Microchip isotachophoresis for green and sustainable pharmaceutical quality control: Method validation and application to complex pharmaceutical formulations.

Universal microchip isotachophoresis (µITP) methods were developed for the determination of cationic and anionic macrocomponents (active pharmaceutical ingredients and counterions) in cardiovascular drugs marketed in salt form, amlodipine besylate and perindopril erbumine. The developed methods are characterized by low reagent and sample consumption, waste production and energy consumption, require only minimal sample preparation and provide fast analysis. The greenness of the proposed methods was assessed using AGREE. An internal standard addition was used to improve the quantitative parameters of µITP. The proposed methods were validated according to the ICH guideline. Linearity, precision, accuracy and specificity were evaluated for each of the studied analytes and all set validation criteria were met. Good linearity was observed in the presence of matrix and in the absence of matrix, with a correlation coefficient of at least 0.9993. The developed methods allowed precise and accurate determination of the studied analytes, the RSD of the quantitative and qualitative parameters were less than 1.5% and the recoveries ranged from 98 to 102%. The developed µITP methods were successfully applied to the determination of cationic and anionic macrocomponents in six commercially available pharmaceutical formulations.

A Tissue Distribution Study of Propafenone in an Intentional Fatal Poisoning Case.

Propafenone (PPF) belongs to the class 1C antiarrhythmics and can cause electrocardiogram-associated adverse/toxic effects. Cases of PPF intoxication are rarely investigated. We developed a novel and selective GC-MS/MS method for the determination of PPF and its tissue distribution in an intentional fatal poisoning case, which is applicable to PPF quantification in the range of therapeutic to lethal concentrations in complex post-mortem samples. A simple and effective sample pretreatment was applied to all analyzed samples. PPF was determined without the need for dilution, even in highly complex samples containing a wide range of analyte concentrations. Quantification was performed using the standard addition method, developed and validated according to the ICH M10 guidelines. The obtained results indicated that the PPF concentration in the serum from blood taken while alive, before therapy, was the highest ever reported in the literature. Despite the intensive therapy after the patients' admission, the PPF concentrations in the lungs, spleen, femoral blood and cardiac blood were fatal or abnormally high. On the other hand, the concentrations in the liver and skeletal muscle were lower or approximately the same as observed in cases with therapeutic doses. To the best of our knowledge, the distribution of PPF has not been investigated in fatal intoxication cases and can be helpful in clinical or forensic toxicology.

Impact of structural similarity on the accuracy of retention time prediction.

Quantitative Structure-Retention Relationships offer a valuable tool for de-risking chromatographic methods in relation to newly formed or hypothetical compounds, arising from synthetic processes or formulation activities. They can also be used to identify optimal separation conditions, or in support of structural elucidation. In this contribution, we provide a systematic study of the relationship between the accuracy of the retention model, the size of the training set and its structural similarity to the predicted compound. We compare structural similarity expressed either on a fingerprint basis (e.g., Tanimoto index), or by Euclidean distance calculated from of subset of molecular descriptors. The results presented indicate that accurate and predictive models can be built from a small dataset containing as few as 25 compounds, provided that the training set is structurally similar to the test compound. When the training set contains compounds selected by minimizing the Euclidean distance calculated from 3 descriptors most correlated with the retention time, root mean square error of 0.48 min and correlation coefficient of 0.9464 were observed for the test sets of 104 compounds. Moreover, these models meet the Tropsha predictivity criteria. These findings potentially bring the prediction of retention times within the practical reach of pharmaceutical analysts involved in chromatographic method development. We also present an optimisation approach to select algorithm settings in order to minimize the prediction error and ensure model predictivity.

Trace determination of perchlorate in drinking water by capillary zone electrophoresis with isotachophoresis sample cleanup and conductivity detection.

