We developed a new model for neonate and infant dosing of phenobarbital with good predictive performance. Clinical application of our model should permit more accurate selection of initial and maintenance doses to achieve target phenobarbital concentrations in Japanese neonates and infants, thereby enabling the clinician to achieve the desired therapeutic effect. A similar approach can be used to validate our model for use in other neonate and infant populations.

Estimates generated using NONMEM indicated that clearance of digoxin (l.h-1) was influenced by the demographic variables of age, total body weight, serum creatinine, the coadministration of spironolactone, and the presence or absence of congestive heart failure. The interindividual variability in digoxin clearance was modeled with proportional errors with an estimated coefficient of variation of 32.1%, and the residual variability was 28.9%. In the validation set of 66 patients, the performance (bias, precision) of the final population model was good (mean prediction error -0.04 ng.ml-1; mean absolute prediction error 0.20 ng.ml-1).

We developed a new model for elderly patient dosing of digoxin with good predictive performance. Clinical application of the findings of the present study to patient care may permit selection of an appropriate initial digoxin maintenance dose, thus enabling the clinician to achieve a desired therapeutic effect. However, the digoxin dosage regimen should be based on an appraisal of the individual patient's clinical need for the drug.

To establish the role of patient characteristics in estimating doses of digoxin for infants and young children using routine therapeutic drug monitoring data, the steady-state blood-level data (n = 245) after repetitive oral administration in 117 hospitalized infants and young children were analyzed using nonlinear mixed effects modeling (NONMEM), a computer program designed for analyzing drug pharmacokinetics in study populations through pooling of data. Analysis of the pharmacokinetics of digoxin was accomplished using a 1-compartment pharmacokinetic model. Estimates generated by NONMEM indicated that the clearance of digoxin (CL/F; L/h) was influenced by the following demographic variables: total body weight (TBW), presence of congestive heart failure (CHF), and infant-young children clearance factor (trough serum concentration of digoxin; Conc). These influences could be modeled by the equation CL/F (L/h) = 0.302 · TBW (kg)¹·¹⁷ · 0.905(CHF) · Conc (trough serum digoxin concentration >1.7 ng/mL)⁻⁰·⁵⁴⁰; F = 0.754, where CHF is 1 for presence of congestive heart failure, 0 otherwise; F is bioavailability, 1 for elixirs, 0.754 for powders; and Conc⁻⁰·⁵⁴⁰ is 1 for digoxin concentration <1.7 ng/mL. Clinical application of the model to patient care may permit selection of an appropriate initial maintenance dose, thus enabling the clinician to achieve the desired therapeutic effect. However, the digoxin dosage regimen for the individual patient should be based on a careful appraisal of his or her clinical need for the drug.

The estimated population parameter values may assist clinicians in the individualisation of digoxin dosage regimens.