Iron preparations are used in the treatment or prophylaxis of sideropenic anemia developed due to... more Iron preparations are used in the treatment or prophylaxis of sideropenic anemia developed due to insuficiency of iron in the body. They can be used orally (tablets, capsules, suspensiones, wines) and parenterally. The aim of this paper was to use experimental ...
ABSTRACT The novel, rapid high performance liquid chromatographic method for the determination of... more ABSTRACT The novel, rapid high performance liquid chromatographic method for the determination of tramadol hydrochloride and its three impurities was developed and validated. The method can simultaneously assay potassium sorbate, used as preservative, and saccharin sodium, used as sweetener in tramadol pharmaceutical formulation. The separation was carried out on a C(18) XTerra (150 mm x 4.6 mm, 5 mm) column using acetonitrile-0.015 M Na(2)HPO(4) buffer (2:8, v/v) as mobile phase (pH value 3.0 was adjusted with orthophosphoric acid) at a flow rate 1.0 ml min(-1), temperature of the column 20 degrees C and UV detection at 218 nm. The method was found to be linear (r > 0.999) in the range of 0.05-0.8 mg ml(-1) for tramadol hydrochloride, 0.1-1.2 mg ml(-1) for impurities B and C and for impurity A (r > 0.995) in the range 0.15-2.4 mg ml(-1). The low RSD values indicate good precision and high recovery values indicate excellent accuracy of the HPLC method. Developed method was successfully applied to the determination of tramadol hydrochloride, its investigated impurities and potassium sorbate in commercial formulation. The recovery of tramadol hydrochloride was 98.25% and RSD was 1.80%. The method is rapid and sensitive enough to be used to analyse Trodon oral drops.
Rapid Communications in Mass Spectrometry, Nov 4, 2015
RATIONALE Undeclared corticosteroids in creams intended for frequent use might cause serious side... more RATIONALE Undeclared corticosteroids in creams intended for frequent use might cause serious side-effects, especially in children. In order to prevent this or find the cause, it was essential to develop a method for quick detection and quantification of low levels of corticosteroids. METHODS Eleven corticosteroids were used in this study: prednisolone, methylprednisolone, prednisolone-21-acetate, fluocinolone acetonide, fluocinolone acetonide-21-acetate, hydrocortisone-21-acetate, dexamethasone, betamethasone, betamethasone dipropionate, clobetasol propionate and triamcinolone. Separation was achieved via liquid chromatography (LC), and mass spectrometric analysis was conducted by electrospray ionization triple-quadrupole mass spectrometry (MS/MS) in the multiple reaction monitoring mode using corticosterone as internal standard. RESULTS Good separation by using a gradient-elution LC/MS/MS method with run time of 25 min enabled the use of a segmented detection method and consecutive decrease in detection limits. The proposed method has been validated in the linearity range of 10-1000 ng/mL with coefficients of determination higher than 0.990. The method has shown to have very low limits of quantification (0.75-3 ng/mL) with satisfactory precision and accuracy for each of the corticosteroids. CONCLUSIONS An LC/MS/MS method for the rapid and simultaneous determination of low levels of eleven topical corticosteroids in creams was developed, optimized and validated. The proposed method can be used for testing of different products indicated for the treatment of atopic dermatitis, including "natural products", and "herbal creams" with "miraculous effects".
The quantitative structure-retention relationship (QSRR) models are not only employed in retentio... more The quantitative structure-retention relationship (QSRR) models are not only employed in retention behaviour prediction, but also in an in-depth understanding of complex chromatographic systems. The goal of the present research is to enable the comprehensive understanding of retention underlying the separation in β-cyclodextrin (CD) modified reversed-phase high performance liquid chromatography (RP-HPLC) systems, through the development of mixed QSRR models. Moreover, the amount of β-CD adsorbed on the stationary phase surface (β-CDA) is added as the model's input in order to evaluate its contribution to both model performances and retention. Nuclear magnetic resonance (NMR) experiments were conducted to confirm the predicted inclusion complex structures and support the application of in silico tools. The most significant descriptors revealed that retention is governed by the steric factors 7.5 Å distant from the geometrical centre of a molecule, 3D arrangement of atoms determining the molecular size and shape, lipophilicity indicated by topological distances, as well as the unbound system's energy, related to the inclusion complex formation. In addition, a notable effect of the pH of the aqueous phase on the retention of ionizable analytes was shown. In the case of pH of the aqueous phase and β-CDA the change in retention behaviour of the studied analytes was observed only at the highest β-CDA value (5.17 μM/m2), but it was not related to the ionization state of analytes. When the analytes did not change the ionization form across the investigated studied pH range, and the acetonitrile content in the mobile phase was 25% (v/v), the retention factor had low values regardless of the β-CDA; under these circumstances the retention is probably acetonitrile driven.
