This paper discusses the development of an analytical method by an alternative separation approach, sequential elution liquid chromatography (SE-LC), to separate permanently charged ions (anions), weak acids, and neutral compounds using anion exchange and reversed-phase columns in tandem. SE-LC separates classes of compounds by group by employing two or more elution modes. Advantages to using SE-LC over conventional HPLC are a greater peak capacity and a reduced separation disorder. Importantly, the same HPLC as used for a conventional HPLC separation may be used to afford a successful SE-LC separation. Mobile phase selection and gradient optimization are integral for a successful SE-LC class separation of permanent anions, weak acids, and neutral compounds and will be discussed in detail in this paper. The most successful (best resolution and repeatability) SE-LC separation was achieved by applying isocratic elution at low pH to elute the weak acids, followed by an acetonitrile gradient to elute the neutral compounds, and last a sodium methanesulfonate gradient to elute the anionic compounds using a superficially porous C18 column coupled with a strong anion exchange (SAX) column. Repeatability (RSD) in the retention times and peak areas of the analytes was less than 0.25 % and 1.5 %, respectively.

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

The process of developing new reversed-phase liquid chromatography methods can be both time-consuming and challenging. To meet this challenge, statistics-based strategies have emerged as cost-effective, efficient and flexible solutions. In the present study, we use a Bayesian response surface methodology, which takes advantage of the knowledge of the pKa values of the compounds present in the analyzed sample to model their retention behavior. A multi-criteria decision analysis (MCDA) was then developed to exploit the uncertainty information inherent in the model distributions. This strategic approach is designed to integrate seamlessly with quantitative structure retention relationship (QSRR) models, forming an initial in-silico screening phase. Of the two methods presented for MCDA, one showed promising results. The method development process was carried out with the optimization phase, generating a design space that corroborates the results of the selection phase.

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In liquid chromatography (LC), discrepancies in liquid properties such as elution strength and viscosity lead to a mismatch between the sample diluent and mobile phase. This mismatch can result in peak deformation, including peak splitting or even breakthrough, particularly when large sample volumes are injected. The formation of a T-junction between sample solution and mobile phase flow stream, a technique previously used in supercritical fluid chromatography, is the key enabler of feed injection in LC. This T-junction allows the injection needle to infuse the sample directly into the mobile phase. It ensures that the diluent is continuously mixed with the mobile phase before introduced onto the column, thereby reducing the initial solvent mismatch. The degree of dilution depends on the ratio between mobile phase flow rate (Qmp) and feed rate (Qfeed) at which the sample is infused. Our study examined the effect of several parameters on the feed injection of large sample volumes from purely organic diluents in reversed-phase LC. These parameters included the type of diluent, compound retention factor (k), injected sample volume (Vinj), and Qmp. With varied Qfeed, all compounds revealed a similar range of optimal values for Qr = (Qmp-Qfeed)/Qfeed between 2 and 5, a range unaffected by Vinj and Qmp. For Qr > 5, the slope of the plate height curves (H vs. Qr) decreases with increasing k, potentially extending the range of optimal Qr-values. However, the best Qr-value for a separation is determined by the compound with the smallest k, simplifying optimization. Using feed injection, we were able to reduce plate heights by up to a factor of 8 compared to classic flow-through injection of large sample volumes.

Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic compounds. Over 5000 descriptors for each standard were calculated using AlvaDesc software and then selected through Genetic Algorithm. The selected descriptors were used as variables for models construction and to obtain a better understanding of the retention behaviour of phenols during reverse-phase separation. Three distinct molecule sets, including fifty-two phenolic compounds (Set 1), 32 flavonoids (Set 2) and 15 mono-substituted flavonoids were divided into training and validation sets to build Partial Least Square, Multiple Linear Regression and Partial Least Square-Artificial Neural Network models. To assess the predictivity of the models, these were tested on a bergamot juice sample. Partial Least Square and Partial Least Square-Artificial Neural Network exhibit the lowest prediction error, and the latter showed the best predictive power in real sample recognition. The building and implementation of such predictive models showed to be a powerful tool to identify phenolic compounds based on retention data and avoiding the use of expensive and sophisticated detectors such as tandem MS.

