Ana Protić

Study of forced degradation behavior of Eletriptan hydrobromide by LC and LC–MS and development of stability-indicating method

The objective of the present study was to report the stability profile of novel antimigrain drug Eletriptan hydrobromide based on the information obtained from forced degradation studies. The drug was subjected to acid (0.1-1 mol L(-1) HCl), neutral and base (0.1-1 mol L(-1) NaOH) hydrolysis and to oxidative decomposition (3-15% (v/v) H(2)O(2)). Photolysis and thermo degradation at 75 degrees C were carried out in methanol solution and in solid state with both Eletriptan hydrobromide bulk drug and the tablet formulation. The products formed under different stress conditions were investigated by LC and LC-MS. The experimental conditions for LC were chosen by employing experimental design and multicriteria decision making methodology. These powerful tools enabled the accomplishment of satisfactory resolution with the shortest possible analysis time. Analytes were separated on a C(18) column (XTerra, 150 mm x 3.9 mm, 5 microm) with the mobile phase composed of methanol-water solution of TEA (pH 6.52, 1%, v/v) (30:70, v/v) pumped at 1 mL min(-1) flow rate. The column temperature was set at 50 degrees C and the detection at 225 nm using DAD detector. The LC method was suitably modified for LC-MS analysis which was further used to characterize the arisen degradation products. The possible degradation pathway was outlined based on the results. The drug appeared to be instable towards every stress condition but oxidation. The stability was not jeopardized even under more exaggerated conditions such as increased temperature of the solutions to 75 degrees C, increased strength of acid/alkali solutions and prolonged testing period. Validation of the LC-DAD method was carried out in accordance with ICH guideline. The method met all required criteria and was applied when testing the commercially available tablets.

Multicriteria optimization methodology in development of HPLC separation of mycophenolic acid and mycophenolic acid glucuronide in human urine and plasma.

Multicriteria optimization methodology was applied for development of isocratic reversed-phased HPLC method for simultaneous determination of mycophenolic acid (MPA) and mycophenolic acid glucuronide (MPAG) in human urine and plasma. In the first stage of method development, pH value of the water phase, percentage of acetonitrile, temperature of the column and flow rate of the mobile phase were investigated using fractional factorial design. Afterwards, the optimal conditions were found employing central composite design and Derringers desirability function. Two goals were considered, the retention factor of the MPAG to be in the range, between 0.8 and 1.118 which allowed well separation of MPAG from the urine and plasma peaks, and the shortest possible total analysis run time. Then, the obtained sigmoid functions were used to transform the optimization criteria into the desirability values. The satisfactory chromatographic conditions were obtained with mobile phase consisted of acetonitrile-phosphate buffer (pH 2.4; 0.04 M KH(2)PO(4)) (28:72, v/v). The separation was performed on C(18) Chromolith column (100 mm x 4.6 mm) with flow rate of 5 mL/min, the temperature of the column was 25 degrees C and the chosen wavelength for simultaneous determination of MPA and MPAG was 215 nm. The MPAG eluted at 0.552 min and the duration of run was 3.092 min. Afterwards, the method was subjected to validation. Linearity was observed over the concentration range of 1-50 microg/mL for MPA and 1-500 microg/mL for MPAG in urine and 1-60 microg/mL for MPA and 1-70 microg/mL for MPAG in plasma matrix. The method showed intra-day and inter-day precision with relative standard deviation lower then 5% and accuracy as recovery (%) between 100+/-5%.

Quantitative structure-retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography.

Artificial neural network (ANN) is a learning system based on a computational technique which can simulate the neurological processing ability of the human brain. It was employed for building of the quantitative structure-retention relationships (QSRRs) model of antifungal agents-imidazoles or triazoles by structure. Computed molecular descriptors together with the percentage of acetonitrile in mobile phase (v/v) and buffer pH, being the most influential HPLC factors, were used as network inputs, giving the retention factor as model output. The multilayer perceptron network with a 9-5-1 topology was trained by using the back propagation algorithm. Good correlation between experimentally obtained data and ones predicted by using QSRR-ANN on previously unseen data sets indicates good predictive ability of the model.

Structure–response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks

Quantitative structure-property relationship (QSPR) methods are based on the hypothesis that changes in the molecular structure are reflected in changes in the observed property of the molecule. Artificial neural network is a technique of data analysis, which sets out to emulate the human brains way of working. For the first time a quantitative structure-response relationship in electrospray ionization-mass spectrometry (ESI-MS) by means of artificial neural networks (ANN) on the group of angiotensin II receptor antagonists--sartans has been established. The investigated descriptors correspond to different properties of the analytes: polarity (logP), ionizability (pKa), surface area (solvent excluded volume) and number of proton acceptors. The influence of the instrumental parameters: methanol content in mobile phase, mobile phase pH and flow rate was also examined. Best performance showed a multilayer perceptron network with the architecture 6-3-3-1, trained with backpropagation algorithm. It showed high prediction ability on the previously unseen (test) data set with a coefficient of determination of 0.994. High prediction ability of the model would enable prediction of ESI-MS responsiveness under different conditions. This is particularly important in the method development phase. Also, prediction of responsiveness can be important in case of gradient-elution LC-MS and LC-MS/MS methods in which instrumental conditions are varied during time. Polarity, chargeability and surface area all appeared to be crucial for electrospray ionization whereby signal intensity appeared to be the result of a simultaneous influence of the molecular descriptors and their interactions. Percentage of organic phase in the mobile phase showed a positive, while flow rate showed a negative impact on signal intensity.

Development and validation of reversed phase high performance liquid chromatographic method for determination of moxonidine in the presence of its impurities.

A simple, rapid, isocratic reversed-phase high-performance liquid chromatographic method was developed and validated for the analysis of moxonidine and its impurities in tablet formulations. The chromatographic separation was achieved on a Symmetry shield C18 column (250 mm × 4.6 mm, 5 μm) by employing a mobile phase consisting of methanol-potassium phosphate buffer (0.05 M) mixture (15:85, v/v) (pH 3.5) at a flow rate of 1 ml min⁻¹; detection at 255 nm. Central composite design technique and response surface method were used to evaluate the effects of variations of selected factors (buffer pH value, column temperature, methanol content) in order to achieve the best isocratic separation within short analysis time (less than 10 min), as well as for robustness test considerations. The method fulfilled the validation criteria: specificity, linearity, accuracy, precision, limit of detection and limit of quantitation. The method was successfully applied for the analysis of commercial moxonidine tablets.

FORCED DEGRADATION STUDY OF TORASEMIDE: CHARACTERIZATION OF ITS DEGRADATION PRODUCTS

Torasemide was subjected to forced degradation studies. Stress conditions were varied concerning hydrolysis (acid, base, and neutral), oxidation, photolysis, and thermal degradation in order to identify the potential degradation products and consequently establish the possible degradation pathways and intrinsic stability of the drug. The study was performed according to ICH guidelines and drug was found to be relatively stable in the solid form. It showed that torasemide degraded significantly under acidic, neutral and alkaline conditions and resulted in formation of degradation product R2. When temperature was increased the degradation was accelerated. Also, the drug showed slight instability under extreme oxidative stress conditions which resulted in formation of two degradation products in total. The drug and degradation products have been separated employing gradient elution method on Zorbax SB C18 analytical column. To characterize the degradation products LC–MSn was applied. The mass fragmentation pattern was established using single quadrupole and ion trap mass analyzers. Finally, the most possible degradation mechanism of torasemide in different experimental conditions was proposed.

Quantitative structure–retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brains way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans.

Chemometrically Assisted Development and Validation of LC–UV and LC–MS Methods for Simultaneous Determination of Torasemide and its Impurities

Complete evaluation of chromatographic behavior and establishment of optimal experimental conditions for determination of torasemide and its four impurities are determined by experimental design. Fractional factorial and 3(n) full factorial design were employed for efficient and rapid optimization of liquid chromatography-ultraviolet and liquid chromatography-mass spectrometry (LC-MS) methods. Separation is achieved on a Zorbax SB C(18) analytical column (250 x 4.6 mm, 5 µm) with mobile phase consisting of acetonitrile and 10 mM ammonium formate (pH 2.5 with formic acid) in gradient mode. The flow rate is 1 mL min(-1), the temperature of the column is 25 °C and UV detection is performed at 290 nm. The efficiency of ionization in electrospray ionization is higher than in atmospheric pressure chemical ionization mode; therefore, it is further used for analysis of torasemide and its impurities. Both methods meet all validation criteria. The calibration curves show high linearity with the coefficients of correlation (r) greater than 0.9982. The obtained recovery values (95.78-104.92%) and relative standard deviation values (0.12-5.56%) indicate good accuracy and precision. Lower limit of detection (LOD) and limit of quantitation (LOQ) values are obtained with the LC-MS method, indicating higher sensitivity of the proposed method.

IMPURITY PROFILING OF MYCOPHENOLATE MOFETIL WITH THE ASSISSTANCE OF DESIRABILITY FUNCTION IN METHOD DEVELOPMENT

Multicriteria decision making methodology in combination with experimental design has been applied for optimization of stability-indicating LC/DAD method of Mycophenolate mofetil and its degradation products. Via fractional factorial design variables with statistically significant impact on retention parameters of the investigated substances were defined and then thoroughly optimized toward four chromatographic responses combining central composition design and desirability function. The separation was achieved on Chromolith column (C18e, 100 × 4.6 mm) with the mobile phase consisted of acetonitrile—0.015 M KH2PO4 (pH 4.4) (28:72, v/v). The flow rate of the mobile phase was 2 mL/min and the detection was performed using PDA detector at 215 nm. The method has been suitably modified for LC/MS/MS analysis. The ammonium-acetate buffer was used instead of phosphate buffer and pH of the water phase was adjusted with acetic acid. The flow rate was 1 mL min−1. The substance was subjected to stress conditions (0.01 M NaOH, 1 M HCl, 3–30% H2O2, 70°C and day light). It resulted in formation of Mycophenolic acid and two oxidation products. The substance showed to be stable only towards photolysis. The LC/MS/MS values and fragmentation pattern of two unknown oxidation products were used in its characterization.

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

AbstractApplying 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 β–CD complexes and predict which part of the molecule structure will most likely incorporate into the β–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 AbstractComplex association constants as inputs of QSRR model in β-cyclodextrin modified HPLC system and development of green chromatography methods