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  • Review
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29 April 2026

42 Pages

Quorum Sensing and Quorum Quenching in Pseudomonas aeruginosa and Staphylococcus aureus Infections: Therapeutic Potential, Limitations and Clinical Challenges

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Billennium Spółka Akcyjna, Tadeusza Czackiego 15/17, 00-043 Warsaw, Poland
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Faculty of Medicine, Wroclaw Medical University, 50-376 Wroclaw, Poland
3
Department of Pharmacognosy with Medicinal Plants Garden, Lublin Medical University, 20-093 Lublin, Poland
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German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC), Department of Infectious Diseases, Medical Microbiology and Hygiene, Medical Faculty Heidelberg University, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
Antibiotics2026, 15(5), 447;https://doi.org/10.3390/antibiotics15050447
This article belongs to the Special Issue Limits and Risks of Quorum Quenching in Medicine
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Abstract

Antimicrobial resistance (AMR) represents a major global health threat, largely driven by antibiotic overuse and the protective role of bacterial biofilms. Quorum sensing (QS), a bacterial communication system regulating virulence and biofilm formation, has emerged as a promising therapeutic target. Quorum quenching (QQ), which disrupts QS without directly inhibiting bacterial growth, is considered a potential anti-virulence strategy that may reduce selective pressure for resistance. This review critically evaluates recent advances in QQ research, focusing on its clinical applicability, limitations, and risks. We analyzed studies from the last five years involving natural compounds, synthetic molecules, nanoparticles (NPs), and combination therapies targeting key pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus in models of lung diseases, mainly cystic fibrosis, chronic wounds, burns, and implant-associated infections. While numerous compounds demonstrate significant in vitro anti-biofilm and anti-virulence activity, major challenges remain, including limited in vivo validation, pharmacokinetic constraints, toxicity concerns, microbiome disruption, and the potential development of tolerance or functional resistance. Although QQ offers a promising adjunctive approach to conventional antibiotics, its long-term clinical feasibility requires comprehensive evaluation of evolutionary dynamics, host–microbe interactions, and safety profiles.

1. Introduction

Antimicrobial resistance (AMR), particularly bacterial AMR, is now recognized as one of the most serious global health threats, significantly reducing the effectiveness of infection treatment and prevention strategies. Important insight into the scale of this challenge has been provided by large-scale quantitative analyses, including a systematic study by the Antimicrobial Resistance Collaborators [1], which estimated that AMR accounted for 1.14 million direct deaths in 2021 and may cause 1.91 million deaths annually by 2050 if effective interventions are not implemented [2,3,4].
Table 1 [5] presents the World Health Organisation (WHO) Bacterial Priority Pathogens List (BPPL), including the classification of pathogens into critical, high-, and medium-priority groups, to guide research and development efforts as well as public health interventions. The 2024 WHO BPPL covers 24 pathogens, spanning 15 families of antibiotic-resistant bacterial pathogens. The critical priority category is reserved for pathogens exhibiting resistance to last-resort treatment options, which are distinguished by their high potential for resistance gene transfer, the severity of associated infections, and their substantial global health burden [5].
Table 1. WHO Bacterial Priority Pathogens List, 2024 [5].
This challenge is further exacerbated by the predominant mode of bacterial growth in the host ecosystem, where microorganisms rarely persist as planktonic cells, which are highly susceptible to adverse conditions, but instead tend to form biofilms, i.e., highly organized microbial assemblies enclosed within a self-generated extracellular polymeric substance (EPS) matrix that functions as a protective barrier [6]. In general, biofilms are composed of approximately 10–25% microbial cells and 75–90% EPS, although the exact composition of the matrix may differ depending on the microorganisms involved [7,8].
Biofilms play a pivotal role in amplifying antimicrobial resistance through multiple protective mechanisms, serving as hotspots for genetic exchange and environmental persistence [9]. A high degree of heterogeneity within the biofilm promotes the formation of persister cells—metabolically inactive cells that lack transcription, translation, and proton motive force [10,11]. Biofilms are widely acknowledged as key etiological factors in numerous infections, contributing to approximately 65% to 80% of cases [12]. Their prevalence in medical implants, living and dead tissues makes the infections they cause difficult and costly to treat [13]. Biofilm formation constitutes a critical survival strategy for bacteria and significantly contributes to their virulence. The reduced efficacy of antimicrobial therapy in biofilm-associated infections results from multiple interacting factors that enhance bacterial tolerance and resistance to antibiotics. Consequently, conventional monotherapeutic approaches are often insufficient, underscoring the need for the development of alternative and complementary therapeutic strategies. Promising approaches include bacteriophage-based therapies, combination treatments integrating antibiotics with biofilm-disrupting agents or efflux pump inhibitors, and strategies targeting bacterial communication systems, such as quorum sensing (QS) inhibition through quorum quenching (QQ) [1,9,14].
Because the transition from planktonic cells to a highly structured biofilm is strictly governed by QS-dependent gene expression, this communication network is the primary driver of biofilm maturation. QQ directly counteracts this process by degrading or competitively blocking the signalling molecules required for such coordination. Consequently, the application of QQ effectively halts biofilm formation and destabilizes existing structures, stripping pathogens of their protective shield and restoring their susceptibility to conventional antimicrobials [15].
This review summarizes current knowledge on QS systems and QQ mechanisms as a conceptual basis for the analysis of anti-virulence and anti-biofilm strategies. Particular attention is given to natural QQ compounds, synthetic molecules, nanoparticle (NPs)-based platforms, and combination approaches investigated against clinically relevant biofilm-forming pathogens. The discussion focuses primarily on Pseudomonas aeruginosa and Staphylococcus aureus, which serve as the principal models for evaluating the therapeutic potential, translational limitations, and clinical relevance of QQ-based interventions. Across the manuscript, emphasis is placed not only on synthesizing available evidence, but also on highlighting emerging concepts and potentially valuable future research directions in this field. Overall, this approach is intended to provide both a structured synthesis of current evidence and a broader perspective on promising translational and research avenues for the future development of QQ-based therapies.

