ABSTRACT
Polymicrobial biofilms are a conglomeration of diverse microbial consortia encased in a self‐produced exopolysaccharide layer that forms on any biotic or abiotic surface. They are more resilient and persistent due to their enhanced drug resistance compared to monospecies biofilms, making it more difficult to eliminate using standard antimicrobial therapies. The present review discusses various inter‐ and intra‐kingdom interactions taking place in polymicrobial biofilms and accounts for the various underlying drug resistance mechanisms in this complex and heterogeneous niche. In addition, this review provides insights into developing new diagnostic approaches by exploiting metabolites and byproducts produced by drug‐resistant pathogens and other microorganisms in polymicrobial biofilms. As drug resistance is an ever‐evolving mechanism in polymicrobial biofilms, synergistic combinations of natural products and antibiotics alone are not a panacea for eradicating these drug‐resistant polymicrobial biofilms. Therefore, this review summarises both chemical and physical measures undertaken to combat these drug‐resistant biofilms and stresses the need to employ ‘omics’ approaches, gene editing technologies and the integration of artificial intelligence/machine learning tools as future perspectives to eradicate these complex biofilms.
Keywords: anti‐biofilm, biofilm, drug resistance, interkingdom, multispecies, polymicrobial
Polymicrobial biofilms: interkingdom interactions, resistance and therapeutic stratergies. This review highlights the heightened drug resistance and complexity of polymicrobial biofilms, emphasising their persistence over mono‐species counterparts. It explores microbial interactions, resistance mechanisms and emerging strategies, including metabolite‐based diagnostics, gene editing and AI/ML tools for effective biofilm eradication.
1. Introduction to Polymicrobial Biofilms
Biofilms are structured microbial cell collectives embedded in a self‐secreted extracellular polymeric substance (EPS). They are referred to as polymicrobial biofilms when comprised of different genera or members of various kingdoms like bacteria and fungi (Khan et al. 2021; Fang et al. 2024). While single‐species cultures often display predictable patterns of antibiotic susceptibility (Laborda et al. 2021), polymicrobial biofilms exhibit a myriad of virulence factors such as co‐aggregation, host colonisation and resistance to a battery of antibiotics. In addition, they also express several quorum‐sensing (QS) regulated virulence factors too (Gaston et al. 2020). Polymicrobial biofilms can form at different sites of the body causing oral, ear, respiratory and wound infections. The complex interactions taking place between microorganisms in these niches make antibiotic treatment quite challenging. About 80% of chronic wounds contain polymicrobial biofilms which are more severe than mono‐species biofilms. They cause more inflammation and tissue damage in host models and are 10 times more resistant to antibiotics when compared to single‐species biofilms (Cavallo et al. 2024). As polymicrobial biofilms hold a dynamic microbial population, this review aims to provide an overview of various interspecies interactions in polymicrobial biofilms, their drug resistance mechanisms and future insights about developing various sensors for their detection. This review distinguishes itself from prior literature, which often singularly addresses microbial interactions (Anju et al. 2022), resistance mechanisms (Khan et al. 2021), or therapeutic interventions (Jeong et al. 2024). The distinction of this review lies in the comprehensive integration of interkingdom interactions, multi‐layered resistance mechanisms, advanced diagnostic strategies utilising nanotechnology and cutting‐edge therapeutic modalities, including AI/ML frameworks, gene editing and phage enzymes (non‐clinical). Specifically, this work provides in‐depth analyses of previously underrepresented areas such as fungal–fungal interactions (e.g., Candida albicans – Candida glabrata ) (Tati et al. 2016; Senthilganesh et al. 2022) and offers a thorough evaluation of diagnostic nanotechnologies, encompassing quantum dots, magnetic nanoparticles and surface‐enhanced Raman scattering (SERS)‐active platforms. Furthermore, critical assessments of physical therapies, including cold atmospheric plasma (CAP) and photodynamic therapy, are incorporated, a feature largely absent or superficially covered in other reviews. By emphasising multi‐omics integration and AI/ML for predictive modelling and precision medicine (Zuberi et al. 2017; Pohl 2022), this review transcends a mere descriptive catalogue, offering a translational framework designed to guide clinicians and researchers toward actionable therapeutic and diagnostic avenues.
2. Interspecies Interactions in Polymicrobial Biofilms
The pathogenesis mediated by polymicrobial infections is quite complex and often involves either competition or cooperative interactions mediated by a network of chemical cues (Anju et al. 2022). Competition may occur for nutrients and space; on the other hand, cooperation may assist in the growth of the other organisms (Orazi and O'Toole 2019). Polymicrobial interactions can be synergistic, additive, and sometimes antagonistic as well. During synergy, the establishment of the first pathogen creates a gateway for the entry of the second or additional pathogens to colonise the host, which increases the severity of the infection (Palmer Jr. et al. 2001). Additive interactions occur during colonization of pathogens on natural or inert indwelling surfaces that are in contact with the human body. Typically, additive effects are observed due to the combined effect of two or more non‐pathogenic organisms (Brogden et al. 2005). Antagonistic interactions are generally mediated by one bacterium interfering with the other, creating an unfavourable niche for the latter. This phenomenon is commonly referred to as microbial interference, wherein probiotic bacteria kill or suppress the virulence of the pathogen in a multispecies biofilm, which is a classic example (Falagas et al. 2008).
2.1. Bacterial–Bacterial Interactions
Co‐aggregation and co‐localization are the common mechanisms through which bacterial interactions occur in a polymicrobial biofilm. During co‐aggregation, bacteria with different genotypes attach to one another through specific molecules (Rickard et al. 2003). In co‐localisation, one bacterium which can be referred to as a ‘helper bacterium’, provides favorable growth conditions to the other bacteria, aiding in biofilm development (Rickard et al. 2003). The helper bacterium conditions the biofilm‐forming surface, initiating the process of co‐aggregation. Specific surface adhesins present on biofilms facilitate the attachment of the secondary colonizer or sometimes self‐induce phenotypic changes among the group of bacteria, leading to co‐aggregation on the biofilms. This co‐aggregation process also appears to be an important virulence factor, as it leads to the production of copious amounts of matrix components that prevent the entry of antibiotics (Doloman and Sousa 2024). Interactions between Pseudomonas aeruginosa and Staphylococcus aureus are typically found in cystic fibrosis (CF) lungs and chronic wounds wherein P. aeruginosa can enhance or suppress S. aureus (Keim et al. 2024). Antagonistic interactions are also observed in catheter‐associated urinary tract infections (CAUTI) where Proteus mirabilis inhibits the growth of Escherichia coli and C. albicans (Fisher et al. 2011).
2.2. Fungal–Bacterial Interactions
The interaction of C. albicans with other pathogenic bacteria appears to be the most important fungal–bacterial interaction encountered in polymicrobial infections. This interaction is commonly observed in infections related to indwelling medical devices such as urinary catheters (Rubini et al. 2021), prosthetic joints, cardiac valves, urinary stents (Sardi et al. 2013) and also in surgical site infections. C. albicans , along with S. epidermidis , is the most predominant polymicrobial infection involved in Catheter‐Associated Urinary Tract Infection (CAUTI), primarily a hospital‐acquired infection (Harriott and Noverr 2011). Bacteria facilitate the attachment of C. albicans to the bladder mucosa of the urinary tract (Fisher et al. 2011) in turn, C. albicans supports S. epidermidis by enhancing its growth and increasing its drug resistance (Adam et al. 2002; El‐Azizi et al. 2004). Apart from S. epidermidis , Candida spp. also interacts with Streptococci in the oral cavity. This interaction is mediated by the adhesins of S. gordonii (SspB) and Als3 of C. albicans (Silverman et al. 2010). In addition to Candida, some other fungal members also seem to play crucial roles in fungal–bacterial interactions. Aspergillus fumigatus and P. aeruginosa often co‐infect the lungs of CF patients. Galactosaminogalactan secreted by A. fumigatus enhances P. aeruginosa biofilm formation and antibiotic resistance, while phenazines produced by P. aeruginosa inhibit A. fumigatus growth (Ostapska et al. 2022). A similar type of interaction can be observed between Malassezia sympodialis (a skin commensal fungus), P. aeruginosa and S. aureus (Lee et al. 2020). Another study points out that Veillonella parvula promotes the growth and virulence of key root caries‐associated microorganisms, Streptococcus mutans and C. albicans , leading to the development of a polymicrobial biofilm with heightened cariogenic potential (Li et al. 2024). At times, fungal–bacterial interactions can be antagonistic as well. Extracellular vesicles of M. restricta, which is another skin commensal, inhibit the proliferation and biofilm formation of S. aureus (Liu et al. 2024). Polymicrobial biofilms with fungal members also exhibit increased biofilm biomass, metabolic activity (Belizario et al. 2024) along with an increase in drug resistance (Shivaji et al. 2023).
2.3. Fungal–Fungal Interactions
Fungal–fungal interactions within polymicrobial biofilms, though less frequently reported than bacterial or fungal–bacterial associations, are clinically significant in certain infections. Vulvovaginal candidiasis (VVC), for instance, is primarily caused by C. albicans and increasingly by non‐albicans Candida species such as C. glabrata (Yao et al. 2025). In mixed‐species biofilms, C. glabrata exhibits a unique ecological advantage by adhering to C. albicans hyphae, a process that facilitates enhanced colonization and deeper penetration of host mucosal tissues (Tati et al. 2016). This physical association is not merely structural; it supports metabolic cooperation and allows C. glabrata with inferior filamentation to exploit the invasive hyphal network of C. albicans for tissue invasion.
Mechanistically, biofilms formed by these dual species demonstrate augmented resistance to azoles and echinocandins compared to single‐species biofilms. This resistance is attributed to a synergistic upregulation of efflux pump genes (e.g., CDR1, CDR2 and MDR1 in C. albicans , and PDH1, SNQ2 in C. glabrata ), which actively expel antifungal agents from the biofilm matrix (Senthilganesh et al. 2022). Furthermore, the enriched extracellular matrix (ECM), particularly β‐1,3‐glucans and mannans, acts as a physical and chemical barrier that impedes antifungal penetration, while simultaneously sequestering host immune components, thereby contributing to immune evasion and persistence in the host (Nobile and Johnson 2015). Beyond Candida species, interactions between lipophilic yeasts like Malassezia spp. and Candida spp. in cutaneous biofilms have been documented. Malassezia spp. contributes lipase activity and biofilm biomass, supporting the persistence of Candida spp. in the skin's lipid‐rich environment (Cafarchia et al. 2011). This relationship is particularly problematic in seborrheic dermatitis and atopic dermatitis, where polymicrobial fungal biofilms exacerbate inflammation and treatment resistance.
Filamentous fungi such as A. fumigatus and Fusarium spp. are also co‐isolated in immunocompromised individuals, especially those with pulmonary infections following haematopoietic stem cell transplantation or in COVID‐19‐associated pulmonary aspergillosis (CAPA) (Al‐Hatmi et al. 2016; Hoenigl et al. 2022). Co‐infection with Aspergillus spp. and Fusarium spp. increases the complexity of antifungal therapy as these organisms exhibit high intrinsic resistance to multiple antifungal classes and often produce biofilms enriched with galactosaminogalactan and hydrophobins that further shield embedded cells from host immunity and antifungal agents (Loussert et al. 2010). In rare cases, Aspergillus spp. have also been reported to coexist with Rhizopus spp. in invasive fungal rhinosinusitis, particularly in diabetic ketoacidosis patients, highlighting the clinical relevance of such dual‐filamentous fungal infections (Skiada et al. 2020). Although non‐Candida fungal–fungal biofilms are less frequently described, their occurrence in high‐risk populations suggests an underappreciated aspect of polymicrobial biofilm pathology. A contributing factor to this scarcity of reports may be diagnostic challenges, as co‐fungal biofilms are often overshadowed by dominant bacterial or fungal species in polymicrobial infections. Advanced molecular diagnostics, such as metagenomics and next‐generation sequencing, have only recently begun to uncover these complex fungal–fungal consortia. Overall, interspecies and interkingdom interactions in polymicrobial biofilms exhibit both synergistic and antagonistic interactions. Synergistic interactions often result in more resilient polymicrobial biofilms that are twice as drug‐resistant as mono‐species biofilms.
3. Antimicrobial Resistance and Tolerance in Polymicrobial Biofilms
As observed in the planktonic counterparts, the same genetic mechanisms are also involved in inducing antimicrobial resistance (AMR) among biofilm‐forming communities (Orazi and O'Toole 2019) (Figure 1).
FIGURE 1.
Mechanisms of antimicrobial resistance and tolerance in polymicrobial biofilms.
