Prediction Of Retention Times Research Articles

Prediction of High Performance Liquid Chromatography Retention Time for Some Organic Compounds Based on Ab initio QSPR Study

Analysis of the quantitative relationship between structure and characteristics of 18 polyaromatic hydrocarbons has been done by quantum Ab initio Quantitative Structure-Property Relationship (QSPR) study at Hartree Fock level of theory. Moment dipole was used as the quantum chemical descriptors, whereas molecular weight and number of rings were applied for constitutional descriptor and the valence connectivity index as steric descriptors. The compound’s electronic structure was studied by molecular modeling and retention time (Tr) data were obtained from the literature. Multi-linear regression analysis has been performed by randomly splitting the initial data set into on fitting data set and a test data set. The best result provided by QSPR analysis is the following model equation:
 log tR = 1.276 + 0.016MW+0.323Rc-0.423χ1-0.147χ2 with n =18 r=0.917 r2 =0.841 SE=0.182 Fcalc/Ftable = 5.408.
 The retention times of PAH compounds with two and three rings were successfully predicted by QSPR models.

QSPR Prediction of Retention Times of Methylxanthines and Cotinine by Bioplastic Evolution

High-performance liquid-chromatographic retention times of methylxanthines and cotinine in human plasma and urine are modelled by structure–property relationships. Bioplastic evolution is an evolutionary perspective conjugating the effect of acquired characters, and relations that emerge among the principles of evolutionary indeterminacy, morphological determination and natural selection. It is applied to design co-ordination index, which is used to characterize retentions of methylxanthines, etc. Parameters used to calculate co-ordination index are formation enthalpy, molecular weight and surface area. Morphological and co-ordination indices provide strong correlations. Effect of different types of features like thermodynamic, fractal, etc., are analyzed. The molar formation enthalpy, fractal dimensions, etc. distinguished methylxanthines and cotinine in linear fits. Different behaviour depends on number of C+N+O atoms.

Generic approach to the method development of intact protein separations using hydrophobic interaction chromatography.

We describe a liquid chromatography method development approach for the separation of intact proteins using hydrophobic interaction chromatography. First, protein retention was determined as function of the salt concentration by isocratic measurements and modeled using linear regression. The error between measured and predicted retention factors was studied while varying gradient time (between 15 and 120min) and gradient starting conditions, and ranged between 2 and 15%. To reduce the time needed to develop optimized gradient methods for hydrophobic interaction chromatography separations, retention-time estimations were also assessed based on two gradient scouting runs, resulting in significantly improved retention-time predictions (average error<2.5%) when varying gradient time. When starting the scouting gradient at lower salt concentrations (stronger eluent), retention time prediction became inaccurate in contrast to predictions based on isocratic runs. Application of three scouting runs and a nonlinear model, incorporating the effects of gradient duration and mobile-phase composition at the start of the gradient, provides accurate results (improved fitting compared to the linear solvent-strength model) with an average error of 1.0% and maximum deviation of -8.3%. Finally, gradient scouting runs and retention-time modeling have been applied for the optimization of a critical-pair protein isoform separation encountered in a biotechnological sample.

Peptide retention time prediction in hydrophilic interaction liquid chromatography. Comparison of separation selectivity between bare silica and bonded stationary phases

Peptide separation selectivity of four different HILIC sorbents was compared through the development of sequence-specific retention prediction algorithms for the datasets of 36–40 thousands peptides each. Hydrophilicity of these sorbents at pH 4.5 (peptide retention under acetonitrile:water gradients) increases in the following order: Luna HILIC < XBridge Amide < Atlantis Silica ∼ Luna HILIC Silica. Bare silica phases are characterized by higher retention coefficients for basic residues (Arg, Lys, His), while interactions with neutral HILIC phases is driven by interaction with the charged residues (Asp, Glu, Arg, Lys, His). Such difference is caused by electrostatic repulsion of negatively charged side chains of Asp and Glu from silanol groups on bare silica sorbents. The sequence-specific features characteristic for both bonded and bare-silica supports were established. Variation of retention coefficients at N- and C-termini showed a peptide orientation effect on silica matrices. The negatively charged surface of silica attracts positively charged groups, thus increasing interaction of N-terminal residues. The effect of peptide helicity is also different between two types of sorbents. The contribution of amphipathic helicity was found to be higher for more hydrophilic silica matrices, causing a reduced predictive accuracy of the models: all four Sequence-Specific Retention Calculator HILIC models had R2-values in 0.973–0.98 range. Development of accurate prediction models provides a solid support for retention prediction filtering of false positive identifications in high-throughput proteomics analyses, guided development of 2D LCMS procedures ((HILIC-RP)). Our analysis also shows that all phases possess a high degree of orthogonality to RPLC (formic acid) with Luna HILIC being the best choice in this regard.

Predictive Liquid Chromatography of Peptides Based on Hydrophilic Interactions for Mass Spectrometry-Based Proteomics

High-performance liquid chromatography (HPLC) is widely used for separation of complex peptide mixtures before mass spectrometry-based proteome analysis. In this analysis, reversed phase HPLC (RPHPLC) using non-polar stationary phases such as surface-modified silica containing alkyl groups (e.g., C18) is typically employed. Because of the high heterogeneity of proteomic samples, multidimensional separation approaches gained increasing attention recently to tackle this complexity and extremely high range of concentrations. In two-dimensional liquid chromatography, hydrophilic interaction chromatography (HILIC) is often a method of choice for combination with RP-HPLC because it uses reversed-phase type eluents and allows efficient separation of polar peptides. Due to the high degree of orthogonality in this two-dimensional separation space, it is tempting to develop approaches for predicting peptide retention times for HILIC-based separations similar to the ones for RP-HPLC. Recent successful efforts in this area were focused on developing retention coefficient (RC)-based approaches. Herein, we explored the feasibility of using a statistical thermodynamic model for prediction of peptide retention times in HILIC separations and determined the phenomenological parameters of the model for a bare silica column. The performance of the developed model was tested using HPLC-MS analysis of a set of synthetic peptides, as well as a tryptic peptide mixture.

Liquid chromatography coupled to quadrupole-Orbitrap high resolution mass spectrometry based method for target analysis and suspect screening of non-ionic surfactants in textiles

In this study, we describe a high-throughput and sensitive method for textiles analysis, using liquid chromatography coupled to quadrupole-Orbitrap high resolution mass spectrometry (LC-Q-Orbitrap HRMS), for the simultaneously quantitative analysis of 40 target alkylphenol polyethoxylates (APEO) oligomers with reference standards and screening of 160 alcohol polyethoxylates (AEO) oligomers without standards in textiles. The APEOs contain nonylphenol ethoxylates (NPEOs) and octylphenol ethoxylates (OPEOs) with an EO number of ethylene oxide of 1–20, while AEOs focus on C11EOs-C18EOs with an EO number of 1–20. After ultrasonic extraction in methanol, the extract was directly separated using a core-shell CORTECS C18+ column and analyzed by Full MS/dd-MS2 (data dependent acquisition) scan in ESI positive mode. Two best sensitivity experimental conditions for APEOs with short EO chains (AP(EO)1-2) and long EO chains (AP(EO)3-20) were investigated, respectively. Most APEO oligomers had wide concentration ranges and the correlation coefficients (R2) were higher than 0.999. The limit of quantitation (LOQ) values for NP(EO)3-20 oligomers ranges from 16.00 to 52.80μg/kg and for OP(EO)3-20 oligomers is from 2.40 to 8.00μg/kg. LOQ for NP(EO)1 and NP(EO)2, OP(EO)1 and OP(EO)2 was 2.40mg/kg and 0.24mg/kg, 1.20mg/kg and 0.16mg/kg, respectively. The average recovery for each APEO oligomer in cotton and polyester matrix was between 78% and 110% at three spiked levels and the relative standard deviation (RSD%) was below 10%. As to AEOs suspects, a HRMS compound database containing 160 AEO oligomers was built and several parameters such as exact m/z, isotopic patterns, predicted product ions and predicted retention time were used for screening and confirmation. The established method was successfully applied for analysis of 40 commercial textile samples. Compared with OPEOs, NPEOs, especially NP(EO)3-15 oligomers, were widely detected in samples and the total concentration ranged from 1.56 to 1376.31mg/kg. AEOs were also found in most samples, among which C12-14, C16 and C18 compounds appeared more frequently and the EO chains mainly ranged from 3 to 15.

Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure-Retention Relationships Studies.

