Determination of valsartan and pitavastatin using synchronous spectrofluorimetry and augmented least squares chemometric models: A comparative study with greenness and blueness assessment.

Hypertension and hyperlipidemia are two common conditions that require effective management to reduce the risk of cardiovascular diseases. Among the medications commonly used for the treatment of these conditions, valsartan and pitavastatin have shown significant efficacy in lowering blood pressure and cholesterol levels, respectively. In this study, synchronous spectrofluorimetry coupled to chemometric analysis tools, specifically concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS), was employed for the determination of valsartan and pitavastatin simultaneously. The developed models exhibited excellent predictive performance with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for valsartan and pitavastatin, respectively. Hence, these models were successfully applied to the analysis of synthetic samples and commercial formulations as well as plasma samples with high accuracy and precision. Besides, the greenness and blueness profiles of the determined samples were also evaluated to assess their environmental impact and analytical practicability. The results demonstrated excellent greenness and blueness scores with AGREE score of 0.7 and BAGI score of 75 posing the proposed method as reliable and sensitive approach for the determination of valsartan and pitavastatin with potential applications in pharmaceutical quality control, bioanalytical studies, and therapeutic drug monitoring.

Early breast milk exposure modifies brain connectivity in preterm infants.

Preterm infants are at increased risk of alterations in brain structure and connectivity, and subsequent neurocognitive impairment. Breast milk may be more advantageous than formula feed for promoting brain development in infants born at term, but uncertainties remain about its effect on preterm brain development and the optimal nutritional regimen for preterm infants. We test the hypothesis that breast milk exposure is associated with improved markers of brain development and connectivity in preterm infants at term equivalent age. We collected information about neonatal breast milk exposure and brain MRI at term equivalent age from 47 preterm infants (mean postmenstrual age [PMA] 29.43 weeks, range 23.28-33.0). Network-Based Statistics (NBS), Tract-based Spatial Statistics (TBSS) and volumetric analysis were used to investigate the effect of breast milk exposure on white matter water diffusion parameters, tissue volumes, and the structural connectome. Twenty-seven infants received exclusive breast milk feeds for ≥75% of days of in-patient care and this was associated with higher connectivity in the fractional anisotropy (FA)-weighted connectome compared with the group who had < 75% of days receiving exclusive breast milk feeds (NBS, p = 0.04). Within the TBSS white matter skeleton, the group that received ≥75% exclusive breast milk days exhibited higher FA within the corpus callosum, cingulum cingulate gyri, centrum semiovale, corticospinal tracts, arcuate fasciculi and posterior limbs of the internal capsule compared with the low exposure group after adjustment for PMA at birth, PMA at image acquisition, bronchopulmonary dysplasia, and chorioamnionitis (p < 0.05). The effect on structural connectivity and tract water diffusion parameters was greater with ≥90% exposure, suggesting a dose effect. There were no significant groupwise differences in brain volumes. Breast milk feeding in the weeks after preterm birth is associated with improved structural connectivity of developing networks and greater FA in major white matter fasciculi.

Complex Trajectories of Brain Development in the Healthy Human Fetus.

This study characterizes global and hemispheric brain growth in healthy human fetuses during the second half of pregnancy using three-dimensional MRI techniques. We studied 166 healthy fetuses that underwent MRI between 18 and 39 completed weeks gestation. We created three-dimensional high-resolution reconstructions of the brain and calculated volumes for left and right cortical gray matter (CGM), fetal white matter (FWM), deep subcortical structures (DSS), and the cerebellum. We calculated the rate of growth for each tissue class according to gestational age and described patterns of hemispheric growth. Each brain region demonstrated major increases in volume during the second half of gestation, the most pronounced being the cerebellum (34-fold), followed by FWM (22-fold), CGM (21-fold), and DSS (10-fold). The left cerebellar hemisphere, CGM, and DSS had larger volumes early in gestation, but these equalized by term. It has been increasingly recognized that brain asymmetry evolves throughout the human life span. Advanced quantitative MRI provides noninvasive measurements of early structural asymmetry between the left and right fetal brain that may inform functional and behavioral laterality differences seen in children and young adulthood.