3.1. Genetic Mechanisms Involved in Drug Resistance
Horizontal gene transfer (HGT) is a key mechanism by which microorganisms acquire antibiotic resistance genes (ARGs). All classical means of gene transfer mechanisms like conjugation (Savage et al. 2013) and transformation (Maeda et al. 2006) are observed in the biofilm lifestyle to transfer ARGs. The high cell biomass within a biofilm promotes efficient conjugation (Madsen et al. 2012). It is also interesting to note that there is an increase in the stability of drug‐resistant plasmids in mixed‐species biofilms in comparison to mono‐species biofilms (Madsen et al. 2012) due to metabolic cooperation, which promotes plasmid stability and the EPS matrix, which protects the biofilm from the selective pressure of antibiotics and environmental stressors.
Beyond environmental factors, HGT in polymicrobial biofilms is not an isolated event but is frequently triggered by stress signals, such as sub‐inhibitory concentrations of antibiotics or reactive oxygen species (ROS), produced by coexisting species within the polymicrobial niche (Michaelis and Grohmann 2023). These interspecies stress signals can upregulate the expression of genes involved in conjugation or increase the permeability of cell membranes, thereby facilitating the uptake of extracellular DNA and accelerating the dissemination of ARGs among diverse microbial populations (Zhu et al. 2023). The HGT of ARGs seems to be common among Gram‐negative bacteria in polymicrobial biofilms. An E. coli strain harboring a plasmid with a carbapenemase resistance gene (blaNDM‐1) was transferred to either P. aeruginosa or Acinetobacter baumannii via conjugation only when the pathogens existed as dual‐species biofilms (Potron et al. 2011).
Beyond conjugation, extracellular DNA (Ready et al. 2006; Hannan et al. 2010) can also be taken up and integrated by other species. When existing as a polymicrobial biofilm on indwelling devices, MRSA can acquire vancomycin and tetracycline resistance from Enterococcus faecium (Weigel et al. 2007). These findings underscore the potential for interspecies transfer of ARGs within an infection site, which can alter the course of antimicrobial treatment. Even a single conjugative event can have wide‐ranging consequences for the spread of AMR, both within the site of infection and among patients. The use of sub‐inhibitory concentrations of antibiotics triggers the conjugation machinery through which antibiotic‐resistant genes can easily be transferred (Lopatkin et al. 2016). The close proximity of cells in a biofilm aids this process and mechanisms like toxin‐antitoxin systems ensure that the transferred drug resistance plasmids are retained in the recipient.
HGT by phage‐mediated transduction in polymicrobial biofilms plays a major role in ARG and virulence dissemination. Bacteriophages carrying Shiga toxin (Stx)‐encoding genes have been shown to infect E. coli biofilms formed on glass slides. This led to the emergence of new Stx‐producing E. coli (STEC) within biofilms (Solheim et al. 2013). The above study indicates that the biofilm environment supports transduction and enhances interactions among strains of the same species. The study of transduction events in biofilms is still in its infancy. Quantitative and mechanistic insights related to lateral transduction and ARG dispersal require high‐resolution imaging and sophisticated molecular tools like Catalysed Reporter Penetration and Signal Amplification Fluorescence In Situ Hybridization (CPRINS‐FISH). CPRINS‐FISH is particularly useful for studying gene transfer events such as transduction and conjugation in polymicrobial biofilms (Grodner et al. 2024).
Overall, polymicrobial biofilms are hotspots for HGT. Several genes encoding virulence factors, i.e., genes encoding toxins, adhesins and immune evasion proteins, may get transferred to avirulent strains (Deng et al. 2019). Key metabolic genes can be transferred to enable shared pathways and enzymes between microbial communities in a polymicrobial biofilm (Lee, Eldakar, et al. 2022). HGT accelerates adaptive evolution in biofilms, which leads to functional diversification within the same biofilm. Single‐cell genomics or metagenomics will help in studying the complex HGT network within biofilms.
3.2. Genetically Encoded Factors in Drug Resistance
β‐lactamases are genetically encoded factors produced by bacteria that inactivate β‐lactam antibiotics. These enzymes can be either chromosomally or plasmid‐encoded, and have played a major role in the emergence of multidrug resistance among Gram‐negative bacteria (Bush 2010). Though their mode of action is well known, their role in mediating drug resistance among polymicrobial communities is less understood.
β‐lactamase‐producing bacteria not only protect themselves but also nearby cells from antibiotic action (Sorg et al. 2016; Kim et al. 2018), which alters the antimicrobial sensitivity profiles of entire polymicrobial communities. β‐lactamase‐mediated ‘bystander protection’ is a prominent example of interspecies resistance in polymicrobial biofilms. In this phenomenon, β‐lactamase‐producing species, such as Moraxella catarrhalis , actively degrade β‐lactam antibiotics within the shared extracellular polymeric matrix (ECM), thereby creating a protective zone that shields coexisting, susceptible pathogens like Haemophilus influenzae and Streptococcus pneumoniae from antibiotic exposure (Perez et al. 2014). This contact‐independent protection highlights how the enzymatic activity of one species can profoundly alter the antimicrobial sensitivity profiles of the entire polymicrobial community. It was noted that β‐lactamase produced by M. catarrhalis protected H. influenzae (Armbruster et al. 2010) and S. pneumoniae (Perez et al. 2014) in dual‐species biofilms which are involved in causing otitis media. These studies reveal that antibiotic‐degrading enzymes can alter the drug susceptibility of other bacteria in a closely associated niche in a contact‐independent manner. Overall, when these interactions take place in vivo, they result in total antibiotic treatment failure. β‐lactamase activity can be commonly found in chronic wounds, CF and urinary tract infections, as polymicrobial biofilms are prevalent in these conditions. In addition, β‐lactamase inhibitors become less effective in a biofilm owing to heightened β‐lactamase expression by biofilm‐residing microbes (Hengzhuang et al. 2013). This underscores the importance of early biofilm detection when enzyme levels are lower, and treatment is more likely to be effective. Hence, the development of β‐lactamase biosensors or nanoparticle sensors that can detect minute traces of β‐lactamase in the nascent stage of biofilm development will be immensely helpful for treatment. Significantly, administering β‐lactamase inhibitors along with biofilm dispersal enzymes will aid in the inhibitors penetrating the polymicrobial biofilms.
3.3. Efflux Pump Mediated Resistance in Candida Polymicrobial Biofilms
In mixed‐species fungal biofilms, drug resistance is primarily mediated by two major efflux systems. ABC superfamilies and MFS pumps are known to mediate the resistance of Candida species, in which ABC superfamilies are driven by ATP hydrolysis, and MFS pumps need the proton‐motive force across the membrane (Cannon et al. 2009). The most widely studied genes associated with efflux function are CDR1, CDR2 and MDR1 in C. albicans , while in C. glabrata , they generally consist of CDR1, CDR2 (PDH1) and SNQ2. Interestingly, the efflux pumps were more active in the mixed biofilms than in single ones, indicating that mixed‐species Candida biofilms exhibit greater drug resistance than mono‐species Candida biofilms (Vipulanandan et al. 2018).
The observed enhancement of efflux pump activity in mixed‐species Candida biofilms compared to mono‐species counterparts signifies a crucial role for microbial crosstalk in augmenting drug resistance. These interkingdom interactions can lead to the upregulation of efflux pump genes, such as CDR1, CDR2 and MDR1, potentially through shared signalling molecules or metabolic byproducts (Taff et al. 2013). This synergistic increase in pump efficiency contributes to a higher minimum inhibitory concentration (MIC) for antifungals, making eradication significantly more challenging. As drug efflux pumps are the major culprits in mediating drug resistance, blocking transcription factors that control efflux pump gene regulation by gene editing techniques (Lei et al. 2023) will be a novel method. However, the real challenge might lie in delivering the CRISPR–Cas components inside the biofilm. Targeted delivery systems like liposomes can be used to deliver both antibiotics and efflux pump inhibitors into biofilms through the ECM to reach the Candida cells. Such metabolite‐induced changes play a significant role in patient treatment because the antibiotic dose needed for eradication may be vastly different from that of monoculture. This also emphasises the need to educate physicians about the severity of polymicrobial biofilms and develop appropriate strategies.
AMR in polymicrobial biofilms is a multifaceted threat worldwide. Understanding the resistance mechanisms and keystone factors involved in resistance becomes essential to devise new containment strategies and find alternative targets to disrupt these polymicrobial biofilms.
3.4. Metabolic Interactions Among Polymicrobial Communities
In addition to AMR, polymicrobial communities exhibit distinct mechanisms of antibiotic tolerance (Figure 1). Metabolic cross‐feeding and QS molecules intricately modify the microenvironment within polymicrobial biofilms, directly influencing antibiotic resistance and tolerance (Cui and Kim 2024). For instance, the cross‐feeding of specific metabolites can enhance plasmid retention and conjugation efficiency, thereby facilitating the spread of resistance genes. Concurrently, QS signals, such as AI‐2 produced by H. influenzae or farnesol from C. albicans , can increase biofilm thickness, induce persister cell formation, and even upregulate efflux pumps in coexisting species, collectively rendering the biofilm more tolerant to antimicrobial agents (Grari et al. 2025). These interactions demonstrate that polymicrobial resistance is not merely additive, but rather a synergistic and emergent property arising from complex interspecies communication. Primary metabolites secreted by microbes in a polymicrobial community greatly influence antibiotic sensitivity by both increasing or decreasing the drug resistance of pathogens. In a CF infection, P. aeruginosa , upon cross‐feeding on products of mucin fermentation by anaerobes, became sensitive to ampicillin (Flynn et al. 2016). In contrast, the interactions among members of a cross‐feeding community composed of E. coli , Salmonella enterica , and Methylobacterium extorquens produced an opposite result, whereby E. coli sensitivity to tetracycline was lower in coculture than in monoculture (Adamowicz et al. 2018). Together, these data illustrate that polymicrobial interactions can produce unexpected antibiotic sensitivity profiles. Apart from cross‐feeding metabolites, other compounds like indole (Hirakawa et al. 2005) and volatiles like 2,3‐butanedione have been reported to modulate the antimicrobial sensitivity profile by inducing changes in gene expression (Whiteson et al. 2014). Ammonia, another volatile, has been reported to alkalinise the surrounding environment, thereby rendering ampicillin inactive (Cepl et al. 2014). As metabolites of certain bacteria assist other individuals or a group of bacteria, inducing dysbiosis at the site of infection might disrupt this metabolite sharing. Engineered probiotic strains can be applied to the infection site, which might disrupt the biofilm and alter the microbial ecology at the infection site either by killing the pathogen(s) or by competing for the secreted metabolites in the biofilm (Chappell and Nair 2020; Tan et al. 2020). As indole and volatiles like 2,3‐butanedione and ammonia are reported to be produced in polymicrobial biofilms, gas sensors (Astuti et al. 2019) can be developed as bedside diagnostic tools to detect minute traces of these volatiles.
Multiple lines of evidence indicate that antibiotic resistance is closely linked to bacterial physiology. Thus, unravelling the metabolic factors that influence bacterial susceptibility to antibiotics becomes important. Identification of these physiological byproducts, especially in vivo during infection, can help identify compounds that enhance antibiotic efficacy and reveal new therapeutic targets. Additionally, understanding the physiological shifts that occur during the development of resistance may expose vulnerabilities in antibiotic‐resistant bacteria (Laborda et al. 2024).
3.5. Bacterial Communication Signals
Bacterial communication signals like QS molecules have been reported to be implicated in the virulence of both Gram‐positive and Gram‐negative bacteria residing within multispecies biofilms. These signals have been shown to impact drug sensitivity within mixed‐species biofilms (Ryan et al. 2008). The diffusible signal factor (DSF) secreted by S. maltophilia reduces the sensitivity of P. aeruginosa biofilms to polymyxins. It is also reported that QS molecules can increase the thickness of the biofilm. The AI‐2 signals produced by H. influenzae increased the thickness and number of viable cells in M. catarrhalis biofilms, resulting in increased tolerance to antibiotics (Orazi and O'Toole 2017). S. aureus also escapes from vancomycin antimicrobial activity by producing ROS induced by farnesol, a QS compound produced by C. albicans . It is believed that the production of ROS upregulates the efflux pumps of S. aureus , thereby enhancing its resistance (Kong et al. 2017). 2‐Heptyl‐4‐hydroxyquinolone N‐oxide (HQNO) produced by P. aeruginosa protects S. aureus biofilms from cell wall targeting antibiotics by inhibiting the electron transport chain (ETC) of S. aureus (Orazi and O'Toole 2017). This leaves S. aureus in a metabolically inactive state, allowing it to escape from the action of antibiotics, as many drug classes are effective only against actively growing cells. Though QS is not directly involved in drug resistance, it aids in biofilm formation, and it regulates the production of EPS, which impedes antibiotic penetration. QS induces the formation of persister cells that are tolerant to antibiotics. Chemical profiling of the polymicrobial biofilms might unravel various QS molecules present in different types of infections. This information could be used to create QS fingerprints, which may help in early detection of QS compounds and design inhibitors or quorum quenchers as a treatment strategy.
