Physiologically informed in vitro framework reveals context-dependent combinatory activity of niclosamide-colistin against Gram-negative bacteria.

Abstract

Background: Synergy between antibiotic pairs is typically discovered using chequerboard assays that assume uniform, static drug exposure; however, such conditions rarely apply in vivo. Dynamic and heterogeneous tissue environments create spatial and temporal mismatches in drug exposure that can uncouple synergistic interactions, leading to unexpected treatment failure.

Objective: This study aims to develop a physiologically relevant in vitro model that integrates infection-site microenvironments and drug-specific pharmacokinetics. This platform was applied to investigate how spatial and temporal factors affect antibiotic synergy, using niclosamide and colistin as a case study for inhaled delivery to infected lung airways.

Methods: Opportunistic Gram-negative bacterial species with varied susceptibility to niclosamide and colistin were tested. Synergy was assessed using microdilution chequerboard assays under both standard and physiologically altered conditions. In vitro models incorporating mucus interactions and pharmacokinetic parameters were used to examine the effects of spatial and temporal decoupling on the activity of the combination.

Results: Changes in pH and cation concentration altered both individual drug potency and combination effects, consistent with the ionizable nature of niclosamide and membrane-stabilizing roles of divalent cations. Simulated rapid clearance of niclosamide reduced its contribution to synergy, suggesting that the combined effects are time-sensitive. Mucin impaired niclosamide diffusion and diminished combination efficacy, indicating that spatial separation can disrupt synergistic interactions.

Conclusions: Microenvironmental complexity and drug kinetics significantly influence antibiotic synergy. Incorporating physiologically relevant spatial and temporal variables into in vitro models may improve clinical prediction and guide rational design of combination therapies.