Antibiofilm and Quorum-sensing Agents Against Pseudomonas aeruginosa and Staphylococcus aureus: a Systematic Review of Preclinical Studies, 2022-2026

1. Department of Medical Laboratory Sciences, Faculty of Science, Al-Balqa Applied University, Al-Salt, Jordan.

1. INTRODUCTION

Bacterial biofilms are surface-attached communities embedded in a self-produced matrix, and they tolerate antibiotics and host defences far better than planktonic cells (Flemming et al., 2016). Pseudomonas aeruginosa and Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA), are two of the most important biofilm formers in chronic wounds, cystic fibrosis airways, implanted devices and hospital-acquired infections, and both are on the World Health Organization's priority list of resistant pathogens (WHO Advisory Group, 2025). They are also frequently found together in polymicrobial wound infections (Hotterbeekx et al., 2017).

Quorum sensing (QS) is the cell-density-dependent signalling that coordinates biofilm development and virulence. P. aeruginosa uses the Las, Rhl and PQS systems, whereas S. aureus relies on the accessory gene regulator (agr) system (Le & Otto, 2015; Lee & Zhang, 2015). QS does not act on biofilm in the same direction in the two organisms: in S. aureus, activation of agr drives extracellular proteases and phenol-soluble modulins that can disperse biofilms (Boles & Horswill, 2008). Down-regulating agr lowers the production of agr-regulated toxins but can favour biofilm: agr-negative strains formed biofilm far more often than agr-positive strains, and agr-blocking compounds increased biofilm formation (Vuong et al., 2000). An agr inhibitor would therefore be expected to stabilise, not reduce, an established biofilm. Blocking QS, rather than killing the cell, is a long-standing antivirulence strategy that is expected to exert less selective pressure for resistance (Mühlen & Dersch, 2016).

A large preclinical literature now reports plant extracts, essential oils, nanoparticles, peptides, enzymes, repurposed drugs and synthetic molecules as antibiofilm or anti-QS agents against these two pathogens. Its quality is uneven. Many papers claim QS inhibition from indirect readouts such as pyocyanin or swarming, concentrations at or above the minimum inhibitory concentration (MIC) confound antibiofilm effects with killing, and docking scores are used as if they were evidence of activity. What a given concentration means also differs between classes (a crude extract is a mixture and a nanoparticle formulation is a delivery system), so the studies cannot be read under one set of pharmacokinetic or pharmacodynamic assumptions. Earlier reviews tend to cover one agent class or one pathogen, and few separate studies that measured QS directly from those that inferred it (Shariati et al., 2024).

We therefore did a systematic review of preclinical studies published from 2022 onward that tested an exogenous antibiofilm or anti-QS agent against P. aeruginosa or S. aureus/MRSA. Our aims were to (1) describe the agents, models and outcomes, (2) grade each study by how directly it tested QS, and (3) appraise risk of bias in the in vitro and animal evidence.

2. MATERIALS AND METHODS

2.1. Protocol and reporting

The review follows the PRISMA 2020 statement (Page et al., 2021). The databases were last searched in August 2026.

2.2. Eligibility criteria

We included preclinical in vitro and in vivo studies published in English from 2022 onward that tested an exogenous antibiofilm or anti-quorum-sensing agent (natural products, synthetic compounds, nanoparticles, peptides and enzymes, phages, or repurposed drugs) and that named Pseudomonas aeruginosa or Staphylococcus aureus/MRSA in the title. We required the organism in the title to keep each study clearly about one of the two organisms. We did not validate this rule against abstracts or indexed keywords; to show its effect, we audited the titles of the records it removed (Section 3.1). We excluded human trials, narrative and systematic reviews, editorials and correction notices, retracted articles, studies of native genes or regulators without an exogenous agent (knockout-only), and purely in silico studies. Because the protocol was not registered, no criteria were fixed in advance in a public record. The restriction to studies published from 2022 onward and naming either organism in the title was decided after the initial search and is therefore a post hoc criterion.

2.3. Information sources and selection

PubMed and Scopus were searched (full strategies in Supplementary Table S1). The PubMed query combined antibiofilm terms, quorum-sensing terms and biofilm terms and excluded clinical trials, randomised controlled trials and records indexed to humans. The Scopus query used the same three concept groups in title, abstract and keywords and excluded clinical-trial terms and the Medicine subject area. Cochrane CENTRAL, which mainly indexes human trials, was also searched at first; its 2 records were not eligible and it was then removed from the review, so they are not counted. Title and abstract screening judged topic only; the restrictions to 2022 onward and to the two target organisms in the title were applied afterwards, when records were assessed for eligibility. The searches had no date limit, which is why records published before 2022 were retrieved and then excluded (Section 3.1). Duplicates were removed across databases before loading (892 records) and within the screening log by DOI, exact-title and fuzzy-title matching (1 record). Records were screened by title and abstract, and the full texts of the remaining records were assessed against the criteria. Records whose full text could not be obtained after searching PubMed Central, Unpaywall and Zotero and attempting retrieval through institutional access were excluded and are counted separately in the flow diagram.

