An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro–in vivo correlation

Parojčić, Jelena; Ibrić, Svetlana; Djurić, Z.; Jovanović, Milica; Corrigan, Owen I.

doi:10.1016/j.ejps.2006.11.010

Cited by 47 publications

(20 citation statements)

References 28 publications

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“…DOE and ANN are used in design of drug formulations, [6,7] optimization of manufacturing processes, [8] quality control, drug stability, [9] in vitro examinations of drug release, [10] and in vitroin vivo correlation. [11] In the present study, the optimization method was applied to the development of manufacturing process of spray-dried powder composite, with naratriptan hydrochloride as a model of a drug substance that is used in solid dosage forms in small doses. Naratriptan hydrochloride is a relatively novel antimigraine drug substance from the group of selective 5-hydroxytryptamine agonists (triptans) and it is present on the market as a 2.5-mg-strength tablet.…”

Section: Introductionmentioning

confidence: 99%

Application of Design of Experiments and Multilayer Perceptron Neural Network in Optimization of the Spray-Drying Process

2011

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Section: Introductionmentioning

confidence: 99%

Application of Design of Experiments and Multilayer Perceptron Neural Network in Optimization of the Spray-Drying Process

2011

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“…24,33) In fact, various reports provide scientific evidence for predictive non-linear correlations. 1,[34][35][36][37] For example, first-order dissolution with delayed in vivo absorption normally assumes a non-linear IVIVC. 35) In our study, calculated in vivo absorption of each formulation by numerical deconvolution was preferably fitted with a quadratic regression model to the in vitro release data as IVIVC 1 and IVIVC 2 (Figs.…”

Section: In Vitro Dissolution Studies Of Coated Matrix Tabletsmentioning

confidence: 99%

Development of an Extended-Release Formulation for Apremilast and a Level A <i>in Vitro</i>–<i>in Vivo</i> Correlation Study in Beagle Dogs

Tang

Shigui

et al. 2016

CHEMICAL & PHARMACEUTICAL BULLETIN

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The primary objective of the present study was to develop extended-release matrix formulations of apremilast for the oral delivery and to study their in vitro and in vivo correlation. Five extended-release formulations containing hydroxypropylmethylcellulose (HPMC) as the retarding excipient with different release rate of apremilast were prepared. Dissolution tests of all the formulated tablets were performed in water, pH 4.0 and 6.8 buffer solutions. The in vitro release kinetics was studied and supported by Korsmeyer-Peppas's equation as it presented highest values of correlation coefficients (r 2 up to 0.966). Among all formulated tablets, F2 (HPMC 25%) and F4 (HPMC 35%) were selected to perform an in vivo study in beagle dogs to obtain various pharmacokinetic parameters, i.e., peak plasma concentration (C max ), time to peak plasma concentration (t max ), area under the plasma-concentration vs. time curve (AUC). Higher t max and t 1/2 , lower C max and elimination coefficient (K e ) were observed for both extended formulations compared to marketed immediate-release products (Otezla ® ). Level A in vitro-in vivo correlations were created with the help of Wagner-Nelson and numeric deconvolution methods. Both formulations showed good in vitro-in vivo correlations whose accuracies were further verified by an internal validation.

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“…b) The Learning of BP network After determining the structure of BP network, it needs to learn and correct threshold value and weight value of the network, in order to realize the mapping relationship between the input and the output [15]. The main contents of this article are that to establish and implement the neural network forecasting model about temperature of the hot zone.…”

Section: A) the Layer And The Number Of Neurons Of Networkmentioning

confidence: 99%

Research on Energy Saving Method for IDC CRAC System based on Prediction of Temperature

Yan¹,

Wu²,

Xing³

et al. 2015

ijacsa

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Abstract-Amid the information era, energy consumption of IDC Computer Room Air Conditioning (CRAC) system is becoming increasingly serious. Thus there is growing concern over energy saving and consumption reduction. Based on the analysis of the energy saving application of the air conditioning system in the present computer room, a new energy saving method of the IDC CRAC system, which presents energy saving decision based on the prediction of temperature, is proposed. Its principle is the collection of CPU utilization reflected the change of equipment working load, the temperature in hot spots and cold area. Then, to build a BP Neural Network model, taking the working load and the temperature in hot spots for the actual input, taking the temperature in a cold area for actual output. The BP Neural Network model can predict the temperature in hot spots of the next, when a set of real-time data into the model. Choosing a reasonable and effective decision-making scheme of the air conditioning system can realize energy saving control. Preliminary simulation results show, through the establishment of BP network model obtain approximation error of training samples and the prediction error of testing samples, both to highlight the advantages of the model. Finally, the distribution of temperature change about CRAC system whole day obtained by simulation shows that the proposed energy-saving method can reduce the energy consumption of IDC, fully embodies the effect of energy saving.

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An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro–in vivo correlation

Cited by 47 publications

References 28 publications

Application of Design of Experiments and Multilayer Perceptron Neural Network in Optimization of the Spray-Drying Process

Application of Design of Experiments and Multilayer Perceptron Neural Network in Optimization of the Spray-Drying Process

Development of an Extended-Release Formulation for Apremilast and a Level A <i>in Vitro</i>–<i>in Vivo</i> Correlation Study in Beagle Dogs

Research on Energy Saving Method for IDC CRAC System based on Prediction of Temperature

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