Quantitative structure-retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical descriptors derived from the three-dimensional (3D) spatial coordinates of computationally predicted structures for the analytes. Therefore, it is sensible to calculate these structures correctly, as any error is likely to carry over to the resulting QSRR models. This study compares molecular modeling, semiempirical, and density functional methods (both B3LYP and M06) for structure optimization. Each of the calculations was performed in a vacuum, then repeated with solvent corrections for both acetonitrile and water. We also compared Natural Bond Orbital analysis with the Mulliken charge calculation method. The comparison of the examined computational methods for structure calculation shows that, possibly due to the error inherent in descriptor creation methods, a quick and inexpensive molecular modeling method of structure determination gives similar results to experiments where structures are optimized using an expensive and time-consuming level of computational theory. Also, for structures with low flexibility, vacuum or gas phase calculations are found to be as effective as those calculations with solvent corrections added.

Application of Genetic Programming (GP) in Prediction of Gas Chromatographic Retention Time of some Pesticides

Application of Genetic Programming (GP) in Prediction of Gas Chromatographic Retention Time of some Pesticides

QSPR in forensic analysis – The prediction of retention time of pesticide residues based on the Monte Carlo method

A method for the prediction of retention indices of pesticides using the Monte Carlo method and with optimal molecular descriptors based on local graph invariants and the SMILES notation of studied compounds has been presented. Quite satisfactory results were obtained with the proposed method, since a robust model with good statistical quality was developed. The predictive potential of the applied approach was tested and the robustness of the model was proven with different methods. The best calculated QSPR model had following statistical parameters: r2 = 0.9182 for the training set and r2 = 0.8939 for the test set. Structural indicators defined as molecular fragments responsible for the increases and decreases of gas chromatographic retention indices activity were calculated.

Error measures in quantitative structure-retention relationships studies

An analysis and comparison of the use of four commonly used error measures (mean absolute error, percentage mean absolute error, root mean square error, and percentage root mean square error) for evaluating the predictive ability of quantitative structure-retention relationships (QSRR) models is reported. These error measures are used for reporting errors in the prediction of retention time of external test analytes, that is, analytes not employed during model development. The error-based validation metrics were compared using a simple descriptive statistic, the sum of squared residuals (SSR) of outliers to the edge of an error window. The comparisons demonstrate that Percentage Root Mean Squared Error of Prediction (RMSEP) provides the best estimate of the predictive ability of a QSRR model, having the lowest SSR value of 20.43.

Prediction of liquid chromatography retention times of erectile dysfunction drugs and analogues using chemometric approaches

ABSTRACTA chemometric model, which utilizes quantitative structure–retention relationships, has been developed for predicting liquid chromatography retention times (RTs) of 41 erectile dysfunction (ED) drugs and their analogues in a reversed-phase ultrahigh-performance liquid chromatography–mass spectrometry system. In this study, multiple linear regression (MLR) calculations were used in conjunction with a genetic algorithm (GA) that was used for reducing the number of molecular descriptors down to 10. MLR calculations were found to provide a reliable and robust RT prediction models for 41 ED drugs and analogues (EDDs) of sildenafil and vardenafil types. In terms of validation statistics, its predictability was successfully tested. The predicted RTs can be possibly used for identifying EDDs in liquid chromatography–tandem mass spectrometry experiments in a more reliable way.

The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition

The application of predicted LC retention time to support metabolite identification was evaluated for a metabolomics MS/MS database containing 532 compounds representative for the major human metabolite classes. LC retention times could be measured for two C18 type columns using a mobile phase of pH=3.0 for positive ESI mode (n=337, 228) and pH=8.0 for negative ESI mode (n=410, 233). A QSRR modelling was applied with a small set of model compound selected based on the Kennard-Stone algorithm. The models were implemented in the R environment and can be applied to any library. The prediction model was built with two molecular descriptors, LogD2 and the molecular volume. A limited set of model compounds (LC CalMix, n=16) could be validated on two different C18 reversed phase LC columns and with comparable prediction accuracy. The CalMix can be used to compensate for different LC systems. In addition, LC retention prediction was found, in combination with SWATH-MS, to be attractive to eliminate false positive identification as well as for ranking purpose different metabolite isomeric forms.