3.6. Role of Extracellular Matrix in Drug Resistance
Interkingdom interactions often result in an altered biofilm matrix, thereby affecting the antimicrobial sensitivity of the polymicrobial members residing within the matrix. S. aureus cells attach to the hyphae of C. albicans , thereby protecting themselves from elevated doses of antibiotics (Harriott and Noverr 2009). Extracellular DNA and β‐1,3‐glucans found in the matrix of C. albicans offer protection to S. aureus from vancomycin within the mixed‐species biofilm by creating a drug‐absorbing matrix (De Brucker et al. 2015; Kean et al. 2017). The same mechanism also protects E. coli from ofloxacin (De Brucker et al. 2015). ECM directly contributes to drug resistance in biofilms by trapping β‐lactamases and aminoglycoside‐modifying enzymes. Studying the ECM with Raman spectroscopy (Bergholt et al. 2019) will aid in the chemical characterization of the ECM, which will help in designing nanocarriers that can penetrate the ECM and deliver their payloads.
Antimicrobial tolerance in polymicrobial biofilms is the main cause of persistent infections. Some strains form persister cells that survive high antimicrobial concentrations, contributing to long‐term tolerance. The altered biofilm matrix also leads to the formation of more resilient biofilm communities. These factors complicate the diagnosis of polymicrobial biofilms, as to date there is no gold standard procedure for the diagnosis of biofilms per se. It also drives the scientific community to pursue novel therapeutic approaches that target key cross‐feeding metabolites, QS inhibitors, and matrix‐degrading enzymes.
4. Location and Niches of Polymicrobial Biofilms
Polymicrobial biofilms form on both biotic and abiotic surfaces. Body surfaces and niches such as skin, respiratory, digestive, and mucosal tracts harbour polymicrobial biofilms. On the other hand, abiotic surfaces like indwelling medical devices are often colonised by polymicrobial biofilms (Damyanova and Paunova‐Krasteva 2025) (Figure 2). Some important niches are mentioned below.
FIGURE 2.
Location and niches of polymicrobial biofilms.
4.1. Oral Cavity
Dental caries (tooth decay) is a polymicrobial biofilm disease driven by the diet and microbiota‐matrix interactions that occur on a solid surface. Multiple types of biofilms are formed in the oral cavity, where interspecies and cross‐kingdom associations occur. The keystone pathogens such as Actinomyces actinomycetemcomitans, Porphyromonas gingivalis , Bacteroides forsythus , and S. mutans contribute to polymicrobial biofilms via production of invasins, proteases, and adhesins (Damyanova et al. 2024).
4.2. Respiratory and Mucosal Tract
CF is a hereditary disease wherein a mutation in the CFTR (CF transmembrane conductance regulator) gene leads to imbalances in ion transport across epithelial cells, particularly in the respiratory system. In the respiratory tract, this leads to airway obstruction, impaired breathing, and increased susceptibility to infection (Döring et al. 2011). The presence of mucus provides an ideal environment for the development of biofilm infections (Moreau‐Marquis et al. 2008). Airway obstruction makes patients susceptible to colonisation by opportunistic pathogens, initially with S. aureus and later with P. aeruginosa , initiating a harmful cycle of infection. The type III secretion system P. aeruginosa delivers exotoxins into host cells, which result in lung damage and respiratory failure (Döring et al. 2011). In addition, A. fumigatus and P. aeruginosa often co‐infect the lungs of CF patients. The environmental conditions determine whether these two pathogens undertake a beneficial or antagonistic interaction (Peleg et al. 2010).
4.3. Skin
Polymicrobial biofilms present on skin are usually pathogenic, causing chronic infection. Biofilms isolated from wounds are typically multispecies, composed of more than two microorganisms as wounds have diverse microbial entry points and a favourable environment for growth. The most commonly found bacteria are from the genera Staphylococcus, Enterococcus, Corynebacteria, and Pseudomonas (Pérez‐Díaz et al. 2016). Multiple species produce several endotoxins that result in tissue damage and suppression of cell metabolism (Anhê et al. 2021). Apart from chronic wounds, burn wounds are also colonised by polymicrobial biofilms due to loss of skin barrier and systemic immune dysfunction. Both aerobic and anaerobic bacteria, including Gram‐positive and Gram‐negative genera such as Pseudomonas, Serratia, and Staphylococcus as well as fungi like Candida are involved (Maitz et al. 2023).
4.4. Vagina
Polymicrobial biofilms in the vaginal environment are predominantly involved in the pathogenesis of bacterial vaginosis (BV) (Sousa et al. 2023) and VVC both prevalent among women of reproductive age. VVC is predominantly caused by C. albicans and non‐albicans (NAC) members existing as polymicrobial biofilm communities, which contribute to their drug resistance. Drug‐resistant fungal strains are emerging at a rapid pace, underscoring the urgent need for new antifungal therapeutics.
It is now clearly evident that each niche area is populated by a specific group of pathogens. The microenvironmental conditions dictate the microbial community that colonises a particular site and also determine the architecture of the biofilm. The location and niches of polymicrobial biofilms are important from a diagnostic point of view, as they determine the sample collection volume and method, which play a crucial role in biomarker development.
4.5. Indwelling Implant‐Mediated Biofilm Infections
Pathogens use medical implants as a substrate to form biofilms, as these implants remain in the body for a long time. The biofilms formed on these surfaces cause reinfection and tissue damage. The various medical implants and the bacteria associated with polymicrobial biofilms are as follows (Figure 2).
4.5.1. Central Venous Catheters and Urinary Catheters
Antibiotic administration, chemotherapy, infusion, and intravenous nutrition are often administered through central venous catheters. Based on the duration of catheterisation, biofilms may extend across catheter surfaces and into their lumens (Fang et al. 2024). Biofilms formed in catheters are responsible for several systemic infections and are also responsible for the spread of infection throughout the body. Pathogens can enter the urinary tract through the lumen of the catheter or along its external route and build a monomicrobial or polymicrobial biofilm. Among the most common causative agents of this type of infection are S. epidermidis , Enterococcus faecalis , E. coli , Proteus mirabilis , P. aeruginosa , Staphylococcus saprophyticus and K. pneumoniae (Werneburg 2022). Biofilm formation in the urinary catheter leads to blockage of the catheter, and the pathogens also secrete toxins that damage the surrounding tissue (Anhê et al. 2021).
4.5.2. Orthopaedic Implants
Orthopaedic implants are placed inside the body during trauma or fractures by undergoing surgical procedures. Postoperative complications may occur after surgical intervention, the most common being infection in the implantation area. Biofilm formation on these implants is a persistent problem, often necessitating surgical removal of the implant. Multispecies biofilms involved in these types of infections include P. aeruginosa , E. faecalis , K. pneumoniae , MRSA, MSSA and methicillin‐resistant Staphylococcus epidermidis (MRSE) (Caldara et al. 2022).
Implant‐mediated infections due to polymicrobial biofilms are difficult to diagnose owing to the lack of specific symptoms. Antimicrobial treatment often fails as polymicrobial biofilms are drug‐resistant. To date, removal and replacement of the implant seem to be the only viable treatment measures. In view of this, recent research is focused on nanoscale surface modification of implant surfaces (Kumar et al. 2025) to create fouling‐resistant indwelling implants.
5. Approaches to Study Polymicrobial Biofilms
Both in vitro and in vivo systems are essential for modelling and studying biofilm development. In vitro systems help in determining effective concentrations of an antibiofilm agent; whereas in vivo systems are helpful in validating the effective concentrations in different animal models (Table 1).
TABLE 1.
In vitro polymicrobial culturing systems and detection systems of polymicrobial biofilms.
| Type of culturing system | Application | References | |
|---|---|---|---|
| Static biofilm models | Colony biofilm | ||
| Colonies are grown over nutrient agar. Basic model prone to chemical gradients. Reproducible and simple making it suitable for high‐throughput screening | Antibiotic susceptible testing. Helps in identifying polysaccharide production and morphological switch | Robertson et al. (2024) | |
| Microtitre plate assay | |||
| Simple liquid miniaturised biofilm culturing system. Based on microbial attachment to wall surface | Simple system used to quantitate biofilms. Evaluate the efficacy of antibiofilm agent against nascent and young biofilms. | Campo‐Pérez et al. (2023) | |
| Biofilm ring test | |||
| Immobilisation of magnetic beads by bacterial aggregates. Biofilm kinetics can be observed. Requires no washing and staining. Biofilms observed under SEM | Early adhesion events can be observed. Ease of automation. No washing or staining procedures | Olivares et al. (2016) | |
| Calgary biofilm device | |||
| Pegs placed on 96 well microtitre plate. Biofilms develop on pegs. | Biofilms formed on pegs can be observed. Biofilm formation over time can be studied. | Almshawit et al. (2014) | |
| Dynamic model system | Kadouri system | ||
| Microtitre Plate based method with constant replenishment of nutrient medium | Forms mature biofilms leading to huge biomass. Biomass can be used for omics studies | Merritt et al. (2011) | |
| Flow cell | |||
| Flat transparent chambers supplied by culture media which can be observed under a microscope. Optimum for image analysis. | Non‐destructive real‐time biofilm observation. Good image quality but costly and requires technical expertise | Arzmi et al. (2023) | |
| CDC biofilm reactors | |||
| Bacteria adhere to coupons. Coupons can be tailor made based on size, shape and material. | Test disinfectant efficiencies | Mendez et al. (2020) | |
| Microfermentors | |||
| Chemostat‐based culture system. Biofilms develop over a removable spatula made of material of choice | Large sample volume as high biofilm biomass is produced. Microscopic and genetic analysis can be performed. Easily turned into microcosm | Létoffé et al. (2022) | |
| Microfluidic biochips | |||
| Biochip with temperature controlled dielectric microsensors | Non‐invasive method to study biofilm population dynamics. | Yuan et al. (2023) | |
| Bioflux device | |||
| 96 individual microfluidic channel with controlled shear flow | Requires very little reagents and energy supply. Environmental conditions can be controlled. Single‐cell analysis is possible within a community | Pouget et al. (2022) | |
| Biosensor | Mechanism | ||
| Biofilm detection systems | Nanoparticle‐based biosensor | ||
| Fluorescent glutathione‐stabilised gold nanoclusters (GSH‐Au NCs) | Detects EPS of Gram‐positive and Gram‐negative biofilms | Evstigneeva et al. (2023) | |
| Magnetic nanoparticles (MNPs) | Magnetosomes bounds with stains or probes aids in detection | Felfoul et al. (2007) | |
| Surface‐enhanced Raman scattering (SERS) active nanostructures | Ag NPs mediated detection of S. aureus and P. aeruginosa | Li et al. (2020) | |
| Electrochemical biosensors | Electric signals from aptamers, antibodies, or nucleic acids is used to differentiate S. aureus and P. aeruginosa | Pourakbari et al. (2019) | |
| DNA‐based nano‐diagnostics | |||
| Nanopore sequencing | Species‐level identification by sequencing entire 16S rRNA gene by nanopore sequencing | Nyaga et al. (2024) | |
| DNA‐functionalised nanoparticles | Detection of signals from DNA‐NPs functionalised with pathogen‐specific aptamers or probes | Sousa et al. (2025) | |
5.1. In Vitro Model Systems
Though there are several traditional model systems, ranging from simple microtiter plate assays to sophisticated microfluidic biochips, they possess an inherent disadvantage, as none of them are suitable for evaluating the efficacy of antimicrobials due to their reproducibility issues in biofilm formation. So, the American Society for Testing and Materials (ASTM International) has implemented standardised test methods to provide the guidelines and specifications for the accurate and reproducible formation of biofilms and testing of antimicrobial substances. To evaluate disinfectant capacity, the CBD and the CDC biofilm reactor are utilised. The colony biofilm model has also recently been adapted to develop a standard test method (Malone et al. 2017; Coenye et al. 2018). Some of the recent in vitro static and dynamic biofilm models are summarised in Table 1. To mimic the CF lung system, an in vitro continuous‐flow model was developed for the cultivation of polymicrobial biofilms and planktonic cultures (O'Brien et al. 2021).
5.2. In Vivo Model Systems
The main advantage of in vivo biofilm models over in vitro models is that they provide a dynamic and complex nature of the interactions between the host immune system and the pathogenic biofilms. The development of both vertebrate and non‐vertebrate in vivo models for biofilm research has been carried out for several years, prompting this section of the review to focus on several recently developed in vivo models. Mouse corneas have been employed as in vivo models to study the virulence and biofilm infections of S. aureus (Sadaka et al. 2014), P. aeruginosa (Saraswathi and Beuerman 2015), S. aureus and Fusarium falciforme mixed biofilm infections (Ponce‐Angulo et al. 2020). Apart from eye infections, in vivo models have been developed to study bacterial and fungal vaginosis (Hymes et al. 2013; Nash et al. 2016). In connection with device‐related infections, in vivo models have been developed for cochlear implants and neurological devices. Mouse models were utilised to study novel therapeutic interventions, antibacterial coatings, and novel materials against cranial implant infections and central nervous system catheter infections (Glage et al. 2017). There is a scarcity of in vivo models to study polymicrobial biofilms as the reproducibility of polymicrobial infections is a major challenge. Mono‐species models are tailored to specific pathogens or infection sites and may not be adaptable for studying multiple species biofilms, because of the need to distinguish and analyse various microbial species within the biofilm matrix. This requires identification of species‐specific markers, imaging technologies, and molecular tools. Developing efficient in vitro and in vivo models to study polymicrobial infections to date remains a translational challenge (Highmore et al. 2022). In vitro models may yield accurate findings for infection treatment, but they lack key traits that can be extrapolated for industrial use. For example, there is no universally accepted model for wound biofilms in clinical settings, while in agriculture, advancing our understanding of agri‐biofilms similarly relies on academic research to develop strategies that enhance food production and ensure food security. So, a unified effort between academia, industry, and government is essential for the efficient and effective translation of biofilm technology (Highmore et al. 2022).