2. Quorum Sensing (QS) and Quorum Quenching (QQ)

2.1. Quorum Sensing (QS) Overview

QS is a signalling mechanism that enables microorganisms to adapt to environmental conditions and regulate gene expression in a coordinated manner through small diffusible molecules—Autoinducers (AIs) [16]. AI quantity rises with the increasing population of bacterial cells. These signalling molecules accumulate in the surrounding environment and, once a critical threshold concentration is reached, bind to specific receptor proteins, triggering coordinated changes in gene expression. Through this strategy, many pathogenic bacteria maintain the adaptation to changing environmental conditions by regulating genes involved in biofilm formation, virulence factor production, antibiotic synthesis, and horizontal gene transfer processes such as transformation and conjugation [15].
Recently, the mechanism of QS has been extensively studied due to its technological and medical applications including combating microbial pathogenicity, biofilm-related infections, and abiotic factors and conditions.

2.2. Main QS Systems in Pseudomonas aeruginosa

In Gram-negative bacteria, QS is predominantly mediated by acyl-homoserine lactones (AHLs), classified as AI-1 autoinducers. Figure 1 [17] presents the LuxI/LuxR system, which is considered the canonical AHL-dependent quorum-sensing mechanism in Gram-negative bacteria that was first described in Vibrio fischeri [17].
Figure 1. Acyl-homoserine lactone (AHL)-dependent LuxI/LuxR quorum sensing (QS) pathway in Vibrio fischeri as a species-specific model of Gram-negative bacterial signalling. At low cell density, the autoinducer (AI) N-(3-oxohexanoyl)-L-homoserine lactone (3OC6-HSL; purple dots), synthesized by LuxI, passively diffuses across the cell membrane into the extracellular environment. Under these conditions, expression of the luxICDABEG operon is minimal. As the bacterial population increases, 3OC6-HSL accumulates in the surrounding medium and, upon reaching a threshold concentration, equilibrates across the membrane. Intracellularly, the AI binds to the transcriptional regulator LuxR, forming an active LuxR–AHL complex, which induces transcription of the luxICDABEG operon. This activation triggers the bioluminescence system, resulting in a marked increase in light emission. This model represents a canonical LuxI/LuxR QS system; however, QS system architecture may vary among different Gram-negative bacteria.
Homologues of the LuxI/LuxR-type system have been described in several other Gram-negative bacteria. In P. aeruginosa, however, QS is organized into a more elaborate regulatory network composed of four systems. Among them, LasI/LasR and RhlI/RhlR represent two AHL-dependent signalling circuits that direct the synthesis of N-(3-oxo-dodecanoyl)-L-homoserine lactone (3OC12-HSL) and N-butanoyl-L-homoserine lactone (C4-AHL), respectively [18,19,20]. Beyond these canonical AHL-based modules, P. aeruginosa also employs the non-AHL Pseudomonas quinolone signal (PQS) system and the more recently characterized integrated quorum-sensing signal (IQS) system [21]. Las and Rhl are LuxI/LuxR systems, in which AHLs—3OC12-HSL and C4-AHL—produced by cognate LuxI-type synthases bind and stabilize LuxR-type transcriptional regulators, enabling DNA binding. Subsequently, the LuxR–AHL complexes interact with specific short DNA sequences named lux-boxes, thereby modulating gene expression. Regarding virulence, the Las system controls the production of several proteases, elastase, the siderophores pyoverdine and pyochelin and exotoxin A [22]. The Rhl system mainly regulates the synthesis of rhamnolipids and pyocyanin (PYO) [23]. The third QS system, PQS, is associated with a 2-alkyl-4-(1H)-quinolone (AHQ) and its precursor, 2-heptyl-4-(1H)-quinolone (HHQ). Both molecules activate the transcriptional regulator PqsR enhancing the expression of the pqsABCDE operon, which triggers the production of several virulence factors (mainly PYO and lectins) and biofilm formation. Finally, the fourth putative QS system, IQS, has been described to integrate QS and stress response. Thus, it has been reported that IQS partially takes over the central role of the Las system under phosphate depletion stress conditions [24]. To provide a structured overview of the contribution of quorum sensing to P. aeruginosa pathogenicity, the main QS-regulated virulence determinants are categorized by function in Table 2 [25,26,27,28,29,30].
Table 2. Quorum sensing-controlled virulence factors in P. aeruginosa and their role in pathogenesis.
The significance of QS in human diseases associated with P. aeruginosa has been well established. According to numerous investigations, 90% of P. aeruginosa samples that can cause infections have working HSL systems. In one instance, it has been demonstrated that patients with cystic fibrosis (CF) typically have N-(3-oxododecanoyl)-HSL, the major P. aeruginosa AIs. Polysaccharide alginate, a crucial part of the matrix created by P. aeruginosa strains, is a QS-controlled virulence factor that shields biofilms from macrophage destruction [32].

2.3. Main QS Systems in Staphylococcus aureus

S. aureus infections are particularly problematic due to frequently occurring antibiotic resistance in S. aureus isolates, among which methicillin-resistant S. aureus (MRSA) are the most important clinically [33].
In Gram-positive bacteria, QS is predominantly based on autoinducing peptides (AIPs) derived from the processing of larger precursor proteins. Figure 2 presents the characteristic architecture of a Gram-positive AIP-dependent QS system, which typically includes a membrane-bound histidine kinase receptor and a cognate cytoplasmic response regulator acting at the transcriptional level [34,35].
Figure 2. Autoinducing peptides (AIP)-dependent quorum-sensing (QS) signalling pathway in Gram-positive bacteria [34,35]. Similarly to acyl-homoserine lactone (AHL)-based QS systems, the extracellular concentration of secreted AIP autoinducers rises as bacterial cell density increases. Upon activation, the phosphorylated response regulators function as deoxyribonucleic acid (DNA)-binding transcription factors that control the expression of specific target genes. In some Gram-positive bacteria, such as the agr QS system of Staphylococcus aureus, the genes encoding the autoinducer precursor, the histidine kinase receptor, and the cognate regulatory protein are organized within a single operon, whose transcription is itself induced by QS. This arrangement establishes a positive feedback loop and promotes a rapid shift from low-cell-density (LCD) to high-cell-density (HCD) quorum-dependent gene expression.
In S. aureus, the agr locus represents the best-characterized AIP-based quorum-sensing system. It operates through two promoters, P2 and P3, which direct the synthesis of ribonucleic acid (RNA) II and RNAIII. The RNAII transcript encodes four essential elements: AgrD, the precursor peptide [36], AgrB, responsible for its maturation and export [36], AgrC, the sensor histidine kinase [37], and AgrA, the associated response regulator [38]. After processing by AgrB, the mature cyclic thiolactone-containing peptide is released outside the cell, where it accumulates as the bacterial population expands. Once sufficient extracellular levels are reached, it is recognized by AgrC, resulting in receptor autophosphorylation [39] and subsequent activation of AgrA. Phosphorylated AgrA increases transcription from both agr promoters, strengthening the system through positive feedback, and also promotes RNAIII production from P3 [40]. RNAIII then acts as the principal downstream effector, controlling the expression of numerous virulence-associated factors, including leukocidins, hemolysins, toxic shock syndrome toxin, exoproteases, and lipases.
Beyond the agr system, S. aureus also possesses the luxS/AI-2 pathway [41,42]. This pathway, based on AI-2 generated through LuxS-dependent synthesis of the precursor 4,5-dihydroxy-2,3-pentanedione (DPD), has been implicated mainly in the regulation of biofilm formation, rather than in direct control of classical virulence factors [41,42,43,44,45]. Available data indicate that luxS/AI-2 signalling influences the expression of biofilm-associated regulators, including icaR, icaA, and rbf, thereby modulating PIA-dependent biofilm development [41,42,43,44,45].

