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

When cyclodextrins (CDs) are used in chromatography analytes' retention time is decreased with an increase 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 approach implies extensive and time-consuming HPLC experiments, the goal of this research was to investigate the possibility of using in silico prediction tools instead. Quantitative structure-retention relationship (QSRR) model previously developed to explain the retention behavior of risperidone, olanzapine and their structurally related impurities in ß-CD modified HPLC system was applied to predict retention factor 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 ß-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 predicted 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 ß-CD modified systems.

Characterization of Biomolecules with Antibiotic Activity from Endophytic Fungi Phomopsis Species.

Recently, growing interest is devoted to investigation of bioactive secondary metabolites of endophytic fungi. Thus, as an extension to our previous achievements related to antimicrobial potential of endophytic fungi, Phomopsis species isolated from conifer needles was selected as appropriately promising natural source for drug discovery. Its dichloromethane and ethanol extracts considerably inhibited growth of Escherichia coli and Staphylococcus aureus. Moreover, the individual compounds of dichloromethane extract have been separated, collected and purified using semi preparative liquid chromatographic analysis and comprehensively characterized using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR). Based on their antimicrobial activity and unique structural characteristics in comparison with well-established drugs from the same therapeutic category, two dominant compounds (Z)-(Z)-2-acetoxyprop-1-en-1-yl-3-(3-((E)-3,4-dihydroxypent-1-en-1-yl)oxiran-2-yl)acrylate (denoted as 325-3) and (Z)-(Z)-2-acetoxyprop-1-en-1-yl 3-(3-((E)-4-hydroxy-3-oxopent-1-en-1-yl)oxiran-2-yl)acrylate (denoted as 325-5) were recognized as valuable leading structures for future discovery of novel antibiotics.

Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response.

In this study, a quantitative structure-property relationship model was built in order to link molecular descriptors and chromatographic parameters as inputs towards CAD responsiveness. Aminoglycoside antibiotics, sugars, and acetylated amino sugars, which all lack a UV/vis chromophore, were selected as model substances due to their polar nature that represents a challenge in generating a CAD response. Acetone, PFPA, flow rate, data rate, filter constant, SM5_B(s), ATS7s, SpMin1_Bh(v), Mor09e, Mor22e, E1u, R7v+, and VP as the most influential inputs were correlated with the CAD response by virtue of ANN applying a backpropagation learning rule. External validation on previously unseen substances showed that the developed 13-6-3-1 ANN model could be used for CAD response prediction across the examined experimental domain reliably (R2 0.989 and RMSE 0.036). The obtained network was used to reveal CAD response correlations. The impact of organic modifier content and flow rate was in accordance with the theory of the detector's functioning. Additionally, the significance of SpMin1_Bh(v) aided in emphasizing the often neglected surface-dependent CAD character, while the importance of Mor22e as a molecular descriptor accentuated its dependency on the number of electronegative atoms taking part in charging the formed particles. The significance of PFPA demonstrated the possibility of using evaporative chaotropic reagents in CAD response improvement when dealing with highly polar substances that act as kosmotropes. The network was also used in identifying possible interactions between the most significant inputs. A joint effect of PFPA and acetone was shown, representing a good starting point for further investigation with different and, especially, eco-friendly organic solvents and chaotropic agents in the routine application of CAD.

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.

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 ß-CD complexes and predict which part of the molecule structure will most likely incorporate into the ß-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 (R2) 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. Graphical Abstract Complex association constants as inputs of QSRR model in ß-cyclodextrin modified HPLC system and development of green chromatography methods.

Use of selective serotonin re-uptake inhibitors and the heart rate corrected QT interval in a real-life setting: the population-based Rotterdam Study.

AIMS: Selective serotonin re-uptake inhibitors (SSRIs), specifically citalopram and escitalopram, are thought to cause QTc prolongation, although studies have shown contradictory results. Nevertheless, a maximum citalopram dosage of 20 mg in high risk patients (e.g. >60 years of age) is recommended. We aimed to investigate the association between use of (individual) SSRIs and QTc in a population-based study in older adults. METHODS: This study, which was part of the prospective Rotterdam Study (period 1991-2012), included participants with up to five electrocardiograms (ECGs). We used linear mixed models to compare QTc F (QT corrected according to Fridericia) measured during use of individual SSRIs with QTc F measured during non-use of any antidepressant. For citalopram, analyses were additionally restricted to a maximum dosage of 20 mg in participants aged 60 years and older. RESULTS: We included 12 589 participants with a total of 26 620 ECGs of which 436 ECGs were made during SSRI use. The mean QTc F was similar during use of any drugs from the SSRI class and during non-use. After stratifying to individual SSRIs, ECGs recorded during use of citalopram had the longest QTc compared with ECGs recorded during non-use (+12.8 ms, 90% CI 7.5, 18.2). This result remained similar in the analysis comprising participants aged 60 years and older with a maximum prescribed daily dosage of 20 mg citalopram. CONCLUSIONS: Although no SSRI class effect was observed, use of citalopram was associated with a longer QTc F, even after considering the recommended restrictions. Other SSRIs may not give a clinically relevant QTc F prolongation.