2.4. Data extraction

For each study we extracted the country, study type, agent and class, target pathogen and strain, model system, concentrations, exposure time, biofilm and QS measures and results, in vivo outcomes, cytotoxicity testing and funding. Extraction was done by the authors, and was verified against the full text by two reviewers. Where a paper contradicted itself, we used the value in the Results or the figure and recorded the discrepancy in the workbook.

Each study was assigned one primary agent class, so that the classes are mutually exclusive and sum to the number of included studies. The rule was applied in this order: nanoparticle (including green-synthesised and plant-mediated nanoparticles, and carriers loaded with an antibiotic or plant compound), peptide or enzyme, phage, probiotic or postbiotic, then combination (two or more agents of different classes tested together, or compared, as the study intervention), then repurposed drug, natural product and synthetic or novel compound. The first rule that applied decided the class. Development stage was not used for classification.

2.5. QS evidence tiers

Each study was assigned a QS evidence tier according to what the authors themselves measured. Direct: a QS-specific assay in the target pathogen, such as AHL or autoinducer measurement, QS-gene expression, or a P. aeruginosa or S. aureus reporter strain; AHL degradation counted as Direct only when the pathogen's own AHL or QS read-out was measured, and otherwise as Surrogate reporter. Surrogate reporter: the only QS assay was run in a heterologous organism, such as violacein inhibition in Chromobacterium violaceum or an Agrobacterium tumefaciens AHL biosensor; a study that also had a Direct assay was classed as Direct. Indirect: QS-regulated phenotypes only (for example pyocyanin, protease or swarming), or a QS claim without an assay. In silico only: docking or simulation with no wet-lab QS data. Not assessed: QS was not tested. The Surrogate reporter tier was added after the first extraction round; 27 studies first coded Direct were moved into it on the basis of the extracted QS fields.

2.6. Risk of bias

In vitro studies were appraised with a nine-item checklist that we built for this review (research question, strain characterisation, controls, standard assay, dose range, statistics, replication, cytotoxicity data, reproducibility). It is not a published or validated instrument and has no validation history; its items follow general preclinical reporting-quality criteria. Each item was scored yes, no or unclear using the operational definitions in Table S3. A study was rated high risk of bias if the control item was scored no or three or more items were scored no, low risk if at least eight items were scored yes, controls were yes and neither statistics nor replication was scored no, and moderate risk otherwise. Vertebrate animal studies (mouse, rat, rabbit or zebrafish) with an infection or efficacy outcome were appraised with SYRCLE's risk-of-bias tool (Hooijmans et al., 2014). SYRCLE was designed for laboratory vertebrates, and items such as random housing do not apply to nematode or insect-larva assays, so studies with invertebrate hosts only (C. elegans, G. mellonella, Drosophila) were not rated with it. Studies that used a vertebrate together with an invertebrate were appraised on the vertebrate component. Toxicity-only animal studies were not appraised. Ratings were not used to exclude studies. The ratings came from a rule-based first pass (Table S3). The scanned was run for the full text for 177 of the 252 in vitro appraisals and for 22 of the 30 SYRCLE appraisals. For the other 75 in vitro appraisals and 8 SYRCLE appraisals, it was run on the extraction row only, before the full texts were retrieved. Their items for statistics, replication and reproducibility were therefore scored unclear (item 5 was scored from the extraction row and item 8 from the coded cytotoxicity flag), and the in vitro studies were rated moderate by the rule. The two reviewers checked all ratings, including these, against the full texts.

2.7. Verification and synthesis

Two reviewers independently checked the extraction, the tiers and the ratings against the full texts, and disagreements were settled by discussion. Because outcomes were reported in different units (percent, fold change, log reduction) and with different designs, no meta-analysis was done; results are summarised descriptively as counts of studies. Docking scores were not compared across studies because the scoring programs differ. We did not model pharmacokinetics or pharmacodynamics, and agents from different classes were not assumed to be comparable at a given concentration; results are therefore described by class. A sensitivity analysis repeated the main summaries for studies with Direct QS evidence.

