When constructing pharmacodynamic (PD) models, you will often encounter the adjectives “direct” and “indirect” describing the associated PD model. This terminology was very confusing to me when I was learning about PD modeling. Hopefully a brief explanation will help you.
Let’s start with the direct PD model. In this type of model, the drug is directly responsible for the pharmacodynamic response being measured. One example of a direct PD model is the pharmacodynamic response to moxifloxacin (AVELOX®). As moxifloxacin concentrations increase, the QT interval also increases. Thus, the PD measure (QT interval) is directly related to the drug (moxifloxacin) concentration.
The indirect PD model is slightly different in that the drug does not directly affect the pharmacodynamic response. Instead, the drug affects a precursor which then influences the pharmacodynamic measure. An example of an indirect PD model is the pharmacodynamic response to warfarin (Coumadin). As warfarin levels increase, the inhibition of prothrombin synthesis is inhibited, which in turn has anti-coagulant effects. In this case, there is a separation (space and time) between the PD measure and the action of the drug (inhibition of synthesis of prothrombin).
In a direct model, the drug is directly responsible for the PD response that is measured. In the indirect model, the drug is “indirectly” responsible for the PD response measured. When trying to decide which type of PD model you should use, most people will start with the indirect model because it is more consistent with our understanding of receptor-mediated drug effects and signaling cascades. However, the indirect model is difficult to use if the PD response profile follows the drug concentration profile. Thus, when no temporal delay in response is seen, direct response models should be used. On the other hand, when there are time delays between peak drug levels and peak PD effects, the indirect model should be used. For more information, look up publications by William J Jusko of the University of Buffalo.
I hope you have a better understanding of direct and indirect pharmacodynamic models. Happy modeling!