Predicting the anti-hypertensive effect of nitrendipine from plasma concentration profiles using artificial neural networks.
Nitrendipine is an effective and safe calcium-channel blocker for the treatment of mild to moderate hypertension. The aim of this study is to show that an artificial neural network (ANN) model of the relationship between nitrendipine plasma levels and pharmacodynamic effects can be built and used for pressure-drop prediction after oral administration of the drug in spite of the poor correlation between plasma concentrations and the effect. To achieve the goal, the following steps were taken: evaluation of the quality of the database for training the ANN, definition of the optimal input set for the ANN, and prediction of the diastolic pressure drop using the ANN. The possible consequences of successful ANN modelling are an optimisation of the drug administration regimen, to achieve the best possible effect, as well as optimal drug formulation for drugs with complicated pharmacokinetic/pharmacodynamic relationships.
Belic
A
A
Faculty of Electrical Engineering, University of Ljubljana, Slovenia. ales.belic@fe.uni-lj.si/
Grabnar
I
I
Belic
I
I
Karba
R
R
Mrhar
A
A
D002121:Calcium Channel Blockers / Q000097:blood* / Q000493:pharmacokinetics*; D018592:Cross-Over Studies; D019992:Databases as Topic; D003971:Diastole; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D016571:Neural Networks, Computer; D009568:Nitrendipine / Q000097:blood* / Q000493:pharmacokinetics*; D016037:Single-Blind Method; D013810:Therapeutic Equivalency
D002121:Calcium Channel Blockers; D009568:Nitrendipine