3. RESULTS

3.1. Study selection

The searches identified 3,349 records. After 893 duplicates were removed, 2,456 records were screened and 520 were excluded on title and abstract. Of 1,936 records assessed for eligibility, 1,684 were excluded: 774 because the target organism was not in the title, 740 because they were published before 2022, 145 because the full text could not be obtained, 18 because the target organism was a different species and 7 for other reasons (including one physical intervention that was not an exogenous agent). Because the searches had no date limit, the pre-2022 records were retrieved and removed at this stage. In total, 252 studies were included (Figure 1). A keyword audit of the 774 titles removed by the organism rule found that about 290 named another organism, 59 used broad terms such as ESKAPE, Gram-negative or drug-resistant bacteria, and the remainder named no organism or one not on our keyword list. Only nine titles contained a target-genus word or abbreviation: five named S. epidermidis, three named staphylococci without a species, and one was a peptide name. We did not read the abstracts of the removed records, so the number of eligible studies missed by the title rule is unknown.

Figure 1. PRISMA 2020 flow diagram: 3,349 records identified, 893 duplicates removed, 2,456 screened, 1,936 full texts assessed, and 252 studies included.

3.2. Characteristics of the included studies

Publication was steady from 2022 to 2025 (50, 47, 53 and 63 studies) with 39 studies already published in 2026. The studies came from 80 country labels, led by China (40 studies), India (39), Iran (23), Egypt (11) and South Korea (10). Most studies were in vitro only; 92 (37%) added docking or simulation, 50 (20%) used an animal host (32 vertebrate, 18 invertebrate), 13 used a host-cell, tissue or plant infection model and 6 tested a food, device or material application. Mice were the most frequent animal host, followed by Caenorhabditis elegans, Galleria mellonella larvae and rats. Mammalian-cell cytotoxicity or haemolysis was tested in 66 studies (26%) and not in 186.

P. aeruginosa was the target in 163 studies, S. aureus/MRSA in 72 and both species in 17. Natural products were the largest agent class (115 studies), followed by nanoparticles (39), synthetic or novel compounds (37), peptides and enzymes (21), repurposed drugs (18), multi-class combinations (15), probiotics and postbiotics (5) and phages (2).

3.3. QS evidence

A Direct QS assay, that is one run in the target pathogen itself, was present in 140 of 252 studies (56%). A further 27 (11%) used only a surrogate reporter such as violacein inhibition in Chromobacterium violaceum, QS-regulated phenotypes only were reported in 35 (14%), docking or simulation only in 14 (6%), and QS was not assessed in 36 (14%). Direct evidence was more common for P. aeruginosa (58%) than for S. aureus/MRSA (54%) and for studies on both species (35%).

Table 1. QS evidence tier by target organism (number of studies).

Direct: a QS-specific assay in the target pathogen (AHL or autoinducer measurement, QS-gene expression, a P. aeruginosa or S. aureus reporter strain; AHL degradation counts only when the pathogen's own AHL or QS was measured). Surrogate reporter: the only QS assay was run in another organism (for example violacein in C. violaceum or an Agrobacterium AHL biosensor). Indirect: QS-regulated phenotypes only (for example pyocyanin, protease or swarming) or a QS claim without an assay. In silico only: docking or simulation with no wet-lab QS data. Not assessed: QS was not tested. Tiers reflect what the authors themselves measured (Section 2.5).

By agent class, Direct evidence was highest for repurposed drugs (13 of 18) and lowest for multi-class combinations (6 of 15); natural products had 63 of 115 and the most surrogate-only studies (19).

Table 2. QS evidence tier by primary agent class (number of studies).

Each study is counted once, under its primary class, so the rows sum to the 252 included studies. Classes are mutually exclusive and assigned by the rule in Section 2.4; for example, green-synthesised or plant-mediated nanoparticles are classed as Nanoparticle, and only studies that test agents of different classes together or against each other are classed as Combination. Direct: a QS-specific assay in the target pathogen (AHL or autoinducer measurement, QS-gene expression, a P. aeruginosa or S. aureus reporter strain; AHL degradation counts only when the pathogen's own AHL or QS was measured). Surrogate reporter: the only QS assay was run in another organism (for example violacein in C. violaceum or an Agrobacterium AHL biosensor). Indirect: QS-regulated phenotypes only (for example pyocyanin, protease or swarming) or a QS claim without an assay. In silico only: docking or simulation with no wet-lab QS data. Not assessed: QS was not tested. Tiers reflect what the authors themselves measured (Section 2.5).

3.3.1. Agents by class

The included studies are numbered 1–252 in the order of Supplementary Table S2, which gives the agent, model, concentrations, biofilm and QS results for every study; in the text, studies are cited by author and year.

