Unified methodology of neural analysis in decision support systems built for pharmaceutical technology

Mendyk, Aleksander; Jachowicz, Renata

doi:10.1016/j.eswa.2006.02.019

Cited by 64 publications

(38 citation statements)

References 18 publications

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“…The trained ANN model can also be used to optimize controlled release formulations when searching algorithms such as genetic algorithms are integrated in the model. Application of ANNs in pharmaceutical technology can be grouped into two main classes: the optimization of dosage form composition, and the optimization of preparation technology of a particular dosage form (156). The examples in Table 3 have confirmed that neural networks provide a very useful tool for development of the most modern drug delivery systems, such as microemulsions (140), gels (127,141), hydrogels (131,132,134), capsules (126), pellets (130), and tablets (127,128,146,147,159).…”

Section: Neural Network In Pharmaceutical Product Developmentmentioning

confidence: 92%

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 Product Developmentmentioning

confidence: 92%

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

Wesołowski

Suchacz

2012

j aoac int

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“…Sensitivity analysis was done using the method described by Żurada et al,25 with some modifications24 to enable knowledge-based selection of crucial variables and obtain collective results from the set of best trained ANNs.…”

Section: Methodsmentioning

confidence: 99%

“…The neural analysis was performed using own-written simulator Nets2010 and numerous applications for data preprocessing,24 all working in the Linux environment. Literature in graphic form was digitalized using g3data version 1.5.2 26.…”

Section: Methodsmentioning

confidence: 99%

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

Mendyk¹,

Tuszyński²,

Polak³

et al. 2013

DDDT

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BackgroundThe 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.MethodsChemoinformatics 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.ResultsThe 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.ConclusionIt 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.

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“…12 The modeling approach was presented as a useful method during construction of expert systems to support formulation development process. 13 The present study introduces the concept of using empirical modeling based on data in the literature to obtain a predictive model for in vitro deposition of drug particles. The methodology is based …”

Section: Data Setmentioning

confidence: 99%

Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations

Pacławski

Szlęk

Lau

et al. 2015

IJN

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In vitro study of the deposition of drug particles is commonly used during development of formulations for pulmonary delivery. The assay is demanding, complex, and depends on: properties of the drug and carrier particles, including size, surface characteristics, and shape; interactions between the drug and carrier particles and assay conditions, including flow rate, type of inhaler, and impactor. The aerodynamic properties of an aerosol are measured in vitro using impactors and in most cases are presented as the fine particle fraction, which is a mass percentage of drug particles with an aerodynamic diameter below 5 μm. In the present study, a model in the form of a mathematical equation was developed for prediction of the fine particle fraction. The feature selection was performed using the R-environment package “fscaret”. The input vector was reduced from a total of 135 independent variables to 28. During the modeling stage, techniques like artificial neural networks, genetic programming, rule-based systems, and fuzzy logic systems were used. The 10-fold cross-validation technique was used to assess the generalization ability of the models created. The model obtained had good predictive ability, which was confirmed by a root-mean-square error and normalized root-mean-square error of 4.9 and 11%, respectively. Moreover, validation of the model using external experimental data was performed, and resulted in a root-mean-square error and normalized root-mean-square error of 3.8 and 8.6%, respectively.

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Unified methodology of neural analysis in decision support systems built for pharmaceutical technology

Cited by 64 publications

References 18 publications

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

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

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

Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations

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Unified methodology of neural analysis in decision support systems built for pharmaceutical technology

Cited by 64 publications

References 18 publications

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

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

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

Empirical modeling of the fine particle fraction for&nbsp;carrier-based pulmonary delivery formulations

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Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations