Chromatographic Conditions Research Articles

Development and validation of a high-performance liquid chromatography method with fluorescence detection for the quantification of the resistance protein P-gp in cancer cells.

A method using high-performance liquid chromatography coupled with fluorescence detection (HPLC-FLD) was developed and validated to quantify the innovative tool LightSpot®-FL-1, a selective permeability-glycoprotein (P-gp)-targeted fluorescent conjugate used to measure P-gp expression in cell samples. Quantifying P-gp is a major challenge in oncology as its overexpression in many cancer cells results in Multidrug Resistance (MDR) associated with chemotherapy failure. To develop the method reported herein, both sample preparation and analysis parameters were investigated. Optimal chromatographic conditions were achieved with 5µL injections at a 1mL/min flow rate on a reversed-phase Zorbax® Eclipse Plus 3.5µm C18 column (150×4.6mm) with isocratic acetonitrile/water (85/15, by volume) elution. Detection was performed with 505nm excitation and 510nm emission wavelengths. Validation studies were designed and performed according to the International Council for Harmonization (ICH) guidelines for bioanalytical method validation. The limit of quantification (LOQ) and limit of detection (LOD) were determined to be 0.5 and 0.2nmol/L, respectively. The linearity range was demonstrated between 10 and 500nmol/L, and the trueness and precision of the method were validated. Good stability was shown in three relevant analytical conditions. The greenness of the developed method was also demonstrated with the AGREE, AGREEprep and MoGAPI tools. Finally, the rapid, precise and sensitive validated analytical method was successfully applied to determine the difference in P-gp expression in three cancer cell lines: DU4475, CCRF-CEM and KG-1a.

Revealing New Analytical Insights into RNA Complexes: Divalent siRNA Characterization by Liquid Chromatography and Mass Spectrometry.

Accurate characterization of therapeutic RNA, including purity and identity, is critical in drug discovery and development. Here, we utilize denaturing and non-denaturing chromatography for the analysis of ∼25 kDa divalent small interfering RNA (di-siRNA), which comprises a complex 2:1 triplex structure. Ion pair reversed-phase (IPRP) liquid chromatography (LC) experiments with UV absorbance and mass spectrometry (MS) showcase a single denaturing LC method for identity confirmation, impurity profiling, and sequencing with automated MS data interpretation. IPRP, size exclusion chromatography (SEC), and melting temperature (Tm) experiments showcase the need for consideration of chromatographic conditions in evaluating noncovalent siRNA structures─here, low-temperature IPRP experiments (generally considered "non-denaturing") indicate denaturation of the noncovalent complex for certain di-siRNA sequences, while SEC data indicate that di-siRNA aggregation can be vastly underestimated due to sample dilution prior to LC experiments. Furthermore, SEC data critically show the propensity of denatured di-siRNA samples to renature under SEC mobile phase conditions upon exposure to high ionic/salt solutions, corroborated by Tm experiments. This work highlights the need for consideration of the noncovalent nature of certain RNA therapeutics during sample preparation, method development, and analytical characterization and the often sequence-specific strength of complex formation, which may significantly affect analytical results obtained for such molecules. Attention to the highly dynamic nature of the duplex RNA structure, which is heavily influenced by its environment, must be considered during analytical method development.

Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning

In chemistry, empirical paradigms prevail, especially within the realm of chromatography, where the selection of separation conditions frequently relies on the chemist’s experience. However, the underlying rationale for such experiential knowledge has not been established or analysed. This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. On this basis, an “artificial intelligence (AI) experience” is generated through a knowledge discovery framework, where the relationship between the retardation factor (RF) value from TLC and retention volume from CC is unveiled in the form of explicit equations. These equations demonstrate satisfactory accuracy and generalizability, providing a scientific basis for the selection of the experimental conditions, and contributing to a better understanding of chromatography.

Comprehensive untargeted polar metabolite analysis using solvent switching liquid chromatography tandem mass spectrometry.