Simeprevir oxidative degradation product: Molecular modeling, in silico toxicity and resolution by synchronous spectrofluorimetry.

In this article, one of the potential degradation products of the novel antiviral drug simeprevir was isolated and characterized by means of infrared (IR) and mass spectrometry. Moreover, comparative molecular docking, ADMET (absorption, distribution, metabolism, excretion - toxicity) and insilico toxicity prediction studies were applied to evaluate the activity of simeprevir and its degradation product. Furthermore,a simple, accurate and selective second derivative synchronous spectrofluorimetric method was developed for the determination of simeprevir in the presence of its oxidative degradation product.The synchronous fluorescence spectra of both compounds were measured in ethanol at pH 2.0 usingΔλ of 140 nm and the peak amplitude of the second derivative spectra were measured at 442 nm. The method was rectilinear over the concentration range of 0.2 to 2.0 µg/ml and validated according to the ICH (International Conference on Harmonization) guidelines. Moreover, the method was statistically compared to the reverse-phase high-performance liquid chromatography (RP-HPLC) method and good results were obtained.

Eco-friendly graphene quantum dots as a novel spectrofluorimetric probe for lamivudine quantification with evaluation of its greenness and blueness profiles.

In this study, graphene quantum dots (GQDs) were employed for quantitatively analyzing lamivudine using a fluorescence quenching technique. This approach allows for sensitive determination of the concentration of lamivudine in different matrices without requiring derivatization. The mechanism behind the fluorescence intensity quenching between GQDs and lamivudine molecules was explored using the Stern Volmer equation, revealing dynamic quenching behavior. Additionally, various factors affecting fluorescence quenching efficiency such as pH, GQDs concentration, and incubation time were carefully tuned. Moreover, our developed method successfully met ICH guidelines for validation parameters including linearity, accuracy, precision, and selectivity demonstrating excellent performance. The results showed good accuracy and precision, with a mean recovery value of 101.91% for method accuracy and a relative standard deviation of 0.682 and 1.489 for intraday and interday precision, respectively. Finally, the greenness and blueness of the developed method were also investigated to assess its environmental friendliness and analytical practicality. Greenness evaluation using the AGREE tool demonstrated that the developed method has a low environmental impact with an AGREE score of 0.75, Besides, the blueness evaluating using the BAGI tool indicated that the developed method is practical, reliable, and well-suited for routine analysis of lamivudine in various samples.

Augmented least squares, a powerful chemometric approach for the spectroscopic analysis of the antiretroviral therapy abacavir, lamivudine and dolutegravir in their ternary mixture.

Augmented least squares models such as concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are powerful chemometric approaches that can be applied for spectroscopic analysis of many pharmaceutical compounds. Herein, both CRACLS and SRACL have been employed for UV spectral analysis of three antiretroviral therapy namely abacavir (ACV), lamivudine (LMV) and dolutegravir (DTG) in their ternary mixture. A partial factorial design has been utilized for calibration set construction then both CRACLS and SRACLS models have been optimized regarding the number of iterations and principal components, respectively, using a leave-one-out cross-validation procedure. It was found that a higher number of iterations and principal components were required for modelling the minor component DTG indicating more augmentation procedures to improve the models' accuracy. Validation of the proposed models was performed using external validation set of 13 mixtures and different validation parameters have been evaluated regarding models' predictive abilities. Both models showed excellent performance for analyzing ACV and LMV with relative root mean square error of prediction (RRMSEP) below 2 %. However, higher RRMSEP values around 5 % were observed for the minor component DTG suggesting that these models should be utilized with caution when analyzing minor components in mixtures. Furthermore, the suggested models have been applied for analyzing ACV, LMV and DTG in their pharmaceutical formulation and excellent agreement was observed between the suggested models and the reported chromatographic method posing these models as powerful chemometric approaches for quality control analysis of many pharmaceutical compounds.

