Abstract
Randomised concentration controlled trials (RCCT) have been used to learn about the concentration–effect relationship and to evaluate treatment strategies. They provide a model for discovering the target concentration, which is an essential prerequisite for using pharmacokinetic knowledge and measured concentrations to individualise dose. Target concentration intervention (TCI) is a rational approach to using the information from drug effect and concentration observations to achieve a clinical outcome. It employs pharmacokinetic and statistical models to learn about the patient's pharmacokinetic parameters. It differs from therapeutic drug monitoring (TDM) which uses empirical dose adjustment based on the idea of a therapeutic range. A pharmacokinetic–pharmacodynamic (PKPD) model can be used to describe the response to a drug. PKPD parameter variability can be divided between subject (BSV) and within subject (WSV) components. These sources of variability are either predictable (e.g. using covariates such as weight and renal function) or non-predictable and thus apparently random. Using covariates to account for subject differences may reduce the predictable component of BSV. TCI can minimize the random part of BSV but cannot reduce WSV. Safe and effective use of a drug can be defined by a criterion for acceptable variation around the target concentration (SEV). Covariates can be used to reduce overall parameter variability (BSV plus WSV) so that it is less than or equal to SEV. Covariates may be able to predict subjects who are at risk of unacceptably high WSV and thus identify people who should not be treated with a specific drug because WSV is greater than SEV. If SEV is larger than WSV, then there is an opportunity to use TCI to reduce BSV and improve the ability to predict the right dose in an individual.
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