Development of Analytical Quality by Design Compliant Chaotropic Chromatography Method for Ziprasidone and Its Five Impurities Determination.

In this study, an AQbD-compliant chaotropic chromatography method for ziprasidone and the determination of its five impurities was developed. The influence of critical method parameters (initial and final methanol fraction in the mobile phase, gradient duration) on the set of selected critical method attributes (t_imp. V, t_imp. V - t_imp. I, S and ) was studied by Box-Behnken design. The errors resulting from the calculation of the model coefficients were propagated to the selected responses by Monte Carlo simulations, and their predictive distribution was obtained. The design space was computed (π ≥ 80%), and a working point was selected: initial methanol fraction 38.5%, final methanol fraction 77.5%, and gradient duration 16.25 min. Furthermore, the quantitative robustness of the developed method was tested using the Plackett-Burman design. P_imp II and P_imp V were found to be significantly affected, the first by mobile phase flow rate and the second by gradient duration. Finally, the method was validated, and its reliability for routine quality control in capsules was confirmed.

Analytical method development supported by DoE-DS approach for enantioseparation of (S,S)- and (R,R)-moxifloxacin.

In this paper, method for enantiomeric purity testing of fourth-generation fluoroquinolone, moxifloxacin hydrochloride, was developed and validated. Exceptional enantioselectivity for this assay was achieved using cyclodextrin type Chiral Stationary Phase (CSP), phenylcarbamate-ß-cyclodextrin CSP, and mobile phase consisted of acetonitrile and triethylammonium acetate (TEAA) buffer. Analytical Quality by Design (AQbD) methodology, comprising Design of Experiments (DoE) - Design Space (DS) approach, was used for method development. In order to select appropriate Critical Method Parameters (CMPs), risk assessment was done using combined three step strategy that involved Ishikawa diagram - CNX (Control, Noise and eXperimental) - FMEA (Failure Mode and Effect Analysis). Three CMPs were further selected and investigated in this study: acetonitrile content in the mobile phase (30-50%, v/v), triethylamine content in the TEAA buffer (0.1-1.5%, v/v) and aqueous phase pH (3.5-4.5). Monte Carlo simulations were performed and 3D-DS was computed. One point situated in the center of 3D-DS was selected as working point for method validation, with the following values of CMPs: acetonitrile content in the mobile phase set to 37% (v/v), triethylamine content in TEAA 0.8% and pH value of the aqueous phase set at 4.0. Also, 2D-DS was created (with fixed one factor - pH value of aqueous phase at 4.0) which also gave us confirmation that the selection of working conditions was suitable. The proposed enantioselective method was further on tested for its quantitative robustness, as well as for its suitability for the intended purpose through validation studies.

Predicting liquid chromatography-electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities.

A priori estimation of analyte response is crucial for the efficient development of liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure-property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC-ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC-ESI/MS that provided a holistic overview of the analyte's response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA-GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.

Analytical Quality by Design: Achieving Robustness of an LC-CAD Method for the Analysis of Non-Volatile Fatty Acids.

An alternative to the time-consuming and error-prone pharmacopoeial gas chromatography method for the analysis of fatty acids (FAs) is urgently needed. The objective was therefore to propose a robust liquid chromatography method with charged aerosol detection for the analysis of polysorbate 80 (PS80) and magnesium stearate. FAs with different numbers of carbon atoms in the chain necessitated the use of a gradient method with a Hypersil Gold C18 column and acetonitrile as organic modifier. The risk-based Analytical Quality by Design approach was applied to define the Method Operable Design Region (MODR). Formic acid concentration, initial and final percentages of acetonitrile, gradient elution time, column temperature, and mobile phase flow rate were identified as critical method parameters (CMPs). The initial and final percentages of acetonitrile were fixed while the remaining CMPs were fine-tuned using response surface methodology. Critical method attributes included the baseline separation of adjacent peaks (α-linolenic and myristic acid, and oleic and petroselinic acid) and the retention factor of the last compound eluted, stearic acid. The MODR was calculated by Monte Carlo simulations with a probability equal or greater than 90%. Finally, the column temperature was set at 33 °C, the flow rate was 0.575 mL/min, and acetonitrile linearly increased from 70 to 80% (v/v) within 14.2 min.

