Semi-mechanistic pharmacokinetic–pharmacodynamic modelling of antibiotic drug combinations

Abstract Background Deriving suitable dosing regimens for antibiotic combination therapy poses several challenges as the drug interaction can be highly complex, the traditional pharmacokinetic–pharmacodynamic (PKPD) index methodology cannot be applied straightforwardly, and exploring all possible dose combinations is unfeasible. Therefore, semi-mechanistic PKPD models developed based on in vitro single and combination experiments can be valuable to suggest suitable combination dosing regimens. Aims To outline how the interaction between two antibiotics has been characterized in semi-mechanistic PKPD models. We also explain how such models can be applied to support dosing regimens and design future studies. Sources PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations. Content Thirteen publications were identified where ten had applied subpopulation synergy to characterize the combined effect, i.e. independent killing rates for each drug and bacterial subpopulation. We report the various types of interaction functions that have been used to describe the combined drug effects and that characterized potential deviations from additivity under the PKPD model. Simulations from the models had commonly been performed to compare single versus combined dosing regimens and/or to propose improved dosing regimens. Implications Semi-mechanistic PKPD models allow for integration of knowledge on the interaction between antibiotics for various PK and PD profiles, and can account for associated variability within the population as well as parameter uncertainty. Decisions on suitable combination regimens can thereby be facilitated. We find the application of semi-mechanistic PKPD models to be essential for efficient development of antibiotic combination regimens that optimize bacterial killing and/or suppress resistance development.