Modeling strategies for pharmaceutical blend monitoring and end-point determination by near-infrared spectroscopy.
Identifieur interne : 002088 ( Main/Exploration ); précédent : 002087; suivant : 002089Modeling strategies for pharmaceutical blend monitoring and end-point determination by near-infrared spectroscopy.
Auteurs : Benoît Igne [États-Unis] ; Anna De Juan [Espagne] ; Joaquim Jaumot [Espagne] ; Jordane Lallemand [France] ; Sébastien Preys [France] ; James K. Drennen [États-Unis] ; Carl A. Anderson [États-Unis]Source :
- International journal of pharmaceutics [ 1873-3476 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- Acetaminophen (chemistry), Carboxymethylcellulose Sodium (chemistry), Cellulose (chemistry), Chemistry, Pharmaceutical (methods), Excipients (chemistry), Lactose (chemistry), Least-Squares Analysis, Models, Theoretical, Principal Component Analysis, Spectroscopy, Near-Infrared, Stearic Acids (chemistry).
- MESH :
- chemical , chemistry : Acetaminophen, Carboxymethylcellulose Sodium, Cellulose, Excipients, Lactose, Stearic Acids.
- methods : Chemistry, Pharmaceutical.
- Least-Squares Analysis, Models, Theoretical, Principal Component Analysis, Spectroscopy, Near-Infrared.
Abstract
The implementation of a blend monitoring and control method based on a process analytical technology such as near infrared spectroscopy requires the selection and optimization of numerous criteria that will affect the monitoring outputs and expected blend end-point. Using a five component formulation, the present article contrasts the modeling strategies and end-point determination of a traditional quantitative method based on the prediction of the blend parameters employing partial least-squares regression with a qualitative strategy based on principal component analysis and Hotelling's T(2) and residual distance to the model, called Prototype. The possibility to monitor and control blend homogeneity with multivariate curve resolution was also assessed. The implementation of the above methods in the presence of designed experiments (with variation of the amount of active ingredient and excipients) and with normal operating condition samples (nominal concentrations of the active ingredient and excipients) was tested. The impact of criteria used to stop the blends (related to precision and/or accuracy) was assessed. Results demonstrated that while all methods showed similarities in their outputs, some approaches were preferred for decision making. The selectivity of regression based methods was also contrasted with the capacity of qualitative methods to determine the homogeneity of the entire formulation.
DOI: 10.1016/j.ijpharm.2014.06.061
PubMed: 25003830
Affiliations:
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Electronic address: andersonca@duq.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Duquesne Center for Pharmaceutical Technology, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282</wicri:regionArea><placeName><region type="state">Pennsylvanie</region></placeName></affiliation></author></analytic><series><title level="j">International journal of pharmaceutics</title><idno type="eISSN">1873-3476</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Acetaminophen (chemistry)</term><term>Carboxymethylcellulose Sodium (chemistry)</term><term>Cellulose (chemistry)</term><term>Chemistry, Pharmaceutical (methods)</term><term>Excipients (chemistry)</term><term>Lactose (chemistry)</term><term>Least-Squares Analysis</term><term>Models, Theoretical</term><term>Principal Component Analysis</term><term>Spectroscopy, Near-Infrared</term><term>Stearic Acids (chemistry)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Acides stéariques ()</term><term>Acétaminophène ()</term><term>Analyse en composantes principales</term><term>Carboxyméthylcellulose de sodium ()</term><term>Cellulose ()</term><term>Chimie pharmaceutique ()</term><term>Excipients ()</term><term>Lactose ()</term><term>Modèles théoriques</term><term>Méthode des moindres carrés</term><term>Spectroscopie proche infrarouge</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Acetaminophen</term><term>Carboxymethylcellulose Sodium</term><term>Cellulose</term><term>Excipients</term><term>Lactose</term><term>Stearic Acids</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Chemistry, Pharmaceutical</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Least-Squares Analysis</term><term>Models, Theoretical</term><term>Principal Component Analysis</term><term>Spectroscopy, Near-Infrared</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Acides stéariques</term><term>Acétaminophène</term><term>Analyse en composantes principales</term><term>Carboxyméthylcellulose de sodium</term><term>Cellulose</term><term>Chimie pharmaceutique</term><term>Excipients</term><term>Lactose</term><term>Modèles théoriques</term><term>Méthode des moindres carrés</term><term>Spectroscopie proche infrarouge</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The implementation of a blend monitoring and control method based on a process analytical technology such as near infrared spectroscopy requires the selection and optimization of numerous criteria that will affect the monitoring outputs and expected blend end-point. Using a five component formulation, the present article contrasts the modeling strategies and end-point determination of a traditional quantitative method based on the prediction of the blend parameters employing partial least-squares regression with a qualitative strategy based on principal component analysis and Hotelling's T(2) and residual distance to the model, called Prototype. The possibility to monitor and control blend homogeneity with multivariate curve resolution was also assessed. The implementation of the above methods in the presence of designed experiments (with variation of the amount of active ingredient and excipients) and with normal operating condition samples (nominal concentrations of the active ingredient and excipients) was tested. The impact of criteria used to stop the blends (related to precision and/or accuracy) was assessed. Results demonstrated that while all methods showed similarities in their outputs, some approaches were preferred for decision making. The selectivity of regression based methods was also contrasted with the capacity of qualitative methods to determine the homogeneity of the entire formulation.</div></front></TEI><affiliations><list><country><li>Espagne</li><li>France</li><li>États-Unis</li></country><region><li>Catalogne</li><li>Pennsylvanie</li></region><settlement><li>Barcelone</li></settlement></list><tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Igne, Benoit" sort="Igne, Benoit" uniqKey="Igne B" first="Benoît" last="Igne">Benoît Igne</name></region><name sortKey="Anderson, Carl A" sort="Anderson, Carl A" uniqKey="Anderson C" first="Carl A" last="Anderson">Carl A. Anderson</name><name sortKey="Drennen, James K" sort="Drennen, James K" uniqKey="Drennen J" first="James K" last="Drennen">James K. Drennen</name></country><country name="Espagne"><region name="Catalogne"><name sortKey="De Juan, Anna" sort="De Juan, Anna" uniqKey="De Juan A" first="Anna" last="De Juan">Anna De Juan</name></region><name sortKey="Jaumot, Joaquim" sort="Jaumot, Joaquim" uniqKey="Jaumot J" first="Joaquim" last="Jaumot">Joaquim Jaumot</name></country><country name="France"><noRegion><name sortKey="Lallemand, Jordane" sort="Lallemand, Jordane" uniqKey="Lallemand J" first="Jordane" last="Lallemand">Jordane Lallemand</name></noRegion><name sortKey="Preys, Sebastien" sort="Preys, Sebastien" uniqKey="Preys S" first="Sébastien" last="Preys">Sébastien Preys</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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