Optimal Effective Concentration Combinations and synergy evaluations for binary antimicrobial combinations

Background: This study provides a comparative analysis of Optimal Effective Concentration Combinations (OPECCs) and synergy evaluations derived from the Loewe additivity and Bliss independence models for binary antimicrobial combinations Methods: Binary combinations of Benzalkonium chloride, Chlorhexidine, Cetylpyridinium chloride, and Ciprofloxacin were tested against Results: All OPECCs were determinable for each binary combination pair. For each binary concentration component, the OPECC lay below the respective minimum effective concentration in single application. The synergy scores obtained with both models ranged from -13.4 (antagonistic) to 11.2 (synergistic), with consistently higher scores for the Bliss model. However, the concentration pairs at maximum synergy, determined using the respective matrices, showed inconsistent antibacterial assessments. No pattern could be derived regarding the antibacterial effect of these concentrations in relation to the OPECCs, nor between the two synergy models. The general synergy score of a combination also does not inevitably reflect the results at effective concentrations. Conclusion: The comparison demonstrated that the assumptions like "additivity" or "independence" underlying these models can result in concentration pairs at maximum synergy that may not necessarily be effective. As a consequence, the synergy evaluation methods tested do not account for the effectiveness of the assessed concentration pairs. In contrast, the model-independent OPECC method identifies effective concentration combinations directly from experimental data, without reliance on interaction assumptions or further data processing. The separating curve is based on directly measured optical density (OD) values of the binary concentration combinations, thus representing the real situation. By offering an alternative or complementary approach to existing models, the OPECC method may support more accurate identification of effective antimicrobial combinations and provide valuable insights for the development of optimized treatment strategies in the context of rising antimicrobial resistance.