Natural products (n = 115; Supplementary Table S2, nos. 1–115). Plant extracts and fractions were the commonest agents, for example Torilis japonica against S. aureus (Kim et al., 2022b), Lavandula dentata (Aboulwafa et al., 2025) and ginger rhizome (Sagar et al., 2024). Single plant compounds included psoralen (Wen et al., 2024b), thymoquinone (Al-Rabia et al., 2024), piperine (Das et al., 2023), berberine (Wang et al., 2022), baicalin and baicalein (Mao et al., 2023b), and shikonin (Kim et al., 2024). Thirteen studies tested essential oils, among them Sphagneticola (Hassan et al., 2025), Aquilaria (Wang et al., 2025), Tagetes (Khan et al., 2023), Cupressus (Al-Mijalli et al., 2023), Matricaria (Qaralleh et al., 2024), Satureja (Maravić-Vlahoviček et al., 2025), thyme (Shalmali & Srividya, 2026), Pelargonium (Benaissa et al., 2026), Salvia (Cinar Koc et al., 2024), Cistus (Benaissa et al., 2024) and Thymus (Coimbra et al., 2022), and one screened 40 oils (Aziz et al., 2024). Lichen metabolites were studied in (Bharti et al., 2022; Koçöz & Gökalsin, 2026; Le et al., 2024), and microbial products such as rhamnolipids (Khan & Jain, 2025; Saadati et al., 2022), mannosylerythritol lipids (Ma et al., 2026), prodigiosin (Liu et al., 2024) and a Lactiplantibacillus glycolipid (Patel et al., 2022) were also tested.

Nanoparticles (n = 39; Supplementary Table S2, nos. 116–154). Silver-based nanomaterials were frequent (Abbas et al., 2022; Aflakian & Hashemitabar, 2025; Al-Momani et al., 2023; Azimzadeh et al., 2025; Azimzadeh et al., 2024; Karthikeyan et al., 2025; Khdir & Muhammed, 2025; Owrang & Gholami, 2024; Saleh & Hassan, 2024), together with zinc oxide (Alipour-Khezri et al., 2024; Esnaashari et al., 2026; Karthikeyan et al., 2025; Langarudy et al., 2025; Nath et al., 2025) and chitosan formulations (Bernal-Mercado et al., 2022; Esnaashari et al., 2026; Maršík et al., 2024; Naderi et al., 2025; Ramezani et al., 2026; Saleh & Hassan, 2024). Several carriers delivered a known antibiotic or a plant compound, such as berberine in mesoporous silica (Abd El-Hamid et al., 2024), vancomycin in membrane-derived vesicles (Dou et al., 2025) and curcumin in zein (Badr-Eldin et al., 2024).

Synthetic and novel compounds (n = 37; Supplementary Table S2, nos. 155–191). These were mostly designed against a single QS receptor: LasR inhibitors (Huang et al., 2026; Vetrivel et al., 2023), PqsR antagonists (Chen et al., 2026; Murray et al., 2022) and homoserine lactone analogues (Liu et al., 2025; Wei et al., 2022); other series included sulfoxide derivatives (Mao et al., 2023a; Ye et al., 2023), N-acylcysteines (Abo-Dya et al., 2022) and halogenated indoles and pyrimidines (Sim et al., 2025; Sim et al., 2024).

Combinations and multi-class studies (n = 15; Supplementary Table S2, nos. 192–206). This group joins agents of different classes, for example citral with triclosan (Chadha et al., 2024), baicalin with cefotaxime (Meng et al., 2026), thymoquinone with nisin (Chen et al., 2023), eugenol with colistin (Kong et al., 2023) naringin with ciprofloxacin (Dey et al., 2024) and vitamin D3 with thymoquinone (Biswas et al., 2025a). It had the lowest share of Direct QS evidence (40%).

Peptides and enzymes (n = 21; Supplementary Table S2, nos. 207–227). Quorum-quenching lactonases (Curcic et al., 2025; Djokic et al., 2022; Porzio et al., 2023; Zhang et al., 2023a; Zhang et al., 2023b) and antimicrobial or designed peptides (Aflakian et al., 2022; Aflakian et al., 2023; Albasri et al., 2026; Dias et al., 2023; Hamid et al., 2025; Leistikow et al., 2024; Masihzadeh et al., 2023; van Gent et al., 2022) predominated, with DNase I (Deng et al., 2022) and recombinant lysostaphin (Yue et al., 2023) as matrix or cell-wall enzymes.

Repurposed drugs (n = 18; Supplementary Table S2, nos. 228–245). Anti-inflammatory drugs (Esnaashari et al., 2023; Mirpour & Zahmatkesh, 2024; Rastgar et al., 2022), the antiretroviral lamivudine (Alyahyawy et al., 2025), the antifungal micafungin (Hijazi et al., 2023), gliptins (Khayat et al., 2022), antihypertensives (Elfaky et al., 2023; Mahfouz et al., 2026) and the gold compound auranofin (Yam et al., 2023) were tested.