Metabolomics analyses enable the examination and identification of endogenous biochemical reaction products, revealing information on the metabolic pathways and processes active within a living cell or organism. Determination of metabolic shifts can provide important information on a treatment or disease. Unlike other omics fields that typically have analytes of the same chemical class with common building blocks, those that fall under the nomenclature of metabolites encompass a wide array of different compounds with very diverse physiochemical properties. Development of a comprehensive metabolomic pipeline therefore can be a troublesome and complicated process for the analyst. Often single liquid chromatography-mass spectrometry methods on unfractionated samples are carried out in order to be time-efficient, however this could potentially produce data with a low number of identifiable metabolites. In the present studies, we developed a comprehensive polar metabolomics pipeline for cell-based metabolomics. SH-SY5Y neuroblastoma cells were selected as the sample matrix for method development since they are one of the most widely used cell lines for human neurotoxicity studies. This was accomplished by investigating and optimising different mass spectrometry source and chromatographic conditions to enhance the signal of polar metabolites. Optimised hydrophilic interaction liquid chromatography (HILIC) based metabolomic methods at different pH values were examined in positive, negative, and polarity switching modes to determine which combination yielded the highest number of confidently identified metabolites. Additionally, the use of sequentially running two methods was also compared to determine the degree of overlap and whether there is merit in running two separate methods on one sample. It was determined that solvent switching between two optimised methods, acidic chromatographic conditions in positive mode and basic chromatographic conditions in negative mode, yielded the highest number of unique identifiable metabolites. This could be run in a single analytical batch due to the large pH range of the column. A quick switch method in-between each method allowed both conditioning the column and preparation of the MS source conditions for the sequential method.

Exploratory multivariate analysis using R Language for method development in liquid chromatography.

The visual evaluation of data derived from screening and optimization experiments in the development of new analytical methods poses a considerable time investment and introduces the risk of subjectivity. This study presents a novel approach to processing such data, based on factor analysis of mixed data and hierarchical clustering - multivariate techniques implemented in the R programming language. The methodology is demonstrated in the early-stage screening and optimization of the chromatographic separation of 15 structurally diverse drugs that affect the central nervous system, using a custom R Language script. The presented explorative approach enabled the identification of key parameters affecting the separation and significantly reduced the time required to evaluate the comprehensive dataset from the screening experiments. Based on the data analysis results, the optimal combination of stationary phase and mobile phase composition was selected, considering retention, overall resolution, and peak shape of compounds. Additionally, compounds vulnerable to changes in selected chromatographic conditions were identified. As a complement to the presented R Language script, a web-based application ChromaFAMDeX has been developed to offer an intuitive interface that enhances the accessibility of the used statistical methods. Accompanying the publication, the R script and the link to the standalone application are provided, enabling replication and adaptation of the methodology.

Prediction of Proteolysis-Targeting Chimeras Retention Time Using XGBoost Model Incorporated with Chromatographic Conditions.

Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that target undruggable proteins, enhance selectivity and prevent target accumulation through catalytic activity. The unique structure of PROTACs presents challenges in structural identification and drug design. Liquid chromatography (LC), combined with mass spectrometry (MS), enhances compound annotation by providing essential retention time (RT) data, especially when MS alone is insufficient. However, predicting RT for PROTACs remains challenging. To address this, we compiled the PROTAC-RT data set from literature and evaluated the performance of four machine learning algorithms─extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and support vector machines (SVM)─and a deep learning model, fully connected neural network (FCNN), using 24 molecular fingerprints and descriptors. Through screening combinations of molecular fingerprints, descriptors and chromatographic condition descriptors (CCs), we developed an optimized XGBoost model (XGBoost + moe206+Path + Charge + CCs) that achieved an R2 of 0.958 ± 0.027 and an RMSE of 0.934 ± 0.412. After hyperparameter tuning, the model's R2 improved to 0.963 ± 0.023, with an RMSE of 0.896 ± 0.374. The model showed strong predictive accuracy under new chromatographic separation conditions and was validated using six experimentally determined compounds. SHapley Additive exPlanations (SHAP) not only highlights the advantages of XGBoost but also emphasizes the importance of CCs and molecular features, such as bond variability, van der Waals surface area, and atomic charge states. The optimized XGBoost model combines moe206, path, charge descriptors, and CCs, providing a fast and precise method for predicting the RT of PROTACs compounds, thus facilitating their annotation.

