Abstract:A new, simple, novel, accurate, precise, reliable, rapid and linear reverse phase high-performance liquid chromatography (RP-HPLC) method was developed and fully validated for simultaneous qualitative and quantitative estimation of Rosuvastatin (ROS), Clopidogrel (CLOP) and Aspirin (ASP) in bulk and pharmaceutical dosage form as per International Conference on Harmonization (ICH) guidelines. In the present work, good chromatographic separation was achieved by isocratic method using a Hypersil BDS C18 column … Show more
“…Upon the literature survey, a few spectrophotometric methods have been proposed for the analysis of ASP, CLP, and ATV or ROS in pharmaceutical preparations 11–16 . Besides, few liquid chromatography with tandem mass spectrometry (LC‐MS/MS) and high‐performance liquid chromatography (HPLC) methods have been proposed for the analysis of ASP, CLP, and ATV or ROS in pharmaceutical preparations 17–21 …”
Section: Introductionmentioning
confidence: 99%
“…[11][12][13][14][15][16] Besides, few liquid chromatography with tandem mass spectrometry (LC-MS/MS) and high-performance liquid chromatography (HPLC) methods have been proposed for the analysis of ASP, CLP, and ATV or ROS in pharmaceutical preparations. [17][18][19][20][21] The UV absorption spectra of ASP, CLP, and ATV in mixture (1) and ASP, CLP, and ROS in mixture (2) in distilled water at their nominal concentrations ratio in tablets and capsules show strong overlap (Figures 2 and 3). Therefore, it is not possible to directly and simultaneously determine the cited medicines in the two mixes using spectrophotometry.…”
In this study, the simultaneous determination of aspirin, clopidogrel, and either atorvastatin or rosuvastatin in their fixed‐dose combination (FDC) formulations has been reported. As a straightforward substitute for employing distinct models for each component, UV spectrophotometry was applied with chemometric approaches and artificial neural networks to achieve this. Three chemometric techniques, including principal component regression (PCR), partial least‐squares (PLS), and classical least‐squares (CLS), were applied in addition to the radial basis function‐artificial neural network (RBF‐ANN). The validation of a set of laboratory‐prepared combinations of aspirin, clopidogrel, and atorvastatin in one ternary mixture and aspirin, clopidogrel, and rosuvastatin in a second ternary mixture was assessed, and the results from the use of these approaches were recorded and compared. The absorbance data matrix matching the concentration data matrix in CLS, PCR, and PLS was created using measurements of absorbances in the range of 250–280 nm at intervals of 0.2 nm in their zero‐order spectra. Then, in order to forecast the unknown concentrations, calibration or regression was created utilizing the concentration and absorbance data matrices. Using RBF‐ANN for the simultaneous determination of aspirin, clopidogrel, and atorvastatin or rosuvastatin in their formulations was achieved by providing the input layer with 151 neurons; there are 2 hidden layers and 3 output neurons were obtained. The green profile of the developed methods has been assessed and compared with previously reported spectrophotometric methods. The suggested techniques were effectively applied to FDC dosage forms that contained the cited medications.
“…Upon the literature survey, a few spectrophotometric methods have been proposed for the analysis of ASP, CLP, and ATV or ROS in pharmaceutical preparations 11–16 . Besides, few liquid chromatography with tandem mass spectrometry (LC‐MS/MS) and high‐performance liquid chromatography (HPLC) methods have been proposed for the analysis of ASP, CLP, and ATV or ROS in pharmaceutical preparations 17–21 …”
Section: Introductionmentioning
confidence: 99%
“…[11][12][13][14][15][16] Besides, few liquid chromatography with tandem mass spectrometry (LC-MS/MS) and high-performance liquid chromatography (HPLC) methods have been proposed for the analysis of ASP, CLP, and ATV or ROS in pharmaceutical preparations. [17][18][19][20][21] The UV absorption spectra of ASP, CLP, and ATV in mixture (1) and ASP, CLP, and ROS in mixture (2) in distilled water at their nominal concentrations ratio in tablets and capsules show strong overlap (Figures 2 and 3). Therefore, it is not possible to directly and simultaneously determine the cited medicines in the two mixes using spectrophotometry.…”
In this study, the simultaneous determination of aspirin, clopidogrel, and either atorvastatin or rosuvastatin in their fixed‐dose combination (FDC) formulations has been reported. As a straightforward substitute for employing distinct models for each component, UV spectrophotometry was applied with chemometric approaches and artificial neural networks to achieve this. Three chemometric techniques, including principal component regression (PCR), partial least‐squares (PLS), and classical least‐squares (CLS), were applied in addition to the radial basis function‐artificial neural network (RBF‐ANN). The validation of a set of laboratory‐prepared combinations of aspirin, clopidogrel, and atorvastatin in one ternary mixture and aspirin, clopidogrel, and rosuvastatin in a second ternary mixture was assessed, and the results from the use of these approaches were recorded and compared. The absorbance data matrix matching the concentration data matrix in CLS, PCR, and PLS was created using measurements of absorbances in the range of 250–280 nm at intervals of 0.2 nm in their zero‐order spectra. Then, in order to forecast the unknown concentrations, calibration or regression was created utilizing the concentration and absorbance data matrices. Using RBF‐ANN for the simultaneous determination of aspirin, clopidogrel, and atorvastatin or rosuvastatin in their formulations was achieved by providing the input layer with 151 neurons; there are 2 hidden layers and 3 output neurons were obtained. The green profile of the developed methods has been assessed and compared with previously reported spectrophotometric methods. The suggested techniques were effectively applied to FDC dosage forms that contained the cited medications.
