FUZZY-LOGIC PHARMACOKINETIC MODELING - APPLICATION TO LITHIUM CONCENTRATION PREDICTION

Introduction: We hypothesized that fuzzy logic could be used for pharm acokinetic modeling. Our objectives were to develop and evaluate a mod el for predicting serum lithium concentrations with fuzzy logic, Metho ds: Steady-state pharmacokinetic data had been previously collected in 10 elderly patients (age range, 67 to 80 years) with depression who w ere receiving lithium once daily, Each patient had serial serum lithiu m concentration determinations over one 24-hour period. The resulting 137 data sets initially consisted of five input variables (age, weight , serum creatinine, lithium dose, and time since last dose) and one ou tput variable (serum lithium concentration; range, 0.2 to 1.24 mmol/L) , Results: A fuzzy rulebase was created with 87 randomly chosen data s ets, and predictions of serum lithium concentration were made on the b asis of the remaining 50 data sets. All of the input variables except age and weight were identified as contributing to the fuzzy logic mode l. The average magnitude of the error in the predictions was 0.13 mmol /L (root mean squared error) with a bias (mean of the prediction error s) of 0.03 mmol/L. Conclusions: This study indicates that the use of f uzzy logic for pharmacokinetic modeling of Lithium for serum concentra tion predictions is feasible.