Unveiling the altered metabolic pathways induced by nivolumab in non-small cell lung cancer via GC-MS metabolomics approach coupled with multivariate analysis.

This research investigates the effects of the immunotherapeutic agent nivolumab on the metabolism of lung cancer cells (NCI-H1975) using GC-MS metabolomic profiling. Multivariate analysis such as unsupervised PCA and supervised OPLS-DA along with univariate analysis and pathway analysis were employed to explore the metabolomic data and identify altered metabolic pathways induced by nivolumab treatment. The study revealed distinct metabolic alterations in cancer cells, linked to proliferative and survival advantages, such as enhanced glycolysis, increased glutaminolysis, and modified amino acid metabolism. Key findings indicate elevated levels of glycolysis-related metabolites (glycine, alanine, pyruvate, and lactate) and TCA cycle intermediates (succinate, fumarate, malate) in cancer cells, with a significant decrease following nivolumab treatment. Additionally, lower levels of aspartic acid and citrate in cancer cells imply altered nucleotide synthesis and fatty acid production essential for tumor growth. Treatment with nivolumab also reduced oleic acid levels, indicative of its effect on disrupted lipid metabolism. Our research shows nivolumab's potential to modify metabolic pathways involved in lung cancer progression, suggesting its dual role in cancer therapy: as an immune response modulator and a metabolic pathway disruptor.

Therapeutic implications of dapagliflozin on the metabolomics profile of diabetic rats: A GC-MS investigation coupled with multivariate analysis.

BACKGROUND: Diabetes mellitus is a complex metabolic disorder with systemic implications, necessitating the search for reliable biomarkers and therapeutic strategies. This study investigates the metabolomics profile alterations in diabetic rats, with a focus on the therapeutic effects of Dapagliflozin, a drug known to inhibit renal glucose reabsorption, using Gas Chromatography-Mass Spectrometry analysis. METHODS: A GC-MS based metabolomics approach combined with multivariate and univariate statistical analyses was utilized to study serum samples from a diabetic model of Wistar rats, treated with dapagliflozin. Metabolomics pathways analysis was also performed to identify the altered metabolic pathways associated with the disease and the intervention. RESULTS: Dapagliflozin treatment in diabetic rats resulted in normalized levels of metabolites associated with insulin resistance, notably branched-chain and aromatic amino acids. Improvements in glycine metabolism were observed, suggesting a modulatory role of the drug. Additionally, reduced palmitic acid levels indicated an alleviation of lipotoxic effects. The metabolic changes indicate a restorative effect of dapagliflozin on diabetes-induced metabolic perturbations. CONCLUSIONS: The comprehensive metabolomics analysis demonstrated the potential of GC-MS in revealing significant metabolic pathway alterations due to dapagliflozin treatment in diabetic model rats. The therapy induced normalization of key metabolic disturbances, providing insights that could advance personalized diabetes mellitus management and therapeutic monitoring, highlighting the utility of metabolomics in understanding drug mechanisms and effects.

Synchronous spectrofluorimetry and chemometric modeling: A synergistic approach for analyzing simeprevir and daclatasvir, with application to pharmacokinetics evaluation.

Simeprevir and daclatasvir represent a cornerstone in the management of Hepatitis C Virus infection, a global health concern that affects millions of people worldwide. In this study, we propose a synergistic approach combining synchronous spectrofluorimetry and chemometric modeling i.e. Partial Least Squares (PLS-1) for the analysis of simeprevir and daclatasvir in different matrices. Moreover, the study employs firefly algorithms to further optimize the chemometric models via selecting the most informative features thus improving the accuracy and robustness of the calibration models. The firefly algorithm was able to reduce the number of selected wavelengths to 47-44% for simeprevir and daclatasvir, respectively offering a fast and sensitive technique for the determination of simeprevir and daclatasvir. Validation results underscore the models' effectiveness, as evidenced by recovery rates close to 100% with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for simeprevir and daclatasvir, respectively. Moreover, the proposed models have been applied to determine the pharmacokinetics of simeprevir and daclatasvir, providing valuable insights into their distribution and elimination patterns. Overall, the study demonstrates the effectiveness of synchronous spectrofluorimetry coupled with multivariate calibration optimized by firefly algorithms in accurately determining and quantifying simeprevir and daclatasvir in HCV antiviral treatment, offering potential applications in pharmaceutical formulation analysis and pharmacokinetic studies for these drugs.