Other classes. Probiotic or postbiotic preparations were tested in 5 studies (Aljohani et al., 2023; Azami et al., 2022; Díaz et al., 2024; Liang et al., 2022; Mitsuwan et al., 2022), and phages in 2 (Franco et al., 2026; Van Helden et al., 2026).

Figure 2 summarizes the classes. Output rose from 50 studies in 2022 to 63 in 2025 (2026 is a partial year), and natural products were the largest class in every year (panel A). The share of studies with Direct QS evidence was lowest for combinations (40%) and highest for repurposed drugs (72%) (panel B). Most nanoparticle studies (29 of 39) stopped at in vitro testing, whereas 50 of the 252 studies reached an animal host, 32 vertebrate and 18 invertebrate (panel C). Figure 3 maps how the study keywords co-occur.

Figure 2. (A) Studies per year by agent class. (B) QS evidence tier within each agent class (probiotic/postbiotic and phage studies are pooled as Other). (C) Highest test level reached per study, by agent class; cells show the number of studies and the percentage of the class.

Figure 3. Co-occurrence map of study keywords. A term was assigned to a study if it matched the text of the title, agent, model system, QS-outcome, in vivo organism or mechanism fields, with three exceptions that use the coded fields: the organism nodes (P. aeruginosa 163 + 17 on both species = 180; S. aureus/MRSA 72 + 17 = 89), the molecular docking node (92 studies coded as in silico) and the mouse node (25 studies with a mouse in the coded in vivo field). A term was drawn as a node if it was assigned to at least 8 studies (the inclusion terms biofilm and quorum sensing were left out) and had at least one edge, and an edge was drawn if two terms occurred together in at least 14 studies. Node size is 60 + 5n and line width is 0.4 + 4.5 × (w / maximum w), where n is the number of studies with the term and w the number of studies with both terms; colour shows the term group. Positions come from a force-directed layout and carry no meaning.

3.4. Animal studies

Of 50 studies with an animal host, 46 measured an infection or efficacy outcome and 4 tested toxicity only. We report vertebrate and invertebrate hosts separately, because invertebrates lack an adaptive immune system, mammalian tissue barriers and mammalian metabolic clearance. Of the 46 efficacy studies, 31 used a vertebrate model (30 mammalian: mouse, rat or rabbit; and one zebrafish; 27 with Direct QS evidence, 87%) and 15 used an invertebrate host (C. elegans, G. mellonella or Drosophila; 10 with Direct QS evidence, 67%). Of the four toxicity-only studies, three were in invertebrates and one in a vertebrate. No animal study was found for 202 of the 252 studies (80%).

Table 3. Studies using an animal host (n = 50: 27 mammalian, 3 mammalian plus invertebrate, 2 zebrafish, 18 invertebrate). SYRCLE appraised: yes for infection or efficacy studies with a vertebrate host; toxicity-only and invertebrate-only studies were not appraised. Studies are listed in the order of Supplementary Table S2.

Direct: a QS-specific assay in the target pathogen (AHL or autoinducer measurement, QS-gene expression, a P. aeruginosa or S. aureus reporter strain; AHL degradation counts only when the pathogen's own AHL or QS was measured). Surrogate reporter: the only QS assay was run in another organism (for example violacein in C. violaceum or an Agrobacterium AHL biosensor). Indirect: QS-regulated phenotypes only (for example pyocyanin, protease or swarming) or a QS claim without an assay. In silico only: docking or simulation with no wet-lab QS data. Not assessed: QS was not tested. Toxicity only: no infection or efficacy outcome was measured. Tiers reflect what the authors themselves measured (Section 2.5).

