Open Access Article
This Open Access Article is licensed under a
Creative Commons Attribution 3.0 Unported Licence

Molecularly imprinted polymers as next-generation weapons against the AMR crisis

Ammar Ibrahim*ab, Alvaro Garcia*a, Amin M. Alrajhi*c and Sergey Pilestky*a
aDepartment of Chemistry, University of Leicester, Leicester, UK. E-mail: afii2@le.ac.uk; a.f.ibrahim89@gmail.com; agc14@leicester.ac.uk; sp523@leicester.ac.uk
bMinistry of Oil, North Refineries Company, Republic of Iraq
cMinistry of Environment, Water and Agriculture, Kingdom of Saudi Arabia. E-mail: e16907@mewa.gov.sa

Received 24th January 2026 , Accepted 14th March 2026

First published on 20th March 2026


Abstract

Antimicrobial resistance (AMR), referred to as the “Silent Pandemic”, has become a massive threat to public health of the current and next generations. Molecularly imprinted polymers (MIPs) have emerged as a possible tool to fight AMR, due to their unique features, such as high affinity and specificity for relevant targets, easy manufacturing and good stability in physiological conditions. This review explores recent developments in combating AMR using MIPs as antibiotic drug carriers, using polymers for targeting components of the outer membrane of Gram-negative bacteria, blocking cell signalling by capturing quorum-sensing molecules, or binding alarmone nucleotide molecules.


Introduction

Antimicrobial resistance (AMR) is defined as the capacity of pathogens (such as bacteria, parasites, viruses, and fungi) to resist the effect of medicines, grow and continue in their pathogenic role,1,2 which prolongs the treatment, or, in some cases, causes death to the infected patient. The incorrect use of antimicrobial drugs and the lack of public knowledge contribute to organisms developing the ability to resist drugs. Annually, 10 million deaths are expected due to AMR by 2050.3 The extent of the problem has escalated to a high-risk level, with huge financing pressure upon healthcare organisations. In Europe, the annual estimation for AMR treatment is about 9 billion euros. Moreover, the Center for Disease Control and Prevention (CDC) has reported that AMR treatment costs in the USA are about 35 billion dollars per year.4

Antibiotics were the first common drugs applied and approved for microbial infection treatment. A wide range of antibiotics are still used, such as β-lactams, sulphonamides,5 aminoglycosides,6 tetracyclines,7 lipopeptides,8 oxazolidinones,9 glycopeptides,10 streptogramins,11 macrolides,12 lincosamides,13 and quinolones.14,15 They can kill microorganisms or curb their growth via various mechanisms, such as inhibiting cell wall synthesis or disrupting the cell wall structure, hindering protein synthesis through interaction with some ribosomes, affecting nucleic acid synthesis, and intervening in some metabolic pathways (Fig. 1). They have contributed to the eradication of many diseases and decreased the rate of fatalities in comparison with the pre-antibiotic era.16 The ability of microorganisms to develop defences against antibiotics complicates the design and approval of new drugs. Thus, between 2010 and 2014, only four novel antibiotics were approved as new drugs.17 In 2010, the World Health Organisation (WHO) classified antibiotic resistance as one of the three most dangerous threats to global health.18 In 2021, WHO reported that “The antibacterial clinical and preclinical pipeline is stagnant and far from meeting global needs. Since 2017 only 12 antibiotics have been approved, 10 of which belong to existing classes with established mechanisms of antimicrobial resistance (AMR)”.19


image file: d6tb00194g-f1.tif
Fig. 1 Mechanisms of antibiotic action. DHF – dihydrofolic acid and THF – tetrahydrofolic acid. Reproduced from ref. 30 with permission from Elsevier, copyright (2021).

The primary challenge of AMR stems from the sophisticated array of resistance mechanisms that allow bacteria to withstand conventional antibiotic treatments, such as modification of penicillin-binding proteins (PBPs),20–22 decrease of the cell membrane permeability,23–25 pumping the drug outside the cell by efflux pumps,26,27 or drug inactivation by β-lactamase enzymes.28–30 This review provides a concise overview of bacterial resistance mechanisms and evaluates the evolution of molecularly imprinted polymers (MIPs) as a strategic tool against infection. Furthermore, it highlights specific bacterial cell membrane biomarkers as promising targets for the development of nanoparticles designed to disrupt essential survival pathways.

