Evaluation of an in vitro in vivo correlation for nebulizer delivery using artificial neural networks.Citation formats

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Evaluation of an in vitro in vivo correlation for nebulizer delivery using artificial neural networks. / de Matas, Marcel; Shao, Qun; Silkstone, Victoria Louise; Chrystyn, Henry.

In: Journal of Pharmaceutical Sciences, Vol. 96, No. 12, 12.2007.

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de Matas, Marcel ; Shao, Qun ; Silkstone, Victoria Louise ; Chrystyn, Henry. / Evaluation of an in vitro in vivo correlation for nebulizer delivery using artificial neural networks. In: Journal of Pharmaceutical Sciences. 2007 ; Vol. 96, No. 12.

Bibtex

@article{b9ef50fb179647e4aae69b97f4a35c0b,
title = "Evaluation of an in vitro in vivo correlation for nebulizer delivery using artificial neural networks.",
abstract = "The ability to generate predictive models linking the in vitro assessment of pharmaceutical products with in vivo performance has the potential to enable greater control of clinical quality whilst minimizing the number of in vivo studies in drug development. Artificial neural networks (ANNs) provide a means of generating predictive models correlating critical product characteristics to key performance attributes. In this regard, ANNs have been used to model historical data exploring the relative lung bioavailability of salbutamol from several different nebulizers. The generated ANN model was shown to relate urinary salbutamol excretion at 30 min post-inhalation, which is the index of relative lung bioavailability of salbutamol, to specific fractions of the particle size distribution, to subject body surface area and to the methods of nebulization. This model was validated using unseen data and gave good agreement with pharmacokinetic outcomes for 17 data records. The model gave improved predictions of urinary salbutamol excretion for individual subjects compared to the published linear correlation generated using the same data. It is therefore concluded that ANN models have the potential to provide reliable estimates of pharmacokinetic performance that relate to lung deposition, for nebulized medicines in individual subjects.",
author = "{de Matas}, Marcel and Qun Shao and Silkstone, {Victoria Louise} and Henry Chrystyn",
year = "2007",
month = dec,
doi = "10.1002/jps.20965",
language = "English",
volume = "96",
journal = "Journal of Pharmaceutical Sciences",
issn = "0022-3549",
publisher = "John Wiley & Sons Ltd",
number = "12",

}

RIS

TY - JOUR

T1 - Evaluation of an in vitro in vivo correlation for nebulizer delivery using artificial neural networks.

AU - de Matas, Marcel

AU - Shao, Qun

AU - Silkstone, Victoria Louise

AU - Chrystyn, Henry

PY - 2007/12

Y1 - 2007/12

N2 - The ability to generate predictive models linking the in vitro assessment of pharmaceutical products with in vivo performance has the potential to enable greater control of clinical quality whilst minimizing the number of in vivo studies in drug development. Artificial neural networks (ANNs) provide a means of generating predictive models correlating critical product characteristics to key performance attributes. In this regard, ANNs have been used to model historical data exploring the relative lung bioavailability of salbutamol from several different nebulizers. The generated ANN model was shown to relate urinary salbutamol excretion at 30 min post-inhalation, which is the index of relative lung bioavailability of salbutamol, to specific fractions of the particle size distribution, to subject body surface area and to the methods of nebulization. This model was validated using unseen data and gave good agreement with pharmacokinetic outcomes for 17 data records. The model gave improved predictions of urinary salbutamol excretion for individual subjects compared to the published linear correlation generated using the same data. It is therefore concluded that ANN models have the potential to provide reliable estimates of pharmacokinetic performance that relate to lung deposition, for nebulized medicines in individual subjects.

AB - The ability to generate predictive models linking the in vitro assessment of pharmaceutical products with in vivo performance has the potential to enable greater control of clinical quality whilst minimizing the number of in vivo studies in drug development. Artificial neural networks (ANNs) provide a means of generating predictive models correlating critical product characteristics to key performance attributes. In this regard, ANNs have been used to model historical data exploring the relative lung bioavailability of salbutamol from several different nebulizers. The generated ANN model was shown to relate urinary salbutamol excretion at 30 min post-inhalation, which is the index of relative lung bioavailability of salbutamol, to specific fractions of the particle size distribution, to subject body surface area and to the methods of nebulization. This model was validated using unseen data and gave good agreement with pharmacokinetic outcomes for 17 data records. The model gave improved predictions of urinary salbutamol excretion for individual subjects compared to the published linear correlation generated using the same data. It is therefore concluded that ANN models have the potential to provide reliable estimates of pharmacokinetic performance that relate to lung deposition, for nebulized medicines in individual subjects.

U2 - 10.1002/jps.20965

DO - 10.1002/jps.20965

M3 - Article

C2 - 17630647

VL - 96

JO - Journal of Pharmaceutical Sciences

JF - Journal of Pharmaceutical Sciences

SN - 0022-3549

IS - 12

ER -