3.5. Risk of bias

Of the 252 in vitro appraisals, 143 were rated low, 93 moderate and 16 high risk of bias. All 75 appraisals made from the extraction row only were moderate by the rule, so these account for 75 of the 93 moderate ratings; among the 177 appraisals scored from the full text, 143 were low, 18 moderate and 16 high. The two reviewers confirmed these ratings against the full texts. The share of studies with Direct QS evidence was similar in the low (57%) and moderate (56%) groups and lower in the high group (7 of 16). Of the 46 animal infection or efficacy studies, 31 included a vertebrate model; 30 were rated with SYRCLE, all with some concerns, and one (Yam et al., 2023) could not be rated because its methods were not available. Allocation concealment was never reported; random housing, caregiver blinding and random outcome assessment were reported in 2 studies each and assessor blinding in 1; sequence generation and baseline similarity were each reported in 11. The other 15 studies used invertebrate hosts without a recorded vertebrate model (C. elegans, G. mellonella or Drosophila) and were not rated with SYRCLE. The 4 toxicity-only studies (Kim et al., 2022a; Matijasević et al., 2025; Pratiwi et al., 2026; Samreen & Ahmad, 2025) were not appraised, so the 50 animal studies divide into 31 with a vertebrate host, 15 invertebrate-only efficacy studies and 4 toxicity-only studies. The 31 vertebrate studies are 30 in mammals (Abbas et al., 2022; Abd El-Hamid et al., 2024; Abdel Bar et al., 2022; Al-Monofy et al., 2025; Al-Rabia et al., 2024; Alyahyawy et al., 2025; Badr-Eldin et al., 2024; Chen et al., 2026; Dehbashi et al., 2024; Deng et al., 2022; Dou et al., 2025; Elfaky et al., 2023; He et al., 2023; He et al., 2026; Huang et al., 2026; Jeong et al., 2026; Kamer et al., 2023; Khayat et al., 2022; Kim et al., 2024; Li et al., 2026; Lu et al., 2023; Mahfouz et al., 2026; Masihzadeh et al., 2023; Turkey et al., 2026; Yam et al., 2023; Ying et al., 2025; Yuan et al., 2022; Zhang et al., 2023a; Zhong et al., 2026; Zhu et al., 2025) and one in zebrafish (Djokic et al., 2022), and the 15 invertebrate-only studies (Aljohani et al., 2023; Bajire et al., 2023; Balkrishna et al., 2026; Balkrishna et al., 2025; Bernabè et al., 2022; Curcic et al., 2025; Gómez et al., 2022; Hacioglu et al., 2024; Jin et al., 2024; Kong et al., 2023; Porzio et al., 2023; Wei et al., 2022; Wen et al., 2024a; Wen et al., 2024b; Yan et al., 2024). Three of these studies (Chen et al., 2026; Li et al., 2026; Yuan et al., 2022) also used an invertebrate host and were appraised on the mammalian component.

Figure 4 shows the item-level results. In vitro, the weakest items were cytotoxicity data (26% yes), reporting of statistics (63%), replicates (64%) and sufficiently detailed methods (61%). Among the 30 rated vertebrate studies, allocation concealment was not reported in any, and sequence generation, baseline similarity, blinding and random housing were mostly unreported.

Figure 4. Item-level appraisal. (A) In vitro items for all 252 studies. (B) SYRCLE items for the 30 vertebrate studies rated (29 mammalian and one zebrafish; the 31st vertebrate study, Yam et al., 2023, could not be rated; the studies are listed in Section 3.5; the 15 invertebrate-only efficacy studies and 4 toxicity-only studies are not shown). Ratings were a rule-based first pass. All ratings were then checked against the full texts by two reviewers. "Unclear" means the report was insufficient to judge.

3.6. Sensitivity analysis

Among the 140 studies with Direct QS evidence, natural products accounted for 63 (45%, against 46% of all 252 studies) and P. aeruginosa for 95 (68%, against 65%), so the Direct subset leans slightly towards P. aeruginosa. Of the 145 records excluded because the full text could not be obtained, 18 had first been extracted from an abstract or snippet, which gave 270 extracted studies before exclusion. Had these 18 been kept, the Direct share would have been 54% (145 of 270) instead of 56%.

4. DISCUSSION

4.1. Principal findings

This review mapped 252 preclinical studies from 2022 to 2026. Three features stand out: natural products make up 46% of the evidence (Supplementary Table S2, nos. 1–115), P. aeruginosa is studied more than twice as often as S. aureus/MRSA, and only 12% of studies tested an agent in a vertebrate infection model (Table 3), with a further 6% in invertebrate hosts. Most studies (56%) ran a QS-specific assay in the target pathogen itself; another 11% used only a surrogate reporter such as violacein in C. violaceum, and the remaining 34% inferred QS activity from phenotypes or docking, or did not test it. Earlier reviews have covered natural compounds against P. aeruginosa (Shariati et al., 2024) and the P. aeruginosa QS systems as drug targets (Soukarieh et al., 2018); the present review adds S. aureus/MRSA and separates studies by what was actually measured.

4.2. How strong is the QS evidence?

The tiering separates what authors measured from what they concluded. A Direct assay was less frequent for S. aureus/MRSA (54%) than for P. aeruginosa (58%), and lowest for studies on both species (35%) and for multi-class combinations (40%). Surrogate readouts such as violacein inhibition in C. violaceum or an Agrobacterium AHL biosensor test QS interference in a model organism, not in the target pathogen, and they cannot be assumed to predict activity against the QS systems of P. aeruginosa or S. aureus. We therefore placed studies whose only QS assay was of this kind in a separate Surrogate reporter tier (27 studies, 11%; for example; Çevikbaş et al., 2024; Hernández-Moreno et al., 2023; Mohan et al., 2024; Tritripmongkol et al., 2022) and did not count them as Direct; counted as Direct, they would have raised that share to 66%. Several other issues recurred during full-text verification and should temper reading of the Direct tier. Some QS-gene expression results were obtained at or above the MIC, where lower expression may reflect reduced growth. Antibiofilm effects at such concentrations likewise cannot be separated from killing, so claims of antivirulence activity are strongest where sub-MIC dosing and growth controls were reported. This follows the pathoblocker principle that an antivirulence agent should disarm the bacteria without killing them (Schütz & Empting, 2018), and sub-inhibitory concentrations of some agents have been reported to increase motility and biofilm formation in certain strains (Touati et al., 2025).