Development of a new method using dispersive liquid-liquid microextraction with hydrophobic natural deep eutectic solvent for the analysis of multiclass emerging contaminants in surface water by liquid chromatography-mass spectrometry.

A new analytical method was developed for the determination of 14 multiclass emerging organic contaminants in surface waters using LC-MS, and Dispersive Liquid-Liquid Microextraction (DLLME) for extraction. Different Natural Deep Eutectic Solvents (NADESs) composed of terpenes and organic acids were tested as extraction solvents and characterized by Fourier Transform Infrared Spectroscopy (FTIR), Hydrogen Nuclear Magnetic Resonance Spectroscopy (1H-NMR), Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA), density, and viscosity, eliminating the need to use traditional chlorinated solvents. NADES produced with butyric acid and thymol showed the best results and was selected for application for the first time in the extraction of emerging organic contaminants of different classes in water samples. Vortex was used as the dispersion mode, eliminating the use of the dispersion solvent. Chromatographic conditions and sample preparation were optimized using multivariate experimental designs. The optimized chromatographic conditions included the column oven temperature, mobile phase modifiers, and stationary phase type. The optimized conditions for sample preparation included the extraction temperature and pH, salting out effect, and extraction solvent volume. The analytical performance was evaluated through repeatability and intermediate precision tests, with RSD values below 20%, and recoveries between 70 and 120%. The coefficient of determination was greater than 0.98 for all analytes. LOQs varied between 1.5 and 35 μg L-1. DLLME is a simple technique, it does not require expensive and specific equipment. Furthermore, replacing traditional chlorinated solvents with NADES makes the procedure more environmentally friendly. The method presented here can be applied to a wide range of analytes for the analysis of fresh, brackish, and salt waters. Up to the present moment, this is the first study using NADES based thymol and butyric acid for the determination of multiclass emerging contaminants in surface waters samples.

Chiral supercritical fluid chromatography of monoacylglycerol and diacylglycerol enantiomers in biological samples: Adjusting selectivity via column coupling.

The distinction of lipid isomers is gaining more attention in lipidomics due to their different biochemical properties in the organism. Herein, we aimed to develop a method for the analysis of monoacylglycerol (MG) and diacylglycerol (DG) enantiomers in biological samples using chiral supercritical fluid chromatography and mass spectrometry (SFC-MS). Amylose-based chiral columns showed a certain degree of separation of MG and DG isomers, but low selectivity for the acylglycerol classes in total lipid extracts, which could not be improved by modifier composition or other chromatographic conditions. The coelution of MG and DG enantiomers with highly concentrated triacylglycerols (TGs) negatively affected their MS determination based on the peak area ratio, therefore the interclass selectivity of chiral SFC was adjusted by coupling with an achiral column. The connection of the amylose tris-(3,5-dimethylphenylcarbamate) chiral column with octadecyl achiral column using a methanol as a modifier provided an excellent interclass separation of acylglycerols with the resolution of 5.53 and 15.17 for oleic acid-based MG/DG and DG/TG classes, respectively. The developed method enabled the determination of MG and DG enantiomers in complex total lipid extracts of biological samples in a 15 min gradient without time-consuming sample prefractionation. Chiral SFC-MS analysis of egg yolk, human plasma, and porcine brain samples showed different ratios of enantiomers, suggesting their unique roles within each sample type.

