Pharmacokinetics neural network prediction

In an early application of in silico approaches to predict human VD, Ritschel and coworkers described an approach using artificial neural networks (ANN), in this case for VDp [34]. However, this was not a truly in silico-only approach as the ANN that yielded accurate predictions of human VD required animal pharmacokinetic data as input parameters, along with in vitro data (protein binding and logP). 

Yamamura S (2003) Clinical application of artificial neural network (ANN) modeling to predict pharmacokinetic parameters of severely ill patients. Adv Drug Deliv Rev 5 1233-1251. 

Neural networks are a relatively new tool in data modelling in the field of pharmacokinetics [54—56]. Using this approach, non-linear relationships to predicted properties are better taken into account than by multiple linear regression [45]. Human hepatic drug clearance was best predicted from human hepatocyte data, followed by rat hepatocyte data, while in the studied data set animal in vivo data did not significantly contribute to the predictions [56]. 

General regression neural network (GRNN) was introduced by Donald Specht in 1991 [33], and it has been successfully used in pharmacokinetic studies, including human intestinal absorption [34], blood-brain barrier prediction [35], human serum albumin binding [35], milk-plasma ratio [35], and drug clearance [36], Recently it has been applied for the prediction of Tetrahymenapyriformis toxicity [37],... 

Probabilistic neural network (PNN) is similar to GRNN except that it is used for classification problems [54], It has been used for pharmacodynamics [55], pharmacokinetics [34,56] studies and has recently been applied for genotoxicity [43,50,57] and torsade de pointes prediction [58], PNN classifies compounds into their data class through the use of Bayes s optimal decision rule ... 

Schneider G, Coassolo P, Lave T. Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques. J Med Chem 1999 42 5072-6. 

Ritschel WA, Akileswaran R, Hussain AS. Apphcation of neural networks for the prediction of human pharmacokinetic parameters. Meth Find Exp Clin Pharmacol 1995 17 629-43. 

Plasma area under the concentration—time curves (AUCs) of 57 NCEs were determined following oral cassette administration (5—9 NCEs/cassette) to mice. Physicochemical properties [such as, molecular weight, calculated molar refractivity, and calculated lipophilicity (clogP)] and molecular descriptors [such as presence or absence of N-methylation, cyclobutyl moiety, or heteroatoms (non-C,H,0,N)] were calculated or estimated for these compounds. This structural data, along with the corresponding pharmacokinetic parameters (primarily AUC), were used to develop artificial neural network models [8]. These models were used to predict the AUCs of compounds under synthesis [10]. This approach demonstrates that predictive models could be developed which potentially predict in vivo pharmacokinetics of NCEs under synthesis. Similar examples have been reported elsewhere [11—13]. 

Schneider, G. Coassolo, P. Lave, T. Combining In Vitro and In Vivo Pharmacokinetic Data for Prediction of Hepatic Dmg Clearance in Hnmans by Artificial Neural Networks and Multivariate Statistical Techniques, J. Med. Chem. 42, 5072-5076 (1999). 

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