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
  • [1] Changes in plasma aldosterone concentration following anti-hypertensive medication and the effect on LV geometry and function
    Yoshida, C.
    Tsujino, T.
    Goda, A.
    Naito, Y.
    Nakao, S.
    Yuba, M.
    Otsuka, M.
    Matsumoto, M.
    Kawabata-Lee, M.
    Ezumi, A.
    Masuyama, T.
    JOURNAL OF HYPERTENSION, 2008, 26 : S417 - S417
  • [2] Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks
    Tutak, Magdalena
    Brodny, Jaroslaw
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2019, 16 (08)
  • [3] EFFECT OF ANTI-HYPERTENSIVE TREATMENT ON PLASMA-LIPIDS AND LIPOPROTEINS
    SAUVANET, JP
    ROUFFY, J
    SEMAINE DES HOPITAUX, 1982, 58 (09): : 563 - 568
  • [4] EFFECT OF ANTI-HYPERTENSIVE TREATMENT ON PLASMA-LIPIDS AND LIPOPROTEINS
    SAUVANET, JP
    ROUFFY, J
    PATHOLOGIE BIOLOGIE, 1981, 29 (07): : 435 - 440
  • [5] Predicting temperature profiles in producing oil wells using artificial neural networks
    Farshad, FF
    Garber, JD
    Lorde, JN
    ENGINEERING COMPUTATIONS, 2000, 17 (6-7) : 735 - 754
  • [6] Predicting stable alluvial channel profiles using emotional artificial neural networks
    Gholami, Azadeh
    Bonakdari, Hossein
    Samui, Pijush
    Mohammadian, Majid
    Gharabaghi, Bahram
    APPLIED SOFT COMPUTING, 2019, 78 : 420 - 437
  • [7] PREDICTING PERMEABILITY FROM POROSITY USING ARTIFICIAL NEURAL NETWORKS
    ROGERS, SJ
    CHEN, HC
    KOPASKAMERKEL, DC
    FANG, JH
    AAPG BULLETIN-AMERICAN ASSOCIATION OF PETROLEUM GEOLOGISTS, 1995, 79 (12): : 1786 - 1797
  • [9] RELATIONSHIP BETWEEN ANTI-HYPERTENSIVE ACTION AND PLASMA CONCENTRATION OF PINDOLOL - PRELIMINARY-STUDY
    WEISS, Y
    LORIA, Y
    LAVENE, D
    GEORGES, D
    SAFAR, M
    MILLIEZ, P
    NOUVELLE PRESSE MEDICALE, 1977, 6 (11): : 927 - 930
  • [10] Effect of lipid-lowering and anti-hypertensive drugs on plasma homocysteine levels
    Dierkes, Jutta
    Luley, Claus
    Westphal, Sabine
    VASCULAR HEALTH AND RISK MANAGEMENT, 2007, 3 (01) : 99 - 108