Journal of Pharmaceutical and Biomedical Analysis, 2022
Nowadays, method development is strongly focused on reducing time needed for method development a... more Nowadays, method development is strongly focused on reducing time needed for method development and execution. This subject specially concerns gradient elution methods regarding the usual need for troubleshooting assistance with uncertain outcome during the method transfer from one laboratory to another. One of the main reasons for this situation is the dwell volume difference between HPLC systems. Therefore, the aim of this study was to propose a novel method development methodology that would integrate the dwell volumes differences in the optimization process. The proposed approach could be quite useful in industry that has insight in HPLC instruments planned to be used during the method life cycle. It was tested on the model mixture consisting of dabigatran etexilate mesylate and its nine impurities by use of experimental design methodology. Three different (U)HPLC instruments with high dwell volume differences were selected to challenge the methodology. Plan of experiments was defined with Plackett-Burman design for screening phase and D-optimal design for optimization phase. Initial and final amount of organic modifier, time of the gradient elution and pH value of the aqueous phase were selected as variables significant for the gradient programme profile and included in the optimization stage along with dwell volume values. The separation criteria s between critical peak pairs was selected as output for method optimization while indirect modelling together with Monte Carlo simulations enabled selection of optimal and robust chromatographic conditions. They included 24% (v/v) of initial amount of acetonitrile, 54% (v/v) of the final amount of acetonitrile, 15 min of gradient elution run time and pH value equal to 4.9. The proposed method was successfully validated, met all validation criteria and thus proved its utility.
When cyclodextrins (CDs) are used in chromatography analytes' retention time is decreased wit... more 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 β-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 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 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 β-CD modified systems.
In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in exce... more In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in excess is accompanied by an alteration of its solubilising capacity and a change in the stationary phase's properties. As an implication, the prediction of the analytes' retention in MLC mode becomes a challenging task. Mixed Quantitative Structure - Retention Relationships (QSRR) modelling represents a powerful tool for estimating the analytes' retention. This study compares 48 successfully developed mixed QSRR models with respect to their ability to predict retention of aripiprazole and its five impurities from molecular structures and factors that describe the Brij - acetonitrile system. The development of the models was based on an automatic combining of six attribute (feature) selection methods with eight predictive algorithms and the optimization of hyper-parameters. The feature selection methods included Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), ReliefF, Multiple Linear Regression (MLR), Mutual Info and F-Regression. The series of investigated predictive algorithms comprised Linear Regressions (LR), Ridge Regression, Lasso Regression, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosted Trees (GBT) and K-Nearest neighbourhood (k-NN). A sufficient amount of data for building the model (78 cases in total) was provided by conducting 13 experiments for each of the 6 analytes and collecting the target responses afterwards. Different experimental settings were established by varying the values of the concentration of Brij L23, pH of the aqueous phase and acetonitrile content in the mobile phase according to the Box-Behnken design. In addition to the chromatographic parameters, the pool of independent variables was expanded by 27 molecular descriptors from all major groups (physicochemical, quantum chemical, topological and spatial structural descriptors). The best model was chosen by taking into consideration the Root Mean Square Error (RMSE) and cross-validation (CV) correlation coefficient (Q2) values. Interestingly, the comparative analysis indicated that a change in the set of input variables had a minor impact on the performance of the final models. On the other hand, different regression algorithms showed great diversity in the ability to learn patterns conserved in the data. In this regard, testing many regression algorithms is necessary in order to find the most suitable technique for model building. In the specific case, GBT-based models have demonstrated the best ability to predict the retention factor in the MLC mode. Steric factors and dipole-dipole interactions have proven to be relevant to the observed retention behaviour. This study, although being of a smaller scale, is a most promising starting point for comprehensive MLC retention prediction.