Greenness Assessment of High‐Performance Liquid Chromatography Method for Simultaneous Estimation of Apigenin, Apocynin, and Gallic Acid With Quality by Design Approach

ABSTRACTThis study presents the development of an eco‐friendly high‐performance liquid chromatography (HPLC) method for the simultaneous determination of apigenin, apocynin, and gallic acid, integrating analytical quality by design (AQbD) and green analytical chemistry (GAC) principles. The objective was to enhance method efficiency while reducing environmental impact, energy consumption, and solvent use. An AQbD approach was employed, starting with quality risk assessment and scouting analysis, followed by optimization using Box–Behnken design (BBD) to screen three key chromatographic parameters. The optimal chromatographic conditions were determined using a C18 column (250 mm × 4.6 mm, 5 µm) with a mobile phase of methanol: water (80%:20% v/v), a flow rate of 1 mL/min, ultraviolet detection at 228 nm, and a temperature of 25°C. The method demonstrated linearity over a concentration range of 1–5 µg/mL for the target compounds, with gallic acid as an internal standard. The method's greenness profile was assessed using various evaluation tools, confirming its compliance with GAC principles. The results indicate that the proposed HPLC method is not only effective and reliable but also environmentally sustainable, making it suitable for routine use in quality control laboratories. The integration of AQbD and GAC principles highlights the method's contribution to more sustainable and efficient analytical practices.

Quantitative Determination of a Series of Oxysterols by an Optimized LC-MS/MS Analysis in Different Tissue Types.

Oxysterols, as metabolites of cholesterol, play a key role in cholesterol homeostasis, autophagosome formation, and regulation of immune responses. Disorders in oxysterol metabolism are closely related to the pathogenesis of neurodegenerative diseases. To systematically investigate the profound molecular regulatory mechanisms of neurodegenerative diseases, it is necessary to quantify oxysterols and their metabolites in central and peripheral biospecimens simultaneously and accurately. However, there are a lot of unsolved problems with the existing methods, such as the hindrance of applying a single method to different biological specimens or the challenge of simultaneous quantification due to differential groups on the ends of the oxysterol side chains. Herein, according to the physicochemical properties and structure of oxysterols, an optimized liquid chromatography-tandem mass spectrometry method for the quantification of oxysterols was established by optimizing the sample preparation process, chromatographic conditions, mobile phase pH, and solvent selection. Seven oxysterols were detected by this method, including 27-hydroxycholesterol, 7α-hydroxycholesterol, 7α,27-dihydroxycholesterol, 7-dehydrocholesterol, 7α-hydroxy-3-oxo-4-cholestenoic acid, 3-hydroxy-5-cholestenoic acid, and 24(S)-hydroxycholesterol. Non-derivatization extraction with methyl tert-butyl ether was used for different biospecimens, followed by simultaneous chromatographic separation of oxysterols on a phenyl hexyl column. By repeated validation, this method exhibited satisfactory linearity, precision, recovery, sensitivity, repeatability, and stability, and it was successfully applied to the detection of oxysterols in the plasma, cerebral cortex, and liver of mouse. In summary, our optimized method enables concurrent analysis and quantification of oxysterols and their metabolites in various biospecimens, presenting a broad range of applicability.

DETERMİNATİON OF PROTON PUMP İNHİBİTORS BY HİGH-PERFORMANCE LİQUİD CHROMATOGRAPHY

Aim of the work: Analysis of proton pump inhibitors by high-performance liquid chromatography. Materials and methods. The studies were conducted on the following comparison standard samples and drugs. Standard samples: omeprazole (working standard Lot: B636472_01), pantoprazole (working standard: PANTBP/2023/01), dexlansoprazole (working standard: RLP/0890922). The following drugs were analyzed in the study: Omeprazole 20 mg capsule N30, Pantoprazole 20 mg tablet N28, Dexlansoprazole 30 mg capsule N30. Acetonitrile (Sigma-Aldrich series: I1306730339), triethylamine (Sigma-Aldrich series: S6010152947), 85% phosphoric acid (Merck KGaA, series: K52615773 029) and ultrapurified water were used as working solvents. Results: The identity and quantity of the main active components included in the composition of omeprazole, pantoprazole and dexlansoprazole preparations were determined by the HPLC method. Optimal chromatographic conditions for the determination of the amount of HPLC in all three preparations were developed and prepared, and the wavelength of 285 nm, injection volume of 10 μl, temperature of 25°C, flow rate of 1.0 ml/min and “Purospher Star RP-8 endcapped” (4.6 x 150 mm, particle size 5 μm) column were determined to be optimal. The proposed method is distinguished by its accuracy, simplicity and short implementation time. The method allows for effective quality control of preparations during their production and use.

