A Computational Strategy for Dose Adaptation at the Population and Group Levels

With the intention to achieve the best therapeutic outcomes, dose adaptation underpins the clinical practice by tailoring dose and time in order to maximize efficacy while minimizing toxicity. Depending on the drug properties and the clinical context, three successive levels of dose adaptation can be considered, i.e., approaches based on population, group and individual. To make a rational choice for the dose adaptation level and determine the best drug regimens, we here propose a modeling and simulation strategy based on the platform provided by the population pharmacokinetic/pharmacodynamic methodology at population and group levels. In order to compare the performance of different dose and time schedules, we introduced probabilistic conceptualized time- and concentration-based therapeutic indicators. Using carbamazepine as a drug model and a recently reported population pharmacokinetic (Pop-PK) model for the group of patients of 60 years and older, we were able to quantitatively study the performance of different group-dosing regimens in order to find the best ones. As indicated by our results, TID regimen was clearly favored among others, confirming thus suggestions in several clinical reports. Moreover, different time schedules that can reach the same therapeutic target for this group were identified through our methodology, giving thus a wider choice for the clinical practice.