Growth controls differ in how well they exclude artefacts. A single end-point OD600 reading, or a viable count at one time, shows whether growth differs at that moment but not why. A longer lag phase, a slower growth rate or a lower plateau can each leave a treated culture below its control at the read-out. A treated culture that is still in early exponential phase when the control has reached stationary phase has fewer cells, less autoinducer and lower expression of QS-regulated genes and virulence factors, and this looks like quorum quenching or biofilm inhibition although signalling was not specifically affected. Continuous kinetic profiling, a plate-reader growth curve read at short intervals over the whole assay, separates these cases. It gives the lag time, maximum growth rate and plateau, so authors can show that treated and control cultures were at the same growth stage when sampled, or normalise the QS read-out to cell density. We did not extract whether studies reported a growth curve, only whether an untreated or growth control was mentioned (checklist item 3), so we cannot say how many Direct-tier studies ruled out this artefact. The Direct tier shows that QS was measured in the target pathogen, not that a growth delay was excluded as the explanation. Timing and dose matter as well, because an agent that delays growth at one concentration may leave QS unaffected at a lower one.

Claims of dual antibiofilm and anti-QS activity also need care, because QS does not act on biofilm in the same direction in the two species. In S. aureus, agr activation promotes dispersal (Boles & Horswill, 2008) and agr-negative strains or agr-blocking compounds can enhance biofilm (Vuong et al., 2000), so an agent that inhibits agr might be expected to stabilise or even increase biofilm; a fall in biofilm together with an agr readout then needs another explanation, such as killing or effects on adhesion or matrix. Our tiers record whether QS was measured, not whether the direction of the QS effect fits the biofilm result, so a Direct tier in S. aureus does not by itself show that the antibiofilm effect is QS-mediated.

4.3. In silico work

Docking appeared in 92 studies (37%). Binding energies depend on the program and scoring function, and docking scores correlate poorly with measured affinity for many target families (Wang et al., 2016), so we did not compare them across studies. In one study the printed energies against PqsA were −176.99 kcal/mol for hypericin and −70.21 kcal/mol for cineole, which cannot be binding free energies (Chakraborty et al., 2024). Typical docking scores for drug-like molecules lie between about −6 and −12 kcal/mol, which at 298 K corresponds to dissociation constants of roughly 40 µM to 2 nM (ΔG = RT ln Kd). The tightest non-covalent protein–ligand pair usually quoted, biotin–streptavidin, has a ΔG of about −18 to −20 kcal/mol (Kd near 10⁻¹⁴ M), and −177 kcal/mol would imply a Kd near 10⁻¹³⁰ M. Per non-hydrogen atom the values are also implausible: cineole has 11 heavy atoms (−6.4 kcal/mol per atom) and hypericin 38 (−4.7), whereas ligand efficiencies of real binders rarely exceed about 0.5 kcal/mol per heavy atom. The source of these numbers (a different unit or scoring term, or an error) is not clear, so we kept only the claim that hypericin scored better than cineole and excluded the values from any comparison. Fourteen studies supported QS activity by in silico work alone (Alossaimi et al., 2025; Chakraborty et al., 2024; Dey et al., 2024; Ganesh et al., 2022; Hamid et al., 2025; Kim et al., 2025; Koosehlar et al., 2024; Lal et al., 2023; Maldonado-Pérez et al., 2025; Nurfauziah et al., 2026; Omar et al., 2025; Piruthivraj et al., 2024; Shilpi et al., 2026; Trognon et al., 2022). Docking is best read as hypothesis-generating unless it is paired with a wet-lab QS assay.

4.4. Translation

Animal data were sparse. Of the 46 infection studies, 31 used a vertebrate model and 30 of these were rated as having some concerns (Figure 4B; SYRCLE; Hooijmans et al., 2014), because randomisation, blinding and allocation concealment were rarely reported. The 15 studies in invertebrate hosts were not rated, because SYRCLE does not fit them and we did not adapt a separate checklist. These hosts lack an adaptive immune system, mammalian tissue barriers and mammalian metabolic clearance, so they are screening models, and their results should not be read as evidence of mammalian efficacy. Mammalian-cell cytotoxicity or haemolysis was tested in only 26% of studies, and in at least one study embryo toxicity occurred below the active antibiofilm dose (Pratiwi et al., 2026), which questions the therapeutic window. Inhibitor specificity, pharmacokinetics and host-cell toxicity are recognised obstacles to clinical use of QS inhibitors (Schütz & Empting, 2018; Touati et al., 2025). Reporting also varied: several papers gave results only in figures, used units inconsistently, or described their findings more strongly in the abstract than the data supported. We recorded such internal discrepancies in the Notes column of the supplementary workbook.

