Model‐informed precision dosing of teicoplanin for the rapid achievement of the target area under the concentration‐time curve: A simulation study

Teicoplanin, a glycopeptide antimicrobial, is recommended for therapeutic drug monitoring, but it remains unclear how to target the area under the concentration‐time curve (AUC). This simulation study purposed to demonstrate the potential of the Bayesian forecasting approach for the rapid achievement of the target AUC for teicoplanin. We generated concordant and discordant virtual populations against a Japanese population pharmacokinetic model. The predictive performance of the Bayesian posterior AUC in limited sampling on the first day against the reference AUC was evaluated as an acceptable target AUC ratio within the range of 0.8–1.2. In the concordant population, the probability for the maximum a priori or Bayesian posterior AUC on the first day (AUC(0–24)) was 61.3% or more than 77.0%, respectively. The Bayesian posterior AUC on the second day (AUC(24–48)) was more than 75.1%. In the discordant population, the probability for the maximum a priori or Bayesian posterior AUC(0–24) was 15.5% or 11.7–80.7%, respectively. The probability for the maximum a priori or Bayesian posterior AUC(24–48) was 23.4%, 30.2–82.1%. The AUC at steady‐state (AUC(SS)) was correlated with trough concentration at steady‐state, with a coefficient of determination of 0.930; the coefficients on days 7 and 4 were 0.442 and 0.125, respectively. In conclusion, this study demonstrated that early sampling could improve the probability of AUC(0–24) and AUC(24–48) but did not adequately predict AUC(SS). Further studies are necessary to apply early sampling‐based model‐informed precision dosing in the clinical settings.