DC pole | Wartość | Język |
dc.contributor.author | Mendyk, Aleksander | - |
dc.contributor.author | Pacławski, Adam | - |
dc.contributor.author | Szafraniec-Szczęsny, Joanna | - |
dc.contributor.author | Antosik, Agata | - |
dc.contributor.author | Jamróz, Witold | - |
dc.contributor.author | Paluch, Marian | - |
dc.contributor.author | Jachowicz, Renata | - |
dc.date.accessioned | 2020-04-21T09:47:04Z | - |
dc.date.available | 2020-04-21T09:47:04Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | AAPS PharmSciTech, 2020, iss. 3, art. no. 111 | pl_PL |
dc.identifier.issn | 1530-9932 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/13574 | - |
dc.description.abstract | Low solubility of active pharmaceutical compounds (APIs) remains an
important challenge in dosage form development process. In the manuscript, empirical
models were developed and analyzed in order to predict dissolution of bicalutamide (BCL)
from solid dispersion with various carriers. BCL was chosen as an example of a poor watersoluble
API. Two separate datasets were created: one from literature data and another based
on in-house experimental data. Computational experiments were conducted using artificial
intelligence tools based on machine learning (AI/ML) with a plethora of techniques including
artificial neural networks, decision trees, rule-based systems, and evolutionary computations.
The latter resulting in classical mathematical equations provided models characterized by the
lowest prediction error. In-house data turned out to be more homogeneous, as well as
formulations were more extensively characterized than literature-based data. Thus, in-house
data resulted in better models than literature-based data set. Among the other covariates, the
best model uses for prediction of BCL dissolution profile the transmittance from IR spectrum
at 1260 cm−1 wavenumber. Ab initio modeling–based in silico simulations were conducted to
reveal potential BCL–excipients interaction. All crucial variables were selected automatically
by AI/ML tools and resulted in reasonably simple and yet predictive models suitable for
application in Quality by Design (QbD) approaches. Presented data-driven model
development using AI/ML could be useful in various problems in the field of pharmaceutical
technology, resulting in both predictive and investigational tools revealing new knowledge. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/ | * |
dc.subject | artificial intelligence | pl_PL |
dc.subject | dissolution modeling | pl_PL |
dc.subject | multivariate modeling | pl_PL |
dc.subject | multi-scale modeling | pl_PL |
dc.subject | solubility enhancement | pl_PL |
dc.title | Data-driven modeling of the bicalutamide dissolution from powder systems | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.relation.journal | AAPS PharmSciTech | pl_PL |
dc.identifier.doi | 10.1208/s12249-020-01660-w | - |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
|