Identification of impurities in macrolides by liquid chromatography–mass spectrometric detection and prediction of retention times of impurities by constructing quantitative structure–retention relationship (QSRR)

Macrolides are multicomponent drugs whose impurity control is always a challenge demanding analysis method with good sensitivity and selectivity. Three separate, sensitive, accurate liquid chromatography tandem mass spectrometry methods (LC–MS) were developed for the measurement of three 16-membered ring macrolides (josamycin, josamycin propionate and midecamycin acetate) and related substances in commercial samples. The characteristics of impurities in macrolides were summarized as useful guidance for the impurity analysis of this class of drugs. For each drug, a large number of unknown components have been detected with the high-sensitive MS detector and possible structures of the majority of them were postulated based on the summarized fragmentation rules of 16-membered ring macrolides. A QSRR model was constructed by multilinear regression to predict the retention times of identified impurities which were not detected by the LC–MS methods, without obtaining their reference standards. Satisfactory performance was obtained during leave-one-out cross-validation with a predictive ability (Q2) of 0.95. The generalisation ability of the model was further confirmed by an average error of 2.3% in external prediction. The best QSRR model, based on eight molecular descriptors, exhibited a promising predictive performance and robustness.

A simple method for HPLC retention time prediction: linear calibration using two reference substances

BackgroundAnalysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (tR) and predicted tR in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of tR accurately.MethodsIn the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The tR of compounds on a HPLC column can be calculated by standard retention time and linear relationship.ResultsThe method was validated in two medicines on 30 columns.ConclusionIt was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances.

Analysis of recombinant monoclonal antibodies in hydrophilic interaction chromatography: A generic method development approach

Hydrophilic interaction liquid chromatography (HILIC) is a well-established technique for the separation and analysis of small polar compounds. A recently introduced widepore stationary phase expanded HILIC applications to larger molecules, such as therapeutic proteins. In this paper, we present some generic HILIC conditions adapted for a wide range of FDA and EMA approved recombinant monoclonal antibody (mAb) species and for an antibody-drug conjugate (ADC). Seven approved mAbs possessing various isoelectric point (pI) and hydrophobicity as well as a cysteine conjugated ADC were used in this study. Samples were digested by IdeS enzyme and digests were further fragmented by chemical reduction. The resulting fragments were separated by HILIC. The main benefit of HILIC was the separation of polar variants (glycovariants) in a reasonable analysis time at the protein level, which is not feasible with other chromatographic modes. Three samples were selected and chromatographic conditions were further optimized to maximize resolution. A commercial software was used to build up retention models. Experimental and predicted chromatograms showed good agreement and the average error of retention time prediction was less than 2%. Recovery of various species and sample stability under the applied conditions were also discussed.

Ideal versus real automated twin column recycling chromatography process

The full baseline separation of two compounds (selectivity factors α<1.03) is either impractical (too long analysis times) or even impossible when using a single column of any length given the pressure limitations of current LC instruments. The maximum efficiency is that of an infinitely long column operated at infinitely small flow rates. It is determined by the maximum allowable system pressure, the column permeability (particle size), the viscosity of the eluent, and the intensity of the effective diffusivity of the analytes along the column.Alternatively, the twin-column recycling separation process (TCRSP) can overcome the efficiency limit of the single-column approach. In the TCRSP, the sample mixture may be transferred from one to a second (twin) column until its band has spread over one column length. Basic theory of chromatography is used to confirm that the speed-resolution performance of the TCRSP is intrinsically superior to that of the single-column process. This advantage is illustrated in this work by developing an automated TCRSP for the challenging separation of two polycyclic aromatic hydrocarbon (PAH) isomers (benzo[a]anthracene and chrysene) in the reversed-phase retention mode at pressure smaller than 5000psi. The columns used are the 3.0mm×150mm column packed with 3.5μm XBridge BEH-C18 material (α=1.010) and the 3.0mm or 4.6mm×150mm columns packed with the same 3.5μm XSelect HSST3 material (α=1.025). The isocratic mobile phase is an acetonitrile–water mixture (80/20, v/v). Remarkably, significant differences are observed between the predicted retention times and efficiencies of the ideal TCRSP (given by the number of cycles multiplied by the retention time and efficiency of one column) and those of the real TCRSP. The fundamental explanation lies in the pressure-dependent retention of these PAHs or in the change of their partial molar volume as they are transferred from the mobile to the stationary phase. A revisited retention and efficiency model is then built to predict the actual performance of real TCRSPs. The experimental and calculated resolution data are found in very good agreement for a change, Δvm=−10cm3/mol, of the partial molar volume of the two PAH isomers upon transfer from the acetonitrile–water eluent mixture to the silica-C18 stationary phase.