Derivative spectroscopy and wavelet transform as green spectrophotometric methods for abacavir and lamivudine measurement.

Herein, two different sustainable and green signal processing spectrophotometric approaches, namely, derivative spectroscopy and wavelet transform, have been utilized for effective measurement of the antiretroviral therapy abacavir and lamivudine in their pharmaceutical formulations. These methods were used to enhance the spectral data and differentiate between the absorption bands of abacavir and lamivudine in order to accurately measure their concentrations. For determining abacavir and lamivudine, the first derivative spectrophotometric method has been applied to the zero-order and ratio spectra of both drugs. The same approach has been tested using the continuous wavelet transform method where a second order 2.4 of rbio and bior wavelet families were found to be optimum for measuring both drugs. Validation of the proposed methods affirmed their reliability in terms of linearity over the concentration range 1.5-30 µg/mL and 1.5-36 µg/mL for abacavir and lamivudine, respectively, precision (RSD < 2 %), and accuracy with mean recoveries ranging between 98 % and 102 %. Additionally, these spectrophotometric methodologies were applied to real pharmaceutical preparations and yielded results congruent with a prior chromatographic method. Most prominently, the proposed methods stood out for their greenness and sustainability with 97 points as evaluated by the analytical eco-scale method and a score value of 0.79 as analyzed by AGREE method, thereby making them suitable for resource-limited settings and highlighting the potential for broader application of green analytical methods in pharmaceutical analysis.

Efficient and eco-friendly detection of gabapentin using nitrogen-doped carbon quantum dots: an analytical and green chemistry approach.

This study presents the development of an eco-friendly and highly selective mitrogen-doped carbon quantum dot based sensor (N-CQDs) for the detection of gabapentin - a commonly misused drug. A detailed characterization of N-CQDs spectral features and their interaction with gabapentin is provided. The optimal conditions for sensing, including pH value, buffer volume, N-CQDs concentration, and incubation time, were established. The results showed excellent fluorescence quenching at 475 nm (λex = 380 nm) due to the dynamic quenching mechanism, and the sensor demonstrated excellent linearity in the 0.5-8.0 µg mL-1 concentration range with correlation coefficients of more than 0.999, a limit of detection (LOD) of 0.160 and limit of quantification (LOQ) of 0.480 µg mL-1. The accuracy of the proposed sensor was acceptable with a mean accuracy of 99.91 for gabapentin detection. In addition, precision values were within the acceptable range, with RSD% below 2% indicating good repeatability and reproducibility of the sensor. Selectivity was validated using common excipients and pooled plasma samples. The proposed sensor accurately estimated gabapentin concentration in commercial pharmaceutical formulations and spiked plasma samples, exhibiting excellent comparability with previously published methods. The 'greenness' of the sensing system was evaluated using the Analytical GREEnness calculator, revealing low environmental impact and strong alignment with green chemistry principles with a greenness score of 0.76. Thus, the developed N-CQDs-based sensor offers a promising, eco-friendly, and effective tool for gabapentin detection in various situations, ranging from clinical therapeutics to forensic science.

Integrated comparative metabolite profiling via NMR and GC-MS analyses for tongkat ali (Eurycoma longifolia) fingerprinting and quality control analysis.