An analytical test procedure for the direct determination of trace levels of perchlorate in drinking water by isotachophoresis combined with capillary zone electrophoresis was developed. A capillary electrophoresis analyzer with column coupling technology, capable of combining capillaries with different internal diameters, was employed in combination with conductivity detection. This combination of the capillary electrophoresis techniques facilitated preconcentration of the trace analytes and elimination of potentially interfering macro-components. To eliminate the influence of weak and moderately strong acids on the migration of perchlorate, acidic leading electrolyte (pH 3.2) in the isotachophoresis step and acidic background electrolyte (pH 3.9) in the zone electrophoresis step were chosen. The addition of polyvinylpyrrolidone into the electrolytes enhanced the resolution of perchlorate from other anions, especially remaining anionic macro-components. The developed method is characterized by good repeatability of migration time (relative standard deviation less than 0.2%) as well as peak area (relative standard deviation less than 5.9%), linearity (R = 0.9996), recoveries (100-112%), and sample throughput (90 samples/24 h). The limit of quantitation for perchlorate in drinking water was achieved at 12.5 nmol/L (1.25 µg/L). This approach is more sensitive and more robust than transient isotachophoresis and offers advantages over some more established analytical techniques such as ion chromatography.

Development of Microchip Isotachophoresis Coupled with Ion Mobility Spectrometry and Evaluation of Its Potential for the Analysis of Food, Biological and Pharmaceutical Samples.

An online coupling of microchip isotachophoresis (µITP) with ion mobility spectrometry (IMS) using thermal evaporation interface is reported for the first time. This combination integrates preconcentration power of the µITP followed by unambiguous identification of trace compounds in complex samples by IMS. Short-chain carboxylic acids, chosen as model analytes, were first separated by the µITP in a discontinuous electrolyte system at pH 5-6, and subsequently evaporated at 130 °C during their transfer to the IMS analyzer. Various parameters, affecting the transfer of the separated sample components through the evaporation system, were optimized to minimize dispersion and loss of the analytes as well as to improve sensitivity. The following analytical attributes were obtained for carboxylic acids in the standard solutions: 0.1-0.3 mg L-1 detection limits, 0.4-0.9 mg L-1 quantitation limits, linear calibration range from the quantitation limit to 75 mg L-1, 0.2-0.3% RSD of the IMS response and 98-102% accuracy. The analytical potential of the developed µITP-IMS combination was demonstrated on the analysis of various food, pharmaceutical and biological samples, in which the studied acids are naturally present. These include: apple vinegar, wine, fish sauce, saliva and ear drops. In the real samples, 0.3-0.6% RSD of the IMS response and 93-109% accuracy were obtained.

Potential of microchip electrophoresis in pharmaceutical analysis: Development of a universal method for frequently prescribed nonsteroidal anti-inflammatory drugs.

A novel microchip electrophoresis method with conductivity detection for the determination of nonsteroidal anti-inflammatory drugs (NSAIDs) in several pharmaceutical formulations was developed. The three frequently used NSAIDs - acetylsalicylic acid, diclofenac and ibuprofen were baseline separated on a poly(methyl methacrylate) microchip with coupled separation channels. Elimination of matrix components such as excipients, was realized through online combination of isotachophoresis (ITP) with zone electrophoresis (ZE). ITP-ZE hyphenation can also facilitate preconcentration of target analytes. ITP was carried out in the first separation channel at pH 6.5, while the second channel of the microchip was used for ZE separation and detection of the analytes at pH 7.0. The developed ITP-ZE method was demonstrated to be applicable for direct and reliable determination of NSAIDs in eleven pharmaceutical formulations. The noticeable advantage of this approach is that only simple sample pretreatment (filtration and dilution) is necessary. The method performance parameters, such as linearity (20-250% of nominal concentration of studied NSAIDs in the test samples), accuracy (98-102%) and precision (less than 2% RSD) were obtained. This universal approach is suitable for the determination of frequently used NSAIDs in a single analysis in less than 15 min. In addition to simple sample pretreatment, low running costs and minimal environmental impact could make this method attractive for the analysis of pharmaceutical preparations.

Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis.

Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.

Advantages and Pitfalls of Capillary Electrophoresis of Pharmaceutical Compounds and Their Enantiomers in Complex Samples: Comparison of Hydrodynamically Opened and Closed Systems.