The secret of reversed-phase/weak cation exchange retention mechanisms in mixed-mode liquid chromatography applied for small drug molecule analysis.

Resolving complex sample mixtures by liquid chromatography in a single run is challenging. The so-called mixed-mode liquid chromatography (MMLC) which combines several retention mechanisms within a single column, can provide resource-efficient separation of solutes of diverse nature. The Acclaim Mixed-Mode WCX-1 column, encompassing hydrophobic and weak cation exchange interactions, was employed for the analysis of small drug molecules. The stationary phase's interaction abilities were assessed by analysing molecules of different ionisation potentials. Mixed Quantitative Structure-Retention Relationship (QSRR) models were developed for revealing significant experimental parameters (EPs) and molecular features governing molecular retention. According to the plan of Face-Centred Central Composite Design, EPs (column temperature, acetonitrile content, pH and buffer concentration of aqueous mobile phase) variations were included in QSRR modelling. QSRRs were developed upon the whole data set (global model) and upon discrete parts, related to similarly ionized analytes (local models) by applying gradient boosted trees as a regression tool. Root mean squared errors of prediction for global and local QSRR models for cations, anions and neutrals were respectively 0.131; 0.105; 0.102 and 0.042 with the coefficient of determination 0.947; 0.872; 0.954 and 0.996, indicating satisfactory performances of all models, with slightly better accuracy of local ones. The research showed that influences of EPs were dependant on the molecule's ionisation potential. The molecular descriptors highlighted by models pointed out that electrostatic and hydrophobic interactions and hydrogen bonds participate in the retention process. The molecule's conformation significance was evaluated along with the topological relationship between the interaction centres, explicitly determined for each molecular species through local models. All models showed good molecular retention predictability thus showing potential for facilitating the method development.

Generic approach in a gradient elution HPLC method development that enables troubleshooting free method transfer.

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.

Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach.

The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes' chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99% (Q2: 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy, GBT used almost all independent variables during model building. Finally, it attributed the highest importance to the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic solvent in the mobile phase and the molecular descriptors such as molecular weight (MW), Radial Distribution Function-080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful acetonitrile.

A comprehensive study on retention of selected model substances in ß-cyclodextrin-modified high performance liquid chromatography.

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 ß-cyclodextrin (CD) modified reversed-phase high performance liquid chromatography (RP-HPLC) systems, through the development of mixed QSRR models. Moreover, the amount of ß-CD adsorbed on the stationary phase surface (ß-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 ß-CDA the change in retention behaviour of the studied analytes was observed only at the highest ß-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 ß-CDA; under these circumstances the retention is probably acetonitrile driven.

Corona Charged Aerosol Detector in studying retention and ß-cyclodextrin complex stability using RP-HPLC.

Binding between cyclodextrin (CD) cavity and guest molecule in Reversed Phase High-Performance Liquid Chromatography (RP-HPLC) is dynamic process. In general, increasing CD concentration is inducing inclusion complex formation, leading to reduction of analyte's retention time. Consequently, the shortness in retention time is a measure of complex stability in HPLC. However, under certain experimental conditions, the retention of some analytes could be prolonged even when concentration of CD in the mobile phase is increased. In order to reveal the cause of this unexpected retention behavior, the present study was carried on. The model mixture consisted of risperidone, olanzapine and their related impurities, while ß-CD was selected among CDs, as in the previous study. In order to achieve fast equilibrium between free analyte and ß-CD-analyte complex, ß-CD was not added to the mobile phase, but only to the sample. Detection was performed with Corona Charged Aerosol Detector (CAD), suitable for non-chromophoric ß-CD. When analyzing olanzapine impurity B-ß-CD sample, three peaks were detected, namely free ß-CD, ß-CD-analyte complex and free analyte. The complex stability constant was calculated employing a modification of the Benesi-Hildebrandt equation and CAD has proven to be useful in complex stability constants assessment if retention of free analyte and ß-CD-analyte complex is distinguished. For all other analytes only two peaks could be detected, because free analyte and formed complex are eluting at the same retention time. Under such circumstances, the authors proposed the methodology for calculating stability constants and confirmed its applicability to studied model mixture.

Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography.

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.

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.

Analytical quality by design development of an ecologically acceptable enantioselective HPLC method for timolol maleate enantiomeric purity testing on ovomucoid chiral stationary phase.

