Retention prediction using quantitative structure-retention relationships combined with the hydrophobic subtraction model in reversed-phase liquid chromatography.

The hydrophobic subtraction model (HSM) combined with quantitative structure-retention relationships (QSRR) methodology was utilized to predict retention times in reversed-phase liquid chromatography (RPLC). A selection of new analytes and new RPLC columns that had never been used in the QSRR modeling process were used to verify the proposed approach. This work is designed to facilitate early prediction of co-elution of analytes in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR models were constructed through partial least squares regression combined with a genetic algorithm (GA-PLS) which was employed as a feature selection method to choose the most informative molecular descriptors calculated using VolSurf+ software. The analyte hydrophobicity coefficient of the HSM was predicted for subsequent calculation of retention. Clustering approaches based on the local compound type and the local second dominant interaction were investigated to select the most appropriate training set of analytes from a larger database. Predicted retention times of five new compounds on five new RPLC C18 columns were compared with their measured retention times with percentage root-mean-square errors of 15.4 and 24.7 for the local compound type and local second dominant interaction clustering methods, respectively.

Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics.

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model.

Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM. It was found that only the hydrophobicity (η'H) term in the HSM was required to give the accuracy necessary to predict potential co-elution of analytes. Global QSRR models derived from all 148 compounds in the dataset were compared to QSRR models derived using a range of local modelling techniques based on clustering of compounds in the dataset by the structural similarity of compounds (as represented by the Tanimoto similarity index), physico-chemical similarity of compounds (represented by log D), the neutral, acidic, or basic nature of the compound, and the second dominant interaction between analyte and stationary phase after hydrophobicity. The global model showed reasonable prediction accuracy for retention time with errors of 30 s and less for up to 50% of modeled compounds. The local models for Tanimoto, nature of the compound and second dominant interaction approaches all exhibited prediction errors less than 30 s in retention time for nearly 70% of compounds for which models could be derived. Predicted retention times of five representative compounds on nine reversed-phase columns were compared with known experimental retention data for these columns and this comparison showed that the accuracy of the proposed modelling approach is sufficient to reliably predict the retention times of analytes based only on their chemical structures.

Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review.

With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.

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.

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.

Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, logP, and retention factor ratio to identify optimal analyte training sets for ion chromatography.

Retention prediction for unknown compounds based on Quantitative Structure-Retention Relationships (QSRR) can lead to rapid "scoping" method development in chromatography by simplifying the selection of chromatographic parameters. The use of retention factor ratio (or k-ratio) as a chromatographic similarity index can be a potent method to cluster similar compounds into a training set to generate an accurate predictive QSRR model provided that its limitation - that the method is impractical for retention prediction for unknown compounds - is successfully addressed. In this work, we propose a localised QSRR modelling approach with the aim of compensating the critical limitation in the otherwise successful k-ratio filter-based QSRR modelling. The approach is to combine a k-ratio filter with both Tanimoto similarity (TS) and a ΔlogP index (i.e., logP-Dual filter). QSRR models for two retention parameters (a and b) in the linear solvent strength (LSS) model in ion chromatography (IC), logk=a - blog[eluent], were generated for larger organic cations (molecular mass up to 506) on a Thermo Fisher Scientific CS17 column. The application of the developed logP-Dual filter resulted in the production of successful QSRR models for 50 organic cations out of 87 in the dataset. The predicted a- and b-values of the models were then applied to the LSS model to predict the corresponding retention times. External validation showed that QSRR models for a-, b- and tR- values with excellent accuracy and predictability (Qext(F2)2 of 0.96, 0.95, and 0.96, RMSEP of 0.06, 0.02, and 0.38min) were created successfully, and these models can be employed to speed up the "scoping" phase of method development in IC.

Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems.