Overall, biofilm model systems are important for understanding the dynamics of polymicrobial communities in a biofilm, their one‐to‐one interactions, and their drug resistance mechanisms. In vitro biofilm models provide a more controlled environment that mimics the natural infection setting. They are more suitable for screening biofilm inhibitors and surface coatings of various implant materials. Additionally, they are less expensive and circumvent the need for stringent ethical approvals. In vivo biofilm models replicate the complex host environment and aid in studying mixed‐species biofilms in realistic anatomical niches. However, their high cost and ethical concerns limit their availability across all laboratories.
5.3. Diagnostic Techniques Using Nanomaterials
The diagnosis of polymicrobial biofilms is quite challenging owing to their heterogeneity and diversity. As nanomaterials can be used for real‐time and in situ detection, they are being explored as a new detection tool for polymicrobial biofilms.
5.3.1. Nanoparticle‐Based Biosensors
Unlike the detection of mono‐species biofilms, several challenges are associated with the diagnosis of polymicrobial biofilms. Multiple species coexist and interact with each other, often one species outnumbering the others, with the lesser members going undetected. The thick EPS layer prevents the entry of stains or probes that can be used to differentiate various species. Nascent biofilms are very thin, making them difficult to detect. Some microbial species causing disease cannot be cultured and escape the routine laboratory culturing tests. Nanoparticles might be an ideal choice as they offer a faster, multiplexed, and non‐invasive means of detection (Wu et al. 2024). The common mechanism behind nanoparticle‐based detection of polymicrobial biofilms involves targeted interaction with the biological entity, signal generation, and amplification. Gold, silver, or magnetic nanoparticles can be functionalised so that they can bind to specific microbial biomarkers or metabolites, aiding in their early detection and thereby detecting the pathogen itself. While these methods offer rapid detection, limitations include poor penetration into thick ECM layers, variability in biofilm composition affecting binding efficiency, and signal interference from host fluids or tissues. Besides, reproducibility across platforms and clinical settings remains unproven (Martín‐Gracia et al. 2020). Some nanoparticle (NP)‐based detection techniques are discussed below.
5.3.2. Quantum Dots (QDs)
Fluorescent semiconductor nanocrystals offer multiplex detection due to size‐tunable emission. Fluorescent glutathione‐stabilised gold nanoclusters (GSH‐Au NCs) have been used to detect the ECM components of Gram‐negative and Gram‐positive bacterial biofilms, resulting in fluorescent differential staining of bacterial biofilms (Evstigneeva et al. 2023). As QDs fluoresce when excited by light, different components in a biofilm or different species in a polymicrobial biofilm can be identified by labelling different targets with distinctly labelled QDs. S. aureus and C. albicans , which coexist in a polymicrobial biofilm, can be detected by distinctly labelled QDs binding to staphyloxanthin pigment of S. aureus and β‐1‐3‐glucans of C. albicans (Cui et al. 2021; Tang et al. 2023). Although this multiplexing might seem advantageous, the biofilm matrix itself might act as a barrier by preventing the penetration of QDs. More importantly, the stability of QDs in clinical samples and the intrinsic toxicity of some QDs also limit their use in clinical diagnosis. Future research should focus on the synthesis of non‐toxic QDs, the integration of ML approaches to read QD‐generated biofilm images, and the development of QDs with theranostic properties.
5.3.3. Magnetic Nanoparticles (MNPs)
The basic principle behind MNPs is to enable magnetic separation and enrichment of biofilm components before analysis. Interestingly, antibiotic delivery can also be carried out in the presence of a magnetic field. Magnetosomes produced by magnetotactic bacteria can be used to track the movement of these bacteria using an MRI system. When the magnetosomes are bound with stains or probes, it aids in bacterial detection (Felfoul et al. 2007). A key advantage of MNPs over other nanoparticles is that the detecting agent can be driven into the site of infection (urinary tract, inside the vagina) with the help of an external magnetic force. The major bottleneck hampering quorum‐sensing inhibitors (QSIs) for UTI treatment is that, to date, there is no means of delivering the QSI deep inside the urinary tract. QSIs can be functionalised onto MNPs via specific ligands, which can be directed to the site of infection by an external magnetic field. Key ligands that are found or produced only by the keystone pathogens (AHLs) are crucial for this approach. The integration of MNPs with microfluidic devices (Zhong et al. 2023) will aid in high‐throughput and automated analysis of clinical samples with polymicrobial biofilms. While MNPs allow targeted delivery under magnetic fields, off‐target accumulation and limited effectiveness against deeply seated biofilms in tissues restrict clinical utility. High costs and lack of standardised protocols are further barriers (Liu et al. 2021).
5.3.4. Surface‐Enhanced Raman Scattering (SERS)
SERS‐active nanostructures greatly amplify Raman signals from biofilm components. The advantage of this technique is that it can detect species‐specific signatures from EPS or bacterial cell walls, allowing profiling of polymicrobial communities. Silver nanoparticles were used to perform label‐free SERS detection of S. aureus and P. aeruginosa within mixed‐species biofilms, achieving rapid identification based on spectral differences (Li et al. 2020). The advantage of this technique is that it can detect even nascent biofilms, even when the EPS production is scarce. SERS offers an easy and cost‐effective means to generate molecular fingerprints of the EPS layer of polymicrobial biofilms (Bergholt et al. 2019). However, as the abundance of SERS spectra might vary based on the sample, it can be quite challenging to estimate the pathogen load, which is directly proportional to the severity of infection. Additionally, species‐level differentiation might be difficult as they might have similar EPS compositions. As polymicrobial biofilms are heterogeneous, there is a need to establish standardised protocols and foolproof methods with proper positive and negative controls. These are highly sensitive, but signal variations, biofouling on electrodes, and challenges in distinguishing closely related microbial species in complex samples limit their clinical adoption (Tahir et al. 2021).
5.3.5. Electrochemical Biosensors With Nanomaterials
Electrochemical biosensors translate biological recognition events into measurable electrical signals. Electrodes modified with carbon nanotubes (CNTs), graphene, or metal nanoparticles increase sensitivity in detecting biofilm‐associated metabolites or enzymes. They aid in detecting biofilm‐specific QS molecules (e.g., AHLs) and facilitate real‐time monitoring of biofilm growth during antimicrobial treatment. Utilising aptamers, antibodies, or nucleic acids to recognise species‐specific markers enables differentiation between pathogens like S. aureus and P. aeruginosa (Pourakbari et al. 2019). Electrochemical biosensors can be a handy tool for diagnosing VVC. As lectins (Jacalin) (Senthilganesh et al. 2022) have a high affinity to the cell surface glycans present in the Candida EPS layer, they can be exploited to design lectin‐based biosensors for early detection of VVC infections directly from vaginal swabs when compared to the traditional culturing technique, where the turnaround time is several days (Sá et al. 2020). The use of nucleic acid probes to detect species‐specific DNA (Sousa et al. 2021) can assist in identifying different microbial species within a biofilm. The main advantage of electrochemical sensors is that the nanomaterials amplify the signals; even low components of the biofilm can be detected, aiding antibiotic treatment. Due to their small size, NPs enable device miniaturisation. This makes electrochemical biosensors portable, enhancing bedside diagnosis or on‐field testing. However, electrochemical biosensors face a significant disadvantage due to biofouling, i.e., biofilm formation over the electrodes, thereby impeding detection.
Overall, NP‐based diagnosis of biosensors faces several challenges, like identifying the right biomarker to differentiate between closely related microbial species. This is important as the biosensor should distinguish the intended pathogens from overlapping signals from a mixed‐species environment and biological samples. Any NP should be able to penetrate the complex and dense ECM and should be reproducible across different biosensing platforms. It is increasingly evident that NP biosensors should be integrated with microfluidics, which will enable high‐throughput screening of samples. Subsequently, AI/ML algorithms should be developed that can read and predict the complex impedance generated from a mixed‐species biofilm and distinguish the impedance spectra arising from the components present in the biological fluid. Finally, the development of bedside or field site theranostic systems for different polymicrobial systems that can not only detect but destroy the polymicrobial biofilms will determine the real success of NP‐based biosensors in future.
Overall, nanomaterials‐based diagnosis of polymicrobial biofilms provides enhanced sensitivity and specificity. They facilitate early and ultrasensitive detection of low‐abundance biomarkers and offer species‐level differentiation of polymicrobial communities. Nanomaterials are also being integrated with point‐of‐care devices with theranostic potential.
5.4. DNA‐Based Nano‐Diagnostics
5.4.1. Nanopore Sequencing
Nanopore technology enables the sequencing of the entire 16S rRNA gene, enhancing species‐level resolution in bacterial identification. This is particularly beneficial in distinguishing closely related species within polymicrobial samples. The platform provides near‐instantaneous data acquisition, allowing for preliminary results within hours. This rapid turnaround time is crucial for timely clinical decision‐making (Lao et al. 2024). The main advantage of this technique is that different pathogens can be detected directly from the body and tissue fluids, even identifying unculturable bacteria. Significantly, devices like MinION make it feasible to perform sequencing onsite, such as hospitals and remote areas (Nyaga et al. 2024). Importantly, nanopore sequencing can be used to detect viable but non‐culturable (VBNC) cells that are often found in polymicrobial biofilms. Transcriptomic studies of VBNC bacterial communities might reveal various metabolic pathways that can be new drug targets. While nanopore sequencing can detect VBNCs through transcriptomic studies, fully leveraging this for clinical benefit requires further research into the functional interpretation of these non‐culturable states (Ji et al. 2024). Contamination from the surrounding environment is a major issue that requires rigorous controls and accurate sample processing. While nanopore sequencing's long reads aid species identification and complex genomic analysis, its higher inherent error rate compared to short‐read technologies necessitates robust bioinformatics pipelines to prevent misclassifications (Zheng et al. 2023). This requires bioinformatics tools like ‘BiofOmics’ (Dutta et al. 2024) to interpret the data to derive meaningful insights. Despite the portability of devices like MinION, the overall cost, including reagents, consumables, and the need for skilled personnel, remains a significant barrier to widespread adoption, especially in resource‐limited settings (Oehler et al. 2025).
Several ‘omics’ based approaches have been applied to diagnose polymicrobial biofilms. Metagenomics or metataxonomics was employed to study diabetic foot ulcer biofilms. The study revealed the presence of diverse anaerobic and aerobic bacterial genera that routine culture‐based methods failed to detect (Suryaletha et al. 2018). Other omics techniques like transcriptomics and proteomics provide insight into expressed virulence genes and functional proteins in polymicrobial biofilms at the infection site. With the help of metabolomics and lipidomics, insights about small molecules produced in polymicrobial biofilms can be gained. Applying these omics techniques in dental plaque from patients with diabetes and periodontal disease revealed a novel metabolic pathway (Overmyer et al. 2021). The aforementioned study highlights the power of omics technology in unravelling new metabolites that can be explored as biomarkers. The downside of ‘omics’ technologies is that they generate voluminous data. Overall, multi‐omics technologies have wide applications in polymicrobial biofilm diagnosis. They offer precision diagnosis and help to identify novel biomarkers from polymicrobial biofilm‐associated infections.
5.4.2. DNA‐Functionalised Nanoparticles
This technique revolves around the hybridisation‐based detection of specific nucleic acid sequences. DNA‐NPs can be functionalised with specific DNA sequences (aptamers or probes) that bind to unique biomarkers present on the surfaces of biofilm‐forming bacteria. This specificity allows for the identification of specific microbial species within a polymicrobial biofilm. Upon binding to their target, DNA‐NPs can undergo conformational changes or trigger enzymatic reactions that amplify a detectable signal, such as fluorescence or colorimetric changes, facilitating the identification of the target species (Sousa et al. 2025). DNA‐NPs can be integrated into various biosensing platforms, enhancing the sensitivity and specificity of biofilm detection. The high surface‐to‐volume ratio of NPs allows the attachment of multiple DNA probes, enhancing the detection of low‐abundance species. The stability and specificity of nucleic acid‐tagged NPs should be ensured across different samples to ensure reproducibility. Developing cost‐effective ways for the scalable manufacturing process of NPs is necessary for clinical adoption.