4.5. Strengths and limitations

The strengths of this review are a recent, bounded search window, tiering of QS evidence by what the studies measured, extraction verified against the full text, and a documented change log.

Several limitations apply. Eligibility required the organism to be named in the title, so studies whose titles name only a general term or a broader group may have been missed. Because the year and title criteria were applied at the eligibility stage, many records (774 for the organism not being in the title and 740 for publication before 2022) were removed only then. In addition, 145 otherwise eligible records could not be retrieved in full text and were excluded, mostly closed-access articles, and 18 of these had first been extracted from an abstract or snippet. Only PubMed and Scopus were searched (137 and 115 included studies came from them); Embase, Web of Science and BIOSIS Previews were not, so coverage of the preclinical literature may be incomplete. Language was limited to English. Studies in invertebrate hosts (15) were not rated with SYRCLE, and no alternative checklist was applied to them. Finally, effect sizes were reported in different units, so we did not do a meta-analysis and cannot rank agents by potency.

4.6. Implications

Future studies should test QS in the target pathogen with a specific reporter or gene-expression assay at sub-MIC doses, report kinetic growth curves at every concentration used and compare QS read-outs at matched cell density, include cytotoxicity and a selectivity index, and move promising agents into randomised, blinded infection models with SYRCLE-aligned reporting (Hooijmans et al., 2014). Reviews and funders should treat docking-only and phenotype-only claims as preliminary.

5. CONCLUSIONS

Preclinical antibiofilm and anti-QS research against P. aeruginosa and S. aureus/MRSA is abundant and led by natural products, and 56% of the studies tested QS in the target pathogen itself and a further 11% relied on a surrogate reporter alone. The evidence is weakest where it matters for translation: S. aureus QS data, animal infection models, cytotoxicity and reporting of randomisation and blinding. Claims of QS inhibition should be read together with the dose relative to the MIC and the type of assay.

SUPPLEMENTARY MATERIALS

Supplementary File 1 (PRISMA 2020 checklist); Supplementary File 2 (Tables S1–S3: search strategies, included studies and in vitro risk-of-bias rubric); the extraction workbook (Screening Log, Data Extraction, QA - In Vitro, QA - In Vivo (SYRCLE), PRISMA Tracker, Synthesis, Sensitivity by verification and Version history); and the list of the 18 records (among the 145 without full text) that had first been extracted from an abstract or snippet and were then excluded, provided in the workbook sheet “Archived - no full text”.

ETHICS APPROVAL

Not applicable. This systematic review used published literature only and involved no human participants or animals.

DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES

The authors acknowledge that AI-assisted technologies were partially used in the preparation of this manuscript. Specifically, [Claude Sonnet 5.5 (Anthropic)] was employed to support language refinement and grammar correction. All content was critically reviewed, edited, and approved by the authors to ensure accuracy, originality, and alignment with academic standards. No AI tools were used to generate data, interpret results, or replace the authors’ intellectual contribution.

DATA AVAILABILITY

No new primary data were generated. All extracted data, the search strategies and the list of included studies are provided as Supplementary Materials.

CONFLICT OF INTEREST

The authors declare no conflict of interest related to the research. Editorial disclosure: Prof. Dr. Haitham Qaralleh, Editor-in-Chief of this journal, has a supervisory relationship with the authors. He was not involved in the editorial handling of this manuscript, the selection of reviewers, or any decision on it. Peer review and the editorial decision were managed independently by another member of the editorial board.

FUNDING

This research received no external funding.

AUTHORS’ CONTRIBUTIONS

Conceptualization, S.M. and B.A.; methodology, S.M. and B.A.; literature search and study selection, S.M. and B.A.; data extraction and curation, S.M. and B.A.; risk-of-bias assessment, S.M. and B.A.; formal analysis, S.M.; visualization, B.A.; writing – original draft preparation, S.M.; writing – review and editing, S.M. and B.A.; supervision and project administration, B.A. All authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.

ACKNOWLEDGMENTS

We thank Waed Al-Tarawneh and Osama Musa for independently verifying the extracted data, quorum-sensing evidence tiers and risk-of-bias ratings against the full texts.

ORCID

Sham Ma’aitah : https://orcid.org/0009-0007-6282-2906

Batool AlAmairyin : https://orcid.org/0009-0000-6093-2254

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