Simultaneous estimation of cilostazol and telmisartan using PCR, PLS, CLS, ILS.
Journal name: World Journal of Pharmaceutical Research
Original article title: Simultaneous estimation of cilostazol and telmisartan using pcr, pls, cls and ils
The WJPR includes peer-reviewed publications such as scientific research papers, reports, review articles, company news, thesis reports and case studies in areas of Biology, Pharmaceutical industries and Chemical technology while incorporating ancient fields of knowledge such combining Ayurveda with scientific data.
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Dharmendra Damor, Bhoomi B. Patel, Karan Mittal and Rajshree C. Mashru
World Journal of Pharmaceutical Research:
(An ISO 9001:2015 Certified International Journal)
Full text available for: Simultaneous estimation of cilostazol and telmisartan using pcr, pls, cls and ils
Source type: An International Peer Reviewed Journal for Pharmaceutical and Medical and Scientific Research
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Summary of article contents:
Introduction
The research article discusses the development and application of four multivariate calibration methods—Principal Component Regression (PCR), Partial Least Squares (PLS), Classical Least Squares (CLS), and Inverse Least Squares (ILS)—for the simultaneous estimation of cilostazol and telmisartan. These techniques play a crucial role in the quantitative analysis of the complex spectra associated with the mixture of these two pharmaceutical compounds. This study highlights the advantages of using multivariate techniques over traditional spectrophotometry through enhanced precision and the ability to analyze mixtures without prior separation.
Principal Component Regression (PCR)
Principal Component Regression (PCR) is a multivariate calibration technique that combines regression analysis with Principal Component Analysis (PCA). This method aims to reduce the dimensionality of the data while preserving as much variability as possible. In the context of the study, PCR processes absorbance and concentration data by mean centering it and subsequently calculating the covariance matrix. The relationship between absorbance and concentrations is established through linear regression, allowing researchers to accurately predict the concentrations of cilostazol and telmisartan in unknown mixtures based on their spectral data.
Partial Least Squares (PLS)
Partial Least Squares (PLS) is another essential chemometric method employed for analyzing complex mixtures. This method operates by simultaneously modeling the independent variables (absorbance data) and the dependent variables (concentration data). PLS focuses on maximizing the covariance between these two sets of variables. By applying mean centering to both absorbance and concentration data, PLS constructs linear regression models that facilitate the prediction of drug concentrations in mixtures. The study demonstrates that PLS is effective and reliable for simultaneous quantification of cilostazol and telmisartan, proving to be precise and accurate in its predictions.
Classical Least Squares (CLS) and Inverse Least Squares (ILS)
Classical Least Squares (CLS) and Inverse Least Squares (ILS) are foundational techniques in multivariate analysis that correlate component concentrations to absorbance data through linear relationships. CLS utilizes Beer’s law to establish a direct relationship between absorbance and concentration at various wavelengths. Conversely, ILS treats concentration as a function of absorbance and employs a statistical approach to derive calibration coefficients. The research findings indicate that both CLS and ILS deliver satisfactory results for the simultaneous analysis of cilostazol and telmisartan, showcasing their robustness as methods for quantitative analysis of pharmaceutical mixtures.
Conclusion
In conclusion, the study successfully develops and validates four chemometric methods—PCR, PLS, CLS, and ILS—for the simultaneous determination of cilostazol and telmisartan in laboratory-prepared mixtures. The methods demonstrated high accuracy, precision, and efficiency, with promising statistical parameters supporting their applicability in quality control settings. The findings underscore the significance of using multivariate calibration techniques to tackle the complexities associated with analyzing multi-component drug formulations, paving the way for advancements in pharmaceutical analysis.
FAQ section (important questions/answers):
What is the purpose of the study on cilostazol and telmisartan?
The study aims to develop four multivariate calibration approaches to simultaneously estimate cilostazol and telmisartan using chemometric techniques that enhance precision and analytical capabilities.
What chemometric methods were used in this research?
The research utilized four methods: Principal Component Regression (PCR), Partial Least Squares (PLS), Classical Least Squares (CLS), and Inverse Least Squares (ILS) for the simultaneous determination of cilostazol and telmisartan.
How were the absorbance measurements conducted?
Absorbance measurements were performed using a Shimadzu model 1700 UV-Visible spectrophotometer, scanning the spectral range of 200-400 nm to assess the maximum absorbance of the drugs.
What were the key findings regarding recovery percentages?
The mean recoveries found for cilostazol ranged from 97.53% to 100.59%, while telmisartan recoveries varied from 99.79% to 100.31%, showing the accuracy and reliability of the methods.
What was concluded about the methods developed?
The study concluded that the developed chemometric methods are simple, precise, accurate, and economical, making them suitable for quality control of cilostazol and telmisartan in pharmaceutical formulations.
Who provided the samples used in the research?
Cilostazol was sourced from Swapnroop Drug Pvt. Ltd., while telmisartan was a gift sample from Alembic Pharmaceuticals, Vadodara, ensuring high-quality reagents for the study.
Glossary definitions and references:
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A drug is a biological or chemical substance with therapeutic effects. It is relevant in pharmacy and medicine as it plays a crucial role in treating diseases, managing health conditions, and contributing to healthcare advancements. Understanding drug interactions, efficacy, and safety is essential for healthcare practitioners and researchers.
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Other Science Concepts:
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