DNA‐based techniques when integrated with nanomaterials offer a unique advantage wherein they can detect even miniscule amounts of microbial DNA from complex polymicrobial biofilms. When tagged with species‐specific probes they allow for simultaneous detection of multiple species. Here, nanomaterials play an important role in signal amplification, increasing the limit of detection. Furthermore, direct detection of microbial DNA from biofilms helps in detecting VBNC organisms which are impossible to culture in lab conditions.
6. Polymicrobial Biofilm Inhibitors
Unlike monomicrobial systems, inhibitors targeting polymicrobial biofilms must disrupt a complex interkingdom community comprising diverse virulence factors, ECM components, and communication systems. Based on this, several inhibitors have been discovered worldwide (Figure 3). Some recent studies discuss polymicrobial biofilms in various settings and their clinical significance. They stress the use of a metagenomics approach for diagnosing and preventing polymicrobial biofilms (Anju et al. 2022), and show the potential of combination treatment of natural products with small molecules and nanomaterials in preventing polymicrobial biofilms (Jeong et al. 2024). At times, polymicrobial inhibitors can give rise to more resilient and robust biofilm communities. The resistant or unaffected species may occupy the vacant niche and form biofilms that might be entirely different from the original one. This phenomenon is known as competitive release or niche replacement (Rendueles and Ghigo 2015). So, it is important to discover broad‐spectrum biofilm inhibitors rather than species‐specific biofilm inhibitors. Some of the recent polymicrobial inhibitors along with their mechanism of action are summarised in Table 2.
FIGURE 3.
Types of polymicrobial biofilm inhibitors.
TABLE 2.
Polymicrobial biofilm inhibitors.
| Inhibitor | Targeted pathogens | Mode of action | References |
|---|---|---|---|
| Biofilm matrix disassemblers | |||
| Glycoside hydrolases | S. aureus and P. aeruginosa | Biofilm dispersal, reduction in EPS biomass | Fleming and Rumbaugh (2017) |
| Marine bacterial DNase and biosurfactant | C. albicans and S. epidermidis | Polymicrobial biofilm disruption | Srikanth et al. (2021) |
| DNase I and glucose oxidase | L. monocytogens and Salmonella enterica | Disruption of mature biofilms | Lin et al. (2024) |
| Mutanase, β‐Glucanase, and DNase | Actinomyces naeslundii , Nesseria subflava, Lacticaseibacillus rhamnosus, Streptococcus mutans , Streptococcus mitis and Streptococcus salivarius | Degradation of matrix polysaccharides | Dukanovic Rikvold et al. (2024) |
| Natural compounds | |||
| Fatty acids | |||
| Myristoleic acid | S. aureus and Cutibacterium acnes | Reduces C. acnes hydrophobicity and inhibits biofilm formation | Kim et al. (2021) |
| Lauric acid and myristic acid | S. aureus , E. coli 0157:H7 and C. albicans | Suppression of biofilm genes, haemolytic genes and hyphal cell wall genes | Kim, Lee, Park, Kim, and Lee (2022) |
| Flavonoids | |||
| Curcumin | C. albicans and Acinetobacter baumanii | Biofilm inhibition | Raorane et al. (2019) |
| C. albicans and S. aureus | Biofilm inhibition on silicone material | Tan et al. (2019) | |
| Thymol | C. albicans and C. tropicalis | Disaggregation of biofilms and reduced hyphae formation | Jafri and Ahmad (2020) |
| Berberine | C. albicans and S. aureus | Reduced expression of Cell surface components and QS genes | Gao et al. (2021) |
| Shikonin | Cutibacterium acnes, S. aureus and C. albicans | Prevention of three species biofilm development | Kim et al. (2024) |
| Lawsone | E. coli (EHEC) and C. albicans | Suppression of curli production and hyphal development | Lee et al. (2024) |
| Organic compounds | |||
| Nepodin | C. albicans and S. aureus and A. baumanii | Repressed hyphal and biofilm related genes | (Lee et al. 2019) |
| 3,3′‐Diindolylmethane | C. acnes and C. albicans | Inhibited dual species biofilm formation | Kim, Lee, Park, and Lee (2022) |
| Halogenated phenols | S. aureus and C. albicans | Prevented polymicrobial biofilm formation | (Olanrewaju et al. 2024) |
| Alizarin (phytopigment) | C. acnes, S. aureus , and C. albicans | Prevented multispecies biofilm development | Lee, Kim, et al. (2022) |
| 3,2′‐Dihydroxyflavone | C. albicans and S. aureus | dual‐species biofilm inhibition | (Park et al. 2024) |
| N‐Butylphthalimide | S. epidermidis and C. albicans | Complete abolishment of dual‐species biofilms | (Shaik et al. 2024) |
| Essential oils (EO) | |||
| Thymus vulgaris EO | C. albicans and C. tropicalis | Disaggregation of biofilms and reduced hyphae formation | Jafri and Ahmad (2020) |
| Thymus vulgaris , Origanum vulgare Eugenia caryophyllata EOs | Actinomyces viscosus , Enterococcus faecalis , Streptococcus mutans , S. oralis , S. sanguinis and S. salivarius . | Biofilm inhibition on toothbrush in vitro model | Aires et al. (2020) |
| Sphingolipids | |||
| Amino acids (Aspartic and succinic acid) | P. aeruginosa (living within polymicrobial communities of S. aureus and Burkholderia cenocepacia ) | Dismantling biofilm | Silva et al. (2020) |
| Farnesol | S. aureus and P. aeruginosa | Biofilm inhibition | Tan et al. (2024) |
| Biomolecule‐based therapeutics | |||
| Antimicrobial peptides | |||
| CWR11‐AuNCs (Peptide‐functionalised gold nanoclusters) | S. aureus and A. baumannii | Bactericidal and antibiofilm properties with low cytotoxicity. | (Shen et al. 2022) |
| Peptoids | S. aureus , K. pneumoniae , A. baumannii , P. aeruginosa , and E. cloacae | Antibacterial and antibiofilm activity in vitro, both in media and under host‐mimicking conditions. | Nielsen et al. (2022) |
| CATHPb1 (Host defence peptide) | S. aureus CMCC26003 and V. vulnificus | Potent antimicrobial, antibiofilm, and immunomodulatory activities | Ouyang et al. (2022) |
| Peptides from casein | S. mutans and P. gingivalis | Biofilm inhibition | Qi et al. (2023) |
| MGD2 | B. subtilis , S. typhimurium , S. aureus , MRSA, and P. fluorescens | Biofilm inhibition on titanium surface | Drexelius et al. (2023) |
| Pln 149 | P. gingivalis , S. mutans , and P. intermedius | Reducing biofilm formation |
Lin et al. (2021) |
| Bacteriophage therapy | |||
| Phage cocktail AB SA‐01 and PA‐01 | S. aureus and P. aeruginosa | Cell reduction in biofilm | Kifelew et al. (2020) |
| Phage ϕ Scott | Sphingomonas paucimobilis from multispecies biofilm | Prevention of Sphingomonas paucimobilis from multispecies biofilm | Thompson et al. (2020) |
| Phages ϕ 44AHJD and ϕX174 | S. aureus and E. coli | Decrease in biofilm intensity | Manoharadas et al. (2021) |
| Recombinant T7 phage | E. coli and Vibrio cholera | Lysis of E. coli cells within V. cholera biofilms | Winans et al. (2022) |
| Polyvalent phage STP55 | Salmonella typhimurium and E. coli 0157:H7 | Dual‐species biofilm eradication | Zhu et al. (2023) |
| Phage SAFA and phage EPA1 and Gentamicin | S. aureus and P. aeruginosa | Dual‐species biofilm eradication | Akturk et al. (2023) |
| Phage Motto and fluconazole, cefotaxime, ciprofloxacin, gentamicin, meropenem and tetracycline | P. aeruginosa and C. albicans | Dual‐species biofilm eradication | Manohar et al. (2024) |
| Probiotics, prebiotics and postbiotics | |||
| Cell free supernatants of commercial probiotics | C. albicans , C. glabrata and C. auris | Mixed species biofilm eradication and hyphal inhibition | Kumar et al. (2024) |
| Physical inhibitors | |||
| Sonication | E. coli and Proteus mirabilis | Dislodging biofilm from catheters | Mandakhalikar et al. (2018) |
| Pulse electromagnetic fields (PEMF), magnetic nanoparticle‐assisted electromagnetic therapy | Eubacterium nodatum, Fusobacterium nucleatum ssp. nucleatum, Streptococcus intermedius, Streptococcus anginosus, Streptococcus mutans, Fusobacterium nucleatum ssp. Vicentii and Capnocytophaga ochracea | Antimicrobial affects | Faveri et al. (2020) |
| Plasma treatment | S. mutans or E. faecalis | Biofilm disruption and bacterial killing | Baig et al. (2022) |
| Low temperature plasma treatment | C. albicans, L. casei , and S. mutans | Biofilm inhibition | Figueira et al. (2024) |
| Cold active plasma | L. monocytogenes and Salmonella Typhimurium | Reduced adherence and maturity of biofilms | Govaert et al. (2019) |
| Photodynamic therapy (PDT) | Porphyromonas gingivalis and Streptococcus gordonii | Biofilm inhibition and suppressing QS | Ono et al. (2024) |
| Laser‐induced patterns | S. aureus, S. epidermidis, P. aeruginosa , E. coli and P. mirabilis (grown as monospecies) | Reduction in biofilm | Mcfadden et al. (2022) |
| Metallic nanoparticles | |||
| Zinc Oxide (ZnO NPs) | S. mutans, L. acidophilus and C. albicans | ROS production | Pourhajibagher and Bahador (2021) |
| Gold (AuNPs) | Candida albicans and S. aureus | Biofilm matrix disruption | Khan et al. (2021) |
| Antimony doped tin oxide (SnO2 NPs) | E. coli and S. aureus | Inhibition of dual‐species biofilms | Park et al. (2023) |
| Silver (AgNPs) | E. coli, S. aureus and P. aeruginosa | Disrupt the biofilm matrix, induce oxidative stress and cellular damage | Li et al. (2024) |
| Nanocarriers | |||
| Dendrimers | L. pneumophila, E. coli, A . hydrophila , K. pneumonia and P. aeruginosa | Biofilm disruption | Andreozzi et al. (2016) |
| Polymeric nanoparticles | E. coli and MRSA | EPS disruption | Makabenta et al. (2021) |
| Chitosan‐coated catheters | S. epidermidis and C. albicans | Mixed‐species biofilm inhibition and down regulation of virulence factors | Rubini et al. (2021) |
| Chitosan‐coated surgical sutures | S. epidermidis and C. albicans | Mixed‐species biofilm inhibition | Prabha et al. (2021) |
| Chitosan‐gum Arabic‐coated liposomes‐alizarin nanocarriers | C. albicans and S. aureus | Inhibition of dual‐species mixed biofilm formation | Raj et al. (2022) |
| Liposomal formulation of lauric acid and myristic acid; alizarin | C. albicans and S. aureus | Disrupted dual‐species biofilms | Bharathi et al. (2024), Bharathi et al. (2025) |
6.1. Biofilm Matrix Disassemblers
The ECM of biofilms is composed of polysaccharides, proteins, and eDNA. Enzymes that degrade these matrix components can be loaded onto nanoparticles (NPs) (e.g., DNase, proteases), thereby destabilising the biofilm structure. Matrix‐degrading enzymes, when combined with antibiotics, enhance antibiotic penetration, reduce resistance, and improve biofilm eradication. Phage‐derived enzymes like endolysins and depolymerases (Kim, Son, et al. 2023; Wang et al. 2023) offer a targeted approach in disrupting these polymicrobial biofilms. Enzymes from different phages can be used as a cocktail to disrupt these polymicrobial biofilms. Only a few of these enzymes have entered clinical development pipelines. Endolysin Lys08, derived from E. faecalis phage, was tested in a UTI model with favourable results across pH/temperature ranges (Yang et al. 2020). No phage‐derived enzymes are yet FDA‐approved for biofilm‐specific indications, though clinical trials are underway in Europe (Abedi et al. 2025).
A major challenge lies in the heterogeneity of biofilm matrices, which vary across bacterial species and growth stages of the same species. This variability means that a ‘one‐size‐fits‐all’ enzymatic approach may not be effective, necessitating the development of broad‐spectrum enzyme cocktails or highly specific enzymes for particular biofilm types (Meireles et al. 2016). The stability and activity of these enzymes in complex biological environments, such as host tissues or bodily fluids, are also critical concerns, as they can be susceptible to degradation or inhibition by host proteases or other factors (Torres‐Herrero et al. 2024). Furthermore, the potential for immunogenicity of exogenous enzymes, especially with repeated administration, could trigger adverse host immune responses, limiting their long‐term clinical utility. For nanoparticle‐based delivery, ensuring efficient and targeted delivery of enzymes to the biofilm site while minimising off‐target effects remains a challenge (Torres‐Herrero et al. 2024). Future research should focus on optimising cocktails for specific polymicrobial infections, enhancing enzyme stability and delivery through advanced nanocarrier design, and developing strategies to mitigate potential immunogenicity to unlock the full therapeutic potential of these matrix‐disassembling agents (Kim, Wang, and Ahn 2023; Wang et al. 2023).