Effect of Short-Chain Organic Acids, Cations and Anions on the Retention of Citicoline Under Hydrophilic Interaction Liquid Chromatography Conditions

Effect of Short-Chain Organic Acids, Cations and Anions on the Retention of Citicoline Under Hydrophilic Interaction Liquid Chromatography Conditions

Optimization of the quantification of Cannabidiol and Tetrahydrocannabinol methodology by High Performance Liquid Chromatography

Cannabinoids are terpenophenolic compounds obtained from the species Cannabis sativa (Cannabaceae), which is worldwide distributed across multiple varieties. The concentration of each cannabinoid in a cannabis plant is closely related to the variety. There are specimens in which higher yields of one specific cannabinoid are found than the remaining cannabinoids. International organizations such as the United Nations Office on Drugs and Crime, recommend the application of High-Performance Liquid Chromatography as an analytical method for the quantification of cannabinoids. In the present work, the different variables that influence a chromatographic analysis such as temperature, sample pH, elution flow and wavelength have been evaluated and the chromatographic conditions for the analytical quantification of the metabolites cannabidiol and tetrahydrocannabinol have been standardized.

Modernizing USP Melatonin Liquid Chromatographic Analyses for Increased Throughput and Reduced Environmental Impact.

Melatonin supplements are often used to alleviate jetlag and other sleep-depletion related disorders. Recent studies found large inconsistencies between labeled values and actual contents of melatonin within products, which has led to concerns over the quality of melatonin supplements. In order to facilitate the quality control testing of melatonin supplements, an improved and more modern approach to the liquid chromatographic analysis of melatonin is required. In addition, growing public concern over the environmental footprint of analytical laboratories exacerbates the need to modernize legacy analytical procedures with more eco-friendly or greener approaches. This study aims to optimize the routine liquid chromatographic analyses that are prescribed in the US Pharmacopeia (USP) melatonin monograph on a High Performance Liquid Chromatography (HPLC) system without fundamentally modifying the methods. The melatonin assay and the melatonin related compounds (impurities) test were optimized on a C18 column packed with 2.5 µm particles. The column length and the gradient elution parameters were adjusted following the guidelines on Adjustment of Chromatographic Conditions in USP General Chapter <621>. The mobile phase compositions were optimized to meet the system suitability requirements that were specified in the USP melatonin monograph. The flow rate was optimized for better separation efficiency. The optimized HPLC methods not only met the USP system suitability requirements in relative retention time (RRT), resolution, and relative standard deviation (RSD), but also demonstrated excellent linearity, sensitivity, accuracy, precision (repeatability), and would have a lower environmental impact. The optimized HPLC methods for assay of melatonin and test of its related compounds achieved significantly increased throughput and a reduced environmental impact, without fundamentally modifying the methods. The optimized HPLC methods are significantly faster and more eco-friendly. These methods can be implemented on HPLC systems without a full re-validation.

Liquid Chromatography Column Screening for the Analysis of Corrosion Inhibitor Molecules Using Derringer Desirability Functions.

Corrosion inhibitors (CIs) are extensively employed in the oil and gas industry, yet their analysis remains a challenge. To develop a suitable liquid chromatography method for a wide array of CIs, a column screening was conducted. Nine different chromatographic conditions were tested across eight RPLC and mixed-mode columns (Accucore C18, CORTECS Shield RP18, Acquity HSS T3, Acquity Premier HSS T3, Accucore 150-C4, Accucore PFP, Synergi Polar RP, and Acclaim WCX-1). Seven model mixtures representative of CIs, which included quaternary ammoniums, imidazolines, and phosphoric esters, were considered to probe the columns. Each column exhibited unique analytical performances, peak shape, and separation profiles. To find a compromise to analyze all the mixtures with one method, Derringer desirability functions were used. Shielding of residual silanols proved to be a critical factor. The trifunctional grafting of the Acquity HSS T3 columns appeared to be a promising strategy to minimize residual silanol effects. However, it was the Premier technology, which protects the column's inner walls and frits against parasitic adsorption, which delivered the best overall results. Based on the desirability study, the Acquity Premier HSS T3 column was selected and the gradient was optimized, which enabled the separation of the molecules present in the model mixtures.

Multi-sample analytical workflow for the determination of isoprostanes in oral fluid: A new tool for non-invasive evaluation of oxidative stress.

Oxidative stress is a pathological condition that contributes to the onset of various diseases. In this way, studying oxidative stress could lead to significant discoveries in the field of therapeutic and preventive medicine. Lipid peroxidation is the most significant event in the oxidative stress process and the gold standard biomarkers for endogenous oxidative damage to lipids are isoprostanes (IsoPs). This project aims to develop a reliable analytical method for the liquid-liquid microextraction technique, parallel artificial liquid membrane extraction (PALME) and LC-MS/MS analysis. PALME allowed to obtain a significant enrichment factor and, at the same time, a good sample purification by removing compounds that cause signal suppression, thereby reducing matrix effect. The chromatographic and mass spectrometric conditions have been fine tuned to improve the sensitivity of the method and therefore obtaining very low LOD and LOQ values. The recovery values obtained for the analytes are slightly above 50%, except for 6-keto Prostaglandin F1A (24%). Matrix effects were ≤ -10 %, with LODs ranging between 1 and 5pgmL-1. The developed method is characterized by high sensitivity and low consumption of organic solvents, according to the principles of Green Analytical Chemistry and enables the determination of basal levels of IsoPs in oral fluid by processing 96 samples simultaneously.

Preparation of a 1,1'-Binaphthyl-based Chiral Polyimine Macrocycle Bonded Chiral Stationary Phase by Thiol-ene Click Reaction and Its Enantioseparation Performance in High-Performance Liquid Chromatography.

Chiral macrocycles have emerged as attractive media for chromatographic enantioseparation due to their excellent host-guest recognition properties. In this study, a new chiral stationary phase (CSP) based on 1,1'-binaphthyl chiral polyimine macrocycle (CPM) was reported. The CPM was synthesized by one-step aldehyde-amine condensation of (S)-2,2'-dihydroxy-[1,1'-binaphthalene]-3,3'-dicarboxaldehyde with 1,2-phenylenediamine and bonded on thiolated silica via the thiol-ene click reaction to afford the CSP. The enantioseparation performance of the CSP was evaluated by separating different types of racemates including alcohols, esters, ketones, amides, organic acids, and ethers in both normal-phase (NP) and reversed-phase (RP) elution modes. As a result, enantioseparations of 10 and 15 racemates were achieved in the two elution modes, respectively. Meanwhile, the effects of chromatographic conditions on separation, such as mobile phase composition and injection mass, were studied in detail. Moreover, a comparison of the proposed CSP for the separation of the tested racemates with commercial Chiralcel OD-H and Chiralpak AD-H columns was also conducted, and results revealed that the proposed CSP can achieve some enantioseparations that cannot be achieved by the two commercial columns. This study indicates that the chiral macrocycle is a promising chiral selector for high-performance liquid chromatography.

Red, green, and blue model assessment and AQbD approach to HPTLC method for concomitant analysis of metformin, pioglitazone, and teneligliptin

