Active content determination of pharmaceutical tablets using near infrared spectroscopy as Process Analytical Technology tool

Chavez, Pierre-François; Sacré, Pierre-Yves; Bleye, Charlotte De; Netchacovitch, Lauranne; Mantanus, Jérôme; Motte, Henri; Schubert, M.; Hubert, Philippe; Ziémons, Eric

doi:10.1016/j.talanta.2015.08.018

Cited by 31 publications

(14 citation statements)

References 22 publications

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“…Spectral variable selection is performed in spectroscopic techniques, because otherwise the model would be affected by unwanted variability in the spectra instead of the analyte of interest [38]. Therefore, quantitative models based on Raman and NIR uses selected spectrum ranges [30][31][32][33], hence the same is performed for UV-Vis in this study.…”

Section: Spectral Variable Selectionmentioning

confidence: 99%

“…Then, ten PLS models were built using the calibration subset with 1 to 10 LVs. The coefficient of determination (R cv 2 ) and root mean square error (RMSECV) of the cross-validation subset were calculated for each PLS model, in a similar fashion as reported in literature for other spectroscopy techniques [30][31][32]. The R cv 2 , RMSECV and regression vector were plotted versus the number of LV used in the model as presented in Figure 11.…”

Section: Number Of Latent Variablesmentioning

confidence: 99%

“…This validation approach was developed by the Societé Française des Sciences et Techniques Pharmaceutiques (SFSTP) based on trueness and precision. The accuracy profile has been successfully applied to analytical procedure validation of on-line and at-line NIR and in-line Raman in HME processes [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of an in-line API Quantification Method Using AQbD Principles Based on UV-Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

et al. 2020

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A key principle of developing a new medicine is that quality should be built in, with a thorough understanding of the product and the manufacturing process supported by appropriate process controls. Quality by design principles that have been established for the development of drug products/substances can equally be applied to the development of analytical procedures. This paper presents the development and validation of a quantitative method to predict the concentration of piroxicam in Kollidon® VA 64 during hot melt extrusion using analytical quality by design principles. An analytical target profile was established for the piroxicam content and a novel in-line analytical procedure was developed using predictive models based on UV-Vis absorbance spectra collected during hot melt extrusion. Risks that impact the ability of the analytical procedure to measure piroxicam consistently were assessed using failure mode and effect analysis. The critical analytical attributes measured were colour (L* lightness, b* yellow to blue colour parameters—in-process critical quality attributes) that are linked to the ability to measure the API content and transmittance. The method validation was based on the accuracy profile strategy and ICH Q2(R1) validation criteria. The accuracy profile obtained with two validation sets showed that the 95% β-expectation tolerance limits for all piroxicam concentration levels analysed were within the combined trueness and precision acceptance limits set at ±5%. The method robustness was tested by evaluating the effects of screw speed (150–250 rpm) and feed rate (5–9 g/min) on piroxicam content around 15% w/w. In-line UV-Vis spectroscopy was shown to be a robust and practical PAT tool for monitoring the piroxicam content, a critical quality attribute in a pharmaceutical HME process.

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Section: Spectral Variable Selectionmentioning

confidence: 99%

Section: Number Of Latent Variablesmentioning

confidence: 99%

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Development and Validation of an in-line API Quantification Method Using AQbD Principles Based on UV-Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

et al. 2020

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“…This technique covers wavelength range closer to the mid-infrared and broadens up to the visible region [26,27]. Typically, NIRS is primarily based on absorbance characteristics caused by vibrations of covalent bonds between H, C, O, S, and N, which are the main components of the organic matter [28].…”

Section: Application Of Near-infrared Spectroscopy (Nirs) For Analysimentioning

confidence: 99%

“…This was achieved by quantitative evaluations of the spectral activity of sediment and soil properties [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. However, due to challenges such as the collinearity, band overlaps, and interactions for some soil properties, the spectra of soil, sediment, or suspended solids are often broad and nonspecific [25].…”

Section: Application Of Near-infrared Spectroscopy (Nirs) For Analysimentioning

confidence: 99%

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

Nomngongo¹,

Munonde²,

Mpupa³

et al. 2017

Developments in Near-Infrared Spectroscopy

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Environmental contamination by trace elements is becoming increasingly important problem worldwide. Trace metals such as cadmium, copper, lead, chromium, and mercury are major environmental pollutants that are predominantly found in areas with high anthropogenic activities. Therefore, there is a need for rapid and reliable tools to assess and monitor the concentration of heavy metal in environmental matrices. A nondestructive, cost-effective, and environmentally friendly procedure based on near-infrared reflectance spectroscopy (NIRS) and chemometric tools has been used as alternative technique for the simultaneous estimation of various heavy metal concentrations in environmental sample. The metal content is estimated by assigning the absorption features of metals associated with molecular vibrations of organic and inorganic functional groups in organic matter, silicates, carbonates, and water at 780-2500 nm in the near-infrared region.This chapter, reviewed the application of NIRS combined with chemometric tools such as multiple linear regression (MLR), principal component regression (PCR), and partial least squares (PLS) regression. The disadvantages and advantages of each chemometric tool are discussed briefly.

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Process Analytical Technology (PAT)

Lundsberg‐Nielsen¹,

Schlindwein

Berghaus

2018

Pharmaceutical Quality by Design

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Active content determination of pharmaceutical tablets using near infrared spectroscopy as Process Analytical Technology tool

Cited by 31 publications

References 22 publications

Development and Validation of an in-line API Quantification Method Using AQbD Principles Based on UV-Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

Development and Validation of an in-line API Quantification Method Using AQbD Principles Based on UV-Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

Process Analytical Technology (PAT)

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