“…Several methods have been developed to estimate ESMO in pharmaceutical preparations, including UV-visible- [21,22] and HPLC [23,24] methods. In biological fluids, ESO was determined by LC-MS/MS [25][26][27] and the desorption electrospray mass spectrometry (DESI-MS) method [28].…”
The HPLC-MS technique was tested and verified in this work to quantify esomeprazole in rat plasma samples. A rat investigation demonstrated that co-administration of a single dosage of liquorice juice had no effect on the pharmacokinetic characteristics of esomeprazole, however successive doses of liquorice juice raised the t1/2 and area under the curve of esomeprazole. After introducing liquorice juice into the stomach, the pharmacokinetics of esomeprazole was determined simultaneously by HPLC-MS. The absorption of esomeprazole was rapid; esomeprazole was detected in plasma from the first blood sampling time, and the peak plasma concentration for the esomeprazole-treated group with multiple doses of liquorice was reached one hour after oral administration. At the same time, peak plasma concentration was reached 30 min after oral administration for the esomeprazole-treated group with a single dose of liquorice or distilled water. The plasma concentrations of esomeprazole in the esomeprazole-treated group with single-dose liquorice were comparable to those with single-dose distilled water. The plasma concentrations of esomeprazole were higher in the esomeprazole treated group with multiple doses of liquorice than in the esomeprazole treated group with single-dose distilled water, resulting in a significantly higher AUC, 2.5 times, in the esomeprazole treated group with multiple doses of liquorice. This could be due to decreased metabolism of esomeprazole by multiple doses of liquorice. Since esomeprazole is metabolized in the liver mainly by CYP2C19 and liquorice is a moderate inhibitor of CYP2C19 in humans there was an inhibition of CYP2C19 at multiple doses of liquorice. The inhibited metabolism of esomeprazole by liquorice resulted in a significantly higher Cmax, 1.5 times, and a significantly longer terminal half-life, 1.45 times, than in the control group. Therefore, multiple administration of liquorice could increase the esomeprazole effect since a single dose did not affect esomeprazole metabolism.
“…In the method presented by Tiwari, for the estimation of aspirin, clopidogrel and rosuvastatin in the presence of potassium dihydrogen phosphate buffer of pH 6.0 and acetonitrile, the basic drug clopidogrel is ionized (Tiwari et al, 2019). The ionized clopidogrel interacts with the phosphate counter anion in the mobile phase and is eluted later than aspirin, an acidic drug, irrespective of its polarity.…”
Section: Effect Of Chaotropic Counter Anions On the Retention Behavio...mentioning
The understanding of principles that drive the separation in reversed‐phase chromatography plays an important role in the prediction of the elution of solutes in RP‐HPLC. The separation in RP‐HPLC is based on the principle of adsorption and partition. In addition, the logP value, the pKa value of the drug and chromatographic parameters like mobile phase pH, buffer concentration, organic modifier and mobile phase additives also influence the retention and selectivity of the analyte. It was found that hydrophobic, electrostatic, hydrogen bonding and other specific interactions between the stationary phase and the solutes, along with the hydrophobicity of an analyte molecule (logP), modify the retention behaviour of the analytes. This article gives special attention to the influence of ionization and ion interaction on the separation of analytes. The drug molecules with different logP values containing protonated and deprotonated acids, bases and zwitterions are selected as examples and this article addresses various issues related to the method development, relationships between analyte retention and mobile phase pH and the pKa value of the analyte. The advances in this regard, with highlights on topics such as mechanisms of retention and various factors that influence the retention behaviour of analytes, are also updated with suitable examples.
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