Tablet potency of Tianeptine in coated tablets by near infrared spectroscopy: Model optimisation, calibration transfer and confidence intervals

doi:10.1016/j.jpba.2010.09.029

Journal of Pharmaceutical and Biomedical Analysis

Volume 54, Issue 3, 20 February 2011, Pages 510-516

https://doi.org/10.1016/j.jpba.2010.09.029 Get rights and content

Abstract

A near infrared (NIR) method was developed for determination of tablet potency of active pharmaceutical ingredient (API) in a complex coated tablet matrix. The calibration set contained samples from laboratory and production scale batches. The reference values were obtained by high performance liquid chromatography (HPLC) and partial least squares (PLS) regression was used to establish a model. The model was challenged by calculating tablet potency of two external test sets. Root mean square errors of prediction were respectively equal to 2.0% and 2.7%. To use this model with a second spectrometer from the production field, a calibration transfer method called piecewise direct standardisation (PDS) was used. After the transfer, the root mean square error of prediction of the first test set was 2.4% compared to 4.0% without transferring the spectra. A statistical technique using bootstrap of PLS residuals was used to estimate confidence intervals of tablet potency calculations. This method requires an optimised PLS model, selection of the bootstrap number and determination of the risk. In the case of a chemical analysis, the tablet potency value will be included within the confidence interval calculated by the bootstrap method. An easy to use graphical interface was developed to easily determine if the predictions, surrounded by minimum and maximum values, are within the specifications defined by the regulatory organisation.

Introduction

Near infrared spectroscopy has been used for many years in pharmaceutical firms for water content estimations [1], content uniformity controls [2] or counterfeit product investigations [3]. Pharmaceutical firms adopted NIR spectroscopy because of the numerous advantages offered by this technique: high speed, freedom from pollution, no need for reagents or sample preparations, non destructive and capable of providing information about API and other components of a tablet [4], [5], [6]. However, although NIR spectra contain a lot of information, this technique requires a good knowledge of chemometric tools for analysis and interpretation of the data. It can be assumed that NIR spectra contain two kinds of information: the first can be defined as a chemical part, which is constituted by tablet components (API and constituents) and the second can be defined as a physical part, which is constituted by uncontrollable factors such as light variation or surface of the tablet [7]. Fourier-transform NIR (FT-NIR) spectroscopy associated with multivariate data analysis were used to build a model for tablet potency of coated tablets from Servier. To ensure uniform potency of low-dose drugs, a content uniformity test based on tablet potency results is required for tablet release to the patients. It ensures the presence of accurate and uniform API content in the dosage unit. The sample preparation for conventional analytical methods of dosage unit determinations typically involves dissolving, extracting, and diluting API into a solution of appropriate concentration that can be accurately detected by chromatography.

PLS regression is one of the best known chemometric tools used to carry out this kind of data analysis. However, using the spectra acquired on a second spectrometer (such as a production spectrometer) with the model previously built on a specific spectrometer is a real problem [8]. A technique called Piecewise Direct Standardization (PDS) performs a spectra transformation by using transfer samples recorded by the two spectrometers [9]. The transfer function established is essential to use the same model with spectra acquired on different spectrometers.

The model quality can be assessed by studying root mean square errors (of calibration or prediction), or by studying scores, loadings and error matrix supplied by the model. Nevertheless, none of these results and parameters were able to estimate the confidence intervals of NIR tablet potency calculated by the model. To assess these intervals, an approach using the bootstrap of PLS residuals was tested.

This paper describes and explains the steps to develop a whole tablet potency model for the laboratory and the production environment. A technique based on the bootstrap of the PLS residuals was evaluated to estimate confidence intervals of tablet potency estimations.

Section snippets

General methodology

The aim of the study was the development of an alternative method to HPLC for tablet potency determination in the development and the production environment. The tablet potency estimations have been included in a confidence interval. The general methodology used for tablet potency determination by NIR spectroscopy (model created in the lab or in production) started with the selection of a set of calibration tablets with varied API content spanning the range of analysis. Spectra were collected

Tablet potency model

Spectra of the 45 calibration samples were acquired with the research and development spectrometer between 10,000 and 7800 cm⁻¹ (Fig. 1). Tablet potencies of API were then measured with high performance liquid chromatography to obtain reference values for each tablet. The wavelengths corresponding to the API were identified and selected to avoid the variations induced by different batches of excipients and data were mean centered. Five latent variables were chosen to build the PLS model (Fig. 2)

Conclusions

Quantifying the content of API in a tablet is a real challenge in the pharmaceutical field as it is important to ensure that the correct quantity of substance is delivered to the patient. In this study, a PLS model for tablet potency determination was developed by using NIR spectroscopy. A calibration transfer method was carried out to improve the poor predictions obtained when the model was used with the spectra acquired at the production site. This method required the acquisition of transfer

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Backscattering NIR, Raman (BSR) and transmission Raman spectroscopy (TRS) coupled with chemometrics have shown to be rapid and non-invasive tools for the quantification of active pharmaceutical ingredient (API) content in tablets. However, the developed models are generally specifically related to the measurement conditions and sample characteristics. In this study, a number of calibration transfer methods, including DS, PDS, DWPDS, GLSW and SST, were evaluated for the spectra correction between modelled tablets produced in the laboratory and commercial samples. Results showed that the NIR and BSR spectra of commercial tablet corrected by DWPDS and PDS, respectively, enabled accurate API predictions with the high ratio of prediction error to deviation (RPD_P) values of 2.33 and 3.03. The most successfully approach was achieved with DS corrected TRS data and SiPLS modelling (161 variables) and yielded RMSE_P of 0.72 %, R²_P of 0.946 and RPD_P of 4.35. The proposed calibration transfer strategy offers the opportunities to analyse samples produced in different conditions; in the future, its implication will find extensively process control and quality assurance applications and benefit all possible users in the entire pharmaceutical industry.
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Citation Excerpt :
Calibration transfer techniques aim at minimizing the differences between either instrumental responses or predicted values between different instruments [16,17]. The literature describes many calibration transfer techniques [13,18–28], but the most used are the Direct Standardization and Piecewise Direct Standardization. Few papers, however, have reported methods for calibration transfer from benchtop to handheld spectrometers.
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Tablet potency of Tianeptine in coated tablets by near infrared spectroscopy: Model optimisation, calibration transfer and confidence intervals

Abstract

Introduction

Section snippets

General methodology

Tablet potency model

Conclusions

Anal. Chim. Acta

Int. J. Pharm.

J. Pharm. Biomed. Anal.

J. Pharm. Sci.

J. Pharm. Biomed. Anal.

Vib. Spectrosc.

J. Pharm. Biomed. Anal.

Chemom. Intell. Lab. Syst.

Anal. Chim. Acta

Chemom. Intell. Lab. Syst.

Analysis of low content drug tablet by transmission near infrared spectroscopy: selection of calibration ranges according to multivariate detection and quantification limits of PLS models

J. Pharm. Sci.