Towards Better Process Understanding: Chemometrics and Multivariate Measurements in Manufacturing of Solid Dosage Forms

Matero, Sanni; Berg, Frans van den; Poutiainen, Sami; Rantanen, Jukka; Pajander, Jari

doi:10.1002/jps.23472

Cited by 40 publications

(25 citation statements)

References 211 publications

(310 reference statements)

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“…LVMs, in fact, are powerful tools to relate highly correlated input variables (such as spectroscopic, X-ray, chromatography data) to process and product quality [5]. Several reviews on the use of LVMs as tools for product quality estimation in the pharmaceutical industry are available [33][34][35].…”

Section: Latent Variable Models In the Pharmaceutical Industrymentioning

confidence: 99%

Advanced Process Decision Making Using Multivariate Latent Variable Methods

Ottavian

Tomba²,

Barolo

2016

Methods in Pharmacology and Toxicology

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This chapter is intended to show how latent variable modeling techniques can be used to support\ud several pharmaceutical development and manufacturing activities by exploitation of historical databases\ud deriving from experiments, ongoing manufacturing processes or historical products already developed.\ud Basic theoretical concepts about latent variable modeling and latent variable model inversion are first\ud introduced. Then, some applications are reviewed to show how the pharmaceutical industry can benefit\ud from these modeling techniques to support decision-making activities in process development,\ud formulation design, process scale-up, product transfer, process control, and raw materials acceptability\ud assessment

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Section: Latent Variable Models In the Pharmaceutical Industrymentioning

confidence: 99%

Advanced Process Decision Making Using Multivariate Latent Variable Methods

Ottavian

Tomba²,

Barolo

2016

Methods in Pharmacology and Toxicology

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“…Monitoring amorphous-tocrystalline transition as a function of temperature and/or time Brittle ASD with higher drug content inappropriate for DMA analysis Activation energy for α,β process between that measured by DRS and TSC T g measured higher than calorimetric T g ASD compact preparation may risk the alteration of the physical structure Composition-dependent hardness, reduced, and storage moduli obtained by nano-DMA provide miscibility data Table 14.5 Vibrational spectroscopic and microscopic analysis of ASD structure and possible hurdles Miscibility and molecular interactions (Andrews et al 2010;Breitkreitz and Poppi 2012;Hédoux et al 2011;Padilla et al 2010;Rumondor et al 2011;Wegiel et al 2012;Zeitler et al 2007) Crystallinty/crystallization (Chan et al 2004;Hédoux et al 2011;Hédoux et al 2009;Kao et al 2012;Palermo et al 2012a, b;Priemel et al 2012;Sinclair et al 2011;Vehring et al 2012) Interference/limitations (Gendrin et al 2008;Jørgensen et al 2009;Matero et al 2013;Taylor et al 2001...…”

Section: Solid Solubility Estimation From Melt Rheologymentioning

confidence: 99%

“…Homo-(drug-drug, polymer-polymer) versus heteromolecular (drug-polymer) interactions, conformational states, dipolar self-and interassociation, phase-separation Raman signals are less affected by water peaks but confronted by fluorescence related artifacts from elastic scattering Table 14.5 (continued) Miscibility and molecular interactions (Andrews et al 2010;Breitkreitz and Poppi 2012;Hédoux et al 2011;Padilla et al 2010;Rumondor et al 2011;Wegiel et al 2012;Zeitler et al 2007) Crystallinty/crystallization (Chan et al 2004;Hédoux et al 2011;Hédoux et al 2009;Kao et al 2012;Palermo et al 2012a, b;Priemel et al 2012;Sinclair et al 2011;Vehring et al 2012) Interference/limitations (Gendrin et al 2008;Jørgensen et al 2009;Matero et al 2013;Taylor et al 2001) Vibrational (MIR, NIR, Raman, THz) microscopy (imaging/mapping) serves for fast and unambiguous quantification of the different solid forms of API in matrix, high signal-to-noise ratio and spatiochemical resolution Physical states and d...…”

Section: Solid Solubility Estimation From Melt Rheologymentioning

confidence: 99%

Amorphous Solid Dispersions

Shah¹,

Sandhu²,

Choi³

et al. 2014

Advances in Delivery Science and Technology

112

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“…Thus, advanced manufacturing practices are being implemented in the pharmaceutical, chemical, biotechnological and food industry, enhancing process efficiency and guaranteeing product quality [3][4][5][6][7][8][9]. The consumer goods industry, on the contrary, has been slow at adapting this PAT approach.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an at-line fast and non-destructive Raman spectroscopic method for the quantification of multiple components in liquid detergent compositions

Brouckaert

Uyttersprot

Broeckx

et al. 2016

Analytica Chimica Acta

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Implementation of process analytical technology (PAT) tools in the manufacturing process of liquid detergent compositions should allow fast and non-destructive evaluation of the product quality. The aim of this study was to develop and validate a rapid method for quantifying the chemical compounds of five washing liquid precursors. Raman spectroscopy was applied in combination with a two-step multivariate modeling procedure. In first instance, a SIMCA (Soft Independent Modeling of Class Analogy) model was developed and validated, allowing the distinction between the different laundry detergents. Once the product was correctly identified, it was aimed at predicting the concentration of its individual components using partial least squares (PLS) models. Raman spectra were collected at-line with a total acquisition time of 20 seconds, using a non-contact fiber-optic probe. 3The SIMCA model was perfectly capable of differentiating between the classes of the laundry liquid precursors. Per detergent, the concentration of at least three main ingredients could be predicted with a recovery between 98 % and 102 % and a standard deviation below 2.5 %. Accuracy profiles based on the analysis results of validation samples were then calculated to prove the reliability of the developed regression models. β-expectation tolerance intervals were calculated for each model and for each validated concentration level. The acceptance limits were set at 5 % relative bias, indicating that at least 95 % of future measurements should not deviate more than 5 % from the true value. Furthermore, based on the data of the accuracy profiles, the measurement uncertainty was determined.The developed Raman spectroscopic method demonstrated to be able to rapidly and adequately determine the concentration of the components of interest in the liquid detergent compositions at-line. Keywords

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Towards Better Process Understanding: Chemometrics and Multivariate Measurements in Manufacturing of Solid Dosage Forms

Cited by 40 publications

References 211 publications

Advanced Process Decision Making Using Multivariate Latent Variable Methods

Advanced Process Decision Making Using Multivariate Latent Variable Methods

Amorphous Solid Dispersions

Development and validation of an at-line fast and non-destructive Raman spectroscopic method for the quantification of multiple components in liquid detergent compositions

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