Microalgae, integral to marine ecosystems for their rich nutrient content, notably lipids and proteins, were investigated by using reversed-phase liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (RPLC-Q-TOF-MS/MS). This study focused on lipid composition in three commonly used microalgae species (Spirulina platensis, Chlorella vulgaris, and Schizochytrium limacinum) for functional food applications. The analysis unveiled more than 700 lipid molecular species, including glycolipids (GLs), phospholipids (PLs), sphingolipids (SLs), glycerolipids, and betaine lipids (BLs). GLs (19.9-64.8%) and glycerolipids (24.1-70.4%) comprised the primary lipid. Some novel lipid content, such as acylated monogalactosyldiacylglycerols (acMGDG) and acylated digalactosyldiacylglycerols (acDGDG), ranged from 0.62 to 9.68%. The analysis revealed substantial GLs, PLs, and glycerolipid variations across microalgae species. Notably, S. platensis and C. vulgaris displayed a predominance of fatty acid (FA) 18:2 and FA 18:3 in GLs, while S. limacinum exhibited a prevalence of FA 16:0, collectively constituting over 60% of the FAs of GLs. In terms of PLs and glycerolipids, S. platensis and C. vulgaris displayed elevated levels of arachidonic acid (AA) and eicosapentaenoic acid (EPA), whereas S. limacinum exhibited a significant presence of docosahexaenoic acid (DHA). Principal component analysis (PCA) revealed MGDG (16:0/18:1), DG (16:0/22:5), Cer (d18:1/20:0), and LPC (16:1) as promising lipid markers for discriminating between these microalgae samples. This study contributes to a comprehensive understanding of lipid profiles in three microalgae species, emphasizing their distinct biochemical characteristics and potentially informing us of their high-value utilization in the food industry.

The stability-indicating approach for tavaborole quantification was developed and validated to establish a precise, linear, accurate, and robust HPLC method. The development section includes optimizing the detection wavelength, the mobile phase ratio, and the type of column used to achieve the best possible separation and sensitivity for analysis. The chromatographic conditions were established, considering peak symmetry, resolution, and retention time. The mobile phase composition, comprising a buffer: acetonitrile (75 : 25, %v/v), with an injection volume of 15 μL, showed suitable elution and recovery at 265 nm. A constant column oven temperature of 35 °C and a 1 mL min-1 flow rate were maintained. The pH of the buffer was changed to 3.0 by using orthophosphoric acid. Linearity was observed from 5 to 1000 ppm (r2 = 1.00000). The capacity (retention) factor (k) of 3.43 was observed, indicating significant interaction and good separation. Forced degradation (FD) or stress tests were performed for chemical and physical photolytic stress conditions, and the results observed were within the specified limits. The stability in the analytical solution was observed for up to 35 hours at 5 °C, confirming the stability of the solution. Validation of the developed HPLC method confirmed the system\'s suitability, precision, linearity, accuracy, FD, robustness, and results. All validation criteria for the technique were within acceptable limits.

Aim: A HPLC method was developed and validated for the novel combination of rutin (RN) and donepezil (DNP). Materials & methods: RN and DNP were simultaneously eluted through a C18 column (Ø 150 × 4.6 mm) with a 60:40 v/v ratio of 0.1% formic acid aqueous solution to methanol at 0.5 ml/min. Results: The purposed method was found linear, selective, reproducible, accurate and precise with percent RSD less than 2. The limit of quantification for RN and DNP was found 3.66 and 3.25 μg/ml, respectively. Conclusion: Validated as per the ICH guidelines, the developed method efficiently quantified RN and DNP co-loaded in DQAsomes (121 nm) estimating matrix effect, release profile, entrapment efficiency, loading efficiency and in vivo plasma kinetics.
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A quick, simple, sensitive, efficient and stability-indicating reverse-phase ultraperformance liquid chromatographic method for the estimation of propylparaben, methylparaben and sodium benzoate in a pharmaceutical liquid oral formulation was developed. A Waters Acquity UPLC BEH C18, 50 × 2.1 mm, 1.7 μm i.d. column was used to perform chromatographic separation with a 0.1% perchloric acid mobile phase used as solvent A and a mixture of 0.1 % perchloric acid and methanol in the ratio 20:80 (v/v), respectively, as solvent B. The experiments were carried out at a flow rate of 0.4 ml/min and the detection wavelength was 240 nm. The compartment temperature of the column was set at 40°C and the injection volume was set at 2 μl. The main aim of the research was to develop a single UPLC assay method for promethazine (active ingredient) and preservatives in the oral solution of promethazine HCl and dextromethorphan HBr that contains promethazine (active ingredient) and methylparaben, propylparaben and sodium benzoate (preservatives). An assay of dextromethorphan HBr was developed and validated by another HPLC method. The drug and preservatives were eluted at retention times of 19.3 min for promethazine HCl, 9.3 min for methylparaben, 18.9 min for propylparaben and 8.9 min for sodium benzoate. Validation of the developed method was carried out as stated by the International Conference on Harmonization guidelines ICH Q2B and under USP<1225>. The analytical parameters verified specificity/selectivity, linearity, accuracy, ruggedness and robustness. The linearity ranges of promethazine HCL, methylparaben, propylparaben and sodium benzoate were 10-100, 10-80, 1.0-8.0 and 10-80 μg/ml, respectively, with a correlation coefficient of active ingredients and preservatives of 1.00. Percentage recoveries of promethazine, propylparaben, methylparaben, and sodium benzoate were 100.0-100.2, 99.0-100.3, 99.5-98.0 and 99.0-100.0%. The validated analytical method proves that the method is specific, precise, linear, accurate, sensitive, rugged and stable, indicating the quantification of the active ingredient and all preservatives in liquid oral formulations.