Simultaneous Determination of Hydrochlorothiazide in Combination with Some Antihypertensive Drugs in The Presence of Its Main Impurities in Pure Form and Pharmaceutical Formulations

2019 ◽  
Vol 16 (1) ◽  
pp. 64-84
Author(s):  
Maissa Yacuob Salem ◽  
Nagiba Yehia Hassan ◽  
Yasmin Mohamed Fayez ◽  
Samah Abd ElSabour ◽  
Enas Shaaban Ali
Keyword(s):  
Quality Control ◽  
Antihypertensive Drugs ◽  
Principal Component Regression ◽  
Principal Component ◽  
Pharmaceutical Formulations ◽  
Pure Form ◽  
Simultaneous Estimation ◽  
Least Squares Regression ◽  
Related Impurities ◽  
Ich Guidelines

Background: Hydrochlorothiazide (HCTZ) is potent diuretic that is used alone or in combination with other drugs such as labetalol (Lab) (mixtures Ι) or nebivolol (Neb) (mixtures ΙΙ) to control moderate to sever hypertension. Introduction: This paper demonstrates the establishment of different validated spectrophotometric and chemometric methods for simultaneous estimation of these mixtures in pure form and pharmaceutical formulations in the presence of HCTZ related impurities in quality control laboratories. Methods: (A) Derivative method (D3) of Lab and HCTZ and its related impurities at 245.3nm and 278.5nm respectively, (D1) of Neb and HCTZ at 294.2nm and 282.2nm, respectively. (B) First derivative of ratio spectra method (DD 1) of Lab at 244.3nm, HCTZ at 261.2nm and 275.4nm, while at 294nm for Neb and 269.4nm for HCTZ. (C) Ratio difference method which depends on measuring the distinction between the amplitudes of ratio spectra at 240nm and 288.3nm for Lab and at 270.1nm and 277.4nm for HCTZ for mixture Ι while at 290.4nm and 299.2nm for Neb and at 232.2nm and 254nm for HCTZ for mixture ΙΙ. (D) Mean centering of ratio spectra (MC) and (E) partial least squares regression (PLS) and principal component regression (PCR). Results: These methods were applied over concentration ranges of 10-100 µg/ml, 10-75 µg/ml and 2.5- 25 µg/ml of Lab, Neb and HCTZ, respectively. Methods were validated according to ICH guidelines and statistical comparison of results of reported and proposed methods revealed no difference. Conclusion: The methods were successfully used for the frequent analysis of selected mixtures in quality control laboratories.

SIMULTANEOUS ESTIMATION OF DAPAGLIFOZIN AND SAXAGLIPTIN IN TABLETS USING TWO CHEMOMETRIC-ASSISTED UV-SPECTROPHOTOMETRIC METHODS

INDIAN DRUGS ◽  
2019 ◽  
Vol 56 (03) ◽  
pp. 32-38
Author(s):  
S. S Sonawane ◽  
S. S More ◽  
S. S. Chhajed ◽  
S. J. Kshirsagar ◽  
Keyword(s):  
Linear Regression ◽  
Phosphate Buffer ◽  
Principal Component Regression ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Training Set ◽  
Spectrophotometric Methods ◽  
Accuracy And Precision ◽  
Ich Guidelines ◽  
Validation Set

Two simple, accurate, precise and economical UV spectrophotometric methods, Multiple Linear Regression (MLR) and Principal Component Regression (PCR), were developed for the simultaneous estimation of dapaglifozin (DAPA) and saxagliptin (SAXA) in tablets. Beer’s law was obeyed in the concentration ranges of 10 – 50 μg/mL for DAPA and 5 – 25 μg/mL for SAXA. Synthetic mixtures containing two drugs were prepared to build the training set and validation set in the calibration range using D-optimal mixture design in phosphate buffer pH 6.8 and were recorded at six wavelengths in the range of 230 – 215 nm at intervals of Δλ = 3 nm. Both methods were validated as per ICH guidelines with respect to the accuracy and precision and found suitable for routine analysis of tablets containing DAPA and SAXA without separation.

DEVELOPMENT OF HPTLC METHOD FOR SIMULTANEOUS ESTIMATION OF CIPROFLOXACIN AND ORNIDAZOLE IN COMBINED DOSAGE FORM

INDIAN DRUGS ◽  
2013 ◽  
Vol 50 (05) ◽  
pp. 36-43
Author(s):  
N. R Dighade ◽  
◽  
M. D Shende ◽  
A. V Kasture
Keyword(s):  
Quality Control ◽  
High Resolution ◽  
Thin Layer ◽  
Mobile Phase ◽  
High Performance ◽  
Dosage Form ◽  
Simultaneous Estimation ◽  
Ich Guidelines ◽  
Hptlc Method ◽  
Routine Quality Control