2.4. Role of QQ and Its Mechanisms

The persistence of biofilms and the growing problem of antibiotic resistance highlight the need for alternative antimicrobial strategies. One promising approach involves interference with QS, the regulatory system that coordinates microbial communication. Among such strategies, QQ has attracted particular attention, as it disrupts signalling between microbial cells and can reduce or even completely suppress the production of virulence-associated factors, including those involved in biofilm formation [46]. QQ has evolved to control QS processes by obstructing their key steps, such as:
  • Inhibition of signal molecules synthesis;
  • Inactivation or enzymatic degradation of signal molecules;
  • Competing with signal receptor analogues;
  • Blocking the signal transduction cascades.
Unlike antibiotics, which kill bacteria, the QQ process disrupts bacterial communication without damaging the colony, thereby helping to prevent the development of antibacterial resistance. Research into QS and QQ holds significant implications for biotechnology, medicine, and environmental management [6,47].
QQ can interfere with bacterial communication through several mechanisms. The most common involves the enzymatic inactivation of AHL signalling molecules, mediated by enzymes such as lactonases, acylases, reductases, and oxidases. Other strategies include the use of inducer antagonists that block receptor binding, inhibition of signal molecule synthesis (e.g., by interfering with LuxI activity or through kinase inhibitors in Gram-positive bacteria), and disruption of signal transduction pathways, such as inhibition of the AgrA regulator, which ultimately reduces the expression of QS-controlled virulence factors [6].
QQ-active compounds can significantly reduce or even completely inhibit the expression of virulence factors, including biofilm development. Several QQ strategies involve the use of structural analogues of QS AIs, which may occur naturally or be designed and synthesized using chemical engineering techniques. In addition, many well-characterized QQ agents are enzymes capable of degrading signalling molecules [6]. These properties make QQ an increasingly attractive anti-virulence strategy against biofilm-associated infections caused by major bacterial pathogens. At the same time, its successful therapeutic development requires careful recognition of both its potential and its limitations, as well as further research to clarify its efficacy, safety, and long-term clinical applicability.

