Literature DB >> 33367961

Population Pharmacodynamic Modeling Using the Sigmoid Emax Model: Influence of Inter-individual Variability on the Steepness of the Concentration-Effect Relationship. a Simulation Study.

Johannes H Proost1,2, Douglas J Eleveld3, Michel M R F Struys3,4.   

Abstract

The relationship between the concentration of a drug and its pharmacological effect is often described by empirical mathematical models. We investigated the relationship between the steepness of the concentration-effect relationship and inter-individual variability (IIV) of the parameters of the sigmoid Emax model, using the similarity between the sigmoid Emax model and the cumulative log-normal distribution. In addition, it is investigated whether IIV in the model parameters can be estimated accurately by population modeling. Multiple data sets, consisting of 40 individuals with 4 binary observations in each individual, were simulated with varying values for the model parameters and their IIV. The data sets were analyzed using Excel Solver and NONMEM. An empirical equation (Eq. (11)) was derived describing the steepness of the population-predicted concentration-effect profile (γ*) as a function of γ and IIV in C50 and γ, and was validated for both binary and continuous data. The tested study design is not suited to estimate the IIV in C50 and γ with reasonable precision. Using a naive pooling procedure, the population estimates γ* are significantly lower than the value of γ used for simulation. The steepness of the population-predicted concentration-effect relationship (γ*) is less than that of the individuals (γ). Using γ*, the population-predicted drug effect represents the drug effect, for binary data the probability of drug effect, at a given concentration for an arbitrary individual.

Entities:  

Keywords:  inter-individual variability; pharmacokinetic-pharmacodynamic modeling; sigmoid Emax model; simulation

Year:  2020        PMID: 33367961      PMCID: PMC7759489          DOI: 10.1208/s12248-020-00549-7

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  16 in total

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Authors:  Timothy G Short; Tam Yuk Ho; Charles F Minto; Thomas W Schnider; Steven L Shafer
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Review 2.  Hypnotic and opioid anesthetic drug interactions on the CNS, focus on response surface modeling.

Authors:  T W Bouillon
Journal:  Handb Exp Pharmacol       Date:  2008

3.  Sample size calculations for population pharmacodynamic experiments involving repeated dichotomous observations.

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Journal:  J Biopharm Stat       Date:  2008       Impact factor: 1.051

4.  Pharmacodynamic Interaction of Remifentanil and Dexmedetomidine on Depth of Sedation and Tolerance of Laryngoscopy.

Authors:  Maud A S Weerink; Clemens R M Barends; Ernesto R R Muskiet; Koen M E M Reyntjens; Froukje H Knotnerus; Martine Oostra; Jan F P van Bocxlaer; Michel M R F Struys; Pieter J Colin
Journal:  Anesthesiology       Date:  2019-11       Impact factor: 7.892

5.  Probability to tolerate laryngoscopy and noxious stimulation response index as general indicators of the anaesthetic potency of sevoflurane, propofol, and remifentanil.

Authors:  L N Hannivoort; H E M Vereecke; J H Proost; B E K Heyse; D J Eleveld; T W Bouillon; M M R F Struys; M Luginbühl
Journal:  Br J Anaesth       Date:  2016-05       Impact factor: 9.166

6.  Application of a new approach for the quantitation of drug synergism to the combination of cis-diamminedichloroplatinum and 1-beta-D-arabinofuranosylcytosine.

Authors:  W R Greco; H S Park; Y M Rustum
Journal:  Cancer Res       Date:  1990-09-01       Impact factor: 12.701

7.  Reliability of pharmacodynamic analysis by logistic regression: a computer simulation study.

Authors:  W Lu; J M Bailey
Journal:  Anesthesiology       Date:  2000-04       Impact factor: 7.892

8.  Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine.

Authors:  L B Sheiner; D R Stanski; S Vozeh; R D Miller; J Ham
Journal:  Clin Pharmacol Ther       Date:  1979-03       Impact factor: 6.875

9.  Reliability of pharmacodynamic analysis by logistic regression: mixed-effects modeling.

Authors:  Wei Lu; James G Ramsay; James M Bailey
Journal:  Anesthesiology       Date:  2003-12       Impact factor: 7.892

10.  Response surface modeling of the interaction between propofol and sevoflurane.

Authors:  Peter M Schumacher; Jan Dossche; Eric P Mortier; Martin Luginbuehl; Thomas W Bouillon; Michel M R F Struys
Journal:  Anesthesiology       Date:  2009-10       Impact factor: 7.892

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