Purification of biological matrix prior to HPLC analysis has been complex procedure and source of... more Purification of biological matrix prior to HPLC analysis has been complex procedure and source of great variability of analytical results. The most used biological matrixes used for analysis are plasma, serum, urine and saliva and it has been advisable to use the simplest procedure for purification of these samples. Biological matrixes are complex and variability of its content is the main problem in development of bioanalytical methods. Namely, plasma and urine samples contain large number of endogenous compounds in concentrations much larger than concentration of investigated analyte. The concentrations of investigated analytes are often in very low concentrations and its structure can be very similar to structure of some endogenous compounds. Due to this problem, purification and concentration of biomatrix is one of the most important steps in development of bioanalytical methods. For bioanalytical methods the most important parameters are reliability and repeatability of the analytical results. Validation of bioanalytical chromatographic methods can be conducted according to The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH), Food and Drug Administration (FDA) and European Medicines Agency (EMA). During the validation process selectivity, limit of detection (LOD), lower limits of quantification (LLOQ), range, linearity, precision, accuracy, stability and efficacy of biological sample purification have to be investigated.
QSRR are mathematically derived relationships between the chromatographic parameters determined f... more 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 brain's 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 15.5min under following conditions: gradient time of 12.5min, buffer pH of 3.95 and buffer molarity of 25mM. 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.
Chemometrics and Intelligent Laboratory Systems, 2015
ABSTRACT Artificial neural network (ANN) is a learning system based on a computation technique wh... more ABSTRACT Artificial neural network (ANN) is a learning system based on a computation technique which was employed for building of the quantitative structure-retention relationship (QSRR) model for candesartan cilexetil and its degradation products. Candesartan cilexetil has been exposed to forced degradation conditions and degradation products have been subsequently identified with the assistance of HPLC-MS technique. Molecular descriptors have been computed for all compounds and were optimized together with significant chromatographic parameters employing developed QSRR models. In this way, QSRR has been used in development of HPLC stability-indicating method, optimal conditions towards various outputs have been established and high prediction potential of the created QSRR models has been proved.
Artificial neural network (ANN) is a learning system based on a computational technique which can... more 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.
Iron preparations are used in the treatment or prophylaxis of sideropenic anemia developed due to... more Iron preparations are used in the treatment or prophylaxis of sideropenic anemia developed due to insuficiency of iron in the body. They can be used orally (tablets, capsules, suspensiones, wines) and parenterally. The aim of this paper was to use experimental ...
ABSTRACT The novel, rapid high performance liquid chromatographic method for the determination of... more ABSTRACT The novel, rapid high performance liquid chromatographic method for the determination of tramadol hydrochloride and its three impurities was developed and validated. The method can simultaneously assay potassium sorbate, used as preservative, and saccharin sodium, used as sweetener in tramadol pharmaceutical formulation. The separation was carried out on a C(18) XTerra (150 mm x 4.6 mm, 5 mm) column using acetonitrile-0.015 M Na(2)HPO(4) buffer (2:8, v/v) as mobile phase (pH value 3.0 was adjusted with orthophosphoric acid) at a flow rate 1.0 ml min(-1), temperature of the column 20 degrees C and UV detection at 218 nm. The method was found to be linear (r > 0.999) in the range of 0.05-0.8 mg ml(-1) for tramadol hydrochloride, 0.1-1.2 mg ml(-1) for impurities B and C and for impurity A (r > 0.995) in the range 0.15-2.4 mg ml(-1). The low RSD values indicate good precision and high recovery values indicate excellent accuracy of the HPLC method. Developed method was successfully applied to the determination of tramadol hydrochloride, its investigated impurities and potassium sorbate in commercial formulation. The recovery of tramadol hydrochloride was 98.25% and RSD was 1.80%. The method is rapid and sensitive enough to be used to analyse Trodon oral drops.
