An enzyme inhibitor is characterised by its dissociation constant K, the concentration at which the inhibitor half occupies the enzyme. In non-competitive inhibition the inhibitor can also bind to the enzyme-substrate complex, and the presence of bound substrate can change the affinity of the inhibitor for the enzyme, resulting in a second dissociation constant K. Hence K and K are the dissociation constants of the inhibitor for the enzyme and to the enzyme-substrate complex, respectively. The enzyme-inhibitor constant K can be measured directly by various methods; one especially accurate method is isothermal titration calorimetry, in which the inhibitor is titrated into a solution of enzyme and the heat released or absorbed is measured. However, the other dissociation constant K is difficult to measure directly, since the enzyme-substrate complex is short-lived and undergoing a chemical reaction to form the product. Hence, K is usually measured indirectly, by observing the enzyme activity under various substrate and inhibitor concentrations, and fitting the data via nonlinear regression to a modified Michaelis–Menten equation.
where the modifying factors α and α' are defined by the inhibitor concentration and its two dissociation constants
Thus, in the presence of the inhibitor, the enzymes effective K and V become (α/α)K and (1/α)V, respectively. However, the modified Michaelis-Menten equation assumes that binding of the inhibitor to the enzyme has reached equilibrium, which may be a very slow process for inhibitors with sub-nanomolar dissociation constants. In these cases the inhibition becomes effectively irreversible, hence it is more practical to treat such tight-binding inhibitors as irreversible (see below). kinetically if K is measured independently.|date=July 2022}}-->
The effects of different types of reversible enzyme inhibitors on enzymatic activity can be visualised using graphical representations of the Michaelis–Menten equation, such as Lineweaver–Burk, Eadie-Hofstee or Hanes-Woolf plots. An illustration is provided by the three Lineweaver–Burk plots depicted in the Lineweaver–Burk diagrams figure. In the top diagram the competitive inhibition lines intersect on the y-axis, illustrating that such inhibitors do not affect V. In the bottom diagram the non-competitive inhibition lines intersect on the x-axis, showing these inhibitors do not affect K. However, since it can be difficult to estimate K and K' accurately from such plots, it is advisable to estimate these constants using more reliable nonlinear regression methods.