Individualising aminoglycoside dosage regimens after therapeutic drug monitoring: simple or complex pharmacokinetic methods?
Measurements of aminoglycoside concentration in serum are used to individualise dosage regimens (dose per administration and/or administration interval) with the goal of attaining the desired therapeutic range as quickly as possible. Therapeutic range is defined in terms of peak concentration (to monitor effectiveness) and trough concentration (to avoid toxicity). This article focuses on methods to individualise aminoglycoside dosage regimens in the context of extended dosage intervals. Simple pharmacokinetic methods involve linear dosage adjustment based on peak or trough concentrations or area under the concentration-time curve, or nomograms. The once daily aminoglycoside nomogram determines the dosage interval for aminoglycosides given as a fixed dose per administration, based on a single concentration measurement drawn 6 to 14 hours after the start of the first infusion. This is a preferred method because of its simplicity, strong pharmacodynamic rationale and prospective validation in a large population. However, it does not work when the fixed dose assumed is not relevant, for example for patients with burns, cystic fibrosis, ascites or pregnancy. Furthermore, it has not been validated in children. In these cases, a more sophisticated method is required. Complex pharmacokinetic methods require dedicated software. Non-Bayesian least-squares methods allow the optimisation of both the dose and the dosage interval, but require aminoglycoside concentrations from two or more samples taken in the post-distributive phase during a single dosage interval. With Bayesian least-squares methods, only one concentration measurement is required, although any number of samples can be taken into account. In the Bayesian maximum a posteriori (MAP) method, the parameter estimates are taken as the values corresponding to the maximum of the posterior density. In 'full' Bayesian approaches (also called stochastic control), all the information about the parameters revealed by the posterior distribution is taken into account, and the optimal regimen is found by minimising the expected value of the weighted sum of squared deviations between predicted and target concentrations. If the population model is reasonably well known, Bayesian methods (MAP or stochastic control) should be used because of their good predictive performance. Although only one concentration measurement is required, better precision is afforded by a two-sample strategy, preferably drawn 1 and 6 hours after the start of the first infusion. If the population model is not known, then the non-Bayesian least-squares method is the method of choice, because of its robustness and lack of requirement for prior information about the distribution of parameters in the population.
Tod
M M
MM
Faculty of Medicine Paris XIII, Department of Pharmacotoxicology, Avicenne Hospital and CREPIT, Bobigny, France. michel.tod@avc.ap-hop-paris.fr
Padoin
C
C
Petitjean
O
O
D000617:Aminoglycosides / Q000097:blood*; D019540:Area Under Curve; D001499:Bayes Theorem; D004305:Dose-Response Relationship, Drug; D016903:Drug Monitoring / Q000379:methods*; D004358:Drug Therapy / Q000379:methods*; D004363:Drug Utilization / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D010599:Pharmacokinetics
D000617:Aminoglycosides