An approach to select linear regression model in bioanalytical method validation (original) (raw)
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Linear regression for calibration lines revisited: weighting schemes for bioanalytical methods
Journal of Chromatography B-analytical Technologies in The Biomedical and Life Sciences, 2002
When the assumption of homoscedasticity is not met for analytical data, a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line is to use weighted least squares linear regression (WLSLR). The purpose of the present paper is to stress the relevance of weighting schemes for linear regression analysis and to show how this approach can be useful in the bioanalytical field. The steps to be taken in the study of the linear calibration approach are described. The application of weighting schemes was shown by using a high-performance liquid chromatography method for the determination of lamotrigine in biological fluids as a practical example. By using the WLSLR, the accuracy of the analytical method was improved at the lower end of the calibration curve. Bioanalytical methods data analysis was improved by using the WLSLR procedure.
Journal of AOAC INTERNATIONAL, 2012
The calibration of an analytical method is a very important part of its development, and only the proper statistical and chemometric evaluation of the results, together with understanding this process, allows good results. The purpose of this minireview is to call the reader's attention to the major problems in calibration: curvilinearity, heteroscedasticity, presence of outliers, transformation of results, and distribution and autocorrelation of residuals. The common misunderstandings and mistakes are emphasized to inform the reader. Additionally, the computational package “quantchem” for GNU R environment, allowing full and automatic calibration evaluation, is presented.
Calibration and Validation of Analytical Methods - A Sampling of Current Approaches, 2018
Calibration curve is a regression model used to predict the unknown concentrations of analytes of interest based on the response of the instrument to the known standards. Some statistical analyses are required to choose the best model fitting to the experimental data and also evaluate the linearity and homoscedasticity of the calibration curve. Using an internal standard corrects for the loss of analyte during sample preparation and analysis provided that it is selected appropriately. After the best regression model is selected, the analytical method needs to be validated using quality control (QC) samples prepared and stored in the same temperature as intended for the study samples. Most of the international guidelines require that the parameters, including linearity, specificity, selectivity, accuracy, precision, lower limit of quantification (LLOQ), matrix effect and stability, be assessed during validation. Despite the highly regulated area, some challenges still exist regarding the validation of some analytical methods including methods when no analyte-free matrix is available.
Statistical approach for selection of regression model during validation of bioanalytical method
Macedonian Pharmaceutical Bulletin
The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals) were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple...
An Empirical Study on the Impact of Bioanalytical Method Variability on Estimation of PK Parameters
Chromatographia, 2004
The effect of the experimental error of bioanalytical methods on the estimation of pharmacokinetic (PK) parameters was formerly studied with simulation experiments. The results showed that the precision of affects both accuracy and precision of pharmacokinetic parameters. In particular for drugs with small within-individual pharmacokinetic variability the contribution of bioanalytical imprecision of 15% may become unacceptable. The above-mentioned simulation results were confirmed by a recent experiment where plasma curves were analysed in multifold in the same and in different analytical batches, thereby being able to quantify correlations between bioanalytical within-day and between-day variability and PK variability. The results showed that for compounds or formulations with large betweensubject or within-subject/between-dosing variability more bioanalytical variability may be acceptable than for drugs with small between-subject variability. Acceptability of analytical precision and accuracy depends on the compound and the study design. Influence of bioanalysis on final outcome is generally insignificant. In general it can be stated that most bioanalytical assays used for regulatory support are very suitable if they are operated within current guideline criteria.
Biomedical Chromatography, 2009
While the practice of using a smaller number of non-zero standards (typically seven to eight) has not been entertained in routine bioanalytical work, it is important to innovate and be pragmatic about minimizing the number of calibration standards to promote cost-eff ective and speedy assessment. In this exercise, two important compounds, omeprazole and clopidogrel carboxylic acid, were considered. Additionally, both analytes off ered a 1000-fold calibration curve range with eight non-zero standards to permit a systematic evaluation. Accordingly various scenarios of post-hoc analysis of the calibration data were formulated which included step-wise reduction of the number of calibration standards from a maximum of n = 8 to a minimum of n = 3. In all the scenarios evaluated in this exercise, a calibration curve was reconstructed and both quality control samples and in vivo pharmacokinetics were calculated in each instance. Based on the data generated in this exercise, a minimum of three non-zero calibration standards were adequate to predict the quality control samples with the predefi ned accuracy and precision estimates for both omeprazole and clopidogrel carboxylic acid. Additionally, the in vivo pharmacokinetic characterization of the chosen compounds was not hampered by the reduction of calibration standards (from n = 8 to n = 3). Hence, consideration for reducing number of calibration standards in bioanalytical work may provide a viable alternative in several situations such as formulation screening strategies, routine therapeutic drug monitoring and sparse sample analyses.
Bioanalytical Method Validation and Its Pharmaceutical Application- AReview
Pharmaceutica Analytica Acta, 2014
Bioanalytical methods, based on a variety of physico-chemical and biological techniques such as chromatography, immunoassay and mass spectrometry, must be validated prior to and during use to give confidence in the results generated. It is the process used to establish that a quantitative analytical method is suitable for biomedical applications. Bioanalytical method validation includes all of the procedures that demonstrate that a particular method used for quantitative measurement of analytes in a given biological matrix, such as blood, plasma, serum, or urine is reliable and reproducible for the intended use. The present manuscript focuses on the consistent evaluation of the key bioanalytical validation parameters is discussed: accuracy, precision, sensitivity, selectivity, standard curve, limits of quantification, range, recovery and stability. These validation parameters are described, together with an example of validation methodology applied in the case of chromatographic met...
International Journal of Biology and Biomedical Engineering
Bivariate calibration algorithm is compared with the results obtained by the usage of high-dimensional calibration methods such as partial least squares (PLS) and multi-way partial least-squares (N-PLS) by using UV-Vis spectrophotometric data of first and second-order. The algorithms were applied to the determination of a mixture of an analgesic and a stimulant compound and their actual concentrations of them were calculated by using spectroscopic data. The direct reading of absorbance values at 227 nm and 271 nm were employed for quantification of the compounds in the case of the bivariate method. The approaches of first-order and multi-way methods were applied with a previous optimization of the calibration matrix by constructing sets of calibration and validation with 20 and 10 samples (mixtures) respectively according to a central composite design and their UV absorption spectra were recorded at 200-350 nm. All algorithms were satisfactorily applied to the simultaneous determina...
Journal of AOAC INTERNATIONAL, 2010
Linearity assessment as required in method validation has always been subject to different interpretations and definitions by various guidelines and protocols. However, there are very limited applicable implementation procedures that can be followed by a laboratory chemist in assessing linearity. Thus, this work proposes a simple method for linearity assessment in method validation by a regression analysis that covers experimental design, estimation of the parameters, outlier treatment, and evaluation of the assumptions according to the International Union of Pure and Applied Chemistry guidelines. The suitability of this procedure was demonstrated by its application to an in-house validation for the determination of plasticizers in plastic food packaging by GC.