear over 2-3 orders of magnitude (R(2)>0.999). Using urine samples, the ability to observe and quantify polyphenols in such a complex biological fluid depended on much narrower mass windows (0.050 amu or less) on a TOF analyzer than those used on a quadrupole analyzer (0.7 amu). Although a greater selectivity was possible with the low mass resolution of a triple quadrupole instrument using the MRM approach, for the daidzein metabolite O-DMA, a chromatographically resolvable second peak could only be substantially reduced by using a 0.01 amu mass window. The advantage of a TOF analyzer for product ion data is that the whole MSMS spectrum is collected at high mass accuracy and MRM experiments are conducted in silico after the analysis.

Development and validation of an ultra high-performance liquid chromatographic method for the determination of a diastereomeric impurity in (+)-pinoresinol diglucoside chemical reference substance

(+)-Pinoresinol 4,4'-di-O-beta-D-glucopyranoside ((+)-PDG) is one of the major lignans with various pharmacological activities which could be isolated from Duzhong and other plant species. In this study, a diastereomeric impurity, (-)-pinoresinol 4,4'-di-O-beta-D-glucopyranoside ((-)-PDG), the main impurity was identified in (+)-PDG chemical reference substance (CRS) and a reliable chromatographic method for rapid purity determination of (+)-PDG CRS was firstly developed. The optimal chromatographic condition was found to be using ACN/1,4-dioxane-water (2.5:6:91.5, v/v/v) as mobile phase on a Waters Acquity UPLC HSS T3 column (2.1 mm x 100 mm, 1.8 microm) with column temperature of 37 degrees C. The method was validated and applied to determine the chromatographic purity of five (+)-PDG CRS samples. The content of (-)-PDG in four commercial (+)-PDG CRS was 8.47-20.30%, whereas no (-)-PDG was detected in our in-house prepared (+)-PDG CRS in which purity was confirmed to be 99.80%. The above results confirmed that this method is fast and highly efficient for purity determination of the (+)-PDG CRS.

Metabolism and bioavailability of flavonoids in chemoprevention: current analytical strategies and future prospectus

Flavonoids are structurally diverse and among the most ubiquitous groups of dietary polyphenols distributed in various fruits and vegetables. Many have been proposed to be bioactive compounds in the diet that are responsible for lowering the risk of cancer and have been used in chemoprevention studies using animal models of this disease. As for any xenobiotic, to evaluate the potential risks and benefits of bioflavonoids to human health, an understanding of the physiological behavior of these compounds following oral ingestion is needed as well as their absorption, distribution, metabolism, and excretion (ADME). The study on metabolism and bioavailability is very important in defining the pharmacological and toxicological profile of these compounds. Due to great structural diversity among flavonoids, these profiles differ greatly from one compound to another, so that the most abundant polyphenols in our diet are not necessarily the ones that reach target tissues. Therefore, careful analysis of flavonoids and their metabolites in biological systems is critical. Mass spectrometry in various combinations with chromatographic methods has been a mainstay in applications that involve profiling and quantification of metabolites in complex biological samples. Because of its speed, sensitivity and specificity, liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the technology of choice for sample analysis. This review describes the chemistry of polyphenols and flavonoids, their ADME, and the various mass spectrometry-based strategies used in the analysis of flavonoids, including future trends in this field.

Investigation of retention behaviour of non-steroidal anti-inflammatory drugs in high-performance liquid chromatography by using quantitative structure–retention relationships

In this paper, a quantitative structure-retention relationship (QSRR) method is employed to model the retention behaviour in reversed-phase high-performance liquid chromatography of arylpropionic acid derivatives, largely used non-steroidal anti-inflammatory drugs (NSAIDs). Computed molecular descriptors and the organic modifier content in the mobile phase are associated into a comprehensive model to describe the effect of both solute structure and eluent composition on the isocratic retention of these drugs in water-acetonitrile mobile phases. Multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is used to extract from a large set of computed 3D descriptors an optimal subset. Based on GA-MLR analysis, a five-dimensional QSRR model is identified. All the four selected molecular descriptors belong to the category of GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors. The related multilinear model exhibits a quite good fitting and predictive performance. This model is further improved using an artificial neural network (ANN) learned by error back-propagation. Finally, the ANN-based model displays a remarkably better performance as compared with the MLR counterpart and, based on external validation, is able to predict with good accuracy the behaviour of unknown arylpropionic NSAIDs in the range of mobile phase composition of analytical interest (between 35 and 75% acetonitrile (v/v)).