4. Experimental and Disease Models of QQ in Medicine

4.1. Lung Infections (Cystic Fibrosis Model)

CF is an autosomal recessive disorder resulting from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The CFTR protein functions as an epithelial anion channel responsible for chloride and bicarbonate transport across the membranes of respiratory epithelial cells, thereby playing a central role in maintaining airway homeostasis. Under physiological conditions, proper CFTR activity preserves hydration of the epithelial surface, helps prevent bacterial persistence, and supports correct mucin unfolding in the lungs. By contrast, mutations in the CFTR gene can either impair or completely eliminate the function of the channel, resulting in the accumulation of abnormally thick mucus within the respiratory tract [104]. Chronic pulmonary infections, predominantly driven by antibiotic-resistant pathogens adapted to the unique microenvironment of the CF airway, represent the principal cause of morbidity and mortality in people with CF (pwCF) [105,106].
At present, chronic administration of inhaled antibiotics remains the standard therapeutic approach for controlling persistent bacterial infections in patients with CF. In contrast to oral or intravenous administration, aerosolized delivery enables the achievement of high local drug concentrations within the respiratory tract, thereby optimizing pulmonary pharmacokinetic/pharmacodynamic exposure while minimizing systemic toxicity. Nevertheless, therapeutic efficacy is critically determined by the pathophysiological characteristics of the CF lung microenvironment, which substantially modulate antibiotic deposition, diffusion, and antimicrobial activity following inhalation. CF airway secretions exhibit altered rheological and biochemical properties, including increased viscosity, abnormal mucin organization, and a distinct inflammatory and ionic milieu, compared with healthy airways. In parallel, biofilm formation by CF pathogens and the dense mucus matrix act as major diffusion barriers, limiting antimicrobial penetration and contributing to bacterial persistence. A more detailed understanding of these local determinants is therefore essential for the rational optimization of inhaled antimicrobial regimens directed against CF-associated pathogens [106,107].
Among the pathogens implicated in advanced CF lung disease, P. aeruginosa remains the most prevalent and clinically significant, with the Cystic Fibrosis Foundation Patient Registry reporting its presence in 28.4–43.2% of adult with pwCF [108]. P. aeruginosa is a highly virulent opportunistic pathogen capable of inducing extensive pulmonary damage through the secretion of a broad spectrum of virulence factors [109]. Management of P. aeruginosa infection is primarily based on cyclic or continuous inhaled antibiotic therapy, most commonly with aztreonam, tobramycin, and colistin. Nevertheless, the long-term selective pressure exerted by these regimens promotes the emergence of antibiotic-resistant P. aeruginosa strains, thereby reducing therapeutic effectiveness and contributing to substantial morbidity and mortality [104]. Biofilm-associated growth is a major determinant of P. aeruginosa persistence in the CF airway and is also thought to contribute to the development of pulmonary exacerbations. Limited penetration of antimicrobial agents through the biofilm matrix is considered one of the principal mechanisms responsible for the insufficient efficacy of anti-pseudomonal therapy [107]. Although biofilms are classically described as surface-attached microbial communities forming on damaged tissue or foreign bodies such as urinary catheters, the situation in CF lungs appears distinct. In the lungs of individuals with CF, however, bacteria frequently form self-aggregating clusters suspended within the airway mucus rather than attaching to a solid surface [10,110].
In recent years, multiple experimental studies have evaluated QS inhibitors (QSI) against P. aeruginosa, as summarized in Table 3 [49,50,51,52,58,59,60,61]. Most of these studies have focused on natural compounds. Naturally derived agents are of particular interest in medical research because they are generally biodegradable and often supported by a history of prior biological use, making them attractive candidates for the control of bacterial and fungal infections. However, compared with synthetic compounds, they are frequently characterized by lower potency, reduced stability, and less well-defined pharmacokinetic profiles [21].
Importantly, QS in P. aeruginosa also mediates interspecies interactions that may influence disease progression in CF. Sass et al. demonstrated that P. aeruginosa laboratory isolates can interact synergistically with the antifungal azole voriconazole, resulting in inhibition of biofilm metabolism in several Aspergillus fumigatus laboratory strains; this effect was mediated primarily by pyoverdine, but also by pyocyanin and pyochelin [111]. In addition, PQS has been implicated in intermicrobial communication, ferric iron (Fe3+) chelation, and iron delivery to the bacterial cell membrane in cooperation with siderophores. Under iron-limited conditions, PQS inhibited A. fumigatus biofilm formation, whereas in an iron-rich environment both fungal growth and biofilm formation were significantly enhanced compared with iron-depleted conditions. Notably, this phenotype was not observed in an Aspergillus mutant lacking siderophore expression. These findings suggest that PQS production by P. aeruginosa may promote fungal persistence in CF airways, where iron homeostasis is frequently dysregulated, thereby potentially aggravating disease severity [112].
Mucus viscosity and oxidative stress in cystic fibrosis lungs are both likely to reduce the effectiveness of QQ against P. aeruginosa, although direct CF-specific evidence for many QQ agents remains limited [113]. Garbero et al. showed that in a biomimetic cystic fibrosis mucus model, all tested QS classes (phenazines, lactones and quinolones) molecules were able to cross the mucus barrier, but their diffusion was heterogeneous, ranging from 26% to 64%, with retention governed mainly by chemical interactions with mucins rather than steric filtering alone. Importantly, when the CF mucus layer was integrated with a membrane permeability model, pyocyanin permeability decreased by about 60%, indicating that the pathological mucus environment can selectively limit the transport and bioavailability of bacterial signalling molecules and, by extension, may also hinder the activity of QQ agents [113]. Oxidative stress may further limit the efficacy of QQ approaches in CF by creating a selective environment that favours bacteria with intact or resistant QS-systems. García-Contreras et al. demonstrated that oxidative stress enhances the advantage of strains capable of mounting a robust QS-dependent antioxidant response and, importantly, that exposure to H2O2 together with the QS inhibitor, furanone C-30, increased the proportion of resistant to furanone C-30 mutants of P. aeruginosa, indicating that oxidative stress can promote resistance to QQ [114].
After P. aeruginosa, S. aureus represents the second most frequently encountered pathogen in CF airways. Additional microorganisms implicated in CF lung infection include Burkholderia spp., non-tuberculous mycobacteria (NTM), Stenotrophomonas maltophilia, Achromobacter spp., and fungi such as Aspergillus spp. [10]. To counter resistance and virulence, anti-virulence therapies aim to disarm S. aureus without exerting lethal pressure, thereby reducing resistance selection. QSI targeting the agr system disrupt toxin production and biofilm dispersion, though the clinical translation is challenged by paradoxical biofilm reinforcement in chronic infections [115,116].
Therefore, effective anti-virulence strategies based on QQ should consider the complex genetic background of biofilm formation (e.g., QS genes, EPS extracellular matrix genes, related regulatory networks) and may require combined approaches that target both QS regulation and biofilm structural mechanisms.

4.2. Chronic Wound Infections, Ulcers and Burns

Chronic surgical wound infections (CSWIs) represent a significant burden in clinical practice, leading to prolonged hospitalization, increased healthcare costs, and substantial patient morbidity [117]. CSWIs remain a major clinical challenge, as biofilm formation promotes antimicrobial tolerance and impairs wound healing. Current evidence suggests that biofilm-forming microorganisms are present in 60–80% of chronic wounds, although data from low-resource settings remain limited [118]. Common pathogens such as S. aureus, P. aeruginosa, and E. coli frequently form biofilms, further complicating infection management [119].
P. aeruginosa forms highly impermeable biofilms that are exceptionally difficult to eradicate even with the broad range of therapeutic options available to clinicians, thereby significantly contributing to the chronicity of these wounds due to the frequent re-establishment of biofilms following debridement [120].
The spectrum of bacteria colonizing burn wounds has evolved over time, largely in response to shifts in antibiotic use and emerging resistance patterns. In patients with severe burns, altered tissue pharmacokinetics may lead to suboptimal local antibiotic concentrations, thereby compromising therapeutic efficacy and favouring the selection of resistant strains. As a result, multidrug-resistant (MDR) organisms, including MRSA, Enterococcus spp., Pseudomonas spp., and Acinetobacter spp., have become increasingly prevalent and represent a major challenge in burn wound management [121]. Gram-positive bacteria are typically associated with early burn wound colonization, whereas Gram-negative organisms tend to predominate at later stages of injury. Among Gram-positive pathogens, S. aureus remains the most prevalent globally and is strongly associated with increased morbidity and mortality, particularly in cases of invasive burn wound infection [122].
Antimicrobial wound management may involve several therapeutic modalities, including wound irrigation, gels, and antimicrobial dressings or pads. Their clinical use depends on multiple factors, such as the nature of the active agent, its concentration, antimicrobial spectrum, and cytotoxic potential. Although a range of wound-care products with proven antimicrobial efficacy and relatively low cytotoxicity is currently available, this does not support indiscriminate or prolonged use. Given the limitations of existing agents, particularly restricted exposure time and residual cytotoxicity, there remains a clear need to identify novel and safer therapeutic molecules [123,124,125].
Given the limitations of existing agents, particularly restricted exposure time and residual cytotoxicity, there remains a clear need to identify novel and safer therapeutic molecules [103,104,105], among which QQ represents a promising approach; however, its clinical translation is still constrained by factors such as limited stability, delivery challenges, and variable efficacy in complex infection environments. The chronic wound microenvironment, marked by sustained neutrophil-driven ROS production and elevated metalloproteinase activity, promotes extracellular matrix (ECM) degradation and biofilm formation. As highlighted by studies on chronic wound pathology, this highly inflammatory and protease-rich milieu not only supports microbial persistence but may also limit QQ efficacy—particularly for enzyme-based QQ agents—by promoting their proteolytic degradation and functional inactivation, while simultaneously facilitating the formation of structured, biofilm-based communities that are inherently less susceptible to QS disruption [126].
Nanostructured drug delivery systems may enhance topical antimicrobial therapy in burn wounds by improving local drug availability while reducing systemic exposure. However, their therapeutic performance depends on burn severity, wound infection status, and NP characteristics, whereas optimal formulations should ensure biocompatibility, low toxicity, biodegradability, and controlled drug release [121,127,128].

