Journal of Pharmacokinetics and Biopharmaceutics

, Volume 19, Issue 5, pp 537–552

A kinetic-dynamic model to explain the relationship between high potency and slow onset time for neuromuscular blocking drugs

  • FranÇois Donati
  • Claude Meistelman

DOI: 10.1007/BF01062962

Cite this article as:
Donati, F. & Meistelman, C. Journal of Pharmacokinetics and Biopharmaceutics (1991) 19: 537. doi:10.1007/BF01062962


To account for experimental data showing increased onset time with increased potency of neuromuscular blocking drugs, a pharmacokinetic-pharmacodynamic model is presented. It is characterized by a finite concentration of receptors (R)in the effect compartment. Transfer from central to effect compartment is linearly related to concentration gradient. A sigmoid Emaxmodel is used to describe the relationship between receptor occupancy and effect. Plasma concentrations found in the literature are used. Differential equations are solved numerically for equipotent doses of drugs of different potencies. Because the density of receptors constitutes a significant drain of drug molecules for potent drugs, the model predicts an inverse relationship between speed of onset and potency. The concentration of receptors in the effect compartment Rwhich best fits experimental data obtained in humans is 0.28 Μmol/L. With this value of R,onset times are prolonged when the ED95(dose for 95% blockade) is less than 0.1 Μmol/kg. It is concluded that, in the development of a short-acting nondepolarizing neuromuscular blocking drug, agents having an ED95of 0.1 Μmol/kg or greater appear more promising.

Key words

Modeling neuromuscular blocking drugs pharmacodynamics pharmacokinetics potency onset time 

Copyright information

© Plenum Publishing Corporation 1991

Authors and Affiliations

  • FranÇois Donati
    • 1
    • 2
  • Claude Meistelman
    • 2
  1. 1.Department of AnaesthesiaMcGill UniversityMontrealCanada
  2. 2.Service d'anesthésieInstitut Gustave-RoussyVillejuifFrance
  3. 3.Royal Victoria HospitalMontrealCanada

Personalised recommendations