Personalizing Polymyxin B Dosing Using an Adaptive Feedback Control Algorithm

Polymyxin B is used as an antibiotic of last resort for patients with multidrug-resistant Gram-negative bacterial infections; however, it carries a significant risk of nephrotoxicity. Herein we present a polymyxin B therapeutic window based on target area under the concentration-time curve (AUC) values and an adaptive feedback control algorithm (algorithm) which allows for the personalization of polymyxin B dosing. The upper bound of this therapeutic window was determined through a pharmacometric meta-analysis of polymyxin B nephrotoxicity data, and the lower bound was derived from murine thigh infection pharmacokinetic (PK)/pharmacodynamic (PD) studies. A previously developed polymyxin B population pharmacokinetic model was used as the backbone for the algorithm. Monte Carlo simulations (MCS) were performed to evaluate the performance of the algorithm using different sparse PK sampling strategies. The results of the nephrotoxicity meta-analysis showed that nephrotoxicity rate was significantly correlated with polymyxin B exposure. Based on this analysis and previously reported murine PK/PD studies, the target AUC_0-24 (AUC from 0 to 24 h) window was determined to be 50 to 100 mg · h/liter. MCS showed that with standard polymyxin B dosing without adaptive feedback control, only 71% of simulated subjects achieved AUC values within this window. Using a single PK sample collected at 24 h and the algorithm, personalized dosing regimens could be computed, which resulted in >95% of simulated subjects achieving AUC_0-24 values within the target window. Target attainment further increased when more samples were used. Our algorithm increases the probability of target attainment by using as few as one pharmacokinetic sample and enables precise, personalized dosing in a vulnerable patient population.