4.3. Implant-Associated Infections (IAIs)

Orthopedic implant surgeries have revolutionized musculoskeletal care by improving functional recovery and quality of life. However, postoperative infections associated with implants remain one of the most serious complications, leading to prolonged hospitalization, increased healthcare costs, and functional impairment. The global incidence of surgical site infections after orthopedic implant surgery ranges between 1% and 10%, depending on surgical type, patient factors, and infection control measures [129].
Microbiological analysis suggests that S. aureus persists as the primary etiologic agent of post-operative infections [129], followed by P. aeruginosa and E. coli. These findings align with those of Alelign et al. [130] who identified Gram-positive cocci as the dominant group in 42% of orthopedic infections. However, the growing detection of Gram-negative pathogens such as Klebsiella pneumoniae and Acinetobacter baumannii signifies a shift toward nosocomial infections with multidrug-resistant profiles, corroborating findings from Mussab et al. [131], and Elifranji et al. [132].
The pathogenesis of implant-associated infections (IAIs) remains a fundamental challenge when it comes to their effective prevention. Following implantation, the sterile surface is rapidly conditioned by host proteins; while this process is essential for tissue integration, these proteins simultaneously serve as ligands for bacterial adhesins. Although initial bacterial attachment is reversible, it typically transitions into biofilm formation within hours. The subsequent secretion of an EPS stabilizes this structure, providing the biofilm with significant resistance to both host immune defences and systemic antibiotics. As the biofilm matures and localized nutrient sources become depleted, microbial cells begin to disperse, facilitating the colonization of adjacent tissues and the further spread of the infection [133,134].
A significant limitation of current strategies for minimizing IAIs is that they do not directly address the implant surface, which remains the primary site for bacterial colonization and biofilm development. To address this, antimicrobial implant coatings have been engineered to help prevent and treat these infections [135]. These technologies function through distinct mechanisms: passive surface modifications alter the physico-chemical properties of the implant to inhibit bacterial adhesion without agent release, whereas active surface modifications incorporate pharmacologically active compounds, such as silver (0.33–2.89 g silver on the surface of titanium prostheses) or iodine (10–12 μg/cm2), to exert direct antimicrobial effects. Despite their theoretical potential, the clinical efficacy of these coatings remains poorly defined. A critical gap in current research is the lack of comparative studies between different coating materials [136]. Existing data are largely fragmented across small, single-centre trials, with very few head-to-head assessments available. Consequently, there is insufficient evidence to provide clear guidance for implant selection, particularly when attempting to balance infection prevention against concerns regarding biocompatibility and cost [134].
Fong et al. [137] demonstrated that disulfide bond-containing ajoene analogues effectively attenuate Pseudomonas aeruginosa infection in a murine IAIs model. The study showed that treatment with these QSI significantly reduced bacterial virulence in vivo, without directly targeting bacterial viability. This effect was linked to the suppression of QS-regulated factors such as elastase, rhamnolipids, and pyocyanin, which are essential for biofilm formation and persistence on implanted materials. These findings highlight QSI as a promising strategy for controlling biofilm-related infections on medical devices [137].
In a similar context, QS-targeted interventions have also been explored in Gram-positive pathogens involved in IAIs. Brady et al. [138] demonstrated that inhibition of QS in S. aureus, particularly via the RNAIII-inhibiting peptide (RIP), can reduce biofilm formation on orthopedic implants. This approach interferes with agr signalling, limiting virulence expression and bacterial attachment, which are critical steps in IAIs development [138].
Beyond soluble QS inhibitors, another important direction involves the development of anti-infective biomaterial surfaces with intrinsic QQ properties. Vogel et al. [139] reported the immobilization of the QQ enzyme PvdQ—an N-terminal nucleophile acylase encoded within the pyoverdine (pvd) gene cluster—onto polydimethylsiloxane (PDMS) silicone surfaces. The resulting functionalized material retained QQ activity and significantly limited bacterial colonization on indwelling medical devices, including urinary and intravascular catheters [139].

5. Clinical and Translational Risks of Quorum Quenching (QQ)

Despite the promising anti-virulence potential of QQ strategies, their clinical translation remains constrained by several unresolved biological, ecological, and evolutionary uncertainties. In particular, the risks of tolerance development and unintended disruption of host-associated microbiota underscore the need for comprehensive investigation before widespread therapeutic implementation.