When the ECM is disassembled, it exposes the microbial cells to the host immune defences. This paves the way for naturally eliminating the pathogen without the use of antibiotics. Matrix disassemblers can be used as adjuvants and also be coated on indwelling medical implants to fabricate fouling‐resistant implants.
6.2. Cell Signalling Disruptors
QS mechanism is responsible for coordinating several virulence factors among pathogens. Nano‐formulations of the QSIs can disrupt microbial communication, preventing the expression of the virulence factors. As this approach circumvents the need to use antibiotics to kill pathogens, it is considered a much‐needed approach in the era of AMR. Despite their non‐antibiotic nature, QSIs face significant challenges in clinical translation (Nachiyar et al. 2025). The main challenge lies in delivering the QSI at the site of infection. The heterogeneity of polymicrobial biofilm communities, several host factors and the immune system can affect the effectiveness of QSIs in vivo. Obtaining regulatory approvals for QSIs as therapeutic agents is a major hurdle. As most of the QSIs come from natural sources like plants, their scalability is also a major concern to meet the demands of its widespread use. Furthermore, the mechanisms, pharmacokinetic and pharmacodynamic profiles of QSIs are poorly understood to date. Determining the effective dosage for human tissue, its cytotoxicity, bioavailability and any off‐target effects requires a thorough investigation. QSIs must be pathogen‐specific to avoid disrupting beneficial microbiota which might lead to dysbiosis in the host (Nachiyar et al. 2025). Consequently, research should focus on species‐specific QSIs that target pathogenic bacteria alone without affecting beneficial bacteria. QSIs seem to be a valuable option to tackle drug‐resistant bacteria. They strategically disarm the pathogens, which reduces the risk of resistance evolution.
6.3. Natural Compounds
Though many natural compounds have been investigated as antibiofilm agents, some of the recent studies that report on natural products that are capable of disrupting polymicrobial biofilms are discussed here.
6.3.1. Fatty Acids
Fatty acids disrupt polymicrobial biofilms by targeting the structural and regulatory components that maintain these complex microbial communities. Compounds like lauric acid, myristic acid, and myristoleic acid, which were isolated from saw palmetto oil, have demonstrated efficacy in inhibiting polymicrobial biofilms of S. aureus and Cutibacterium acnes. Myristoleic acid, when combined with tobramycin, significantly reduced biofilm formation and bacterial viability in S. aureus strains, including methicillin‐resistant variants (Kim, Lee, Park, Kim, and Lee 2022). Additionally, lauric acid and myristic acid together (Bharathi et al. 2024) and alizarin alone (Bharathi et al. 2025) when loaded into soy‐lecithin liposomes effectively disrupted the dual‐species biofilms of S. aureus and C. albicans . Fatty acids disrupt biofilms by triggering biofilm dispersal, inhibiting initial stages of biofilm development, destabilising microbial cell membranes and affecting QS pathways, thereby suppressing the virulence of pathogens (Kumar et al. 2020). The main advantage of using fatty acids to disrupt polymicrobial biofilms is that they provide a multifaceted action due to their diverse active compounds. However, more research is needed to develop delivery systems that effectively target biofilms in vivo. Despite their promise, fatty acids face limitations for in vivo biofilm disruption. Key challenges include developing effective delivery systems to overcome their hydrophobicity and ensure sufficient concentration at the biofilm site. While liposomes show in vitro potential, their in vivo stability, encapsulation, and targeting require optimisation (Bharathi et al. 2025). Concerns also exist regarding concentration‐dependent host cell cytotoxicity and the potential, albeit lower, for microbial resistance. Lastly, the varied efficacy across diverse polymicrobial biofilm compositions necessitates further research into their broad‐spectrum applicability (Kumar et al. 2020; Bharathi et al. 2024).
6.3.2. Flavonoids
Flavonoids are generally found in various fruits and vegetables. Nanovesicles of quercetin and kaempferol have been shown to disrupt biofilms of S. mutans , a major contributor to dental plaque. These compounds reduced biofilm mass and viable cell counts, suggesting their potential in oral health applications (Kamarehei et al. 2022). Flavonoids hold promise as antibiofilm agents because they are seen as alternatives to conventional antimicrobial therapy. Their high efficacy can be attributed to the diverse mechanisms of action, such as disruption of cell membrane integrity, inhibition of QS, and efflux pump activity. Studies into their pharmacokinetics, bioavailability, and the synergistic effects with existing antibiotics will be crucial to harnessing their therapeutic potential. Additionally, bioprospecting flavonoid‐rich extracts from fruits and various plant sources could result in novel, phyto‐based antibiofilm agents.
A significant hurdle is their pharmacokinetics and bioavailability, as many flavonoids exhibit poor solubility and stability in vivo, limiting their effective concentration at the biofilm site (Kamarehei et al. 2022). While nanovesicles offer a potential solution for improved delivery, their long‐term stability and targeted accumulation in vivo still require extensive research and optimisation (Chavda et al. 2023). Furthermore, while generally considered safe, the potential for cytotoxicity at higher concentrations or with prolonged exposure needs thorough investigation to determine a safe and effective therapeutic window. The synergistic potential with existing antibiotics is a major advantage that warrants further exploration to develop more potent combination therapies (Liu et al. 2025). Lastly, the scalability of flavonoid production presents a significant challenge. Relying solely on plant extraction may not meet therapeutic demands, necessitating the development of plant cell‐based bioreactors for mass production (Kamarehei et al. 2022).
6.3.3. Essential Oils
Essential oils (EOs) produced by aromatic plants have gained attention as they are reported to inhibit drug‐resistant polymicrobial biofilms. Cinnamaldehyde, carvacrol, and eugenol exhibit strong antibiofilm properties against rapidly growing Mycobacteria. The potential mechanism through which these compounds exert their effects may involve damaging the bacterial cell membrane. Amikacin and clarithromycin, when combined with EOs such as carvacrol and cinnamaldehyde, have shown synergistic effects in reducing biofilm formation and enhancing bacterial killing of Mycobacteria spp. (Huang et al. 2024). Amikacin and clarithromycin naturally possess human toxicity. When combined with these EOs, it may result in the use of a lesser dosage of antibiotics, potentially reducing cytotoxicity (Huang et al. 2024). The reason why EOs are effective against polymicrobial drug‐resistant biofilms is that EOs consist of a myriad of compounds, targeting different structural components of both bacteria and fungi (Prakash et al. 2018). Therefore, it is difficult for the pathogens to gain resistance against such cocktails of compounds. There are several factors to be considered before EOs can be explored for their clinical application. EOs have numerous fatty acid components. Thus, it is necessary to pinpoint the exact component responsible for the activity and to elucidate the mechanism of action in a complex biofilm environment. This might be a disadvantage while working with EOs as their chemical composition varies with season and geography, potentially affecting bioactivity, which could lead to variations in antibiofilm activity. Since EOs are volatile, future research should focus on proper delivery mechanisms and formulations that can inhibit as well as disrupt polymicrobial biofilms. Furthermore, assessing their potential cytotoxicity, stability, dosage determination, and conducting clinical trials to validate their efficacy and safety keeps EOs far from reaching clinical application.
6.3.4. Sphingolipids
Sphingolipids are bioactive lipids that are an integral part of the eukaryotic cell membrane and have recently been reported to disrupt polymicrobial biofilms (Silva et al. 2020). Sphingolipids like sphingosine, phytosphingosine (PHS) and sphinganine were incorporated onto saliva‐coated hydroxyapatite. These hydroxyapatite surfaces reduced the biofilms of S. mutans 10‐fold, suggesting their potential as anti‐oral biofilm agents (Kamarehei et al. 2022). Since sphingolipids can interfere with microbial adhesions, they can inhibit the initial adhesion of biofilms, which is a crucial step in biofilm formation. As sphingolipids can modulate signalling pathways in the host, they can enhance the host's ability to combat biofilm‐associated pathogens. This augurs well for exploring sphingolipids as promising candidates for adjunctive therapies against polymicrobial infections. Future research should focus on elucidating the precise mechanisms by which sphingolipids exert their antibiofilm effects and exploring their efficacy in clinical settings.
Despite their promising potential, the use of sphingolipids as antibiofilm agents faces several limitations that future research needs to address. We must better understand their specificity and spectrum of activity against the wide variety of microbial species found in complex polymicrobial biofilms, as different microbes may respond differently (Ottaviano et al. 2025). Developing effective delivery and stability strategies is crucial, especially for challenging infection sites beyond the oral cavity, ensuring these compounds remain active and bioavailable (Ottaviano et al. 2025). Given that sphingolipids are vital host signalling molecules, careful investigation into their host‐pathogen interactions and potential off‐target effects is necessary to avoid unintended toxicity or immune responses. Furthermore, the risk of resistance development and the long‐term efficacy of sphingolipids require thorough evaluation.
6.3.5. Antibiotic Adjuvants and Fatty Acid Adjuvants
Adjuvants, often used in vaccines, can also enhance antibiotic delivery. Likewise, adjuvants can also be used along with antibiotics, thereby enabling a controlled and sustained release. Incorporating natural compounds as adjuvants can potentiate the effects of antibiotics once they penetrate the biofilm, and a controlled release of the antibiotic prolongs the killing of cells within a biofilm. Myristoleic acid has been identified as a potent enhancer of aminoglycoside antibiotics like tobramycin. At sub‐inhibitory concentrations, it increased antibiotic efficacy, reduced biofilm formation, and altered bacterial morphology, suggesting its role in overcoming antibiotic resistance mechanisms (Park et al. 2022). By disrupting the biofilm matrix, adjuvants aid in the deeper penetration of antibiotics, thereby improving their effectiveness. Adjuvants can also be combined with natural QSIs and biofilm disruptors that can enhance polymicrobial biofilm disruption. Some adjuvants themselves inhibit the QS system, thereby disrupting the chemical coordination required for biofilm formation, and some act on the EPS layer, thereby weakening the integrity of biofilms (Fleming and Rumbaugh 2017). As a step forward, studies should focus on optimising the delivery mechanisms of both antibiotic and fatty acid adjuvants and understanding their interactions with antibiotics to broaden their application for clinical use.
Natural compounds play a crucial role in tackling polymicrobial biofilms as they are broad‐spectrum multi‐target inhibitors. As they are sourced from plants and marine organisms, they exhibit low cytotoxicity when compared to metal nanoparticles. Natural compounds can exhibit a synergistic effect when combined with antibiotics; thereby increasing their efficacy.
6.4. Biomolecule‐Based Therapeutics
6.4.1. Peptide Antibiotics
Peptide antibiotics are low molecular weight proteins that kill bacteria. Unlike bacteriocins, peptide antibiotics exhibit broad‐spectrum activity. Like phage endolysins, they disrupt bacterial cell membranes and, when combined with antimicrobials, they can act against biofilms too. Antimicrobial peptides (AMPs) have several advantages over antibiotics. They possess anti‐persister activity, immunomodulatory activity, and wound healing activity that antibiotics lack. Most importantly, AMPs can be easily manipulated to enhance antimicrobial activity while reducing cytotoxicity (Mahlapuu et al. 2016). The main advantage of AMPs is that they can be tailored to target a specific pathogen without disturbing the normal flora. On the other hand, administration of antibiotics often causes dysbiosis (Batoni et al. 2021). AMPs have been used to combat several polymicrobial biofilm infections, such as wound infections mediated by S. aureus , P. aeruginosa , and A. baumannii (Thapa et al. 2020). Apart from the wound infection model, AMPs have been used to target polymicrobial biofilms in dental implants and the rat vaginitis model. Despite their advantages, AMPs suffer from several limitations like low stability in biological fluids, the need for high concentrations to eradicate polymicrobial biofilms, and difficulty in approval for clinical use (Galdiero et al. 2019; Batoni et al. 2021; Campos et al. 2025). Although these compounds offer a multifaceted approach to eliminate polymicrobial biofilms, their success in clinical application relies on the route of administration and determining their optimum dosage. Nanoparticle delivery systems offer a new avenue in delivering AMPs directly into the biofilm. Nanoencapsulation methods can protect the AMPs from degradation while delivering them into the biofilm matrix. Of late, 3D scaffolds combined with AMPs with materials conducive for tissue engineering are being explored. While peptide antibiotics offer compelling advantages, their path to widespread clinical use is fraught with specific and nuanced challenges. A primary limitation lies in their pharmacokinetic properties, particularly their low stability in biological fluids (Shen et al. 2022). This susceptibility to rapid degradation by proteases in the bloodstream or gastrointestinal tract significantly reduces their in vivo bioavailability and shortens their circulation half‐life (Shen et al. 2022). Consequently, achieving therapeutic concentrations often necessitates high dosages, which can raise concerns about cytotoxicity to host cells. Furthermore, the cost and complexity of peptide synthesis and production remain a considerable hurdle compared to traditional small‐molecule antibiotics (Zheng et al. 2025).
