The use of kinetic-dynamic interactions in the evaluation of drugs

Deterred by the complexity of the mathematics, pharmacologists and clinical pharmacologists have only recently appreciated the usefulness of pharmacokinetics in drug development. Now unfortunately, although the vernacular of the science is known, often the meaning behind the words is lost. It is often assumed that drug levels are linearly related to drug action. Frequently they are not. This review shows, with reference to psychotropic drugs, how, in simple terms, it is possible to relate pharmacokinetics with pharmacodynamics, and how such relationships may provide a greater insight into drug activity and enhance drug development. Assuming that an equilibrium exists between the drug in plasma levels, and at the site of action, the same Michaelis-Menten equations used to relate effect to drug receptor binding can be used for drug level-dynamic interactions. A number of these relationships have been published and are discussed in terms of their derivation and their limitations. The graphical and computerised methods to create complete Emax curves are described and how the parameters of maximal effect, potency, variability and slope can be measured. When the drug is not in equilibrium with its site of action, hysteresis occurs and drug levels are out of phase with activity. Anticlockwise hysteresis, that is, activity increasing with time for a given drug level, can be caused by uptake into an active site, active metabolites, cascade activity, and sensitisation whilst clockwise hysteresis, in which activity decreases with time, can be caused by tolerance, active antagonistic metabolites, learning effects and feedback regulation. Attempts to relate simultaneously kinetics and dynamics by Link models can be difficult and not always necessary. It is assumed in therapeutic drug monitoring that individuals will show the same response for a given drug level. On the contrary, differences in individual subject sensitivity to drugs measured by kinetic-dynamic relationships may provide a greater understanding of the disease itself.