Effect of the surface coverage of endcapped C18-silica on the excess adsorption isotherms of commonly used organic solvents from water in reversed phase liquid chromatography

The excess adsorption isotherms of methanol, ethanol, 2-propanol, acetonitrile, and tetrahydrofuran from water were measured on five different silica-based packing materials by the minor disturbance method. These materials were prepared with the same lot of 5-microm particles (average pore size 90 A), all endcapped with trimethylchlorosilane (TMS), and bonded to octadecyl chains with different surface coverages (0, 0.42, 1.01, 2.03, and 3.15 micromol/m2). The relative adsorption of one eluent by respect to a second one informs on the heterogeneity of the material (alkyl-bonded and bare silica regions) and on the accessibility of the unreacted silanol groups to the mobile phase. It is shown that the total surface area of the adsorbent decreases with increasing degree of surface coverage with octadecyl chains and that the relative surface area of the regions occupied by accessible silanol groups to the regions occupied by alkyl-bonded groups decreases. For the five columns, an average of 10% of the adsorbent surface area is covered of bare silica accessible to the liquid phase, with a minimum of 5% with tetrahydrofuran and a maximum of 12% with ethanol or 2-propanol. Increasing the surface coverage by the C18 chains causes a significant increase of the attraction potential of the hydrophobic surface toward the organic solvent. This result is confirmed by the increase of the number of adsorbate monolayers with increasing bonding density of the octadecyl chains. This number is twice larger for the 315C18 column than for the C1 column.

Comparative study of solvation parameter models accounting the effects of mobile phase composition in reversed-phase liquid chromatography

Solvation parameter models relate linearly compound properties with five fundamental solute descriptors (excess molar refraction, dipolarity/polarizability, effective hydrogen-bond acidity and basicity, and McGowan volume). These models are widely used, due to the availability of protocols to obtain the descriptors, good performance, and general applicability. Several approaches to predict retention in reversed-phase liquid chromatography (RPLC) as a function of these descriptors and mobile phase composition are compared, assaying the performance with a set of 146 organic compounds of diverse nature, eluted with acetonitrile and methanol. The approaches are classified in two groups: those that only allow predictions of retention for the mobile phases used to build the models, and those valid at any other mobile phase composition. The first group includes the use of ratios between the regressed coefficients of the solvation models that are assumed to be characteristic for a column/solvent system, and the application of offsets to transfer the retention from a reference mobile phase to any other. Maximal accuracy in predictions corresponded, however, to the approaches in the second group, which were based on models that describe the retention as a function of mobile phase composition (expressed as the solvent volume fraction or a normalised polarity measurement), where the coefficients were made dependent on the solvent descriptors. The study revealed the properties that influence the retention and distinguish the particular behaviour of acetonitrile and methanol in RPLC.

Modelling of retention of pesticides in reversed-phase high-performance liquid chromatography: Quantitative structure-retention relationships based on solute quantum-chemical descriptors and experimental (solvatochromic and spin-probe) mobile phase descriptors

A quantitative structure-retention relationship (QSRR) analysis based on multilinear regression (MLR) and artificial neural networks (ANNs) is carried out to model the combined effect of solute structure and eluent composition on the retention behaviour of pesticides in isocratic reversed-phase high-performance liquid chromatography (RP-HPLC). The octanol-water partition coefficient and four quantum chemical descriptors (the total dipole moment, the mean polarizability, the anisotropy of the polarizability and a descriptor of hydrogen-bonding based on the atomic charges on acidic and basic chemical functionalities) are considered as solute descriptors. In order to identify suitable mobile phase descriptors, encoding composition-dependent properties of both methanol- and acetonitrile-containing mobile phases, the Kamlet-Taft solvatochromic parameters (polarity-dipolarity, hydrogen-bond acidity and hydrogen-bond basicity, pi*, alpha and beta, respectively) and the 14N hyperfine-splitting constant (aN) of a spin-probe dissolved in the eluent are examined. A satisfactory description of mobile phase properties influencing the solute retention is provided by aN and beta or alternatively pi* and beta. The two seven-parameter models resulting from combination of aN and beta, or pi* and beta, with the solute descriptors were tested on a set of 26 pesticides representative of 10 different chemical classes in a wide range of mobile phase composition (30-60% (v/v) water-methanol and 30-70% (v/v) water-acetonitrile). Within the explored experimental range, the acidity of the eluent, as quantified by alpha, is almost constant, and this parameter is in fact irrelevant. The results reveal that aN and pi*, that can be considered as interchangeable mobile phase descriptors, are the most influent variables in the respective models. The predictive ability of the proposed models, as tested on an external data set, is quite good (Q2 close to 0.94) when a MLR approach is used, but the modelling capability can be further improved using an artificial neural network.