Official method in Ph. Eur. for evaluation of timolol enantiomeric purity is normal-phase high performance liquid chromatography (NP-HPLC) method. Compared to other HPLC modes, NP is depicted as quite expensive with high consumption of organic solvents which leads to chronic exposure of analysts to toxic and carcinogenic effects. In order to overcome above-mentioned drawbacks, the aim of this study was to develop new method with better eco-friendly features. This was enabled by using protein type Chiral Stationary Phase (CSP) in reversed-phase mode that required up to 10 % (v/v) of organic solvent. Therefore, an enantioselective HPLC method was developed and validated for quantification of (S)-timolol and its chiral impurity, (R)-isomer. Optimized separation conditions on ovomucoid column were set using Analytical Quality by Design (AQbD) approach in method development. Optimization step was performed following the Box-Behnken experimental plan and the influence of three critical method parameters (CMPs) towards enantioseparation of the above-mentioned peak pair was examined. CMPs included variation of acetonitrile content in the mobile phase (5-10 %, v/v), pH value of the aqueous phase (6.0-7.0) and ammonium chloride concentration in the aqueous part of the mobile phase (10-30 mmol L-1). The most relevant critical method attributes (CMAs) in this case were the separation criterion between studied critical pair and retention factor of the second eluting peak, (S)-timolol. Qualitative Design Space (DS) was defined by Monte Carlo simulations providing adequate assurance of method's qualitative robustness (π = 95 %). The selected working point situated in the middle of the DS was characterized by following combination of CMPs: acetonitrile content in the mobile phase 7 % (v/v), pH value of the aqueous phase 6.8 and concentration of ammonium chloride in aqueous phase 14 mmol L-1. In the next step, the quantitative robustness was tested by Plackett-Burman experimental design. The validation studies confirmed adequacy of the proposed method for its intended purpose. Finally, Analytical Eco-Scale metric tool was applied to confirm that developed method represents excellent green analytical method compared to the official one.

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.

Relevance of TNF-α, IL-6 and IRAK1 gene expression for assessing disease severity and therapy effects in tuberculosis patients.

INTRODUCTION: Tuberculosis (TBC) is a contagious chronic respiratory disease which despite the known cause, Mycobacterium tuberculosis (Mtb), and many decades of successful therapy, remains one of the leading global health problems. Immune responses against Mtb infection involve both of types of immunity, but cellular immunity, in which certain cytokines and Th1 cells play a key role, is crucial. A better understanding of the functions of the cytokine network involved in the state and progression of TBC could identify specific molecular markers for monitoring of disease activity as well as therapy outcomes in TBC patients. METHODOLOGY: We investigated expression of TNF-α, IL-6 and IRAK1 genes using an RT-qPCR technique in peripheral blood mononuclear cells of 33 TBC patients and 10 healthy individuals. RESULTS: Comparison between TBC patients and healthy individuals revealed statistically significant differences for all analyzed genes. The levels of expression of TNF-α and IL-6 mRNA were higher, while the level of IRAK1 mRNA was lower in the TBC group compared to controls. Moreover, a strong positive correlation was observed between TNF-α and IL-6 gene expression. When clinical parameters were analyzed, increased levels of TNF-α mRNA were detected in patients with a longer duration of therapy (>2 months) compared to those with a shorter therapy duration (< 2 months), and in patients without anemia. CONCLUSIONS: Our results indicate that the inflammatory genes we examined play a crucial role in the pathogenesis of tuberculosis, and that the expression of the TNF-α gene could be a marker for monitoring the clinical effect of the ant-tuberculosis drugs during therapy.

Rare histological subtype of pulmonary artery intimal sarcoma diagnosed by multidisciplinary approach.

Pulmonary artery intimal sarcoma (PAS) is a rare mesenchymal tumor mostly diagnosed in middle-aged women. In a 63-year-old female, the radiological findings showed cavitation in the left upper lobe of the lung and infiltrative tumor mass around the left pulmonary artery. PAS consisted of small, round tumor cells with about 80% of mitotic activity and with myxoid background and specific immunoprofile and diagnosed as undifferentiated sarcoma with round cell features type. The final diagnosis of PAS was established according to the pathohistological, chest computed tomography scan, and surgery finding.

