QSRR modeling of the chromatographic retention behavior of some quinolone and sulfonamide antibacterial agents using firefly algorithm coupled to support vector machine.

Quinolone and sulfonamide are two classes of antibacterial agents with an opulent history of medicinal chemistry features that contribute to their bacterial spectrum, efficacy, pharmacokinetics, and adverse effect profiles. The urgent need for their use, combined with the escalating rate of their resistance, necessitates the development of suitable analytical methods that accelerate and facilitate their analysis. In this study, the advanced firefly algorithm (FFA) coupled with support vector regression (SVR) was used to select the most significant descriptors and to construct two quantitative structure-retention relationship (QSRR) models using a series of 11 selected quinolone and 13 sulfonamide drugs, respectively, to predict their retention behavior in HPLC. Precisely, the effect of the pH value and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides, respectively, were studied. The obtained QSRR models performed well in both internal and external validations, demonstrating their robustness and predictive ability. Y-randomization validation demonstrated that the obtained models did not result by statistical chance. Moreover, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions.

Analysis of the ternary antiretroviral therapy dolutegravir, lamivudine and abacavir using UV spectrophotometry and chemometric tools.

Herein, a simple spectrophotometric method coupled with chemometric techniques i.e. partial least square (PLS) and genetic algorithm (GA) were utilized for the simultaneous determination of the vital ternary antiretroviral therapy dolutegravir (DTG), lamivudine (LMV), and abacavir (ACV) in their combined dosage form. Calibration (25 samples) and validation (13 samples) sets were prepared for these drugs at different concentrations via implementing partial factorial experimental designs. The zero order UV spectra of calibration and validation sets were measured and then subjected for further chemometric analysis. Partial least squares with/without variable selection procedures i.e. genetic algorithm (GA) were utilized to untangle the UV spectral overlapping of these mixtures. Cross-validation and external validation methods were applied to compare the performance of these chemometric techniques in terms of accuracy and predictive abilities. It was found that six latent variables were optimum for modelling DTG, four latent variables for modelling LMV and three latent variables for modelling ACV. Although, good recoveries with prompt predictive ability were attained by these PLS, GA-PLS showed better analytical performance owing to its capability to remove redundant variables i.e. the number of absorbance variables have been reduced to about 21-29%. The proposed chemometric methods can be reliably applied for simultaneous determination of DTG, LMV, and ACV in their laboratory prepared mixtures and pharmaceutical preparation posing these chemometric methods as worthy and substantial analytical tools in in-process testing and quality control analysis of many antiretroviral pharmaceutical preparations.

QSRR modeling for the chromatographic retention behavior of some ß-lactam antibiotics using forward and firefly variable selection algorithms coupled with multiple linear regression.

The justified continuous emerging of new ß-lactam antibiotics provokes the need for developing suitable analytical methods that accelerate and facilitate their analysis. A face central composite experimental design was adopted using different levels of phosphate buffer pH, acetonitrile percentage at zero time and after 15 min in a gradient program to obtain the optimum chromatographic conditions for the elution of 31 ß-lactam antibiotics. Retention factors were used as the target property to build two QSRR models utilizing the conventional forward selection and the advanced nature-inspired firefly algorithm for descriptor selection, coupled with multiple linear regression. The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability. Williams-Hotelling test and student's t-test showed that there is no statistical significant difference between the models' results. Y-randomization validation showed that the obtained models are due to significant correlation between the selected molecular descriptors and the analytes' chromatographic retention. These results indicate that the generated FS-MLR and FFA-MLR models are showing comparable quality on both the training and validation levels. They also gave comparable information about the molecular features that influence the retention behavior of ß-lactams under the current chromatographic conditions. We can conclude that in some cases simple conventional feature selection algorithm can be used to generate robust and predictive models comparable to that are generated using advanced ones.

Stability-Indicating RP-HPLC Methods for the Determination of Fluorometholone in Its Mixtures with Sodium Cromoglycate and Tetrahydrozoline Hydrochloride.

Two stability-indicating reversed-phase liquid chromatographic methods were developed and validated for the determination of fluorometholone (FLU) in its mixtures with sodium cromoglycate (SCG) and tetrahydrozoline hydrochloride (THZ). The first HPLC method (Method 1) was based on isocratic elution of FLU and SCG along with their alkaline degradation products on a reversed phase C18 column (250 × 4.6 mm id)-ACE Generix 5, using a mobile phase consisting of methanol-water (70 : 30, v/v), pH adjusted to 2.5 using orthophosphoric acid at a flow rate of 1.2 mL min(-1) Quantitation was achieved with UV detection at 240 nm. The second HPLC method (Method 2) was based on isocratic elution of FLU, its alkaline degradation product and THZ on a reversed phase C8 column (250 × 4.6 mm)-ACE Generix 5, using a mobile phase consisting of acetonitrile-50 mM potassium dihydrogen orthophosphate (40 : 60, v/v) at a flow rate of 2 mL min(-1) Quantitation was achieved by applying dual-wavelength detection, where FLU and its alkaline degradation product were detected at 240 nm and THZ was detected at 215 nm at ambient temperatures. Linearity, accuracy and precision were found to be acceptable over the concentration range of 5-50 and 10-500 µg mL(-1) for FLU and SCG (Method 1) and over the concentration range of 5-80 and 5-60 µg mL(-1) for FLU and THZ (Method 2), respectively. Besides, the FLU alkaline degradation product was verified using IR, NMR and LC-MS spectroscopy. The two proposed methods could be successfully applied for the routine analysis of the studied drugs either in their pure bulk powders or in their pharmaceutical preparations without any preliminary separation step.