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Opara, Jerneja; Primozic, S.; Cvelbar, P

doi:10.1023/a:1018857108713

Cited by 16 publications

(2 citation statements)

References 6 publications

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“…The results showed that ANNs were useful not only for accurate prediction of the plasma concentration of aminoglycosides, but also for classification of patients whose plasma concentration would be in the therapeutic concentration range. Additionally, numerous papers have been published indicating the application of ANNs for predicting the pharmacokinetic parameters: area under concentration-time curve (AUC), peak plasma concentration (c max ), time to reach peak plasma concentration (t max ) and the assessment of their variability in bioequivalence studies (75), as well as for diagnosis and therapy. For example, two unsupervised ANNs were applied for the classification of the patients in three medical fields based on their electroencephalograms and evoked potentials (76).…”

Section: Neural Network In Pharmaceutical and Clinical Analysismentioning

confidence: 99%

Artificial Neural Networks: Theoretical Background and Pharmaceutical Applications: A Review

Wesołowski

Suchacz

2012

j aoac int

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In recent times, there has been a growing interest in artificial neural networks, which are a rough simulation of the information processing ability of the human brain, as modern and vastly sophisticated computational techniques. This interest has also been reflected in the pharmaceutical sciences. This paper presents a review of articles on the subject of the application of neural networks as effective tools assisting the solution of various problems in science and the pharmaceutical industry, especially those characterized by multivariate and nonlinear dependencies. After a short description of theoretical background and practical basics concerning the computations performed by means of neural networks, the most important pharmaceutical applications of neural networks, with suitable references, are demonstrated. The huge role played by neural networks in pharmaceutical analysis, pharmaceutical technology, and searching for the relationships between the chemical structure and the properties of newly synthesized compounds as candidates for drugs is discussed.

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Section: Neural Network In Pharmaceutical and Clinical Analysismentioning

confidence: 99%

Artificial Neural Networks: Theoretical Background and Pharmaceutical Applications: A Review

Wesołowski

Suchacz

2012

j aoac int

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“…The artificial neural networks (ANNs), which are usually computer programs designed to simulate biological neural systems in both their functional activity and structure, are useful data analysis tools to handle such complex, nonlinear relationships. ANNs were utilized in multiple applications in various areas of science and technology [ 6 ] along with pharmaceutical sciences, including but not limited to development of nimodipine consisting floating tablets [ 7 ], minimisation of the capping tendency during tableting process [ 8 ], optimization of galenical dosage form technological process [ 9 ], prediction of dissolution from ketoprofen consisting solid dispersions [ 10 ], analysis of parameters of direct compression tableting [ 11 ], control of quality attributes of tablets manufactured by wet granulation [ 12 ], prediction of gentamicin [ 13 ], remifentanil [ 14 ] and aminoglycosides [ 15 ] serum concentrations, analysis of pharmacokinetic population data [ 16 ], evaluation of an in vitro-in vivo correlation for nebulizer delivery of salbutamol [ 17 ], oral verapamil products [ 18 ], sustained release paracetamol matrix tablet formulations [ 19 ], and nifedipine osmotic release tablets [ 20 ]. However they are also considered the so-called black-box models as it is not possible to provide analytical solution of their way of data processing; what stays in contradiction to the idea that the model purpose is to reveal information about the analyzed system.…”

Section: Introductionmentioning

confidence: 99%

From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

Mendyk

Güres

Jachowicz

et al. 2015

Computational and Mathematical Methods in Medicine

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The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.

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Application of a Recurrent Neural Network to Prediction of Drug Dissolution Profiles

Goh

Lim

Peh

et al. 2002

Neural Computing & Applications

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The Elman Recurrent Neural Network was employed for the prediction of in-vitro dissolution profiles of matrix controlled release theophylline pellet preparation, leading to the potential use of an intelligent learning system in the development of pharmaceutical products with desired drug release characteristics. A total of six different formulations containing various matrix ratios of substance to control the release rate of theophylline were used for experimentation. By using the leave-one-out cross-validation approach, the dissolution profiles of all the matrix ratios were consumed for training, except for one set that was taken as a reference profile, with which the network predicted profiles were compared. Performance of the network was assessed using the similarity factor, f 2 , a criterion for dissolution profile comparison recommended by the United States Food and Drug Administration. Simulation results indicated that the Elman network was capable of predicting dissolution profiles that were similar to the reference profiles with an error of less than 8%. In addition, the Bootstrap method was used to estimate the confidence intervals of the f 2 values. The results revealed the potential of a neural-network-based intelligent system in solving nonlinear time-series prediction problems in pharmaceutical product development.

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Abstract: The developed methodology could supply inclusion or exclusion criteria for subjects to be included in bioequivalence studies.

Cited by 16 publications

References 6 publications

Artificial Neural Networks: Theoretical Background and Pharmaceutical Applications: A Review

Artificial Neural Networks: Theoretical Background and Pharmaceutical Applications: A Review

From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

Application of a Recurrent Neural Network to Prediction of Drug Dissolution Profiles

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