Pharmaceutical Research

, Volume 27, Issue 11, pp 2433–2445 | Cite as

Reliability of Inhibition Models to Correctly Identify Type of Inhibition

  • Vidula Kolhatkar
  • James E. Polli
Research Paper



Type of inhibition (e.g. competitive, noncompetitive) is frequently evaluated to understand transporter structure/function relationships, but reliability of nonlinear regression to correctly identify inhibition type has not been assessed. The purpose was to assess the ability of nonlinear regression to correctly identify inhibition type.


This aim was pursued through three objectives that compared the competitive, noncompetitive, and uncompetitive inhibition models to best fit simulated competitive and noncompetitive data. The first objective involved conventional inhibition data and entailed simulated data for the common situation where substrate concentration was fixed at a single level but inhibitor concentration varied. The second objective involved Dixon-type data where both substrate and inhibitor concentrations varied. A third objective involved nonconventional inhibition data, where substrate concentration was varied and inhibitor was fixed at a single concentration. Experimental data were also examined.


Nonlinear regression performed poorly in identifying the correct inhibition model for conventional inhibition data, but performed moderately well for Dixon-type data. Interestingly, nonlinear regression performed well for nonconventional inhibition data, particularly at higher inhibitor concentrations. Experimental data support simulation findings.


Conventional inhibition data is a poor basis to determine inhibition type, while Dixon-type data affords modest success. Nonconventional inhibition data merits further consideration.


competitive inhibition dixon plot model noncompetitive inhibition transporter 



This work was supported in part by National Institutes of Health (grant DK67530). WinNonlin software was kindly donated by Pharsight Corp. (Mountain View, CA).

Supplementary material

11095_2010_236_MOESM1_ESM.docx (24 kb)
ESM 1 (DOCX 24 kb)


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.University of Maryland School of PharmacyBaltimoreUSA

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