Raman mapping-based non-destructive dissolution prediction of sustained-release tablets
| العنوان: | Raman mapping-based non-destructive dissolution prediction of sustained-release tablets |
|---|---|
| المؤلفون: | Dorián László Galata, Boldizsár Zsiros, Lilla Alexandra Mészáros, Brigitta Nagy, Edina Szabó, Attila Farkas, Zsombor Kristóf Nagy |
| المصدر: | Journal of pharmaceutical and biomedical analysis. 212 |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Hypromellose Derivatives, Solubility, Delayed-Action Preparations, Clinical Biochemistry, Drug Discovery, Pharmaceutical Science, Methylcellulose, Spectroscopy, Analytical Chemistry, Tablets |
| الوصف: | In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f |
| تدمد: | 1873-264X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c7d51fd847b366d069de073c77e77fa https://pubmed.ncbi.nlm.nih.gov/35180565 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....5c7d51fd847b366d069de073c77e77fa |
| قاعدة البيانات: | OpenAIRE |
| ResultId |
1 |
|---|---|
| Header |
edsair OpenAIRE edsair.doi.dedup.....5c7d51fd847b366d069de073c77e77fa 859 3 unknown 858.680603027344 |
| PLink |
https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsair&AN=edsair.doi.dedup.....5c7d51fd847b366d069de073c77e77fa&custid=s6537998&authtype=sso |
| FullText |
Array
(
[Availability] => 0
)
Array ( [0] => Array ( [Url] => https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c7d51fd847b366d069de073c77e77fa# [Name] => EDS - OpenAIRE [Category] => fullText [Text] => View record in OpenAIRE [MouseOverText] => View record in OpenAIRE ) ) |
| Items |
Array
(
[Name] => Title
[Label] => Title
[Group] => Ti
[Data] => Raman mapping-based non-destructive dissolution prediction of sustained-release tablets
)
Array ( [Name] => Author [Label] => Authors [Group] => Au [Data] => <searchLink fieldCode="AR" term="%22Dorián+László+Galata%22">Dorián László Galata</searchLink><br /><searchLink fieldCode="AR" term="%22Boldizsár+Zsiros%22">Boldizsár Zsiros</searchLink><br /><searchLink fieldCode="AR" term="%22Lilla+Alexandra+Mészáros%22">Lilla Alexandra Mészáros</searchLink><br /><searchLink fieldCode="AR" term="%22Brigitta+Nagy%22">Brigitta Nagy</searchLink><br /><searchLink fieldCode="AR" term="%22Edina+Szabó%22">Edina Szabó</searchLink><br /><searchLink fieldCode="AR" term="%22Attila+Farkas%22">Attila Farkas</searchLink><br /><searchLink fieldCode="AR" term="%22Zsombor+Kristóf+Nagy%22">Zsombor Kristóf Nagy</searchLink> ) Array ( [Name] => TitleSource [Label] => Source [Group] => Src [Data] => <i>Journal of pharmaceutical and biomedical analysis</i>. 212 ) Array ( [Name] => DatePubCY [Label] => Publication Year [Group] => Date [Data] => 2021 ) Array ( [Name] => Subject [Label] => Subject Terms [Group] => Su [Data] => <searchLink fieldCode="DE" term="%22Hypromellose+Derivatives%22">Hypromellose Derivatives</searchLink><br /><searchLink fieldCode="DE" term="%22Solubility%22">Solubility</searchLink><br /><searchLink fieldCode="DE" term="%22Delayed-Action+Preparations%22">Delayed-Action Preparations</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+Biochemistry%22">Clinical Biochemistry</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+Discovery%22">Drug Discovery</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmaceutical+Science%22">Pharmaceutical Science</searchLink><br /><searchLink fieldCode="DE" term="%22Methylcellulose%22">Methylcellulose</searchLink><br /><searchLink fieldCode="DE" term="%22Spectroscopy%22">Spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Analytical+Chemistry%22">Analytical Chemistry</searchLink><br /><searchLink fieldCode="DE" term="%22Tablets%22">Tablets</searchLink> ) Array ( [Name] => Abstract [Label] => Description [Group] => Ab [Data] => In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f ) Array ( [Name] => ISSN [Label] => ISSN [Group] => ISSN [Data] => 1873-264X ) Array ( [Name] => URL [Label] => Access URL [Group] => URL [Data] => <link linkTarget="URL" linkTerm="https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c7d51fd847b366d069de073c77e77fa" linkWindow="_blank">https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c7d51fd847b366d069de073c77e77fa</link><br /><link linkTarget="URL" linkTerm="https://pubmed.ncbi.nlm.nih.gov/35180565" linkWindow="_blank">https://pubmed.ncbi.nlm.nih.gov/35180565</link> ) Array ( [Name] => Copyright [Label] => Rights [Group] => Cpyrght [Data] => OPEN ) Array ( [Name] => AN [Label] => Accession Number [Group] => ID [Data] => edsair.doi.dedup.....5c7d51fd847b366d069de073c77e77fa ) |
| RecordInfo |
Array
(
[BibEntity] => Array
(
[Languages] => Array
(
[0] => Array
(
[Text] => Undetermined
)
)
[Subjects] => Array
(
[0] => Array
(
[SubjectFull] => Hypromellose Derivatives
[Type] => general
)
[1] => Array
(
[SubjectFull] => Solubility
[Type] => general
)
[2] => Array
(
[SubjectFull] => Delayed-Action Preparations
[Type] => general
)
[3] => Array
(
[SubjectFull] => Clinical Biochemistry
[Type] => general
)
[4] => Array
(
[SubjectFull] => Drug Discovery
[Type] => general
)
[5] => Array
(
[SubjectFull] => Pharmaceutical Science
[Type] => general
)
[6] => Array
(
[SubjectFull] => Methylcellulose
[Type] => general
)
[7] => Array
(
[SubjectFull] => Spectroscopy
[Type] => general
)
[8] => Array
(
[SubjectFull] => Analytical Chemistry
[Type] => general
)
[9] => Array
(
[SubjectFull] => Tablets
[Type] => general
)
)
[Titles] => Array
(
[0] => Array
(
[TitleFull] => Raman mapping-based non-destructive dissolution prediction of sustained-release tablets
[Type] => main
)
)
)
[BibRelationships] => Array
(
[HasContributorRelationships] => Array
(
[0] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Dorián László Galata
)
)
)
[1] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Boldizsár Zsiros
)
)
)
[2] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Lilla Alexandra Mészáros
)
)
)
[3] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Brigitta Nagy
)
)
)
[4] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Edina Szabó
)
)
)
[5] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Attila Farkas
)
)
)
[6] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Zsombor Kristóf Nagy
)
)
)
)
[IsPartOfRelationships] => Array
(
[0] => Array
(
[BibEntity] => Array
(
[Dates] => Array
(
[0] => Array
(
[D] => 22
[M] => 11
[Type] => published
[Y] => 2021
)
)
[Identifiers] => Array
(
[0] => Array
(
[Type] => issn-print
[Value] => 1873264X
)
[1] => Array
(
[Type] => issn-locals
[Value] => edsair
)
[2] => Array
(
[Type] => issn-locals
[Value] => edsairFT
)
)
[Numbering] => Array
(
[0] => Array
(
[Type] => volume
[Value] => 212
)
)
[Titles] => Array
(
[0] => Array
(
[TitleFull] => Journal of pharmaceutical and biomedical analysis
[Type] => main
)
)
)
)
)
)
)
|
| IllustrationInfo |