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.

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.

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.

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.

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.

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.

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.

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.

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.

In vitro prediction of human intestinal absorption and blood-brain barrier partitioning: development of a lipid analog for micellar liquid chromatography.

Over the past decades, several in vitro methods have been tested for their ability to predict either human intestinal absorption (HIA) or penetration across the blood-brain barrier (BBB) of drugs. Micellar liquid chromatography (MLC) has been a successful approach for retention time measurements of drugs to establish models together with other molecular descriptors. Thus far, MLC approaches have only made use of commercial surfactants such as sodium dodecyl sulfate (SDS) and polyoxyethylene (23) lauryl ether (Brij35), which are not representative for the phospholipids present in human membranes. Miltefosine, a phosphocholine-based lipid, is presented here as an alternative surfactant for MLC measurements. By using the obtained retention factors and several computed descriptors for a set of 48 compounds, two models were constructed: one for the prediction of HIA and another for the prediction of penetration across the BBB expressed as log BB. All data were correlated to experimental HIA and log BB values, and the performance of the models was evaluated. Log BB prediction performed better than HIA prediction, although HIA prediction was also improved a lot (from 0.5530 to 0.7175) compared to in silico predicted HIA values.

Evaluation of sphingomyelin, cholester, and phosphatidylcholine-based immobilized artificial membrane liquid chromatography to predict drug penetration across the blood-brain barrier.

Over the past decades, several in vitro methods have been tested for their ability to predict drug penetration across the blood-brain barrier. So far, in high-performance liquid chromatography, most attention has been paid to micellar liquid chromatography and immobilized artificial membrane (IAM) LC. IAMLC has been described as a viable approach, since the stationary phase emulates the lipid environment of a cell membrane. However, research in IAMLC has almost exclusively been limited to phosphatidylcholine (PC)-based stationary phases, even though PC is only one of the lipids present in cell membranes. In this article, sphingomyelin and cholester stationary phases have been tested for the first time towards their ability to predict drug penetration across the blood-brain barrier. Upon comparison with the PC stationary phase, the sphingomyelin- and cholester-based columns depict similar predictive performance. Combining data from the different stationary phases did not lead to improvements of the models.

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.

Gradient stationary phase optimized selectivity liquid chromatography with conventional columns.

Stationary phase optimized selectivity liquid chromatography (SOSLC) is a promising technique to optimize the selectivity of a given separation. By combination of different stationary phases, SOSLC offers excellent possibilities for method development under both isocratic and gradient conditions. The so far available commercial SOSLC protocol utilizes dedicated column cartridges and corresponding cartridge holders to build up the combined column of different stationary phases. The present work is aimed at developing and extending the gradient SOSLC approach towards coupling conventional columns. Generic tubing was used to connect short commercially available LC columns. Fast and base-line separation of a mixture of 12 compounds containing phenones, benzoic acids and hydroxybenzoates under both isocratic and linear gradient conditions was selected to demonstrate the potential of SOSLC. The influence of the connecting tubing on the deviation of predictions is also discussed.

Predicting drug penetration across the blood-brain barrier: comparison of micellar liquid chromatography and immobilized artificial membrane liquid chromatography.

Several in vitro methods have been tested for their ability to predict drug penetration across the blood-brain barrier (BBB) into the central nervous system (CNS). In this article, the performance of a variety of micellar liquid chromatographic (MLC) methods and immobilized artificial membrane (IAM) liquid chromatographic approaches were compared for a set of 45 solutes. MLC measurements were performed on a C18 column with sodium dodecyl sulfate (SDS), polyoxyethylene (23) lauryl ether (Brij35), or sodium deoxycholate (SDC) as surfactant in the micellar mobile phase. IAM liquid chromatography measurements were performed with Dulbecco's phosphate-buffered saline (DPBS) and methanol as organic modifier in the mobile phase. The corresponding retention and computed descriptor data for each solute were used for construction of models to predict transport across the blood-brain barrier (log BB). All data were correlated with experimental log BB values and the relative performance of the models was studied. SDS-based models proved most suitable for prediction of log BB values, followed closely by a simplified IAM method, in which it could be observed that extrapolation of retention data to 0% modifier in the mobile phase was unnecessary.

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.

A variable column length strategy to expedite method development.

A variable length method development (or VL-MD)strategy, exploiting the potential of an automatic column coupling system, is proposed and has been applied to a number of different pharmaceutical and environmental samples with a varying degree of complexity. The proposed strategy consistently produced separation methods that had at least an equally good critical pair resolution and an equally short run time to those of methods produced using commercially available MD assistance software. In some cases, the VL-MD strategy allowed the MD time to be drastically shortened from >30 h to an overnight run of only 12 h. The developed strategy has the potential to become fully automated provided that reliable chromatogram read-out software becomes available. The advantage of combining different stationary phase types to improve the available selectivity and the integration into the general VL-MD strategy was also demonstrated.

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.

Evaluation of electron capture detection in reversed-phase HPLC for pharmaceutical analysis.

The coupling of RP-LC to electron capture detection (ECDNi(63)) is described. To reduce the amount of mobile phase entering into the detector, interfacing was performed via a Scott-type spray chamber. The performance of RP-LC/ECDNi(63) was evaluated for pharmaceutical analysis and the results show that the system is able to work in a routine environment using columns with 2 mm id and common amounts of the organic modifiers methanol or ACN in the mobile phase. Because of the high sensitivity and selectivity toward electrophilic compounds, the use of this detector opens possibilities for the analysis of impurities down to the 0.05% level of active pharmaceutical ingredients (API).