The aim of this study was to develop a population pharmacokinetic (PK) model for teicoplanin across childhood age ranges to be used as Bayesian prior information in the software constructed for individualized therapy. We developed a nonparametric population model fitted to PK data from neonates, infants, and older children. We then implemented this model in the BestDose multiple-model Bayesian adaptive control algorithm to show its clinical utility. It was used to predict the dosages required to achieve optimal teicoplanin predose targets (15 mg/liter) from day 3 of therapy. We performed individual simulations for an infant and a child from the original population, who provided early first dosing interval concentration-time data. An allometric model that used weight as a measure of size and that also incorporated renal function using the estimated glomerular filtration rate (eGFR), or the ratio of postnatal age (PNA) to serum creatinine concentration (SCr) for infants <3 months old, best described the data. The median population PK parameters were as follows: elimination rate constant (Ke) = 0.03 · (wt/70)^-0.25 · Renal (h^-1); V = 19.5 · (wt/70) (liters); Renal = eGFR^0.07 (ml/min/1.73 m²), or Renal = PNA/SCr (μmol/liter). Increased teicoplanin dosages and alternative administration techniques (extended infusions and fractionated multiple dosing) were required in order to achieve the targets safely by day 3 in simulated cases. The software was able to predict individual measured concentrations and the dosages and administration techniques required to achieve the desired target concentrations early in therapy. Prospective evaluation is now needed in order to ensure that this individualized teicoplanin therapy approach is applicable in the clinical setting. (This study has been registered in the European Union Clinical Trials Register under EudraCT no. 2012-005738-12.).

	Year	Citations

About