Study of structure‐dependent chromatographic behavior of glycopeptides using reversed phase nanoLC

Analysis of glycosylation is challenging due to micro- and macro-heterogeneity of the protein attachment. A combination of LC with MS/MS is one of the most powerful tools for glycopeptide analysis. In this work, we show the effect of various monosaccharide units on the retention time of glycopeptides. Retention behavior of several glycoforms of six peptides obtained from tryptic digest of haptoglobin, hemopexin, and sex hormone-binding globulin was studied on a reversed phase chromatographic column. We observed reduction of the retention time with increasing number of monosaccharide units of glycans attached to the same peptide backbone. Fucosylation of larger glycans provides less significant retention time shift than for smaller ones. Retention times of glycopeptides were expressed as relative retention times. These relative retention times were used for calculation of upper and lower limits of glycopeptide retention time windows under the reversed phase conditions. We then demonstrated on the case of a glycopeptide of haptoglobin that the predicted retention time window boosts confidence of identification and minimizes false-positive identification. Relative retention time, as a qualitative parameter, is expected to improve LC-MS/MS characterization of glycopeptides.

Peptide Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography: Data Collection Methods and Features of Additive and Sequence-Specific Models.

The development of a peptide retention prediction model for hydrophilic interaction liquid chromatography (XBridge Amide column) is described for a collection of ∼40 000 tryptic peptides. Off-line 2D LC-MS/MS analysis (HILIC-RPLC) of S. cerevisiae whole cell lysate has been used to acquire retention information for a HILIC separation. The large size of the optimization data set (more than 2 orders of magnitude compared to previous reports) permits the accurate assignment of hydrophilic retention coefficients of individual amino acids, establishing both the effects of amino acid position relative to peptide termini and the influence of peptide secondary structure in HILIC. The accuracy of a simple additive model with peptide length correction (R2 value of ∼0.96) was found to be much higher compared to an algorithm of similar complexity applied to RPLC (∼0.91). This indicates significantly smaller influence of peptide secondary structure and interactions with counterions in HILIC. Nevertheless, sequence-specific features were found. Helical peptides that tend to retain stronger than predicted in RPLC exhibit negative prediction errors using an additive HILIC model. N-cap helix stabilizing motifs, which increase retention of amphipathic helical peptides in RP, reduce peptide retention in HILIC independently of peptide amphipathicity. Peptides carrying multiple Pro and Gly (residues with lowest helical propensity) retain stronger than predicted. We conclude that involvement of the peptide backbone's carbonyl and amide groups in hydrogen-bond stabilization of helical structures is a major factor, which determines sequence-dependent behavior of peptides in HILIC. The incorporation of observed sequence dependent features into our Sequence-Specific Retention Calculator HILIC model resulted in 0.98 R2 value correlation, significantly exceeding the predictive performance of all RP and HILIC models developed for complex mixtures of tryptic peptides.

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.

Comparative analysis of native and permethylated human milk oligosaccharides by liquid chromatography coupled to high resolution mass spectrometry.

Human milk oligosaccharides (HMOs) represent the third most abundant components of milk after lactose and lipids. HMOs are indigestible by the suckling infant but can act as prebiotics and have significant biological functions regarding the organism defense against pathogens (such as bacteria or viruses) by preventing interactions with their receptors. Although constituted of only five distinct monosaccharide building blocks, HMOs are highly structurally diverse compounds with many co-existing structural isomers. Here we report the development and comparison of two distinct glycomic platforms based on liquid chromatography coupled to high resolution mass spectrometry (LC-MS) for analyzing HMOs. We have implemented and thoroughly compared the LC-MS of permethylated and native HMOs on reversed phase (RP) and porous graphitic carbon (PGC) columns for their ability to resolve the natural heterogeneity of milk oligosaccharides at the highest sensitivity. Our data essentially underlines the usefulness of analyzing HMOs as permethylated derivatives especially for getting more precise structural information at high sensitivity. For instance, permethylation annihilates gas-phase fucose migration during MS/MS experiments, thus facilitating spectra interpretation and giving access to relevant information regarding oligosaccharide branching and isomer distinction. At the opposite, LC-MS profiling of native HMOs (using PGC) in milk performed best in terms of detected species, while also being much faster in terms of sample preparation. Although less efficient than PGC chromatography, RPLC proved successful for separating pairs of permethylated isomeric HMOs. A key advantage of RP over PGC liquid chromatography is that retention times can be correlated to molecular weights, which can greatly facilitate further HMO identification using retention time prediction. Altogether these data lead us to think that LC-MS analysis of native HMOs (using PGC) can be used as first-line profiling approach while permethylation can be performed afterwards for facilitating structural characterization.