Heterogeneity of adsorption mechanisms in chiral normal-phase liquid chromatography

The adsorption equilibria of two commonly employed strong mobile phase modifiers, ethyl acetate and 2-propanol, on a polysaccharide-based chiral stationary phase have been studied by modeling nonlinear perturbation peaks measured after equilibration of the column with hexane (the weak component of the binary mixture). The investigation of adsorption processes from dilute solutions for species that are strongly retained on the stationary phase could be performed by this approach. On the opposite, limits of traditional linear perturbation technique for isotherm determination, in presence of strong interactions, have been evidenced. Alcohol adsorption has been modeled by a single Langmuir isotherm, while the ester has required a BiLangmuir model. These findings have found to be in a semi-quantitative agreement with available spectroscopic data about 2-propanol and ethyl acetate adsorption on thin silica sol-gel films in contact with a weak solvent. Experimental features observed for racemic separation on polysaccharide-based chiral stationary phases, such as the dependence of the separation factor on the amount and type of the employed additive, have been explained in light of these measurements.

Selectivity in reversed-phase separations

The selectivity difference between 15 different stationary phases was measured using a large number of analytes at 2 or 3 different pH values (3, 7 and 10) with acetonitrile and methanol as the mobile phase modifiers. The packings discussed include standard C(8) and C(18) packings, packings with embedded polar groups, a phenyl packing, a pentafluoro-phenyl packing, an adamantylethyl packing and others. The major selectivity differences observed are discussed in detail. Specific effects such as pi-pi interactions on phenyl packings or hydrogen-bond interactions on phases with embedded polar groups are confirmed.

Heterogeneity of the Adsorption Mechanism of Low Molecular Weight Compounds in Reversed-Phase Liquid Chromatography

The retention mechanism in RPLC mode was investigated based on the acquisition of adsorption isotherm data by frontal analysis measurements and their modeling. This work is a review of the results of four years of adsorption data measurements. The data were acquired on a wide variety of brands of C18-silica columns (from Akzo Nobel, Bishoff, Hypersil, Merck, Phenomenex, Supelco, Vydac, and Waters) with several low molecular weight compounds such as phenol (94 g/mol), caffeine (194 g/mol), tryptophan (204 g/mol), sodium 2-naphthalenesulfonate (235 g/mol), and propranololium chloride (295 g/mol). The mobile phase was a mixture of methanol and water at variable composition. The adsorption isotherms were all convex upward (langmuirian), and the degree of heterogeneity of the adsorption system was determined from the calculation of the adsorption energy distribution using the expectation-maximization method. The adsorption isotherm parameters (number of types of adsorption sites, surface concentration of each type of site, and difference between the adsorption energies E(i) - E(j) on sites i and j), obtained from the mathematical fit of the adsorption data to the appropriate multi-Langmuir adsorption isotherm model, were analyzed and compared. The results allow the drawing of general conclusions regarding the relationships between the size of the analyte and the adsorption properties (saturation capacities, adsorption energies) characterizing the retention mechanism in RPLC mode for neutral, anionic, and cationic compounds.

Adsorption mechanism in reversed-phase liquid chromatography

The effect of the bonding density of the octadecyl chains onto the same silica on the adsorption and retention properties of low molecular weight compounds (phenol, caffeine, and sodium 2-naphthalene sulfonate) was investigated. The same mobile phase (methanol:water, 20:80, v/v) and temperature (T = 298 K) were applied and two duplicate columns (A and B) from each batch of packing material (neat silica, simply endcapped or C1 phase, 0.42, 1.01, 2.03, and 3.15 micromol/m2 of C18 alkyl chains) were tested. Adsorption data of the three compounds were acquired by frontal analysis (FA) and the adsorption energy distributions (AEDs) were calculated using the expectation-maximization method. Results confirmed earlier findings in linear chromatography of a retention maximum at an intermediate bonding density. From a general point of view, the saturation capacity of the adsorbent tends to decrease with increasing bonding density, due to the vanishing space intercalated between the C18 bonded chains and to the decrease of the specific surface area of the stationary phase. The equilibrium constants are maximum for an intermediary bonding density (approximately 2 micromol/m2). An enthalpy-entropy compensation was found for the thermodynamic parameters of the isotherm data. Weak equilibrium constants (small deltaH) and high saturation capacities (large deltaS) were observed at low bonding densities, higher equilibrium constants and lower saturation capacities at high bonding densities, the combinations leading to similar apparent retention in RPLC. The use of a low surface coverage column is recommended for preparative purposes.