BackgroundThe CDSCO of India has authorized a combination of metformin hydrochloride, teneligliptin hydrochloride, and pioglitazone hydrochloride for the treatment of insulin-independent diabetes. For the purpose of estimating metformin, teneligliptin, and pioglitazone combinations as well as individual commercial formulations, there are a plethora of publicly accessible chromatographic techniques. More importantly, the development of these chromatographic procedures has included the use of chemical solvents that are dangerous to both animals and the environment.ObjectivesHowever, to date, there has been no documented chromatographic technique that can concomitantly estimate various commercial formulations of drugs under study employing a uniform chromatographic condition and environmentally friendly solvents. In order to concomitantly estimate drugs under study utilizing unified chromatographic conditions, a green HPTLC method was developed.MethodThe AQbD approach was used to carry out the method development. To determine the most important method parameters and response variables, the analytical risk assessment was conducted using the risk priority number ranking and screening approach. Critical method parameters and response variables were modeled using the response surface modeling approach, which relies on the central composite design. Optimal ranges for the intended method operable design region were determined, and control strategy was framed. The chromatographic separation was carried out on preparative TLC plate precoated with silica gel G-60 F254 using 1.0%W/V ammonium acetate in ethanol: water: triethylamine (6.5:0.4:0.6, V/V) as mobile phase. The detection of the anti-diabetic drugs under study was carried out at 267 nm wavelength.ResultsThe linearity of metformin, teneligliptin, and pioglitazone was found to be 5000–25000 ng/band, 200–1000 ng/band, and 150–750 ng/band, respectively. The %RSD for robustness and precision study was found to be less than 2.0%. The %recovery of method was found to be 98–102%. The assay results were shown to be in compliance with respective labeled claims of anti-diabetic medications when the suggested method was used for concurrent analysis of several formulations and combinations of drugs under study.ConclusionThe suggested technique was evaluated utilizing red–green–blue model scoring tools. The suggested technique was determined to be precise, accurate, rapid, cost-effective, and easy to apply for the estimation of drugs under study.

Development of an HPLC Method for Simultaneous Quantitation of Hyperin, Quercetin and Kaempferol in the Semen cuscutae

In this study, a method for the simultaneous quantification of hyperin, quercetin, and kaempferol in samples of Semen cuscutae was developed using high-performance liquid chromatography (HPLC). The chosen chromatographic conditions were a mobile phase of methanol – 0.1% formic acid in water, running in gradient mode, with a flow rate of 0.9 ml/min, and detection wavelengths of 354, 365, and 369 nm for hyperin, kaempferol, and quercetin, respectively. The results of the quantitative analysis of 5 samples of Semen cuscutae purchased from Phu Tho province showed hyperin content ranging from 0.950 to 1.218 mg/g; quercetin from 0.077 to 0.118 mg/g; and kaempferol from 1.051 to 1.190 mg/g based on the dry weight of the medicinal material. This research may be suitable for the quality control of Semen cuscutae in the market.

Application of quality by design in the development of HPTLC Method for the estimation of ceritinib in bulk and synthetic mixture

The aim of the present work is to develop and validate the High-Performance Thin Layar Chromatography method for the estimation of Ceritinib in bulk and synthetic mixture using the Analytical Quality by Design (AQbD) approach as per ICH Q8 (R2). The AQbD is used to develop a precise HPTLC method. The AQbD parameter elements like QTMP (Quality Target Method Profile- Ceritinib, method type, instrument requirement, sample characteristics, synthetic mixture preparation) CMA—Critical method attributes (chloroform ± 1 ml, methanol ± 1 ml, triethylamine ± 0.05), and Risk assessment (the Ishikawa (fishbone) diagram) were carried out for the development of the HPTLC method for Cerinitib. The Box-Behnken design was utilized for the optimization of chromatographic conditions in HPTLC. The method utilized an aluminum HPTLC plate pre-coated with silica gel G60 F254 as a stationary phase along with chloroform: methanol: triethylamine (8.9: 1.6: 0.07 v/v/v) as a mobile phase. ANOVA results and perturbation plot indicates that methanol and triethylamine had more impact than chloroform on the retardation factor. The retardation factor was found to be 0.37. The absorbance mode at 277 nm was used for the densitometric study. It was found that the linearity ranged from 20 to 100 ng/band, with a correlation coefficient of 0.998. No interference was observed from excipients and mobile phase solvents. The results indicate that the AQbD approach is effectively applied to optimize the retardation factor and the method is appropriate for routine laboratory analysis.