The potential of corona charged aerosol detector for investigation of telmisartan-β-cyclodextrin inclusion complexes

Cyclodextrins (CDs) are widely used in pharmaceutical analysis due to its biodegradability and eco-friendly character. The particular structure of CDs, characterized by hydrophobic cavity, enables the formation of inclusion complexes with variety of organic compounds. As structures lacking chromophores, CDs could not be detected by Photodiode Array (PDA) detector and Corona Charged Aerosol Detector (CAD) was introduced as the most appropriate detector for the formed complexes. The aim of the study was to investigate the degree of complexation between telmisartan, used as a model substance, and β-CD. Moreover, the effect of different β-CD concentrations (5-15 mM) and pH of the aqueous part of mobile phase (3-7) on the degree of complexation was also assessed. The intensity of the formed complex appeared to be the highest when 15 mM β-CD was used for the complexation. Also, it was noticed that the increase in peak areas with an increasing β-CD concentration was more evident at pH 7 in comparison with the same trend at lower pH values. The reproducibility of the measurement was confirmed by low relative standard deviation (RSD) of peak areas within five measurements. These findings support the use of HPLC-CAD methods for studying the process of inclusion complexes formation

Isolation and Determination of Fomentariol: Novel Potential Antidiabetic Drug from Fungal Material

Diabetes mellitus is one of the leading world's public health problems. Therefore, it is of a huge interest to develop new antidiabetic drugs. Apart from traditional therapy of diabetes, nowadays, importance is given to natural substances with antidiabetic potential. Fomes fomentarius is a mushroom widely used for different purposes, due to its range of already confirmed activities. Fomentariol is a constituent of Fomes fomentarius, responsible for its antidiabetic potential. In that respect, it is important to develop a method for isolation and quantification of fomentariol from fungal material, which will be simple and efficient. Multistep, complex extraction applied in the previously reported studies was avoided with ethanol, providing rapid single-step extraction. The presence of fomentariol in ethanolic extract was confirmed by high-resolution mass spectrometry. Semipreparative HPLC method was developed and applied for isolation from ethanol extract and purification of the active compound fomentariol. It was a gradient reversed-phase method with a mobile phase consisting of acetonitrile and 0.1% formic acid in water and total run time of 15 minutes. The amount of 6.5 mg of high-purity fomentariol was determined by quantitative NMR with toluene as internal standard. The isolated and determined amount of substance can be further used for the quantitative estimation of activity of fomentariol

Chemometrically Assisted RP-HPLC Method Development for Efficient Separation of Ivabradine and its Eleven Impurities

The aim of this study was to develop a novel reversed-phase high-performance liquid chromatography (RP-HPLC) method for efficient separation of ivabradine and its 11 impurities. Similar polarity of impurities in the sample mixture made method optimization challenging and accomplishable only when different chemometric tools, such as principal component analysis (PCA), Box–Behnken design (BBD), and desirability function as a multicriteria approach, were employed. The presence of 3 positional isomers (impurities III, V, and VI), keto–enol tautomerism of impurity VII, and diastereoisomers of impurity X made separation of this complex mixture even more challenging. Chromatographic retention parameters obtained with the mobile phase consisting of 30 mM phosphate buffer and acetonitrile (80:20, v/v) on four different RP-HPLC columns at varying pH values (3.0, 4.0, and 5.0) were subjected to the PCA analysis to select the column with the most appropriate selectivity. Then the column temperature, pH of the aqueous component of mobile phase, phosphate buffer molarity and the organic solvent content in the mobile phase were estimated employing BBD. Valid and reliable mathematical models towards resolution of twelve critical peak pairs were obtained. After determination of the desirability making criteria for all responses, desirability functions were established and used in optimization. The proposed optimal chromatographic conditions included the Zorbax Eclipse Plus C18 chromatographic column (100 × 4.6 mm, 3.5 μm), the column temperature of 34 °C, the mobile phase flow rate of 1.6 mL min−1 and the UV detection at 220 nm. The mobile phase consisted of the 28 mM phosphate buffer at pH 6.0 and acetonitrile (85:15, v/v). Separation of one pair of positional isomers was not achieved, so methanol was added to the organic part of mobile phase in small increments with the optimal ratio of methanol to acetonitrile 59:41, v/v. The overall organic component of the mobile phase also increased to 18%, accelerating the chromatographic analysis

Quantitative structure retention relationship modeling as potential tool in chromatographic determination of stability constants and thermodynamic parameters of β-cyclodextrin complexation process

When cyclodextrins (CDs) are used in chromatography analytes’ retention time is decreased with an in- crease in concentration of CD in the mobile phase. Thus complex stability constants can be determined from the change in retention time of the ligand molecule upon complexation. Since the preceding ap- proach implies extensive and time-consuming HPLC experiments, the goal of this research was to inves- tigate the possibility of using in silico prediction tools instead. Quantitative structure–retention relation- ship (QSRR) model previously developed to explain the retention behavior of risperidone, olanzapine and their structurally related impurities in β-CD modified HPLC system was applied to predict retention fac- tor under different chromatographic conditions within the examined domains. Predicted retention factors were further used for calculation of stability constants and important thermodynamic parameters, namely standard Gibbs free energy, standard molar enthalpy and entropy, contributing to inclusion phenomenon. Unexpected prolonged retention with an increase in β-CD concentration was observed, in contrast to the employed chromatographic theory used for the calculation of the stability constants. Consequently, it led to failure in stability constants and thermodynamic parameters calculation for almost all analytes when acetonitrile content was 20% (v/v) across the investigated pH range. Moreover, ionization of investigated analytes and free stationary phase silanol groups are pH dependent, leading to minimization of secondary interactions if free silanol groups are non-ionized at pH lower than 3. In order to prove accuracy of pre- dicted retention factors, HPLC verification experiments were performed and good agreement between predicted and experimental values was obtained, confirming the applicability of proposed in-silico tool. However, the obtained results opened some novel questions and revealed that chromatographic method is not overall applicable in calculation of stability constants and thermodynamic parameters indicating the complexity of β-CD modified systems

Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases

Applying green chromatography methods is currently one of the challenges in liquid chromatography. Among different strategies, using cyclodextrin (CD) mobile phase modifiers was applied in this paper. CDs can form inclusion complexes with a wide variety of hydrophobic organic compounds and, consequently, affect their retention behavior. CD-containing mobile phases possess complicated complexation and adsorption equilibria so retention is dependent not only on chromatographic parameters and properties of the compound but also on properties of compound-CD complex. Docking study was used to calculate association constants of the selected antipsychotics (risperidone, olanzapine, and their impurities) and beta-CD complexes and predict which part of the molecule structure will most likely incorporate into the beta-CD cavity. Quantitative structure-retention relationship model (QSRR) for selected model substances was built employing artificial neural network (ANN) technique. Reliable QSRR model was obtained using molecular descriptors, complex association constants, and chromatographic factors. The multilayer perceptron network with 11-8-1 topology, trained with back propagation algorithm, showed the best performance. Root mean square error for training, validation, and test was 0.2954, 0.3633, and 0.4864, respectively. The correlation coefficient (R-2) between experimentally obtained retention factor values [k(exp)] and values computed or predicted by ANN [k(ANN)] was 0.9962 for training, 0.9927 for validation, and 0.9829 for test, indicating good predictive ability of the model. The optimized network was used for development of green chromatography method for separation of risperidone and its related impurities, as well as olanzapine and its related impurities in a relatively short run time and with low consumption of organic modifier. The developed methods were validated in accordance with ICH Q2 (R1) quideline and all parameters fulfilled the defined criteria. The greenness of the proposed methods has also been demonstrated