A simple and accurate high performance thin layer chromatographic (HPLTC) method has been developed and validated as per ICH guidelines for estimations of Ciprofloxacin (CP) and Ornidazole (ORN) in combined dosage form. The mobile phase was acetonitrile: toluene: water and triethylamine (5.5:1.8:1.5:1.6 V/V) was found to be best which gave high resolution with Rf 0.16 and 0.84 for ciprofloxacin and ornidazole respectively. The linearity of ciprofloxacin and ornidazole was found to be in the range of 0.4 to 0.8 µg/mL and 0.4 to 0.8 µg/mL, respectively. The coefficient of correlation (r2 ) was found to be greater than 0.989 for both the components by this method. The tablet analyses result (n = 5) were found to be > 100.84 % by HPTLC for both the components. The proposed method was found to be simple, accurate and suitable for routine quality control of marketed formulations containing these drugs.

scholarly journals The Determination of Parkinson’s Drugs in Human Urine by Applying Chemometric Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Guzide Pekcan Ertokus
Keyword(s):  
Urine Sample ◽  
Human Urine ◽  
Principal Component Regression ◽  
Principal Component ◽  
Healthy Person ◽  
Drug Analysis ◽  
Uv Spectrophotometry ◽  
Least Squares Regression ◽  
Chemometric Methods

The spectrophotometric-chemometric analysis of levodopa and carbidopa that are used for Parkinson’s disease was analyzed without any prior reservation. Parkinson’s drugs in the urine sample of a healthy person (never used drugs in his life) were analyzed at the same time spectrophotometrically. The chemometric methods used were partial least squares regression (PLS) and principal component regression (PCR). PLS and PCR were successfully applied as chemometric determination of levodopa and carbidopa in human urine samples. A concentration set including binary mixtures of levodopa and carbidopa in 15 different combinations was randomly prepared in acetate buffer (pH 3.5).). UV spectrophotometry is a relatively inexpensive, reliable, and less time-consuming method. Minitab program was used for absorbance and concentration values. The normalization values for each active substance were good (r2>0.9997). Additionally, experimental data were validated statistically. The results of the analyses of the results revealed high recoveries and low standard deviations. Hence, the results encouraged us to apply the method to drug analysis. The proposed methods are highly sensitive and precise, and therefore they were implemented for the determination of the active substances in the urine sample of a healthy person in triumph.

scholarly journals Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets

2006 ◽  
Vol 71 (11) ◽  
pp. 1207-1218
Author(s):  
Dondeti Satyanarayana ◽  
Kamarajan Kannan ◽  
Rajappan Manavalan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mefenamic Acid ◽  
Principal Component Regression ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Uv Spectrophotometry ◽  
Novel Approach ◽  
Marquardt Algorithm ◽  
Calibration Models

Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB? Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study.

QBD BASED RP-HPLC METHOD FOR SCREENING AND ANALYSIS OF TELAPRAVIR AND 7 OTHER ANTIRETROVIRAL AGENTS

INDIAN DRUGS ◽  
2015 ◽  
Vol 52 (02) ◽  
pp. 20-33
Author(s):  
N. S Kumar ◽  
◽  
R Kumaraswamy ◽  
S. Shantikumar ◽  
D. Paul
Keyword(s):  
Quality By Design ◽  
Pharmaceutical Formulations ◽  
Active Pharmaceutical Ingredient ◽  
Hplc Method ◽  
Array Detector ◽  
Simultaneous Estimation ◽  
Rp Hplc ◽  
Ich Guidelines ◽  
Full Factorial

The present study describes the separation and simultaneous estimation of eight anti-retroviral drugs, namely, Telaprevir (TPV), Emtricitabine (ECB), Fosamprenavir (FANV), Tenofavir (TNF), Ritonavir (RNV), Raltegravir (RGV) and Oseltamivir (OSMV) and Zidovudine (ZDV) as an active pharmaceutical ingredient, by RP-HPLC method by applying the principles of Quality by Design (QbD). An application of DoE (Design of Experiments) full factorial design was used for initial screening and optimization. The final optimized method consists of separation being carried out on a Fortis C18 column (150 mm × 4.6 mm, 5μ particle size) using acetonitrile and 10 mm ammonium formate buffer (pH 3 adjusted with formic acid) using a gradient program. The quantitative evaluation was performed with a diode array detector at 251 nm and 230 nm with a flow rate of 1 mL min–1. Suitability of this method for the quantitative determination of the drugs was proved by validation in accordance with the International Conference on Harmonization (ICH) guidelines. The method is selective, precise, robust and accurate and can be used for routine analysis of pharmaceutical formulations in quality control and counterfeit screening.

scholarly journals Multivariate Method Based on Raman Spectroscopy for Quantification of Dipyrone in Oral Solutions

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Luciana Lopes Guimarães ◽  
Letícia Parada Moreira ◽  
Bárbara Faria Lourenço ◽  
Walber Toma ◽  
Renato Amaro Zângaro ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quality Control ◽  
Principal Component Regression ◽  
Principal Component ◽  
Quantitative Model ◽  
Calibration Model ◽  
Analytical Methodology ◽  
Figures Of Merit ◽  
Model Based ◽  
Raman Probe