Endotracheal stent increased survival length in patients with invasive thymic adenocarcinoma.

We reported the first case of inoperative thymic adenocarcinoma successfully palliative treated by the double-stent procedure. In a patient who expressed stridor, computed tomography was done and necrotic mediastinal mass, which protrudes into a trachea, was demonstrated. Fiberoptic bronchoscopy showed tracheal infiltration and 70% stenosis; therefore, surgical resection was inapplicable. Recanalization with repeated argon plasma coagulation and debridement of necrotic mass was performed, followed by placement of the endotracheal stent, radiotherapy, and chemotherapy. After 1 year, the patient developed gastric aspiration and tracheoesophageal fistula; therefore, the esophageal stent was placed. The outcome was lethal, but the placement of endotracheal stent significantly increased a length of survival for the patient with invasive thymic adenocarcinoma.

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.

Clinical features of infection caused by non-tuberculous mycobacteria: 7 years' experience.

INTRODUCTION: Non-tuberculous mycobacteria (NTM) are ubiquitous organisms associated with various infections. The aim of the study was to determine the most relevant clinical characteristics of NTM during the 7-year period. METHODOLOGY: A retrospective study of NTM infections was conducted between January 2009 and December 2016. The American Thoracic Society/Infectious Disease Society of America criteria were used to define cases of pulmonary or an extrapulmonary site. RESULTS: A total of 85 patients were included in the study. Pulmonary cases predominated 83/85 (98%), while extrapulmonary NTM were present in 2/95 (2%) patients. Overall, ten different NTM species were isolated. The most common organisms were slow-growing mycobacteria (SGM) presented in 70/85 (82.35%) patients. Isolated SGM strains were Mycobacterium avium complex (MAC) in 25/85 (29.41%) patients, M. xenopi in 20/85 (23.53%) patients, M. kansasii in 15/85 (17.65%) patients and M. peregrinum and M. gordonae in 5/85 (5.88%) patients each. Isolated rapid-growing mycobacteria (RGM) strains were M. abscessus in 8/85 (9.41%) patients, M. fortuitum in 4/85 (4.71%) patients and M. chelonae in 3/85 (3.53%) patients. Almost all patients (98%; 83/85) had comorbidities. Among 75 (88.24%) patients who completed follow-up, 59 (69.41%), 10 (11.76%) and 6 (7%), were cured, experienced relapse and died, respectively. CONCLUSION: In the present study, pulmonary NTM infections were more frequent compared to extrapulmonary disease forms. SGM were most common isolates with MAC pulmonary disease the most frequently found. Comorbidities have an important role in NTM occurrence. Further investigation should focus on an NTM drug susceptibility testing.

Multicriteria Optimization Methodology in Stability-Indicating Method Development of Cilazapril and Hydrochlorothiazide.

Multicriteria optimization methodology was applied in development of UHPLC-UV-MS method for separation of cilazapril, hydrochlorothiazide and their degradation products. This method is also applicable for analysis of cilazapril, hydrochlorothiazide and their degradation products in combined tablet formulation. Prior to method optimization forced degradation studies were conducted. Cilazapril and hydrochlorothiazide were subjected to acidic (0.1, 0.5 and 1.0 M HCl), basic (0.1, 0.5 and 1.0 M NaOH), thermal (70°C), oxidative (3-30% H2O2) degradation and photodegradation (day light). Cilazapril appeared to be unstable toward acid and base and resulted in formation of cilazaprilat. Hydrochlorothiazide significantly degraded after acid, base and thermal hydrolysis and formed degradation product was 4-amino-6-chlorobenzene-1.3-disulfonamide. For both substances, after oxidative degradation unknown products have arisen. Initial percentage of acetonitrile in mobile phase, final percentage of acetonitrile in mobile phase, time of gradient elution and column temperature were defined as variables to be optimized toward two chromatographic responses by means of central composite design and Derringer's desirability function. The satisfactory chromatographic analysis was achieved on Kinetex C18 (2.6 µm, 50 × 2.1 mm) column with temperature set at 25°C. The final mobile phase consisted of acetonitrile and 20 mM ammonium formate buffer (pH adjusted to 8.5). The flow rate of the mobile phase was 400 µL min-1 and it was pumped in a gradient elution mode.