Nanotechnology and MIPs

In medicine, nanoparticles have become particularly valuable for disease detection, targeted therapy, and drug delivery. Their rational design has enhanced key characteristics such as bioavailability, compatibility, stability, and drug-loading capacity, thereby extending their applications to gene therapy, tissue engineering, and diagnostic systems.31 Among these systems, polymeric nanoparticles (PNPs) have received considerable attention because of their tunable properties, including morphology, shape, size (10–1000 nm), and molecular architecture.32,33 Depending on the intended application, PNPs may be prepared either via direct polymerisation of monomers or from preformed polymers, with each approach producing particles of distinct size, encapsulation efficiency, and morphological features. This synthetic versatility offers researchers the ability to tailor nanoparticles for a broad spectrum of biomedical and industrial applications.34

Among polymeric nanomaterials, MIPs have emerged as a prominent class of synthetic materials distinguished by their ability to achieve highly specific molecular recognition. MIPs are defined as polymers that possess the ability to “remember” a target molecule through the formation of complementary binding sites generated during synthesis. This molecular memory allows selective rebinding of the template, mediated through covalent or non-covalent interactions, in a manner analogous to natural recognition systems.35 The imprinting process involves the pre-assembly of a template with functional monomers, followed by polymerisation in the presence of a cross-linker, which stabilises the recognition cavities. The molecular imprinting process involves the copolymerisation of functional and cross-linking monomers in a solvent in the presence of a template molecule. Once polymerisation is complete, the template is removed, leaving specific recognition sites within the polymer structure. These imprinted cavities, which mirror the template in both geometry and chemical functionality, provide the material with the capacity for selective rebinding (Fig. 2).36–38 MIPs can be synthesised through straightforward, cost-effective, and highly adaptable methods, such as precipitation polymerisation,39,40 emulsion polymerisation,41,42 core–shell imprinting,43,44 and solid-phase methods,45,46 enabling the production of micro- and nano-sized materials with excellent stability, robustness, selectivity, and biocompatibility.47–49 Owing to these properties, MIPs have been successfully applied in a broad range of fields, including the recognition of amino acids and proteins.50–53 nucleotide derivatives,54 pollutants,55,56 drugs and food,57,58 chemical sensors,59 catalysis,60 drug delivery,61 biological antibodies and receptor systems.62,63


image file: d6tb00194g-f2.tif
Fig. 2 Schematic illustration of the synthesis steps of molecularly imprinted polymer(s) (MIPs) and their rebinding performance.

MIPs as antimicrobials

MIPs have gained attention as promising synthetic recognition platforms with potential applications in antibacterial therapy. Over the past decade, MIPs have been investigated for bacterial cell recognition and antibacterial activity, reviewed by Tse Sum Bui et al.64 Strategies have included imprinting entire bacterial cells65–67 or selectively targeting key biomarkers located on the bacterial outer membrane. Such targets include lipopolysaccharides (LPS), which are essential components of Gram-negative cell walls,68–71 quorum-sensing messengers, whose inhibition suppresses biofilm formation,72–77 and drug-deactivating enzymes such as β-lactamase.78 Other examples involve imprinting against flagellin,79 a structural protein responsible for motility; protein A,80 which mediates bacterial adhesion to host tissues and contributes to pathogenicity; and lipoprotein Lpp20,81 a component implicated in outer membrane integrity and immune evasion. Furthermore, the integration of MIPs with conventional antimicrobial agents, such as MIP-loaded polymyxin B, has been demonstrated to enhance antibacterial activity against Pseudomonas aeruginosa.82

More recently, research has diversified into multiple therapeutic strategies in which MIPs are designed according to the nature of the bacterial biomarker and the chosen synthetic route. Table 1 summarises current advances in the development of MIPs for combating bacterial growth and resistance.

