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Synergistic Interactions Between Natural Phenolic Compounds and Antibiotics Against Multidrug-Resistant K. pneumoniae: A Pooled Analysis of 216 In Vitro Tests

Ormeneanu VP, Andrei C, Zanfirescu A, Pușcașu C, Olaru OT, Negreș S

Microorganisms, 2025

2 citations

Abstract

The rapid global emergence of multidrug-resistant (MDR) Klebsiella pneumoniae threatens public health, as treatment options remain limited and resistance to last-line antibiotics is rising. Natural phenolic compounds emerge as promising adjuvants to restore antibiotic activity. This study pooled data from 216 in vitro assays evaluating interactions between phenolic compounds and conventional antibiotics against MDR K. pneumoniae. Fractional inhibitory concentration index (FICI) values were analyzed at the individual-test level, and structure-activity relationships were explored using a binary chemotype flagging approach. Overall, synergy was highly context-dependent, varying by both antibiotic class and phenolic chemotype. Polymyxin B combined with resveratrol demonstrated the most consistent and robust synergy (median FICI = 0.25, synergy rate = 96.2%), with no antagonism observed. For carbapenems, meropenem showed strong synergy when paired with flavonoids containing catechol or gallol motifs (e.g., quercetin, kaempferol), whereas curcumin exhibited inconsistent or antagonistic effects. Variability analysis revealed that combinations with low dispersion, such as polymyxin B + resveratrol, offer greater translational potential than high-variability pairs. These findings highlight the structural determinants of synergy and support further preclinical evaluation of select phenolic compounds as adjuvants to conventional antibiotics in the fight against MDR K. pneumoniae.

How strong is the evidence?

Evidence Hub reads this as a secondary / pooled analysis, done in cell cultures (in vitro), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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