5.1. Risk of Developing Tolerance and Functional Resistance

Bacteria have developed a wide range of molecular mechanisms to survive in extreme conditions, from nutrient limitation to environmental factors. Unlike antibiotics, QSIs act by interfering with QS-mediated gene expression controlling virulence rather than bacterial growth and are therefore considered to impose lower selective pressure for bacterial resistance development. However, resistance to QS inhibition may still emerge. The genetic plasticity of microorganisms and their ability to acquire resistance genes through horizontal gene transfer could hinder the long-term effectiveness of QSI [140]. Nonetheless, this area remains relatively understudied, with only limited reports describing potential QQ-related resistance mechanisms [141]. Maeda et al. [142] were among the first to demonstrate experimentally that bacteria can evolve resistance to QQ. Using P. aeruginosa as a model organism, the authors tested a widely used QQ compound—the synthetic brominated furanone 4-bromo-5-(bromomethylene)-2(5H)-furanone, known as C-30. In transposon mutants, resistance was associated with mutations in mexR and nalC, which encode repressors of the MexAB–OprM resistance operon [143]. These mutations enhanced efflux of the QQ compound, reducing its ability to inhibit several QS-regulated virulence factors and pathogenicity. Consistent with this, C-30 showed diminished efficacy in mexR mutants. Critically, clinical isolates from CF patients (Liverpool epidemic strain 12142) also carried mexR/nalC mutations, suggesting that even in the absence of the QS inhibitor, cells may naturally evolve resistance to QQ compounds in the pathogenic state when confronted with the pressures of antibiotic treatment, and due to standing genetic variation. Hence, antibiotic treatment may incidentally reduce susceptibility to QQ compounds. However, spontaneous mutants with intact mexR and nalC genes were also identified, indicating that additional, yet uncharacterized resistance mechanisms may exist. Together, these findings demonstrate that resistance to QQ compounds may arise through different mechanisms and that these mutations may occur in a clinical setting, although their generality remains unclear [142].
Insights from the P. aeruginosa model and related studies [24] suggest that bacteria may evolve resistance to QS-targeting strategies through several potential mechanisms, including:
  • Mutations in LuxR and LysR-like receptors that enhance their affinity for AIs thereby lowering the required AIs concentration threshold.
  • Increased AIs production to ensure sufficient signal availability for QS activation.
  • Synthesis of modified AIs to become less susceptible to different QSIs.
Testing these predictions using traditional microbiology methodologies is challenging. For example, quantifying resistance to QQ requires isolating resistant organisms from a mixed population. However, unlike conventional antibiotics—where only resistant mutants survive and are easily identified—both resistant and sensitive organisms may continue to grow in the presence of QQ molecules [144].
To overcome this obstacle, Beckmann et al. [144] used the Avida system to simulate the evolution of digital organisms [145]. These are self-replicating computer programmes subject to mutation and natural selection within a computational environment. Their results demonstrate that resistance to QQ can evolve, at least in silico. While signal-receiving-impaired mutants initially disrupted QS effectively, populations exposed to increasing levels of such mutants developed resistance over time. This occurred via a shift in quorum threshold, with organisms triggering quorum at lower population densities than the original strain. Notably, the highest resistance levels evolved under gradually increasing mutant introduction rates, indicating that sustained selective pressure can drive adaptive responses [144].
According to Anguige et al. [146] QQ treatment is only effective below a critical biofilm depth, making drug penetrability a key factor in the design of antivirulence compounds. Resistance to QQ in biofilms may therefore arise through reduced permeability, for example via overproduction of ECM components that sequester QQ agents, such as ndvB-encoded glucans known to bind aminoglycosides [147]. Other theoretical studies show that, for biofilms, the timing of QQ treatment is critical for effective prevention of QS-mediated virulence [148].
Defoirdt [149] reports that resistance to QS inhibition appears to spread more slowly during host infection than resistance to conventional antibiotics. However, the selective dynamics of QS are highly context-dependent, particularly regarding whether QS regulates “private goods” (benefitting only the producing cell) or “public goods” (benefitting the broader population). In vitro studies have demonstrated that resistance can spread when growth depends on QS-controlled private goods, but it may be constrained when public goods are involved, as “sensitive cheater” cells exploit resistant producers [149]. Crucially, recent in vivo experiments using Vibrio campbellii across multiple host infection cycles showed that resistance to QSI did not spread when initially rare. Although it could reach fixation when initially common, its spread was approximately tenfold slower than that of antibiotic resistance [150]. These findings suggest that while QSI is not evolution-proof, it exerts weaker selective pressure than conventional antibiotics during infection, supporting its potential as a more sustainable therapeutic strategy. Nonetheless, caution is warranted, particularly in pathogens harbouring broad-spectrum resistance mechanisms such as multidrug efflux pumps, which may confer cross-resistance to certain QSI compounds. Additional research across diverse pathogen-host systems, particularly in the presence of natural microbiota, is required to assess the generalizability of these results [151].
Although competition experiments between QS-deficient and QS-proficient strains have provided insight into the potential selection of QQ resistance, further studies are required, particularly those involving quenching agents under conditions that better mimic in vivo environments. Current QQ compounds present notable limitations: highly specific inhibitors often display modest activity, whereas more potent molecules may exhibit significant toxicity. In contrast, enzymes such as acylases and lactonases, which degrade homoserine lactone signals, appear both specific and effective, making them promising tools for experimental investigation [152].
Current evidence highlights QSIs as an important and conceptually attractive antivirulence approach, particularly in the broader effort to develop anti-infective therapies that do not rely exclusively on direct bactericidal or bacteriostatic activity. Because they target virulence regulation rather than growth itself, they are generally associated with lower selective pressure than conventional antibiotics, and resistance appears to spread more slowly and less uniformly. This gives QSIs particular significance as potential components of more sustainable therapeutic strategies. However, the true clinical implications of these observations remain incompletely understood, as available evidence is still limited by the small number of experimental systems, the predominance of model-organism data, and the context-dependent nature of QS regulation across infection settings.

5.2. Impact on Host Microbiota

The human body is colonized by diverse microorganisms across its surfaces and in cavities connected to the external environment [153]. These permanent and transient inhabitants form complex interspecies interactions, collectively constituting the microbiome.
The microbiome is a dynamic community of microorganisms interacting closely with the host and environment, significantly influencing physiological processes, health outcomes, and treatment responses [154,155]. Advances in high-throughput sequencing technologies have enabled detailed characterization and functional profiles of these communities, providing deeper insight into their interactions with host organs and tissues [156].
In these microbial communities, QS facilitates both intra- and interspecies communication via signalling molecules known as AIs. This system orchestrates collective actions, such as biofilm development and pathogenic virulence. Crucially, these QS networks are not exclusive to pathogens; rather, they are pervasive throughout diverse microbial ecosystems, actively engaging both commensal and beneficial microorganisms [157].
Consequently, QQ strategies, while targeting pathogenic QS systems, may also interfere with communication within the host microbiota, although the effect of QQ on the microbiota is not yet fully known [157,158]. Such disruption could alter microbial community structure and function, potentially leading to dysbiosis and unintended clinical consequences [158]. Therefore, to develop effective therapeutic interventions, it is necessary to ensure that such therapies are target-specific, and not broad in their actions, which underscores the need for deeper understanding of QS regulation and interactions in specific environments [157].
A comprehensive understanding of the impact of QQ on the human microbiota remains limited by the lack of integrated data. This includes detailed knowledge of microbial composition, interspecies interactions, QS signalling molecules, and the functional effects of these communities on host health and surrounding tissues. Such complexity calls for molecule-specific investigation, making research in this area inherently long-term and resource-intensive [158].

6. Methods

A comprehensive literature search was conducted using databases such as PubMed, Google Scholar, and Scopus, focusing primarily on studies published between 2021 and 2026; however, earlier pivotal publications were also included when deemed relevant. Core search terms included combinations of: (“quorum sensing” OR “quorum quenching”) AND (“biofilm” OR “antimicrobial resistance”); (“quorum sensing” OR “quorum quenching”) AND (“Pseudomonas aeruginosa” OR “Staphylococcus aureus”); (“biofilm inhibition” AND “quorum sensing”) AND (“Pseudomonas aeruginosa” OR “Staphylococcus aureus”). While advanced search methods prioritized literature from the past five years, this review analyses research spanning from 1995 to 2026. Inclusion criteria: (1) peer-reviewed original research articles, (2) systematic and narrative reviews published in high-impact journals, (3) studies focusing on P. aeruginosa and S. aureus as model organisms, and (4) articles published in English. Exclusion criteria: (1) non-peer-reviewed reports, (2) conference abstracts without full-text availability, and (3) predatory or low-quality publications.

7. Conclusions and Final Research Perspectives

AMR continues to undermine the effectiveness of conventional antibiotics and represents one of the most pressing global health challenges, particularly in the context of chronic and biofilm-associated infections. In this context, strategies targeting bacterial virulence rather than viability have attracted increasing attention. This review critically evaluated the current state of QQ research, with a primary focus on P. aeruginosa and S. aureus, two clinically relevant biofilm-forming pathogens that serve as major models for studying QS-driven virulence and its therapeutic targeting. Table 7 provides a comparative summary of the principal QQ strategies, with emphasis on their potency, target specificity, stability, bioavailability, safety profile, cost, and potential for clinical translation.
Table 7. Comparative overview of quorum-quenching (QQ) strategies: potency, specificity, stability, bioavailability, toxicity, cost, and translational potential.
QS plays a critical role in coordinating virulence and bacterial behaviours, including biofilm formation, toxin production, motility, and immune evasion. Interference with this communication system through QQ has emerged as a conceptually attractive anti-virulence approach. Unlike traditional antibiotics, QQ strategies aim to attenuate pathogenicity without directly inhibiting bacterial growth, potentially reducing selective pressure for resistance development. Synergistic regimens combining QQ agents with antibiotics, efflux pump inhibitors, or biofilm-disrupting compounds warrant systematic evaluation. In this context, QQ should not be viewed as a replacement for antibiotics but rather as an adjunctive strategy integrated into multimodal treatment protocols. AI and ML-based screening platforms may accelerate the identification of potent and selective QS inhibitors while reducing R&D costs. Rational drug design, supported by structural biology and molecular modelling, can facilitate the optimization of receptor-specific inhibitors with improved stability and minimized off-target effects. NP-based delivery systems offer additional advantages by enhancing targeted drug delivery, improving biofilm penetration, and enabling controlled release profiles. Enzyme-based QQ strategies and monoclonal antibodies directed against signalling molecules represent further innovative modalities that merit exploration.
Long-term experimental evolution studies are needed to assess whether and how bacteria adapt to sustained QS disruption. The majority of available data originate from simplified experimental systems that do not fully recapitulate the complexity of human infections. Biofilms formed in vivo are structurally heterogeneous, influenced by host immunity, nutrient availability, oxygen gradients, and polymicrobial interactions. Consequently, the therapeutic efficacy observed under controlled laboratory conditions may not directly translate to clinical benefit.
Several limitations must be addressed before QQ can be considered a viable therapeutic strategy. The pharmacokinetic and pharmacodynamic properties of many QQ agents remain poorly defined, particularly with respect to bioavailability, tissue penetration, metabolic stability, and dosing. These issues are especially relevant in conditions such as cystic fibrosis, where preventing biofilm formation could be used at early stage of infection. In addition, the long-term consequences of QS interference remain uncertain, as QQ may still promote adaptive responses, including compensatory mutations or activation of alternative virulence pathways, particularly in pathogens with complex QS networks such as P. aeruginosa.
Another important concern is the potential impact of QQ on the host microbiome. Because QS is also used by commensal bacteria, non-selective interference may disturb beneficial microbial communities. Furthermore, current regulatory frameworks are not well suited to anti-virulence therapies, as conventional clinical endpoints focus mainly on bacterial eradication or mortality rather than reductions in virulence, inflammation, or recurrence.
Standardization of in vivo biofilm models that more accurately mimic human disease is critical for generating reproducible and clinically relevant data. Pharmacokinetic analyses, toxicity assessments, and microbiome monitoring must become integral components of preclinical evaluation. Clinical translation will require carefully designed pilot trials in well-defined patient populations, such as individuals with chronic lung infections, IAIs, or complex wound biofilms. Additionally, interdisciplinary collaboration among microbiologists, clinicians, pharmacologists, bioengineers, and regulatory experts will be necessary to establish realistic development pathways for anti-virulence therapeutics.
AI and ML are transforming efforts to combat AMR by enabling the analysis of large-scale medical, pharmacological, and genomic datasets. These technologies support early prediction of resistant strains and facilitate the development of targeted therapeutic strategies. In clinical settings, AI-driven diagnostic tools improve the speed and accuracy of pathogen identification, thereby reducing antibiotic misuse and optimizing patient care pathways [160].
Beyond diagnostics, AI plays a critical role in accelerating the drug discovery pipeline, particularly through in silico screening of large chemical libraries. For example, a neural network-based study screened approximately 7500 compounds, leading to the identification of abaucin, a molecule effective against Acinetobacter baumannii infections [161]. Such approaches also enable the efficient screening of underutilized chemical space, including natural product databases. With over 300,000 compounds in SciFinder yet to be tested for antibacterial activity, computational methods allow rapid identification of candidates capable of targeting essential bacterial enzymes. Compared to traditional experimental techniques, these methods are more cost- and time-efficient, enabling large-scale analyses within days. However, their applicability remains limited to targets with available three-dimensional structural data in public databases [162]. Furthermore, AI- particularly through ML and deep learning techniques is instrumental not only in the design of novel antibiotics but also in enhancing the efficacy of existing drugs through the development of synergistic combinations [163]. Despite these advances, important challenges remain. The use of AI in healthcare raises ethical concerns related to the handling of sensitive genetic and clinical data, emphasizing the need for rigorous data collection and strict adherence to informed consent protocols [164].
An alternative strategy is drug repurposing, also referred to as drug reprofiling or repositioning. This development approach involves identifying novel pharmacological applications for already approved pharmaceuticals beyond their original intended use. By leveraging existing drugs, developers can bypass the initial stages of the drug discovery pipeline, leading to substantial savings in both time and costs. Furthermore, many pharmaceutical agents possess secondary mechanisms of action that remain partially characterized, enabling them to function effectively against various bacteria, either through direct antibacterial activity or by acting as inhibitors of bacterial virulence [80].
Taken together, the available evidence suggests that QQ are a potentially valuable supportive strategy, especially when combined with existing antimicrobial therapies. Its future clinical relevance will depend on further mechanistic studies, the use of standardized and clinically meaningful infection models, the development of more effective delivery systems, and careful evaluation of safety, including possible effects on the host microbiome. Well-designed translational and early clinical studies will also be necessary to determine whether the promising results observed so far can be translated into real therapeutic benefit. Ultimately, only a balanced and evidence-based approach that takes into account both the opportunities and the current limitations of QQ will show whether it can become a truly useful option in the treatment of biofilm-associated infections.

