Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks

2013
journal article
article
20
dc.abstract.enBackground: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR. Methods: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.pl
dc.affiliationWydział Farmaceutyczny : Zakład Technologii Postaci Leku i Biofarmacjipl
dc.affiliationWydział Farmaceutyczny : Zakład Farmacji Społecznejpl
dc.cm.date2020-01-07
dc.cm.id58748
dc.contributor.authorMendyk, Aleksander - 130937 pl
dc.contributor.authorTuszyński, Paweł K.pl
dc.contributor.authorPolak, Sebastian - 133197 pl
dc.contributor.authorJachowicz, Renata - 129780 pl
dc.date.accessioned2020-01-17T07:50:18Z
dc.date.available2020-01-17T07:50:18Z
dc.date.issued2013pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.physical223-232pl
dc.description.points35pl
dc.description.versionostateczna wersja wydawcy
dc.description.volume7pl
dc.identifier.doi10.2147/DDDT.S41401pl
dc.identifier.eissn1177-8881
dc.identifier.projectROD UJ / OPpl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/131473
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa - Użycie niekomercyjne 3.0*
dc.rights.licenceCC-BY-NC
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/legalcode*
dc.share.typeotwarte czasopismo
dc.subject.enartificial neural networkspl
dc.subject.enin vitro-in vivopl
dc.subject.encorrelationpl
dc.subject.enrelationshippl
dc.subject.enbioavailabilitypl
dc.subject.ensoft computingpl
dc.subtypeArticlepl
dc.titleGeneralized in vitro-in vivo relationship (IVIVR) model based on artificial neural networkspl
dc.title.journalDrug Design, Development and Therapypl
dc.typeJournalArticlepl
dspace.entity.typePublication
dc.abstract.enpl
Background: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR. Methods: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.
dc.affiliationpl
Wydział Farmaceutyczny : Zakład Technologii Postaci Leku i Biofarmacji
dc.affiliationpl
Wydział Farmaceutyczny : Zakład Farmacji Społecznej
dc.cm.date
2020-01-07
dc.cm.id
58748
dc.contributor.authorpl
Mendyk, Aleksander - 130937
dc.contributor.authorpl
Tuszyński, Paweł K.
dc.contributor.authorpl
Polak, Sebastian - 133197
dc.contributor.authorpl
Jachowicz, Renata - 129780
dc.date.accessioned
2020-01-17T07:50:18Z
dc.date.available
2020-01-17T07:50:18Z
dc.date.issuedpl
2013
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.physicalpl
223-232
dc.description.pointspl
35
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
7
dc.identifier.doipl
10.2147/DDDT.S41401
dc.identifier.eissn
1177-8881
dc.identifier.projectpl
ROD UJ / OP
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/131473
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa - Użycie niekomercyjne 3.0
dc.rights.licence
CC-BY-NC
dc.rights.uri*
http://creativecommons.org/licenses/by-nc/3.0/legalcode
dc.share.type
otwarte czasopismo
dc.subject.enpl
artificial neural networks
dc.subject.enpl
in vitro-in vivo
dc.subject.enpl
correlation
dc.subject.enpl
relationship
dc.subject.enpl
bioavailability
dc.subject.enpl
soft computing
dc.subtypepl
Article
dc.titlepl
Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks
dc.title.journalpl
Drug Design, Development and Therapy
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

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