6.4.2. Combinatorial and Rationally Designed Peptides
Rationally designed peptides offer a cutting‐edge approach to combat polymicrobial biofilms. Instead of relying on single AMPs, peptide fusion or rational design can enhance specificity and biofilm disruption efficacy. Fusion of species‐specific peptides with broad‐spectrum AMPs can increase antimicrobial activity against specific pathogens while minimising effects on beneficial microbiota. Peptides engineered to target specific components of the biofilm matrix or microbial cells can effectively disrupt biofilms. For example, 10 synthetic peptides were designed to target both the planktonic cells and biofilms of drug‐resistant P. aeruginosa . Interestingly, one peptide WIK‐14 could destroy the entire ECM of P. aeruginosa (Kim, Son, et al. 2023). The main advantage of the rational design of AMPs is that they can be tailored or fine‐tuned against a particular target in the pathogen. Computational modelling and machine learning algorithms facilitate the design of natural AMPs with optimised amphipathicity and charge distribution, enhancing their ability to penetrate the biofilm matrices. To protect natural AMPs from proteolytic degradation, hydrocarbon staples are being incorporated (Bird et al. 2016). AMPs can be designed to target specific signalling molecules like cyclic di‐GMP to disrupt P. aeruginosa in a polymicrobial biofilm. Although it targets only one species, the metabolites produced by P. aeruginosa might assist other members as they can cross‐feed on these metabolites. The absence of crucial metabolites might result in induced starvation in other members, thereby leading to the disintegration of the entire polymicrobial biofilm. Fusion of different AMPs will create hybrid peptides that exhibit broad‐spectrum biofilm disruption ability and at times can also reduce cytotoxicity. For instance, halophiles produce several AMPs but tend to lose their stability under normal conditions. Combining halophilic peptides with regular AMPs might result in hybrid molecules that are stable at normal conditions, thereby enabling their use in clinical applications.
Though the potential for AMP resistance remains a concern, future research on rationally designed AMPs should focus on in vivo efficacy and safety by conducting animal studies and selecting low‐cost animal models. Rationally designed AMPs can be used in combination with antibiotics to overcome resistance mechanisms and enhance treatment outcomes. Biomolecule‐based agents like AMPs may have several foreseen advantages. Their low toxicity profiles make them suitable agents to be delivered into sensitive niches, wounds, and mucosa. Encapsulating AMPs into nanocarriers and hydrogels aids in directly delivering the biomolecule into the biofilm, favouring better penetration, stability, and controlled release.
6.5. Microbial Therapeutics
Microbial therapeutics are emerging as potent agents in combating recalcitrant polymicrobial biofilms.
6.5.1. Bacteriophage Therapy
Bacteria and fungi tend to coexist within biofilms instead of planktonic states. Usually, such communities include cross‐kingdom microorganisms, making them harder to remove from abiotic surfaces or infection sites. Bacteriophages specifically infect bacteria, offering a targeted approach to biofilm disruption. This approach is urgent as it provides an antibiotic‐free means to tackle drug‐resistant polymicrobial infections. Combining the innate lysing ability of phages with antibiotics has shown enhanced antibiofilm activity (Chan et al. 2016; Joo et al. 2023). The phage KPO1K2 depolymerases effectively reduce biofilm formation of K. pneumoniae and P. aeruginosa in dual‐species biofilms. Though phages are known solely to kill bacteria, they reduced interkingdom biofilms comprising P. aeruginosa and C. albicans . Biofilm reduction was observed when phages were used in synergy along with antifungal and antibacterial drugs (Gliźniewicz et al. 2024). Recent studies highlight the potential of engineered phages armed with biofilm‐degrading enzymes, including depolymerases, endolysins, and quorum‐quenching molecules, to penetrate and dismantle dense extracellular matrices. These enzymes weaken biofilm structural integrity and enhance antimicrobial penetration (Chang et al. 2022). Moreover, CRISPR‐Cas‐armed ‘designer phages’ have been developed to silence ARGs (e.g., β‐lactamases, efflux pumps) within biofilm‐residing bacteria, offering precise eradication of multidrug‐resistant pathogens (Tao et al. 2022; Zuberi et al. 2024). Phage cocktails targeting multiple bacterial species simultaneously are also being explored to overcome the narrow host range of individual phages and address the complexity of polymicrobial infections. Such cocktails, when co‐administered with antibiotics or antifungals, have demonstrated synergistic effects and significant reductions in biofilm biomass (El‐Din et al. 2025). Encapsulation of phages in nanocarriers (e.g., liposomes, hydrogels) has further enhanced phage stability, protected them from host immune clearance, and improved delivery into biofilm matrices (Patil et al. 2025).
Through the means of synthetic biotechnology, phages can be engineered to express biofilm‐degrading enzymes, enhancing their ability to disrupt polymicrobial biofilms. Endolysins are enzymes produced by phages that degrade the peptidoglycan layer of the bacterial cell wall to release progeny phages during the lytic cycle. The endolysin Lys08, from an E. faecalis phage, reduced biofilm density in urinary tract infections. The endolysin showed great stability in various temperatures and pH (Yang et al. 2020). Recently, mycoviruses, which infect fungi, are emerging as novel key players in therapeutic strategies. Their role is particularly intriguing within biofilms, as mycoviruses can temper fungal virulence by downregulating harmful virulence genes, disrupting biofilm formation, and hindering fungal growth (Hamid 2023). A recent report related to the identification of ‘killer viruses’ of Saccharomyces cerevisiae (Myers and James 2022) augurs well for exploring mycoviruses against drug‐resistant Candida, as they also undergo a yeast morphological state during their life cycle. However, leveraging mycoviruses to treat polymicrobial biofilms is still in its infancy and comes with its own set of challenges. Research on the interactions of mycoviruses in polymicrobial biofilms is extremely limited. Despite these hurdles, the potential of mycoviruses to modify fungal behaviour and interfere with biofilm integrity offers exciting avenues for developing novel strategies to combat stubborn polymicrobial infections.
Though phage therapy might seem a natural means to destroy biofilms, its success in clinical application remains a question to date. Identifying the precise pathogens is essential for effective treatment. The isolation of phages with a broad host range poses a challenge, as biofilm infections are always polymicrobial. A phage cocktail with a broad spectrum or engineered phages targeting different pathogens in a multispecies biofilm should be formulated. Determining the optimal titre value of the phages is a challenge because the thickness of the biofilms is unknown at an infection site. Low titre values might give rise to phage‐resistant strains because when the number of phages is relatively low to the bacterial density, not all bacterial cells are infected and lysed (Nang et al. 2023). This allows a significant portion of the bacterial population to survive, leading to the rise of new phage‐resistant mutants (Labrie et al. 2010; Fernández et al. 2017; Oechslin 2018). Moreover, the heterogeneous structure of polymicrobial biofilms, often featuring interwoven bacterial and fungal networks, limits phage diffusion and activity. Strategies such as co‐administering ECM‐degrading enzymes or using microbubble‐assisted ultrasound have been proposed to enhance phage penetration (Gliźniewicz et al. 2024). Phage therapy also faces regulatory and ethical challenges, including difficulties in standardising phage production, determining pharmacokinetics in biofilm‐laden tissues, and addressing concerns about ecological impacts or HGT (Zalewska‐Piątek 2023). Adaptive phage libraries and AI/ML‐guided phage‐host interaction prediction are emerging solutions to these barriers. Phage therapy's reliance on precise pathogen identification limits its applicability in polymicrobial settings. Resistance development, immune system neutralisation of phages, and variability in biofilm penetration are additional hurdles. Regulatory approval processes for phages are also inconsistent globally (Zalewska‐Piątek 2023). As a downside, synergising phages with antibiotics might give rise to antibiotic‐resistant microorganisms as well.
While the above factors might seem like long‐standing issues to resolve, phage therapy still holds an upper hand in treating polymicrobial biofilms. It offers a targeted approach, as it targets only its particular bacterial host rather than beneficial bacteria or fungi. This reduces the issue of dysbiosis, which is often associated with broad‐spectrum antibiotics.
6.5.2. Probiotics, Prebiotics and Postbiotics
Probiotics and their related components appear to be a natural and non‐antibiotic means to combat polymicrobial biofilms. They have recently gained attention as they can disrupt polymicrobial biofilms through different mechanisms. Probiotics are live bacteria that produce bacteriocins and organic acids, resulting in competitive exclusion of the pathogens. Prebiotics act as a food source for beneficial microbes of the host gut, thereby modulating the gut microflora to a more beneficial population. For instance, fructo‐oligosaccharides and inulin increase bifidobacteria and reduce the levels of enteropathogenic bacteria in the gut (Dou et al. 2022). On the other hand, postbiotics are bioactive metabolites (short‐chain fatty acids, bacteriocins and enzymes) produced by probiotics that act upon polymicrobial biofilms (Kumar et al. 2024). Probiotic‐based interventions are constrained by strain variability, survival during gastrointestinal transit, and potential for HGT of resistance genes in the host microbiome (Hitch et al. 2022). Postbiotics, while safer, require dose standardisation and efficacy validation.
Overall, probiotics, prebiotics, and postbiotics offer an antibiotic‐free and sustainable approach to disrupt polymicrobial biofilms. Their main advantage is they can reverse dysbiosis, i.e., restore microbial homeostasis in an infected site through competitive exclusion and microbiota modulation.
6.6. Physical Inhibitors
Physical means of removing biofilms is an alternative option to antibiotic‐mediated methods in the era of AMR. It exploits light‐based methods, sound‐based methods, and high‐energy methods of biofilm removal. These methods target the structural integrity of biofilms through mechanical, thermal, electrical, or photonic means, often enhancing the efficacy of antimicrobial agents.
6.6.1. Sonication
This method uses high‐frequency sound waves to induce cavitation, forming vapour bubbles in a liquid medium. The collapse of these bubbles creates shock waves and ROS formation, leading to cell wall damage in bacteria. This method effectively disrupts the polymicrobial biofilms of P. aeruginosa , S. aureus , and C. albicans (Ciarolla et al. 2022). This method is often associated with potential host tissue damage and inconsistent results owing to the uncontrolled formation of cavitation. The efficacy of this technique depends on several parameters like frequency, intensity, and duration. Sonication may disrupt biofilms, but it might not be lethal to the cells. Adjunctive antimicrobial therapies are required along with sonication.
6.6.2. Electromagnetic Waves
Electromagnetic waves are an innovative and non‐invasive way of disrupting polymicrobial biofilms. Static, alternating, or pulsed magnetic fields are applied to induce metabolic changes, biomass reduction, and ROS production in biofilms. This method is highly useful in treating infections associated with metal implants. The conductive properties of the implant material play an important role in this method. This method significantly reduced the metabolic activity as well as the biomass of P. aeruginosa biofilms (Ciarolla et al. 2022). The main advantage of this method is that EMF can penetrate tissue and target biofilm on implant surfaces. EM fields cannot fully eradicate the biofilms, which might lead to the regrowth of residual bacteria and form biofilms again. The efficacy of EM can be hindered by limited penetration, posing a challenge in disrupting polymicrobial biofilms on medical devices. In addition, non‐uniform heating and potential tissue damage are other limitations of this method.
6.6.3. Plasma Treatment
This technique uses high‐energy plasma to destroy biofilm‐embedded bacteria. CAP is a nonthermal physical means for disrupting polymicrobial biofilms. Bactericidal effects come from charged and neutral active species generated by plasma, including ozone, nitric oxide, superoxide, hydrogen peroxide, singlet oxygen, OH radicals, ultraviolet radiation, electrons and other charged species. This method could eliminate polymicrobial biofilms of E. coli and methicillin‐sensitive and resistant S. aureus (Joshi et al. 2010). Large surface areas can be sterilised using this method. CAP may not fully eliminate biofilms, necessitating the use of antibiotics along with this method. The effectiveness of CAP may vary depending on the microbial species, biofilm thickness and environmental conditions. Sub‐lethal exposure of CAP might lead to stress response in the pathogens, inducing adaptive resistance mechanisms.
6.6.4. Photodynamic Therapy (PDT)
This method involves the application of photosensitisers activated by light to produce ROS that damage biofilm structures. It seems to be very effective in treating wound infections as it provides site‐specific delivery. It impaired the biofilms of P. aeruginosa , MRSA, and S. epidermidis biofilms and showed some success in patients with chronic leg ulcers (Chen et al. 2022). Though PDT has the advantages of broad‐spectrum activity and minimal resistance development, it also suffers from several limitations. Light and photosensitisers might have limited diffusion inside thick biofilms. Optimisation of light intensity and photosensitisers is necessary as polymicrobial biofilms are composed of different microbial species, and the structure of biofilms varies accordingly. Selecting the appropriate photosensitisers with broad‐spectrum activity and deep tissue activity is a major task in this application.
