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

被引:4
|
作者
Belic, A
Grabnar, I
Belic, I
Karba, R
Mrhar, A
机构
[1] Univ Ljubljana, Fac Elect Engn, Ljubljana 1111, Slovenia
[2] Univ Ljubljana, Fac Pharm, Ljubljana 1000, Slovenia
[3] Univ Ljubljana, Fac Chim Justice, Ljubljana 1000, Slovenia
关键词
pharmacokinetics; pharmacodynamics; artificial neural networks; statistics; modelling;
D O I
10.1016/j.compbiomed.2004.07.006
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. (c) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:892 / 904
页数:13
相关论文
共 50 条
  • [41] Towards Predicting Water Levels Using Artificial Neural Networks
    Londhe, Shreenivas N.
    OCEANS 2009 - EUROPE, VOLS 1 AND 2, 2009, : 1223 - 1228
  • [42] Predicting surgical satisfaction using artificial neural networks Response
    Azimi, Parisa
    Benzel, Edward C.
    JOURNAL OF NEUROSURGERY-SPINE, 2014, 20 (03) : 299 - 299
  • [43] Predicting wood thermal conductivity using artificial neural networks
    Avramidis, S
    Iliadis, L
    WOOD AND FIBER SCIENCE, 2005, 37 (04): : 682 - 690
  • [44] Predicting water saturation using artificial neural networks (ANNS)
    Al-Bulushi, Nabil
    Araujo, Mariela
    Kraaijveld, Martin
    PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, 2007, : 57 - +
  • [45] Predicting mutual fund performance using artificial neural networks
    Indro, DC
    Jiang, CX
    Patuwo, BE
    Zhang, GP
    OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 1999, 27 (03): : 373 - 380
  • [46] Predicting Roundabout Lane Capacity using Artificial Neural Networks
    Anagnostopoulos A.
    Kehagia F.
    Damaskou E.
    Mouratidis A.
    Aretoulis G.
    Journal of Engineering Science and Technology Review, 2021, 14 (05) : 210 - 215
  • [47] Predicting Students' Final Performance Using Artificial Neural Networks
    Ahajjam, Tarik
    Moutaib, Mohammed
    Aissa, Haidar
    Azrour, Mourad
    Farhaoui, Yousef
    Fattah, Mohammed
    BIG DATA MINING AND ANALYTICS, 2022, 5 (04) : 294 - 301
  • [48] The contributions of muscarinic receptors and changes in plasma aldosterone levels to the anti-hypertensive effect of Tulbaghia violacea
    Ismaila Raji
    Pierre Mugabo
    Kenechukwu Obikeze
    BMC Complementary and Alternative Medicine, 13
  • [49] Using artificial neural networks for modeling suspended sediment concentration
    Wang, Yu-Min
    Traore, Seydou
    Kerh, Tienfuan
    MMACTEE' 08: PROCEEDINGS OF THE 10TH WSEAS INTERNATIONAL CONFERENCE MATHERMATICAL METHODS AND COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING: COMPUTATIONAL METHODS AND INTELLIGENT SYSTEMS, 2008, : 108 - +
  • [50] Predicting oil saturation from velocities using petrophysical models and artificial neural networks
    Boadu, FK
    JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2001, 30 (3-4) : 143 - 154