Tongkat ali commonly known as Malaysian Ginseng (Eurycoma longifolia) is a herbal root worldwide available in nutraceuticals, either as a crude powder or capsules blended with other herbal products. Herein, a multiplexed metabolomics approach based on nuclear magnetic resonance (NMR) and solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS) was applied for authentic tongkat ali extract vs some commercial products quality control analysis. NMR metabolite fingerprinting identified 15 major metabolites mostly ascribed to sugars, organic and fatty acids in addition to quassinoids and cinnamates. Following that, multivariate analysis as the non-supervised principal component analysis (PCA) and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied revealing that differences were related to fatty acids and 13,21-dihydroeurycomanone being more enriched in authentic root. SPME-GC-MS aroma profiling led to the identification of 59 volatiles belonging mainly to alcohols, aldehydes/furans and sesquiterpene hydrocarbons. Results revealed that aroma of commercial products showed relatively different profiles being rich in vanillin, maltol, and methyl octanoate. Whereas E-cinnamaldehyde, endo-borneol, terpinen-4-ol, and benzaldehyde were more associated to the authentic product. The present study shed the light for the potential of metabolomics in authentication and standardization of tongkat ali and identification of its true flavor composition.

Environmentally sustainable DRS-FTIR probe assisted by chemometric tools for quality control analysis of cinnarizine and piracetam having diverged concentration ranges: Validation, greenness, and whiteness studies.

A novel diffuse reflectance fourier transform infrared spectroscopic method accompanied by chemometrics was optimized to fulfill the white analytical chemistry and green analytical chemistry principles for the quantification of cinnarizine and piracetam for the first time without any prior separation in their challenging pharmaceutical preparation, which has a pretty substantial difference in the concentration of cinnarizine/piracetam (1:16). Furthermore, the suggested method was used for cinnarizine/piracetam dissolution testing as an effective alternative to traditional methods. For the cinnarizine/piracetam dissolution tests, we used a dissolution vessel with 900 mL of phosphate buffer pH 2.5 at 37 °C ± 0.5 °C, then the sampling was carried out by frequent withdrawal of 20 µl samples from the dissolution vessel at a one-minute interval, over one hour, then representative fourier transform infrared spectra were recorded. To create a partial-least-squares regression model, a fractional factorial design with 5 different levels and 2 factors was used. This led to the creation of 25 mixtures, 15 as a calibration set and 10 as a validation set, with varying concentration ranges: 1-75 and 16-1000 µg/mL for cinnarizine/piracetam, respectively. Upon optimization of the partial-least-squares regression model, in terms of latent variables and spectral region, root mean square error of cross-validation of 0.477 and 0.270, for cinnarizine/piracetam respectively, were obtained. The optimized partial-least-squares regression model was further validated, providing good results in terms of recovery% (around 98 to 102 %), root mean square error of prediction (0.436 and 3.329), relative root mean square error of prediction (1.210 and 1.245), bias-corrected mean square error of prediction (0.059 and 0.081), and limit of detection (0.125 and 2.786) for cinnarizine/piracetam respectively. Ultimately, the developed method was assessed for whiteness, greenness, and sustainability using five assessment tools. the developed method achieved a greener national environmental method index and complementary green analytical procedure index quadrants with higher eco-scale assessment scores (91), analytical greenness metric scores (0.87), and red-greenblue 12 algorithm scores (89.7) than the reported methods, showing high practical and environmental acceptance for quality control of cinnarizine/piracetam.

Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications.

In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.

Metabolites Profiling and Bioassays Reveal Bassia indica Ethanol Extract Protective Effect against Stomach Ulcers Development via HMGB1/TLR-4/NF-κB Pathway.