6.6.5. Laser‐Induced Patterns
This method involves modifying the surface of implant materials. Femtosecond laser treatment creates micro/nanoscale patterns that deter bacterial colonisation (Kumar et al. 2025). The change in surface roughness and the generation of hydrophobic surfaces prevents biofilm colonisation (Jalil et al. 2020). Micro‐ or nanopatterning creates surface topographies that prevent microbial adhesion or biofilm regrowth. Laser‐induced periodic surface structures (LIPSS) can be tailored to disrupt biofilm maturation or interfere with microbial signalling. Laser‐induced patterns have several advantages over other physical methods, as they are a non‐contact, minimally invasive means to disrupt polymicrobial biofilms (Wang et al. 2025). This method provides cell‐level precision with minimal off‐target action. However, the cost and complexity of high precision laser systems, scalability, and repeatability have put this system out of reach to be inducted in clinical settings. Overall, though physical inhibitors provide innovative avenues for disrupting polymicrobial biofilms specifically on medical devices, the equipment cost and the requirement of technical expertise to integrate it into clinical practice question its affordability. Though the above facts might limit the application, the advantages of physical inhibitors cannot be ignored. Physical inhibitors are not species‐specific, and they act upon diverse microbial taxa. This is crucial from a polymicrobial biofilm perspective, as it often contains both bacteria and fungi. More importantly, the question of resistance development is less pressing, as physical approaches apply mechanical or energy‐based stress rather than targeting specific proteins.
6.7. Nanotechnology
Nanotechnology offers innovative strategies to prevent, disrupt, or eradicate polymicrobial biofilms. Nanomaterials can be designed to penetrate biofilms by disrupting the ECM, delivering antimicrobial agents directly into the biofilm and preventing initial microbial adhesion.
6.7.1. Metallic Nanoparticles (NPs) With Antimicrobial Properties
Several metal nanoparticles, like silver (Ag NPs), zinc oxide (ZnO NPs) and gold (AuNPs) possess direct antimicrobial activity (Rai et al. 2009). Metallic NPs disrupt the biofilm matrix by penetrating the EPS layer and thereby destabilising its structural stability. They can also induce oxidative stress within microbial cells, causing cellular damage and interfering with replication and other cellular functions by binding to microbial DNA and proteins. Though metallic NPs are efficient in killing pathogens, they can be cytotoxic to human cells, which necessitates evaluation of NPs in cell lines. NPs may also pose an environmental hazard, as the release of excess NPs into natural ecosystems may lead to undesirable ecological consequences. Sub‐lethal concentrations of NPs may induce adaptive resistance in pathogens, thereby nullifying their application.
6.7.2. Nanocarriers
Nanocarriers like liposomes, polymeric nanoparticles, and dendrimers can penetrate the EPS and deliver antibiotics directly to microbes. This enables localised high concentrations of antimicrobials, reducing off‐target effects. Liposomes are spherical vesicles composed of lipid bilayers that can encapsulate both hydrophilic and hydrophobic agents. Their structural similarity to biological membranes makes them effective agents at delivering antimicrobial agents directly into the biofilm. Cationic liposomes interact with the negatively charged biofilms, facilitating adhesion and fusion, thereby enhancing the delivery of antibiotics inside the biofilm. Some liposomal formulations release their contents based on specific stimuli like pH changes, ensuring that the antibiofilm agent is directly released in the vicinity of the biofilms (Hu et al. 2019; Kluzek et al. 2022). Liposomal formulations suffer from some drawbacks, such as the stability of the formulation upon storage, which is important for its efficacy and its cost‐effective scalability process. Obtaining regulatory approval of the liposomal drug delivery system remains a major hurdle. Dendrimers are highly branched synthetic macromolecules with multivalent surface functionalities (Abbasi et al. 2014). The multiple surface groups allow simultaneous binding to various biofilm components. Dendrimers too have several issues that must be addressed. Dendrimers with cationic surface groups can be toxic to mammalian cells requiring further surface modification. More importantly, dendrimers lack specificity, wherein they can damage surrounding tissues and have very poor penetration into mature biofilms.
6.7.3. Multifunctional Hybrid Nanoplatforms
Multiple disruption strategies can be combined into one nanoplatform. For example, Ag NPs, enzymes, and antibiotics can be combined into one system wherein the synergistic effects significantly enhance biofilm disruption (Wan et al. 2016; Meesaragandla et al. 2022). In this case, the enzyme disrupts the EPS layer, facilitating the entry of Ag NP and the antibiotic into the biofilm. As the mode of action of the NP and the antibiotic varies, multiple species in a biofilm can be destroyed by this method. Though this method might seem an ideal one, the stability of each component when combined into one platform always remains a question. Hybrid nanoplatforms' complexity raises issues with reproducibility and regulatory hurdles for approval. Integration of multiple active agents may also lead to unpredictable interactions (Soltanmohammadi et al. 2024). However, their mechanistic diversity, precision, and ability to circumvent traditional resistance mechanisms make the aforementioned nanotechnology‐based approaches ideal candidates for polymicrobial biofilm management.
7. Perspectives and Prospects
It is now understood that polymicrobial biofilms are not a mere conglomeration of microbial species but rather an interactive community with shared behaviours. In an interkingdom interaction between bacteria and fungi, biofilm dynamics are much more complicated since they not only interact with each other but also engage in complex interactions with the host. The enhanced drug resistance among both C. albicans and its associated bacteria when thriving as polymicrobial biofilms, when compared to single community biofilms, is one fine example. This indicates that some important crosstalk exists between these members, and inhibiting this inter‐domain signalling and molecular mechanisms should be a priority in treating polymicrobial infections (Pohl 2022).
The complexity of polymicrobial biofilms necessitates the development of novel therapeutic strategies. Phage therapy presents a promising alternative for targeting bacterial components of polymicrobial biofilms. Although phages target only bacteria in an interkingdom biofilm, eliminating bacteria also removes the key signal compounds that induce virulence in the other members, thereby rendering the entire polymicrobial biofilm vulnerable to antimicrobial treatment. As mixed‐species biofilms often comprise more than two or three species, it necessitates the need for broad‐spectrum antimicrobial agents like AMPs. A key advantage of AMPs is their low potential for inducing resistance, and they can be fine‐tuned to increase their antimicrobial activity. The development of machine learning tools facilitates the rational design of novel AMPs. Gene editing techniques like CRISPR interference (CRISPRi) have been explored to silence genes involved in biofilm formation, offering a targeted approach to biofilm disruption. The luxS gene which synthesises Autoinducer‐2 (AI‐2) was silenced in E. coli , thereby inhibiting biofilm formation (Zuberi et al. 2017). Another study employed CRISPRi to investigate various genes involved in biofilm formation in Pseudomonas fluorescens (Noirot‐Gros et al. 2019). Although the above two studies focused on single‐species biofilms, CRISPRi augurs well to explore several aspects in polymicrobial biofilms too. Single guide RNAs (sgRNAs) can be designed to target various virulence genes, EPS‐producing genes, QS genes, and adhesion genes in polymicrobial biofilms. As discussed in the earlier section, β‐lactamase producing strains manipulate their members existing in a polymicrobial community. sgRNAs can be designed to silence β‐lactamase genes and efflux pump genes in a polymicrobial biofilm. In certain disease conditions like polymicrobial wound infections and VVC, probiotic strains can be engineered to carry the sgRNA to the site of infection, where the probiotic strains can infiltrate the biofilms and selectively target the pathogen or the virulence genes. While CRISPRi shows promise as a non‐antibiotic strategy to suppress microbial virulence, the delivery of the sgRNA directly into the biofilm across the ECM remains a major challenge.
To establish novel treatment measures, it is important to identify new drug targets in polymicrobial biofilms. Integrating various ‘omics’ techniques, such as transcriptomics, proteomics, and metabolomics, is essential to elucidate complex regulatory networks in polymicrobial biofilms. Moreover, multi‐omics studies might help in the unravelling of novel molecular markers that may serve as diagnostic tools or therapeutic targets.
7.1. Prospects of Polymicrobial Biofilm Research
The application of omics technologies in biofilm research generates a substantial amount of data that requires careful interpretation and use of proper data analysis tools. AI offers powerful tools for interpreting large‐scale omics data and uncovering novel therapeutic targets. AI tools can be useful in predicting disease states based on changes in mixed‐species biofilm composition occurring at specific body niches, such as oral or gut biofilms, for predicting biofilm phenotypes and identifying potential therapeutic targets. AI‐driven models can accelerate drug discovery strategies as well as the translation of basic research into clinical studies.
Multi‐omics tools, encompassing genomics, transcriptomics, proteomics, and metabolomics, offer an unparalleled, holistic view into the intricate biological processes governing polymicrobial biofilms. By providing a comprehensive snapshot of genetic potential, gene expression, protein function, and metabolic activity, these approaches are instrumental in unravelling key resistance and virulence pathways that contribute to biofilm persistence and therapeutic recalcitrance (Seneviratne et al. 2020; Séguéla et al. 2025). For example, the application of meta transcriptomics can specifically elucidate the upregulation of QS genes within in vivo polymicrobial infections, thereby revealing critical communication networks and coordinated behavioural shifts that underpin heightened drug resistance (Seneviratne et al. 2020). Furthermore, the synergistic integration of these multi‐omics datasets facilitates the discovery of novel molecular biomarkers, which hold immense potential as diagnostic targets for early and precise detection, as well as therapeutic targets for innovative antimicrobial strategies (Seneviratne et al. 2020; Pohl 2022). The capacity to profile the genetic, transcriptomic, proteomic, and metabolic landscapes of polymicrobial biofilms at an unprecedented resolution is thus pivotal for dissecting their intricate interspecies interactions, adaptive mechanisms and ultimately, for guiding the development of more effective interventions (Seneviratne et al. 2020).
The transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms is rapidly being realised in the fight against polymicrobial biofilms. These advanced computational tools extend beyond traditional data analysis, enabling the rational design of novel AMPs with optimised efficacy and reduced off‐target effects, addressing a critical need in antimicrobial development (Kim, Wang, and Ahn 2023). Beyond therapeutic design, AI/ML algorithms are indispensable for enhancing the interpretation and predictive power of biosensor data (Wu et al. 2024), and for predicting complex biofilm‐specific phenotypes by intelligently analysing vast and heterogeneous datasets derived from omics studies or sophisticated imaging analyses (Nyaga et al. 2024). A compelling example of this integration is the advent of AI‐integrated biosensors, particularly electrochemical systems that leverage sophisticated impedance pattern recognition for rapid, sensitive, and accurate differentiation of polymicrobial infections in real time (Sengupta et al. 2024). Such deep learning‐based AI models have demonstrated exceptional accuracy in identifying biofilm states, even distinguishing between planktonic and biofilm forms of notorious pathogens like Pseudomonas aeruginosa based on subtle variations in optical microscopic images (Sengupta et al. 2024). This level of precise analytical capability is crucial for overcoming the limitations of conventional methods in differentiating microbial states and interactions within complex clinical environments. Moving forward, the fusion of patient‐specific biofilm signatures gleaned from comprehensive omics data with clinical metadata holds the promise of ushering in an era of personalised treatment strategies, thereby advancing the principles of precision medicine to combat persistent biofilm‐associated infections (Lao et al. 2024; Sousa et al. 2025).
Employing omics approaches may help in identifying unique structural and functional characteristics of biofilms in individual patients, creating unique biofilm signatures. These biofilm fingerprints can be combined with the patient's electronic health record data to develop precision medicine. This will assist in tailoring therapies based on the patient‐specific biofilm fingerprints. Polymicrobial biofilms also pose major challenges in environmental and industrial contexts, such as marine biofouling, microbial‐induced corrosion (MIC) and food contamination. Solutions obtained from combating polymicrobial biofilms in clinical settings might hold value in these niches as well.
Author Contributions
Paramasivam Nithyanand: writing – review and editing, writing – original draft. Bharath Reddy Boya: writing – review and editing. Jin‐Hyung Lee: writing – review and editing. Jintae Lee: writing – review and editing, funding acquisition, supervision.
Disclosure
Declaration of generative AI in scientific writing: No artificial intelligence tool was used to write the manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgements
This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (RS‐2025‐00513239) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS‐2024‐00450423).
Nithyanand, P. , Boya B. R., Lee J.‐H., and Lee J.. 2025. “Polymicrobial Biofilms: Interkingdom Interactions, Resistance and Therapeutic Strategies.” Microbial Biotechnology 18, no. 8: e70218. 10.1111/1751-7915.70218.
Funding: This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (RS‐2025‐00513239) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS‐2024‐00450423).
Paramasivam Nithyanand and Bharath Reddy Boya contributed equally to this work.
Data Availability Statement
The authors have nothing to report.
References
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