Combining in Vitro and in Vivo Pharmacokinetic Data for Prediction of Hepatic Drug Clearance in Humans by Artificial Neural Networks and Multivariate Statistical Techniques

1999 ◽  
Vol 42 (25) ◽  
pp. 5072-5076 ◽  
Author(s):  
Gisbert Schneider ◽  
Philippe Coassolo ◽  
Thierry Lavé
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Drug Clearance ◽  
Statistical Techniques ◽  
Pharmacokinetic Data ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Hepatic Drug

scholarly journals A comparative study of the phenolic compounds and the in vitro antioxidant activity of different Brazilian teas using multivariate statistical techniques

2014 ◽  
Vol 60 ◽  
pp. 246-254 ◽  
Author(s):  
Acácio Antonio Ferreira Zielinski ◽  
Charles Windson Isidoro Haminiuk ◽  
Aline Alberti ◽  
Alessandro Nogueira ◽  
Ivo Mottin Demiate ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Phenolic Compounds ◽  
Comparative Study ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
In Vitro Antioxidant Activity

scholarly journals Predicting topical drug clearance from the skin

2020 ◽  
Author(s):  
Maria Alice Maciel Tabosa ◽  
Magdalena Hoppel ◽  
Annette L. Bunge ◽  
Richard H. Guy ◽  
M. Begoña Delgado-Charro
Keyword(s):  
Drug Disposition ◽  
Chemical Properties ◽  
Time Frame ◽  
Drug Clearance ◽  
Pharmacokinetic Data ◽  
Local Concentration ◽  
Topical Formulation ◽  
Sites Of Action

Abstract For topical drug products that target sites of action in the viable epidermal and/or upper dermal compartment of the skin, the local concentration profiles have proven difficult to quantify because drug clearance from the viable cutaneous tissue is not well characterised. Without such knowledge, of course, it is difficult—if not impossible—to predict a priori whether and over what time frame a topical formulation will permit an effective concentration of drug within the skin ‘compartment’ to be achieved. Here, we test the hypothesis that valuable information about drug disposition, and specifically its clearance, in this experimentally difficult-to-access compartment (at least, in vivo) can be derived from available systemic pharmacokinetic data for drugs administered via transdermal delivery systems. A multiple regression analysis was undertaken to determine the best-fit empirical correlation relating clearance from the skin to known or easily calculable drug properties. It was possible, in this way, to demonstrate a clear relationship between drug clearance from the skin and key physical chemical properties of the drug (molecular weight, log P and topological polar surface area). It was further demonstrated that values predicted by the model correlated well with those derived from in vitro skin experiments.

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

2007 ◽  
Vol 96 (12) ◽  
pp. 3293-3303 ◽  
Author(s):  
Marcel de Matas ◽  
Qun Shao ◽  
Victoria Louise Silkstone ◽  
Henry Chrystyn
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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