Critical contribution of nonlinear chromatography to the understanding of retention mechanism in reversed-phase liquid chromatography

The retention of most compounds in RPLC proceeds through a combination of several independent mechanisms. We review a series of recent studies made on the behavior of several commercial C18-bonded stationary phases and of the complex, mixed retention mechanisms that were observed in RPLC. These studies are essentially based on the acquisition of adsorption isotherm data, on the modeling, and on the interpretation of these data. Because linear chromatography deals only with the initial slope of the global, overall, or apparent isotherm, it is unable fully to describe the complete adsorption mechanism. It cannot even afford clues as to the existence of several overlaid retention mechanisms. More specifically, it cannot account for the consequences of the surface heterogeneity of the packing material. The acquisition of equilibrium data in a wide concentration range is required for this purpose. Frontal analysis (FA) of selected probes gives data that can be modeled into equilibrium isotherms of these probes and that can also be used to calculate their adsorption or affinity energy distribution (AED). The combination of these data, the detailed study of the best constants of the isotherm model, the determination of the influence of experimental parameters (e.g., buffer pH and pI, temperature) on the isotherm constants provide important clues regarding the heterogeneity of the adsorbent surface and the main properties of the adsorption mechanisms. The comparison of similar data obtained for the adsorption of neutral and ionizable compounds, treated with the same approach, and the investigation of the influence on the thermodynamics of phase equilibrium of the experimental conditions (temperature, average pressure, mobile phase composition, nature of the organic modifier, and, for ionizable compounds, of the ionic strength, the nature, the concentration of the buffer, and its pH) brings further information. This review provides original conclusions regarding retention mechanisms in RPLC.

Optimization of gradient elution conditions in multicomponent preparative liquid chromatography

Gradient elution is widely applied in analytical chromatography to reduce the separation time and/or to improve the selectivity. Increasingly the potential of modulating the solvent strength during gradient operation is exploited in preparative liquid chromatography. The purpose of this paper is to investigate theoretically the effect of optimizing free parameters available in gradient chromatography (extents and shapes of gradients) on the productivity of isolating a target component in a multicomponent mixture. An equilibrium stage model was used to quantify and compare different modes of operation (isocratic and various variants of gradient elution). By combining experimental design and artificial neural network concepts, optimal conditions were identified for the production of the second eluting component in a ternary mixture. The strong impact of the shape of gradients on process performance is elucidated.

Monoliths for microfluidic devices in proteomics

We report here on the preparation of monolithic capillary columns in view to their integration in a microsystem for on-chip sample preparation before their on-line analysis by electrospray and mass spectrometry (ESI-MS). These monolithic columns are based on polymer materials and consist of reverse phases for peptide separation and/or desalting. They were prepared using lauryl methacrylate (LMA), ethylene dimethacrylate (EDMA) as well as a suitable porogenic mixture composed of cyclohexanol and ethylene glycol. The resulting stationary phases present thus a C12-functionality. The LMA-based columns were first prepared in a capillary format using capillary tubing of 75 microm i.d. and tested in nanoLC-MS experiments for the separation of a commercial Cytochrome C digest composed of 12 peptidic fragments whose isoelectric point values and hydrophobic character cover a wide range. The LMA-based columns were capable of separating the peptidic fragments and their performances were seen to be similar as those of standard commercial columns dedicated to proteomic purposes with calculated separation efficiencies up to 145 x 10(3) plates/m. Monolithic LMA-based phases were then successfully polymerized in microchannels fabricated using the negative photoresist SU-8. After the polymerization, the systems were seen to withstand the pressures applied during the nanoLC-MS separation tests that were carried out in the same conditions as for the monolithic capillary columns. The pressure drop during these tests of the in-microchannel monoliths was as high as 50 bar; however, the separation was not as good as for a capillary format which could be accounted for by the monolith dimensions.

Comparison of methods for characterization of reversed-phase liquid chromatographic selectivity

The goal of the present work is to obtain a better understanding of the chemical factors affecting liquid chromatographic retention. One of the most commonly used formats for liquid chromatographic separations is based on a nonpolar stationary phase, typically an octadecyl-derivatized silica material. A wide variety of these reversed-phase columns are commercially available that differ significantly in their chromatographic retention and selectivity. We seek to quantitatively characterize these differences. Retention data for a range of compounds with many diverse characteristics have been measured on several different octadecyl silica columns (J. Chromatogr. A, submitted for publication). Principal components analysis is used to characterize the different properties of these stationary phases and predict retention factors. The key set factor analysis method and the typical solute method are used in conjunction with the principal components analysis to identify small subsets of solutes that can be used to quantitatively describe the retention of a broad range of compounds. In addition, a quantitative comparison to alternative data analysis methods is made, including linear solvation energy relationships and an iterative subtraction method based on linear regression techniques. Although many earlier studies have reported the application of these methods, this study is the first to make a quantitative comparison of these methods using a highly precise and structurally variable set of test compounds.