Predicting the anti-hypertensive effect of nitrendipine from plasma concentration profiles using artificial neural networks

  • Authors:

  • A. Belič;I. Grabnar;I. Belič;R. Karba;A. Mrhar

  • Affiliations:

  • Faculty of Electrical Engineering, University of Ljubljana, Traška 25, 1111 Ljubljana, Slovenia,;Faculty of Pharmacy, Aškerčeva 7, University of Ljubljana, 1000 Ljubljana, Slovenia;Faculty of Criminal Justice, University of Ljubljana, Kotnikova 8, 1000 Ljubljana, Slovenia;Faculty of Electrical Engineering, University of Ljubljana, Traška 25, 1111 Ljubljana, Slovenia,;Faculty of Pharmacy, Aškerčeva 7, University of Ljubljana, 1000 Ljubljana, Slovenia

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2005

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Abstract

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.