This work employed a quantitative model based on Raman spectroscopy and principal component regression (RS/PCR) to quantify the active ingredient dipyrone (metamizole) in commercially available formulations as an analytical methodology for quality control in the pharmaceutical industry. Raman spectra were collected using a dispersive Raman spectrometer (830 nm, 250 mW excitation, and 20 s exposure time) coupled to a Raman probe. Solutions of dipyrone diluted in water in the range of 80 to 120% of the concentration of commercial formulations (500 mg/mL) were used to develop a calibration model based on PCR to obtain the figures of merit for class I validation from the Brazilian Sanitary Surveillance Agency (ANVISA, RE no. 899/2003). This spectral model was then used to predict the concentration of dipyrone in commercial formulations from distinct brands with 500 mg/mL. A prediction error of 6.5 mg/mL (1.3%) was found for this PCR model using the diluted samples. Commercial formulations had predicted concentrations with a difference below 5.0% compared to the label concentration, indicating the applicability of Raman spectroscopy for quality control in the final product.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

1996 ◽  
Vol 4 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Paul Geladi ◽  
Harald Martens
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Regression Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Statistical Parameters ◽  
Calibration Samples

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.

scholarly journals Application of Cerium (IV) as an Oxidimetric Agent for the Determination of Ethionamide in Pharmaceutical Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Kanakapura Basavaiah ◽  
Nagib A. S. Qarah ◽  
Sameer A. M. Abdulrahman
Keyword(s):  
Quality Control ◽  
Pharmaceutical Formulations ◽  
Molar Absorptivity ◽  
Sample Solution ◽  
Standing Time ◽  
Spectrophotometric Methods ◽  
Ich Guidelines ◽  
Back Titration ◽  
Bulk Drug

Two simple methods are described for the determination of ethionamide (ETM) in bulk drug and tablets using cerium (IV) sulphate as the oxidimetric agent. In both methods, the sample solution is treated with a measured excess of cerium (IV) solution in H2SO4 medium, and after a fixed standing time, the residual oxidant is determined either by back titration with standard iron (II) solution to a ferroin end point in titrimetry or by reacting with o-dianisidine followed by measurement of the absorbance of the orange-red coloured product at 470 nm in spectrophotometry. In titrimetry, the reaction proceeded with a stoichiometry of 1 : 2 (ETM : Ce (IV)) and the amount of cerium (IV) consumed by ETM was related to the latter’s amount, and the method was applicable over 1.0–8.0 mg of drug. In spectrophotometry, Beer’s law was obeyed over the concentration range of 0.5–5.0 μg/mL ETM with a molar absorptivity value of 2.66 × 104 L/(mol·cm). The limits of detection (LOD) and quantification (LOQ) calculated according to ICH guidelines were 0.013 and 0.043 μg/mL, respectively. The proposed titrimetric and spectrophotometric methods were found to yield reliable results when applied to bulk drug and tablets analysis, and hence they can be applied in quality control laboratories.

scholarly journals Temperature changes over the past 2000 yr in China and comparison with the Northern Hemisphere

2013 ◽  
Vol 9 (3) ◽  
pp. 1153-1160 ◽  
Author(s):  
Q. Ge ◽  
Z. Hao ◽  
J. Zheng ◽  
X. Shao
Keyword(s):  
Northern Hemisphere ◽  
Principal Component Regression ◽  
Principal Component ◽  
Warm Period ◽  
Ice Age ◽  
Least Squares Regression ◽  
Temperature Variations ◽  
Temperature Changes ◽  
Confidence Levels ◽  
The Past

Abstract. We use principal component regression and partial least squares regression to separately reconstruct a composite series of temperature variations in China, and associated uncertainties, at a decadal resolution over the past 2000 yr. The reconstruction is developed using proxy temperature data with relatively high confidence levels from five regions across China, and using a temperature series from observations by the Chinese Meteorological Administration, covering the period from 1871 to 2000. Relative to the 1851–1950 climatology, our two reconstructions show four warm intervals during AD 1–AD 200, AD 551–AD 760, AD 951–AD 1320, and after AD 1921, and four cold intervals during AD 201–AD 350, AD 441–AD 530, AD 781–AD 950, and AD 1321–AD 1920. The temperatures during AD 981–AD 1100 and AD 1201–AD 1270 are comparable to those of the Present Warm Period, but have an uncertainty of ±0.28 °C to ±0.42 °C at the 95% confidence interval. Temperature variations over China are typically in phase with those of the Northern Hemisphere (NH) after 1000, a period which covers the Medieval Climate Anomaly, the Little Ice Age, and the Present Warm Period. In contrast, a warm period in China during AD 541–AD 740 is not obviously seen in the NH.

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