Rapid Communications in Mass Spectrometry, Nov 4, 2015
RATIONALE Undeclared corticosteroids in creams intended for frequent use might cause serious side... more RATIONALE Undeclared corticosteroids in creams intended for frequent use might cause serious side-effects, especially in children. In order to prevent this or find the cause, it was essential to develop a method for quick detection and quantification of low levels of corticosteroids. METHODS Eleven corticosteroids were used in this study: prednisolone, methylprednisolone, prednisolone-21-acetate, fluocinolone acetonide, fluocinolone acetonide-21-acetate, hydrocortisone-21-acetate, dexamethasone, betamethasone, betamethasone dipropionate, clobetasol propionate and triamcinolone. Separation was achieved via liquid chromatography (LC), and mass spectrometric analysis was conducted by electrospray ionization triple-quadrupole mass spectrometry (MS/MS) in the multiple reaction monitoring mode using corticosterone as internal standard. RESULTS Good separation by using a gradient-elution LC/MS/MS method with run time of 25 min enabled the use of a segmented detection method and consecutive decrease in detection limits. The proposed method has been validated in the linearity range of 10-1000 ng/mL with coefficients of determination higher than 0.990. The method has shown to have very low limits of quantification (0.75-3 ng/mL) with satisfactory precision and accuracy for each of the corticosteroids. CONCLUSIONS An LC/MS/MS method for the rapid and simultaneous determination of low levels of eleven topical corticosteroids in creams was developed, optimized and validated. The proposed method can be used for testing of different products indicated for the treatment of atopic dermatitis, including "natural products", and "herbal creams" with "miraculous effects".
The quantitative structure-retention relationship (QSRR) models are not only employed in retentio... more The quantitative structure-retention relationship (QSRR) models are not only employed in retention behaviour prediction, but also in an in-depth understanding of complex chromatographic systems. The goal of the present research is to enable the comprehensive understanding of retention underlying the separation in β-cyclodextrin (CD) modified reversed-phase high performance liquid chromatography (RP-HPLC) systems, through the development of mixed QSRR models. Moreover, the amount of β-CD adsorbed on the stationary phase surface (β-CDA) is added as the model's input in order to evaluate its contribution to both model performances and retention. Nuclear magnetic resonance (NMR) experiments were conducted to confirm the predicted inclusion complex structures and support the application of in silico tools. The most significant descriptors revealed that retention is governed by the steric factors 7.5 Å distant from the geometrical centre of a molecule, 3D arrangement of atoms determining the molecular size and shape, lipophilicity indicated by topological distances, as well as the unbound system's energy, related to the inclusion complex formation. In addition, a notable effect of the pH of the aqueous phase on the retention of ionizable analytes was shown. In the case of pH of the aqueous phase and β-CDA the change in retention behaviour of the studied analytes was observed only at the highest β-CDA value (5.17 μM/m2), but it was not related to the ionization state of analytes. When the analytes did not change the ionization form across the investigated studied pH range, and the acetonitrile content in the mobile phase was 25% (v/v), the retention factor had low values regardless of the β-CDA; under these circumstances the retention is probably acetonitrile driven.
Journal of Pharmaceutical and Biomedical Analysis, 2022
Nowadays, method development is strongly focused on reducing time needed for method development a... more Nowadays, method development is strongly focused on reducing time needed for method development and execution. This subject specially concerns gradient elution methods regarding the usual need for troubleshooting assistance with uncertain outcome during the method transfer from one laboratory to another. One of the main reasons for this situation is the dwell volume difference between HPLC systems. Therefore, the aim of this study was to propose a novel method development methodology that would integrate the dwell volumes differences in the optimization process. The proposed approach could be quite useful in industry that has insight in HPLC instruments planned to be used during the method life cycle. It was tested on the model mixture consisting of dabigatran etexilate mesylate and its nine impurities by use of experimental design methodology. Three different (U)HPLC instruments with high dwell volume differences were selected to challenge the methodology. Plan of experiments was defined with Plackett-Burman design for screening phase and D-optimal design for optimization phase. Initial and final amount of organic modifier, time of the gradient elution and pH value of the aqueous phase were selected as variables significant for the gradient programme profile and included in the optimization stage along with dwell volume values. The separation criteria s between critical peak pairs was selected as output for method optimization while indirect modelling together with Monte Carlo simulations enabled selection of optimal and robust chromatographic conditions. They included 24% (v/v) of initial amount of acetonitrile, 54% (v/v) of the final amount of acetonitrile, 15 min of gradient elution run time and pH value equal to 4.9. The proposed method was successfully validated, met all validation criteria and thus proved its utility.
When cyclodextrins (CDs) are used in chromatography analytes' retention time is decreased wit... more 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 β-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 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 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 β-CD modified systems.