The development of quantitative structure retention relationships (QSRR) having sufficient accuracy to support high performance liquid chromatography (HPLC) method development is still a major issue. To tackle this challenge, this study presents a novel QSRR methodology to select a training set of compounds for QSRR modelling (i.e. to filter the database to identify the most appropriate compounds for the training set). This selection is based on a dual filtering strategy which combines Tanimoto similarity (TS) searching as the primary filter and retention time (tR) similarity clustering as the secondary filter, using a database of pharmaceutical compound retention times collected over a wide range of hydrophilic interaction liquid chromatography (HILIC) systems. To employ tR similarity filtering, correlation to a molecular descriptor is used as a measure of retention time. For the retention time of a compound to be modelled a relationship between experimental chromatographic data and various molecular descriptors is calculated using a genetic algorithm-partial least squares (GA-PLS) regression. The proposed dual-filtering-based QSRR model significantly improves the retention time predictability compared to the diverse, global, and TS-based QSRR models, with an average root mean square error in prediction (RMSEP) of 11.01% over five different HILIC stationary phases. The average CPU time for implementing the proposed approach is less than 10min, which makes it quite favorable for rapid method development in HILIC. In addition, interpretation of the molecular descriptors selected by this novel approach provided some insight into the HILIC mechanism.

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.

Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Qext(F2)2>0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min.

Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure-Retention Relationships and Design of Experiments.

A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R2 > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the model's prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools.

Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model.

Quantitative Structure-Retention Relationships (QSRRs) represent a popular technique to predict the retention times of analytes, based on molecular descriptors encoding the chemical structures of the analytes. The linear solvent strength (LSS) model relating the retention factor, k to the eluent concentration (log k=a-blog [eluent]), is a well-known and accurate retention model in ion chromatography (IC). In this work, QSRRs for inorganic and small organic anions were used to predict the regression parameters a and b in the LSS model (and hence retention times) for these analytes under a wide range of eluent conditions, based solely on their chemical structures. This approach was performed on retention data of inorganic and small organic anions from the "Virtual Column" software (Thermo Fisher Scientific). These retention data were recalibrated via a "porting" methodology on three columns (AS20, AS19, and AS11HC), prior to the QSRR modeling. This provided retention data more applicable on recently produced columns which may exhibit changes of column behavior due to batch-to-batch variability. Molecular descriptors for the analytes were calculated with Dragon software using the geometry-optimized molecular structures, employing the AM1 semi-empirical method. An optimal subset of molecular descriptors was then selected using an evolutionary algorithm (EA). Finally, the QSRR models were generated by multiple linear regression (MLR). As a result, six QSRR models with good predictive performance were successfully derived for a- and b-values on three columns (R2>0.98 and RMSE<0.11). External validation showed the possibility of using the developed QSRR models as predictive tools in IC (Qext(F3)2>0.7 and RMSEP<0.4). Moreover, it was demonstrated that the obtained QSRR models for the a- and b-values can predict the retention times for new analytes with good accuracy and predictability (R2 of 0.98, RMSE of 0.89min, Qext(F3)2 of 0.96 and RMSEP of 1.18min).

Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures.

Quantitative structure-retention relationship (QSRR) models are developed to predict the retention times of analytes on five hydrophilic interaction liquid chromatography (HILIC) stationary phases (bare silica, amine, amide, diol and zwitterionic), with a view to selecting the most suitable stationary phase(s) for the separation of these analytes. The study was conducted using six ß-adrenergic agonists as target analytes. Molecular descriptors were calculated based only on chemical structures optimized using density functional theory. A genetic algorithm (GA) was then used to select the most relevant molecular descriptors and these were used to build a retention model for each stationary phase using partial least squares (PLS) regression. This model was then used to predict the retention of the test set of target analytes. This process created an optimized descriptor set which enhanced the reliability of the developed QSRR models. Finally, the QSRR models developed in the work were utilized to provide some insight into the separation mechanisms operating in the HILIC mode. Three performance criteria - mean absolute error (MAE), root mean square error of prediction scaled to retention time (RMSEP), and the number of selected descriptors, were used to evaluate the developed models when applied to an external test set of six ß-adrenergic agonists and showed highly predictive abilities. MAE values ranged from 13 to 25s on four of the stationary phases, with a somewhat higher error (50s) being observed for the zwitterionic phase. RMSEP values of 4.88-11.12% were recorded. Validation was performed through Y-randomization and chemical domain applicability, from which it was evident that the developed optimized GA-PLS models were robust. The high levels of accuracy, reliability and applicability of the models were to a large extent due to the optimization of the GA descriptor set and the presence of relevant structural and geometric molecular descriptors, together with descriptors based on important physicochemical properties, which establish a strong connection between retention time and meaningful chemical properties. The present strategy, while it is a pilot study, holds great promise for broader screening of HILIC stationary phases for desired separation, as well as for acquisition of information about molecular mechanisms of separation under chromatographic conditions.

Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio.

Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule. The present study offers further proof-of-concept of localized QSRR modelling, in which the retention of any given compound is predicted using only the most chromatographically similar compounds in the available dataset. To this end, each compound in the dataset was sequentially removed from the database and individually utilized as a test analyte. In this study, we propose the retention factor k as the most relevant chromatographic similarity measure and compare it with the Tanimoto index, the most popular similarity measure based on chemical structure. Prediction error was reduced by up to 8 fold when QSRR was based only on chromatographically similar compounds rather than using the entire dataset. The study therefore shows that the design of a practically useful structural similarity index should select the same compounds in the dataset as does the k-similarity filter in order to establish accurate predictive localized QSRR models. While low average prediction errors (Mean Absolute Error (MAE)<0.5min) and slopes of the regression lines through the origin close to 1.00 were obtained using k-similarity searching, the use of the structural Tanimoto similarity index, considered as the gold standard in Quantitative Structure-Activity Relationships (QSAR) studies, generally resulted in much higher prediction errors (MAE>1min) and significant deviations from the reference slope of 1.0. The Tanomoto similarity index therefore appears to have limited general utility in QSRR studies. Future studies therefore aim at designing a more appropriate chromatographic similarity index that can then be applied for unknown compounds (that is, compounds which have not been tested previously on the chromatographic system used, but for which the chemical structures are known).

Enhanced methodology for porting ion chromatography retention data.

Porting is a powerful methodology to recalibrate an existing database of ion chromatography (IC) retention times by reflecting the changes of column behavior resulting from either batch-to-batch variability in the production of the column or the manufacture of new versions of a column. This approach has been employed to update extensive databases of retention data of inorganic and organic anions forming part of the "Virtual Column" software marketed by Thermo Fisher Scientific, which is the only available commercial optimization tool for IC separation. The current porting process is accomplished by performing three isocratic separations with two representative analyte ions in order to derive a porting equation which expresses the relationship between old and new data. Although the accuracy of retention prediction is generally enhanced on new columns, errors were observed on some columns. In this work, the porting methodology was modified in order to address this issue, where the porting equation is now derived by using six representative analyte ions (chloride, bromide, iodide, perchlorate, sulfate, and thiosulfate). Additionally, the updated porting methodology has been applied on three Thermo Fisher Scientific columns (AS20, AS19, and AS11HC). The proposed approach showed that the new porting methodology can provide more accurate and robust retention prediction on a wide range of columns, where average errors in retention times for ten test anions under three eluent conditions were less than 1.5%. Moreover, the retention prediction using this new approach provided an acceptable level of accuracy on a used column exhibiting changes in ion-exchange capacity.

Optimizing selectivity during reversed-phase high performance liquid chromatography method development: prioritizing experimental conditions.

Several different conditions can be varied to improve selectivity for reversed-phase chromatography (RPC). A reexamination of literature data suggests that changes in selectivity due to a change of column or mobile phase pH are largely replicated by changes in temperature or mobile phase composition (concentrations of acetonitrile and/or methanol). This suggests a reconsideration of the role of mobile phase pH and the column during method development.

Column selectivity in reversed-phase liquid chromatography. V. Higher metal content (type-A) alkyl-silica columns.

