Biomedical subjects

J Bourquin

Publications and source records attributed to J Bourquin.

15 recordsLinked to original sources

PubMed1998

Comparison of artificial neural networks (ANN) with classical modelling techniques using different experimental designs and data from a galenical study on a solid dosage form.

Artificial Neural Networks (ANN) methodology was used to analyse experimental data from a tabletting study and compared both graphically and numerically to classical modelling techniques (i.e. Response surface methodology, RSM). The aim of this investigation was to describe quantitatively the degree of data fitting achieved and the robustness of the developed models using two types of experimental design (i.e. a statistical, highly organised design and a randomised design). To compare goodness of fit, the R(2) coefficient was used, whereas for the robustness of the models the R(2) coefficient of an independent validation data set was computed. Comparable results were achieved for both ANN and RSM methodology when using the statistical plan. However, the robustness of the models when developed based on a randomised plan was clearly better for the ANN methodology. Based on the results of this study, it appears that the ANN methodology is much less sensitive to the organisational level of a trial design and is therefore better adapted to the data analysis of the results of historical or poorly organised trials. All tablet properties determined were largely influenced by the dwell time during compression as well as by concentration of silica aerogel and magnesium stearate, whereas the other factors showed very much weaker effects.

Biopharmaceutics↗

PubMed1998

Pitfalls of artificial neural networks (ANN) modelling technique for data sets containing outlier measurements using a study on mixture properties of a direct compressed dosage form.

An application of the Artificial Neural Networks (ANN) methodology was investigated using experimental data from a mixture properties study and compared to classical modelling technique (i.e. Response Surface Methodology, RSM) both graphically and numerically. The aim of this investigation was to quantitatively describe the achieved degree of data fitting and robustness of the developed models. For comparing the goodness of fit, the R2 coefficient was used, whereas for the robustness of the models an outlier measurement was integrated in the data set. Comparable results were achieved for both ANN- and RSM methodologies for data fitting. The robustness of the models towards outliers was clearly better for the RSM methodology. All determined mixture properties were mainly influenced by the concentration of silica aerogel, whereas the other factors showed very much lower effects. For that reason the physical properties of this excipient (e.g. its specific surface area) are of importance for the behaviour of the mixtures.

Chemistry, Pharmaceutical↗

PubMed1998

Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form.

Artificial Neural Networks (ANN) methodology was used to assess experimental data from a tablet compression study showing highly non-linear relationships (i.e. measurements of ejection forces) and compared to classical modelling technique (i.e. Response Surface Methodology, RSM). These kinds of relationships are known to be difficult to model using classical methods. The aim of this investigation was to quantitatively describe the achieved degree of data fitting and predicting abilities of the developed models. The comparison between the ANN and RSM was carried out both graphically and numerically. For comparing the goodness of fit, all data were used, whereas for the goodness of prediction the data were split into a learning and a validation data set. Better results were achieved for the model using ANN methodology with regard to data fitting and predicting ability. All determined ejection properties were mainly influenced by the concentration of magnesium stearate and silica aerogel, whereas the other factors showed very much lower effects. Important relationships could be recognised from the ANN model only, whereas the RSM model ignored them. The ANN methodology represents a useful alternative to classical modelling techniques when applied to variable data sets presenting non-linear relationships.

Chemistry, Pharmaceutical↗

PubMed1997

Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development.

Artificial neural networks (ANN) methodology is a new modeling method that has not been broadly applied to pharmaceutical sciences up to now. The aim of this paper is to give a detailed description of the associating networks as well as a description of less well-known networks (i.e., feature-extracting and nonadaptive networks) and their scope of application in pharmaceutical sciences. The descriptions include the historical origin and the basic concepts behind the computing. ANN are based on the attempt to model the neural networks of the brain. Learning algorithms for associating ANN use mathematical procedures usually derived from the gradient descent method whereas feature-extracting ANN map multidimensional input data sets onto two-dimensional spaces. Nonadaptive ANN map data sets and are able to reconstruct their patterns when presented with corrupted or noisy samples. Associating networks can typically be applied in the pharmaceutical field as an alternative to traditional response surface methodology, feature-extracting networks as alternative to principal component analysis, and nonadaptive networks for image recognition. Based on these abilities, the potential application fields of the ANN methodology in the pharmaceutical sciences is broad, ranging from clinical pharmacy through biopharmacy, drug and dosage form design, to interpretation of analytical data. The few applications presented in the pharmaceutical technology area seem promising and should be investigated in more detail.

Algorithms↗

PubMed1997

Application of artificial neural networks (ANN) in the development of solid dosage forms.

The application of ANN in pharmaceutical development has been assessed using theoretical as well as typical pharmaceutical technology examples. The aim was to quantitatively describe the achieved data fitting and predicting abilities of the models developed with a view to using ANN in the development of solid dosage forms. The comparison between the ANN and a traditional statistical (i.e., response surface methodology, RSM) modeling technique was carried out using the squared correlation coefficient R2. Using a highly nonlinear arbitrary function the ANN models showed better fitting (R2 = 0.931 vs. R2 = 0.424) as well as predicting (R2 = 0.810 vs. R2 = 0.547) abilities. Experimental data from a tablet compression study were fitted using two types of ANN models (i.e., multilayer perceptrons and a hybrid network composed of a self-organising feature map joined to a multilayer perception). The achieved data fitting was comparable for the three methods (MLP R2 = 0.911, SOFM-MLP R2 = 0.850, and RSM R2 = 0.897). ANN methodology represents a promising modeling technique when applied to pharmaceutical technology data sets.

Algorithms↗

PubMed1988