In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in exce... more In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in excess is accompanied by an alteration of its solubilising capacity and a change in the stationary phase's properties. As an implication, the prediction of the analytes' retention in MLC mode becomes a challenging task. Mixed Quantitative Structure - Retention Relationships (QSRR) modelling represents a powerful tool for estimating the analytes' retention. This study compares 48 successfully developed mixed QSRR models with respect to their ability to predict retention of aripiprazole and its five impurities from molecular structures and factors that describe the Brij - acetonitrile system. The development of the models was based on an automatic combining of six attribute (feature) selection methods with eight predictive algorithms and the optimization of hyper-parameters. The feature selection methods included Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), ReliefF, Multiple Linear Regression (MLR), Mutual Info and F-Regression. The series of investigated predictive algorithms comprised Linear Regressions (LR), Ridge Regression, Lasso Regression, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosted Trees (GBT) and K-Nearest neighbourhood (k-NN). A sufficient amount of data for building the model (78 cases in total) was provided by conducting 13 experiments for each of the 6 analytes and collecting the target responses afterwards. Different experimental settings were established by varying the values of the concentration of Brij L23, pH of the aqueous phase and acetonitrile content in the mobile phase according to the Box-Behnken design. In addition to the chromatographic parameters, the pool of independent variables was expanded by 27 molecular descriptors from all major groups (physicochemical, quantum chemical, topological and spatial structural descriptors). The best model was chosen by taking into consideration the Root Mean Square Error (RMSE) and cross-validation (CV) correlation coefficient (Q2) values. Interestingly, the comparative analysis indicated that a change in the set of input variables had a minor impact on the performance of the final models. On the other hand, different regression algorithms showed great diversity in the ability to learn patterns conserved in the data. In this regard, testing many regression algorithms is necessary in order to find the most suitable technique for model building. In the specific case, GBT-based models have demonstrated the best ability to predict the retention factor in the MLC mode. Steric factors and dipole-dipole interactions have proven to be relevant to the observed retention behaviour. This study, although being of a smaller scale, is a most promising starting point for comprehensive MLC retention prediction.
Purification of biological matrix prior to HPLC analysis has been complex procedure and source of... more Purification of biological matrix prior to HPLC analysis has been complex procedure and source of great variability of analytical results. The most used biological matrixes used for analysis are plasma, serum, urine and saliva and it has been advisable to use the simplest procedure for purification of these samples. Biological matrixes are complex and variability of its content is the main problem in development of bioanalytical methods. Namely, plasma and urine samples contain large number of endogenous compounds in concentrations much larger than concentration of investigated analyte. The concentrations of investigated analytes are often in very low concentrations and its structure can be very similar to structure of some endogenous compounds. Due to this problem, purification and concentration of biomatrix is one of the most important steps in development of bioanalytical methods. For bioanalytical methods the most important parameters are reliability and repeatability of the analytical results. Validation of bioanalytical chromatographic methods can be conducted according to The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH), Food and Drug Administration (FDA) and European Medicines Agency (EMA). During the validation process selectivity, limit of detection (LOD), lower limits of quantification (LLOQ), range, linearity, precision, accuracy, stability and efficacy of biological sample purification have to be investigated.
QSRR are mathematically derived relationships between the chromatographic parameters determined f... more 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 brain's 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 15.5min under following conditions: gradient time of 12.5min, buffer pH of 3.95 and buffer molarity of 25mM. 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.
Chemometrics and Intelligent Laboratory Systems, 2015
ABSTRACT Artificial neural network (ANN) is a learning system based on a computation technique wh... more ABSTRACT Artificial neural network (ANN) is a learning system based on a computation technique which was employed for building of the quantitative structure-retention relationship (QSRR) model for candesartan cilexetil and its degradation products. Candesartan cilexetil has been exposed to forced degradation conditions and degradation products have been subsequently identified with the assistance of HPLC-MS technique. Molecular descriptors have been computed for all compounds and were optimized together with significant chromatographic parameters employing developed QSRR models. In this way, QSRR has been used in development of HPLC stability-indicating method, optimal conditions towards various outputs have been established and high prediction potential of the created QSRR models has been proved.
Artificial neural network (ANN) is a learning system based on a computational technique which can... more 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.
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Papers by Biljana Otašević