Clinical manifestation of gastric ulcers is frequent, in addition to their costly drug regimens, warranting the development of novel drugs at lower costs. Although Bassia indica is well characterized for its anti-inflammatory and antioxidant potential, capacity of its ethanol extract (BIEE) to prevent stomach ulcers' progression has not been reported. A nuclear protein termed high-mobility group box 1 (HMGB1) plays a key role in the formation of stomach ulcers by triggering a number of inflammatory responses. The main purpose of the current investigation was to evaluate the in vivo anti-inflammatory and anti-ulcerogenic capabilities of BIEE against ethanol-induced gastric ulcers in rats via the HMGB1/TLR-4/NF-B signaling pathway. HMGB1 and Nuclear factor kappa (NF-B) expression, IL-1ß and Nrf2 contents showed an increase along with ulcer development, concurrent with an increase in immunohistochemical TLR-4 level. In contrast, pre-treatment with BIEE significantly reduced HMGB1 and Nuclear factor kappa (NF-B) expression levels, IL-1ß and Nrf2 contents and ulcer index value. Such protective action was further confirmed based on histological and immunohistochemical TLR-4 assays. Untargeted analysis via UPLC-ESI-Qtof-MS has allowed for the comprehensive characterization of 40 metabolites in BIEE mostly belonged to two main chemical classes, viz., flavonoids and lipids. These key metabolites, particularly flavonoids, suggesting a mediation for the anti-inflammatory and anti-ulcerogenic properties of BIEE, pose it as a promising natural drug regimen for treatment of stomach ulcers.

Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression.

Medical imaging has shown that, during early development, the brain undergoes more changes in size, shape and appearance than at any other time in life. A better understanding of brain development requires a spatio-temporal atlas that characterizes the dynamic changes during this period. In this paper we present an approach for constructing a 4D atlas of the developing brain, between 28 and 44 weeks post-menstrual age at time of scan, using T1 and T2 weighted MR images from 204 premature neonates. The method used for the creation of the average 4D atlas utilizes non-rigid registration between all pairs of images to eliminate bias in the atlas toward any of the original images. In addition, kernel regression is used to produce age-dependent anatomical templates. A novelty in our approach is the use of a time-varying kernel width, to overcome the variations in the distribution of subjects at different ages. This leads to an atlas that retains a consistent level of detail at every time-point. Comparisons between the resulting atlas and atlases constructed using affine and non-rigid registration are presented. The resulting 4D atlas has greater anatomic definition than currently available 4D atlases created using various affine and non-rigid registration approaches, an important factor in improving registrations between the atlas and individual subjects. Also, the resulting 4D atlas can serve as a good representative of the population of interest as it reflects both global and local changes. The atlas is publicly available at www.brain-development.org.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture.

BACKGROUND: Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form. OBJECTIVE: In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation. METHOD: For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 µg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS. RESULTS: The models' validation criteria displayed excellent recoveries and lower errors of prediction. CONCLUSIONS: The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results. HIGHLIGHTS: Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.

Innovative electrochemical electrode modified with Al2O3 nanoparticle decorated MWCNTs for ultra-trace determination of tamsulosin and solifenacin in human plasma and urine samples and their pharmaceutical dosage form.

A simple, cheap, sensitive, and time-saving square wave voltammetric (SWV) procedure using a carbon paste electrode modified with aluminum oxide nanoparticle decorated multi-walled carbon nanoparticles (Al2O3-NPs/MWCNTs/CPE) is presented for the ultra-sensitive determination of tamsulosin (TAM) and solifenacin (SOL), one of the most prescribed pharmaceutical combinations in urology. Characterization of the developed electrode was performed using scanning electron microscopy (SEM), X-ray diffraction (XRD) patterns, energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM) and FT-IR spectrophotometry. The voltammetric behavior of TAM/SOL was evaluated using Al2O3-NPs in different content and electrode compositions. The use of Al2O3 functionalized MWCNTs as a CPE modifier increased the process of electron transfer as well as improved the electrode active surface area therefore, ultra-sensitive results were acquired with a linear range of 10-100 and 12-125 ng ml-1 for TAM and SOL respectively, and a limit of the detection value of 2.69 and 3.25 ng ml-1 for TAM and SOL, respectively. Interestingly, the proposed method succeeded in quantifying TAM and SOL with acceptable percentage recoveries in dosage forms having diverged concentration ranges and in the biological fluids with very low peak plasma concentration (C max). Furthermore, the proposed method was validated, according to the ICH criteria, and shown to be accurate and reproducible.

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.