Literature DB >> 8450420

A system approach to pharmacodynamics. Input-effect control system analysis of central nervous system effect of alfentanil.

P Veng-Pedersen1, N B Modi.   

Abstract

Virtually all biological variables, including those affected by drugs, are subject to adaptive self regulation. In the description of the pharmacodynamics (PD) of drugs, it may be necessary to consider the endogenous control system (ECS) as an integral part of the PD. A PDECS model based on system analysis principles is presented and tested on PD data for alfentanil considering the central nervous system activity quantified by a power spectrum analysis of the electroencephalogram. The model was tested in terms of a proposed relative prediction performance criterion that measures the accuracy of future predictions relative to how well the model describes (fits) the past effect data. A mean value of 80% (standard deviation, 28) for relative prediction performance indicates that the model performs well when challenged by the complex multiple infusion scheme used in the test. The overshoot phenomenon observed in the data is considered by the PDECS model as a ECS-based tolerance phenomenon. The proposed development of tolerance is modeled as a variable gain in the ECS processing that influences the effect. Although the development and loss of tolerance is determined by a single rate constant in the tolerance model, the rates of increase and decrease of tolerance may be substantially different. Contrary to other PD tolerance models, the proposed PDECS approach models the tolerance in terms of an effect deviation from an ECS set point. The intrinsic (no tolerance) effect of the drug is isolated in terms of an open loop (no feedback) effect.

Entities:  

Mesh:

Substances:

Year:  1993        PMID: 8450420     DOI: 10.1002/jps.2600820310

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  6 in total

Review 1.  Interchangeability and predictive performance of empirical tolerance models.

Authors:  M Gårdmark; L Brynne; M Hammarlund-Udenaes; M O Karlsson
Journal:  Clin Pharmacokinet       Date:  1999-02       Impact factor: 6.447

2.  Mechanism-based pharmacokinetic-pharmacodynamic modeling-a new classification of biomarkers.

Authors:  Meindert Danhof; Gunnar Alvan; Svein G Dahl; Jochen Kuhlmann; Gilles Paintaud
Journal:  Pharm Res       Date:  2005-08-24       Impact factor: 4.200

Review 3.  A flexible nonlinear feedback system that captures diverse patterns of adaptation and rebound.

Authors:  Johan Gabrielsson; Lambertus A Peletier
Journal:  AAPS J       Date:  2008-02-22       Impact factor: 4.009

4.  Modeling energy intake by adding homeostatic feedback and drug intervention.

Authors:  Peter Gennemark; Stephan Hjorth; Johan Gabrielsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-11-12       Impact factor: 2.745

5.  The use of multiple doses and pharmacodynamic system analysis to distinguish between dispositional delays and time-variant pharmacodynamics.

Authors:  M R Gastonguay; S L Schwartz
Journal:  Pharm Res       Date:  1994-12       Impact factor: 4.200

6.  Validation of a variable direction hysteresis minimization pharmacodynamic approach: cardiovascular effects of alfentanil.

Authors:  N B Modi; P Veng-Pedersen
Journal:  Pharm Res       Date:  1994-01       Impact factor: 4.200

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.