Pharmacokinetic-pharmacodynamic modeling: why?

At present, pharmacokinetic-pharmacodynamic (PK-PD) modeling has emerged as a major tool in clinical pharmacology to optimize drug use by designing rational dosage forms and dosage regimes. Quantitative representation of the dose-concentration-response relationship should provide information for prediction of the level of response to a certain level of drug dose. Several mathematical approaches can be used to describe such relationships, depending on the single dose or the steady-state measurements carried out. With concentration and response data on-phase, basic models such as fixed-effect, linear, log-linear, E(MAX), and sigmoid E(MAX) can be sufficient. However, time-variant pharmacodynamic models (effect compartment, acute tolerance, sensitization, and indirect responses) can be required when kinetics and response are out-of-phase. To date, methodologies available for PK-PD analysis barely suppose the use of powerful computing resources. Some of these algorithms are able to generate individual estimates of parameters based on population analysis and Bayesian forecasting. Notwithstanding, attention must be paid to avoid overinterpreted data from mathematical models, so that reliability and clinical significance of estimated parameters will be valuable when underlying physiologic processes (disease, age, gender, etc.) are considered.