Retention measurements involving 16 test solutes have been carried out for 38 type-A alkyl-silica columns and three bonded-zirconia columns. These measurements have been analyzed in terms of a model previously developed for type-B columns, so as to yield values of five column selectivity parameters (H, S*, A, B, C) for each type-A column. Overall differences in selectivity between type-A and -B columns can be related to the average values of H, S*, etc. for each column type. Compared to type-B columns, type-A columns provide generally stronger retention for carboxylic acids, while solutes that are more hydrophobic or less bulky are more retained on type-B columns. Hydrogen-bond acceptors (e.g. aliphatic amides) and cations (e.g. protonated bases) are strongly retained on type-A versus type-B columns. Compared to type-B columns, bonded-zirconia columns show much increased retention of cations and reduced retention of hydrogen-bond acceptors. Because of relatively large differences in the selectivity of bonded-zirconia, type-A, and type-B columns, it will prove difficult to find columns of different type (e.g. a type-A and a type-B column) which have equivalent selectivity. Type-A columns also tend to be more different from each other (in terms of selectivity) than is the case for type-B columns. As a result, the replacement of a given type-A column by an "equivalent" type-A column also appears unlikely, except for samples that do not contain ionized compounds.

Column selectivity in reversed-phase liquid chromatography. IV. Type-B alkyl-silica columns.

Columns for reversed-phase HPLC (RP-LC) can be characterized by five, retention-related parameters: H (hydrophobicity), S (steric selectivity), A (hydrogen-bond acidity), B (hydrogen-bond basicity), and C (cation-exchange behavior). In the present study, values of the latter parameters have been measured for 92 type-B (low metals content)alkyl-silica columns and compared to column properties such as ligand length,ligand concentration, pore diameter, and the presence or absence of end-capping. With the exception of five columns of unusual design, retention factors, k, for 16 representative test compounds were correlated with values of H, S, etc., within an average +/- 1.2% (1 standard deviation, SD), suggesting that all significant solute-column interactions are recognized by these five column parameters. A single-valued function F(s) is proposed to measure differences in selectivity for any two RP-LC columns whose values of H, S, etc., are known. This allows the easy selection of columns whose selectivity is desired to be either similar to or different from a starting column, for application in either routine analysis or method development.

Measurement of paclitaxel in biological matrices: high-throughput liquid chromatographic-tandem mass spectrometric quantification of paclitaxel and metabolites in human and dog plasma.

A GLP-validated, sensitive and specific LC-MS-MS method for the quantification of paclitaxel and its 6-alpha- and 3'-p-hydroxy metabolites is presented. A 0.400 ml plasma aliquot is spiked with a (13)C(6)-labeled paclitaxel internal standard and extracted with 1 ml methyl-tert.-butyl ether. The ether is evaporated and the residue is reconstituted in 130 microl of 30% aqueous acetonitrile (ACN) containing 0.1% trifluoroacetic acid. Isocratic HPLC analysis is performed by injecting 50 microl of the reconstituted material onto a 50x2.1 mm C(18) column with an ACN-water-acetic acid (50:50:0.1) mobile phase at 200 microl/min flow. Detection is by positive ion electrospray followed by multiple reaction monitoring of the following transitions: paclitaxel (854>509 u), 6-alpha-hydroxy paclitaxel (870>525 u), 3'-p-hydroxy paclitaxel (870>509 u) and internal standard (860>509 u). Quantification is by peak area ratio against the 13C(6) internal standard. The method range is 0.117-117 nM (0.1-100 ng/ml) for paclitaxel and both metabolites using a 0.400 ml human or dog plasma sample. Analysis time per sample is less than 5 min.

Temperature selectivity in reversed-phase high performance liquid chromatography.

Column temperature plays two important roles in reversed-phase high-performance liquid chromatography (RP-HPLC): control of retention (k) and control of selectivity (a). While changes in retention as a function of temperature are ubiquitous, selectivity changes for any given solute pair are more pronounced for ionized samples and samples with more polar substituents. With many samples, column temperature can be selected in a manner that optimizes resolution. The selectivity effects observed for temperature changes in RP-HPLC generally are complementary to those observed for mobile phase strength changes, so it is often possible to improve resolution by simultaneous optimization of temperature and mobile phase percent organic or gradient steepness. Computer simulation is a powerful tool for such optimization experiments. This paper reviews the influence of temperature on chromatographic selectivity for RP-HPLC.