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Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study.

Kazutaka OdaTomoyuki YamadaKazuaki MatsumotoYuki HanaiTakashi UedaMasaru SamuraAkari ShigemiHirofumi JonoHideyuki SaitoToshimi Kimura
Published in: Clinical and translational science (2023)
In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration-time curve (AUC) of vancomycin on the first and second day (AUC 0-24 , AUC 24-48 , respectively) of therapy. A virtual population of 1,000 individuals was created using a population pharmacokinetic (popPK) model, which was validated and incorporated into our model-informed precision dosing (MIPD) tool. The results were evaluated using six additional popPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8-1.2. The Bayesian posterior probability of achieving the AUC 24-48 range increased from 51.3% (a priori probability) to 77.5% after using two-point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC 0-24 range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady state (AUC SS ) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second-day trough and peak sampling provided accurate AUC 24-48 , and first-day sampling may assist in rapidly achieving therapeutic AUC 24-48 , although the AUC SS should be re-estimated in patients with reduced kidney function owing to its unreliable predictive performance.
Keyphrases
  • mass spectrometry
  • methicillin resistant staphylococcus aureus
  • high resolution
  • bone marrow
  • electronic health record
  • artificial intelligence
  • deep learning