Author Contributions

Conceptualization, E.N. and E.P.; methodology, E.N.; software, M.K.; validation, K.P., M.K., B.C. and E.P.; formal analysis, M.K.; investigation, K.P.; resources, E.N. and K.P.; data curation, E.P.; writing—original draft preparation, E.N., M.K., K.P. and E.P.; writing—review and editing, E.P., J.W. and B.C.; visualization, M.K.; supervision, E.P., J.W. and K.P.; project administration, E.N. and E.P.; funding acquisition, E.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

Author Emilia Nowak was employed by the company Billennium Spółka Akcyjna. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
3OC12-HSLN-(3-oxododecanoyl)-L-homoserine lactone
3OC6-HSLN-(3-oxohexanoyl)-L-homoserine lactone
ABRAntibiotic Resistance
AHQ2-alkyl-4-(1H)-quinolone
AHLN-acyl Homoserine Lactone
AIArtificial Intelligence
AIsAutoinducers
AIPsAutoinducing Peptides
AMRAntimicrobial Resistance
ASAAcetylsalicylic Acid
BPPLBacterial Priority Pathogens List
C-304-bromo-5-(bromomethylene)-2(5H)-furanone
C4-AHLN-butanoyl-L-homoserine lactone
CABHLN-cyclohexanoyl-L-homoserine lactone
CADDcomputer-aided drug design
C. elegansCaenorhabditis elegans
CFCystic Fibrosis
CFTRCystic Fibrosis Transmembrane Conductance Regulator
CFUColony-forming units
COPDChronic Obstructive Pulmonary Disease
Co3O4Cobalt(II,III) oxide
CSWIsChronic Surgical Wound Infections
CYPCytochrome P450
DINCCN′-dodecyl-N-isonicotinyl chitosan chloride
DMDACN′,N′-dimethyl-N′-dodecyl-ammonium chloride-N-amino-acetyl chitosan
DMDBCN′,N′-dimethyl-N′-dodecyl-ammonium chloride-N-benzoyl chitosan
DNADeoxyribonucleic Acid
eDNAextracellular DNA
ECMextracelullar matrix
EPSExtracellular Matrix
ExoSExoenzyme S
ExoTExoenzyme T
ExoUExoenzyme U
ExoYExoenzyme Y
FABHLN-(4-{4-fluoroanilino}butanoyl)-L-homoserine lactone
FDAFood and Drug Administration
FRETFluorescence resonance energy transfer
FtsZFilamenting temperature-sensitive mutant Z protein
GLASSGlobal Antimicrobial Resistance and Use Surveillance System
HCDhigh-cell-density
HHQ2-heptyl-4-(1H)-quinolone
IAIsImplant-Associated Infections
IC50Half maximal inhibitory concentration
IQSIntegrated QS Signal
IQRInterquartile Range
LCDlow-cell-density
LPSLipopolysaccharide
LuxIAutoinducer synthase (LuxI-type enzyme)
LuxRTranscriptional regulator (LuxR-type receptor)
MabsMonoclonal Antibodies
MDRMultidrug Resistance
MDR PAMulti Drug Resistant Pseudomonas aeruginosa
MICMinimum Inhibitory Concentration
MLMachine Learning
MRSAMethicillin-Resistant Staphylococcus aureus
NPsNanoparticles
NTMNon-Tuberculous Mycobacteria
P. aeruginosaPseudomonas aeruginosa
PQSPseudomonas Quinolone Signal
PYOPyocyanin
pwCFpeople with Cystic Fibrosis
QQQuorum Quenching
QSQuorum Sensing
QSIQuorum Sensing Inhibitors
R&DResearch and Development
RIPRNAIII-inhibiting peptide
ROSReactive Oxygen Species
RRRifampicin-Resistant
S. aureusStaphylococcus aureus
SDStandard Deviation
SpAStaphylococcal protein A
SqNPQualenyl hydrogen sulfate nanoparticle
SrtASortase A
SSIsSurgical Site Infections
VRSAVancomycin resistant Staphylococcus aureus
TACN1,4,7-triazacyclononane
T6SSType VI secretion system
Ti02Titanium dioxide
WHOWorld Health Organization
ZnOZinc Oxide

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