Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees

Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

doi:10.1016/j.ijpharm.2012.02.031

Cited by 50 publications

(24 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since in dynamic neural networks data is stored and elaborated in time, it is expected that it could be useful in time dependent processes, like drug release prediction or drug stability issues. Therefore, our research group have successfully applied dynamic neural networks in establishing design space for formulation of matrix tablets and compared those results with static networks [ 13 , 29 , 30 ]. Following case study presents our unpublished data and could be useful for the readers as a guide in selection and development of appropriate network for the problem studied.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Even though results obtained using this network were better, there were still shifting values of predicted drug release profiles compared to experimentally observed values at later time points. Therefore, in the next study [ 29 ], the starting point of experiments was changed: since it was demonstrated that mechanical properties of the tablets have impact on drug release, porosity and the tensile strengths were selected to be inputs, together with ratio of the polymer and compression force. Elman dynamic neural network was applied, with topology shown in Figure 12 .…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Elman dynamic neural network was applied, with topology shown in Figure 12 . This type of network was also applied in modeling of drug release from lipid matrix tablets [ 29 ]. After the training session, prediction of drug release from test formulations was better in comparison to OLDNN application.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

et al. 2012

Self Cite

View full text Add to dashboard Cite

Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

et al. 2012

Self Cite

View full text Add to dashboard Cite

show abstract

“…Connecting points in 3D diagram, as trajectory, it is possible to predict the drug release, for both different SDMRs and drug loadings. Obtained diagrams showed that formulations with SDMR from 0.4 to 0.62 and DS loading from 97 to 150 mg/g exhibited prolonged drug release during 8 h. So far, DS release from various matrix tablets was optimized using ANNs models . Additionally, the results reported in this paper showed that MLP network can be successfully used for prediction of prolonged DS release based on knowledge of SDMR in the drug/modified zeolite physical mixtures, which is relevant for suitable DS release during 8 h.…”

Section: Resultsmentioning

confidence: 99%

Application of Artificial Neural Networks in Prediction of Diclofenac Sodium Release From Drug-Modified Zeolites Physical Mixtures and Antiedematous Activity Assessment

Krajišnik

Stepanović-Petrović

Tomić

et al. 2014

Journal of Pharmaceutical Sciences

Self Cite

View full text Add to dashboard Cite

“…Learning is performed so that data are divided into a train set and a test set before the generation algorithm takes train data as input [45]. Many algorithms can be used for building a decision tree and their goal is to select a suitable splitting attribute [34]. Among the most popular are discriminant-based univariate splits algorithms built on the QUEST (Quick, Unbiased, Efficient Statistical Trees) and classification and regression trees (CART) algorithm.…”

Section: Classification Treementioning

confidence: 99%

Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model

Babić

2015

Croat. Oper. Res. Rev.

View full text Add to dashboard Cite

Abstract. Student satisfaction with courses in academic institutions is an important issue and is recognized as a form of support in ensuring effective and quality education, as well as enhancing student course experience. This paper investigates whether there is a connection between student satisfaction with courses and log data on student courses in a virtual learning environment. Furthermore, it explores whether a successful classification model for predicting student satisfaction with course can be developed based on course log data and compares the results obtained from implemented methods. The research was conducted at the Faculty of Education in Osijek and included analysis of log data and course satisfaction on a sample of third and fourth year students. Multilayer Perceptron (MLP) with different activation functions and Radial Basis Function (RBF) neural networks as well as classification tree models were developed, trained and tested in order to classify students into one of two categories of course satisfaction. Type I and type II errors, and input variable importance were used for model comparison and classification accuracy.The results indicate that a successful classification model using tested methods can be created. The MLP model provides the highest average classification accuracy and the lowest preference in misclassification of students with a low level of course satisfaction, although a t-test for the difference in proportions showed that the difference in performance between the compared models is not statistically significant. Student involvement in forum discussions is recognized as a valuable predictor of student satisfaction with courses in all observed models.

show abstract

Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees

Cited by 50 publications

References 35 publications

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

Application of Artificial Neural Networks in Prediction of Diclofenac Sodium Release From Drug-Modified Zeolites Physical Mixtures and Antiedematous Activity Assessment

Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model

Contact Info

Product

Resources

About