Several research disciplines require fast, reliable and highly automated determination of pharmaceutically active compounds and their enantiomers in complex biological matrices. To address some of the challenges of Capillary Electrophoresis (CE), such as low concentration sensitivity and performance degradation linked to the adsorption and interference of matrix components, CE in a hydrodynamically closed system was evaluated using the model compounds Pindolol and Propranolol. Some established validation parameters such as repeatability of injection efficiency, resolution and sensitivity were used to assess its performance, and it was found to be broadly identical to that of hydrodynamically opened systems. While some reduction in separation efficiency was observed, this was mainly due to dispersion caused by injection and it had no impact on the ability to resolve enantiomers of model compounds even when spiked into complex biological matrix such as blood serum. An approximately 18- to 23-fold increase in concentration sensitivity due to the employment of wide bore capillaries was observed. This brings the sensitivity of CE to a level similar to that of liquid chromatography techniques. In addition to this benefit and unlike in hydrodynamically opened systems, suppression of electroosmotic flow, which is essential for hydrodynamically closed systems practically eliminates the matrix effects that are linked to protein adsorption.

Exploration of the Selectivity and Retention Behavior of Alternative Polyacrylamides in Temperature Responsive Liquid Chromatography.

Temperature responsive liquid chromatography (TRLC) allows for separation of organic solutes in purely aqueous mobile phases whereby retention is controlled through temperature. The vast majority of the work has thus far been performed on poly[N-isopropylacrylamide] (PNIPAAm)-based columns, while the performance of other temperature responsive polymers has rarely been compared under identical conditions. Therefore, in this work, two novel TRLC phases based on poly[N-n-propylacrylamide] (PNNPAAm) and poly[N,N-diethylacrylamide] (PDEAAm) are reported and compared to the state of the art PNIPAAm based column. Optimal comparison is thereby obtained by the use of controlled radical polymerizations, identical molecular weights, and by maximizing carbon loads on the silica supporting material. Analysis of identical test mixtures of homologue series and pharmaceutical samples revealed that PNNPAAm performs in a similar way as PNIPAAm while offering enhanced retention and a shift of the useable temperature range toward lower temperatures. PDEAAm offers a range of novel possibilities as it depicts a different selectivity, allowing for enhanced resolution in TRLC in, for example, coupled column systems. Reduced plate heights of 3 could be obtained on the homemade columns, offering the promise for reasonable column efficiencies in TRLC despite the use of bulky polymers as stationary phases in HPLC.

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.

Screening therapeutics according to their uptake across the blood-brain barrier: A high throughput method based on immobilized artificial membrane liquid chromatography-diode-array-detection coupled to electrospray-time-of-flight mass spectrometry.

The Blood-Brain Barrier (BBB) plays an essential role in protecting the brain tissues against possible injurious substances. In the present work, 79 neutral, basic, acidic and amphoteric structurally unrelated analytes were considered and their chromatographic retention coefficients on immobilized artificial membrane (IAM) stationary phase were determined employing a mass spectrometry (MS)-compatible buffer based on ammonium acetate. Their BBB passage predictive strength was evaluated and the statistical models based on IAM indexes and in silico physico-chemical descriptors showed solid statistics (r2 (n - 1) = 0.78). The predictive strength of the indexes achieved by the MS-compatible method was comparable to that achieved by employing the more "biomimetic" Dulbecco's phosphate buffered saline, even if some differences in the elution order were observed. The method was transferred to the MS, employing a diode-array-detection coupled to an electrospray ionization source and a time-of-flight analyzer. This setup allowed the simultaneous analysis of up to eight analytes, yielding a remarkable acceleration of the analysis time.

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.

Determination of in Vitro and in Silico Indexes for the Modeling of Blood-Brain Barrier Partitioning of Drugs via Micellar and Immobilized Artificial Membrane Liquid Chromatography.

In the present work, 79 structurally unrelated analytes were taken into account and their chromatographic retention coefficients, measured by immobilized artificial membrane liquid chromatography (IAM-LC) and by micellar liquid chromatography (MLC) employing sodium dodecyl sulfate (SDS) as surfactant, were determined. Such indexes, along with topological and physicochemical parameters calculated in silico, were subsequently used for the development of blood-brain barrier passage-predictive statistical models using partial least-squares (PLS) regression. Highly significant relationships were observed either using IAM (r2 (n - 1) = 0.78) or MLC (r2 (n - 1) = 0.83) derived indexes along with in silico descriptors. This hybrid approach proved fast and effective in the development of highly predictive BBB passage oriented models, and therefore, it can be of interest for pharmaceutical industries as a high-throughput BBB penetration oriented screening method. Finally, it shed new light into the molecular mechanism involved in the BBB uptake of therapeutics.