Tools for the Individualized Therapy of Teicoplanin for Neonates and Children [PublishAheadOfPrint]

The aim of the study was to develop a population PK model for teicoplanin across childhood age ranges to be used as Bayesian prior information in the constructed software for individualized therapy. We developed a non-parametric population model fitted to PK data from neonates, infants and older children. We then implemented it in the BestDose multiple-model Bayesian adaptive control algorithm to show its clinical utility. It was used to predict the required dosages to achieve teicoplanin optimal pre-dose targets (15 mg/L) from day 3 of therapy. We performed individual simulations in an infant and a child from the original population, who provided early first dosing interval concentration time-data. An allometric model that linked weight to clearance and volume of distribution (Ke and V) and incorporating renal function as a power function of estimated glomerular filtration rate (eGFR) or Post-natal age (PNA)/serum creatinine(SCr) for infants < 3 months, best described the data. The median population PK parameters were as follows: Ke= 0.03*(wt/70)^-0.25 * Renal (h^-1); V=19.5*(wt/70) (L), being Renal= eGFR^0.07 (ml/min/1.73m²) or PNA/SCr (μmol/L). 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 required dosages and administration techniques to achieve the desired target concentrations early in therapy. Prospective evaluation is now needed in order to ensure that this teicoplanin individualized therapy approach is applicable in the clinical setting.

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