Neural networks pharmacokinetic property

Yap CW, Chen YZ (2005) Quantitative Structure-Pharmacokinetic Relationships for drug distribution properties by using general regression neural network. J Pharm Sci 94 153-168. 

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]. 

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]. 

The acidity or basicity of compounds, as expressed by the pAl, is another property of bioactive compounds. This can have substantial influence on aqueous solubility and pharmacokinetics. Models for pK, employing neural nets are still emerging although prior experiences with other properties and the recent work by Clark (111) suggest that neural networks have a useful role in modeling this important property. 

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