Table 1 Molecular imprinting strategies for fighting bacteria
MIP type Applications Therapy's target Notes/outcomes Targeted bacteria Ref.
MIP-hydrogel • Drug delivery (Ciprofloxacin) Eye infections • Sustained release P. aeruginosa 83
• UV absorptivity
Emulsion/bulk/co-precipitation • Drug delivery (Ciprofloxacin) Skin/wound infections • Lower drug dose E. coli 84
• Sustained release
Surface imprinting (bioglass–chitosan) • Drug delivery (Doxycycline) Osteomyelitis/periodontitis • Controlled release S. aureus 85
• Reduced cytotoxicity
MIP-alginate encapsulated • Drug delivery (Vancomycin) Wound dressing • Controlled release E. coli 87
• Clear inhibitory effect
MIP-PVA/gelatine nanofibers • Drug delivery (Gentamicin) Wound dressing • Slow release E. coli 88
• Non-cytotoxic
• Fast wound closure
MIP-BC grafted • Drug delivery (Gentamicin sulphate) Broad spectrum • Controlled release S. aureus 89
• Clear inhibitory effect
Boronic acid-supported MIP • Boron affinity Inhibiting protein synthesis • High bacterial killing effect E. coli, S. epidermidis 90
• Drug delivery (Chloramphenicol)
Boronic acid-supported MIP • Boron affinity Targeting LPS (cell wall) • Remarkable affinity towards LPS E. coli 91
• Photothermal therapy • Selective recognition/inhibition
MIP-AuNPs • Photothermal therapy Targeting LPS • High affinity towards LPS E. coli 92
• Selective bacterial inhibition
MIP-homoserine lactone autoinducer • Block quorum sensing Biofilm formation • Sequestration of signalling molecules MRSA, P. aeruginosa 73 and 75
• Reduced biofilm
MIP + z-pppGppp (Alarmone) • Capture alarming nucleotides Stress adaptation inhibition • Effective in vivo dose reduction E. coli 93
• Growth inhibition
MIP-Lysozyme Cryogel • Lysozyme release Bacterial inhibition • Constant release S. aureus, E. coli 94
MIP-AgNPs • Antibacterial Cell wall disruption (β-linkage) • Selective recognition E. coli, S. epidermidis 95
• High antibacterial effect
MIP-clindamycin (PU nanofibers) • Drug delivery Acne vulgaris • Successful in vivo treatment S. aureus, P. aeruginosa, K. pneumoniae, Proteus vulgaris 96
• CFU reduction
• No cytotoxic effect


MIPs as drug delivery carriers

Bacterial eye infections, caused by pathogens such as Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli, are commonly treated through diverse therapeutic strategies. Among ocular drug delivery systems, soft contact lenses have attracted attention as potential carriers for sustained release of antibacterial agents. Ciprofloxacin (CFX), a fluoroquinolone antibiotic widely prescribed for corneal ulcers and bacterial eye infections, has been extensively employed in such systems. In this context, Kioomars et al.83 reported the development of a molecularly imprinted polymer (MIP) hydrogel contact lens designed to combine the antibacterial activity of ciprofloxacin with the structural robustness of MIPs. Acrylamide monomers were used to synthesise the hydrogel matrix, enabling controlled loading and sustained release of CFX. Notably, the antibacterial effect increased over time, suggesting that the sustained release of CFX from the MIP network gradually disrupted bacterial defences.

In addition to its drug delivery function, the synthesised MIP hydrogel exhibited optical properties desirable for ophthalmic applications. It effectively absorbed a broad spectrum of ultraviolet (UV) radiation while maintaining approximately 85% transmission of visible light. This thereby provides both therapeutic and protective benefits. The study further identified reliable monomer-to-template ratios for optimised hydrogel synthesis, offering promising parameters for ocular drug loading and release. However, an important consideration is that both MIPs and NIPs displayed antibacterial effects against Gram-positive and Gram-negative strains. This raises potential concerns regarding the cytotoxicity of the polymer matrices themselves, which warrants careful evaluation of their safety for corneal, lens, and retinal tissues before clinical translation. The development of ciprofloxacin-loaded MIPs for the treatment of wound infections has been investigated by Galván Romero et al.84 Their study demonstrated antibacterial activity against Staphylococcus aureus and Escherichia coli, with minimum inhibitory concentrations (MICs) ranging from 0.016 to 0.125 mg L−1 and 0.004 to 0.028 mg L−1, respectively. Importantly, no cytotoxic effects were observed on fibroblast cells. These findings suggest that MIPs represent a promising strategy for the treatment of skin infections; however, further in vivo assessments are required.

Bacterial infections of bones and teeth, such as osteomyelitis or periodontitis, pose serious clinical challenges, particularly in the context of increasing AMR. Khademi and Kharaziha85 developed an antibacterial and osteogenic treatment using surface imprinting to synthesise MIP-loaded doxycycline (DOX) over imprinted bioglass microspheres (BGMs) coated with chitosan. BGMs were synthesised via the sol–gel method,86 followed by deposition of chitosan to produce chitosan-coated BGMs (BGMs@Cs). Acrylamide monomers were then polymerised over the BGMs@Cs in the presence of DOX as a template, which was subsequently removed by elution to form the final MIP structure (Fig. 3). In vitro antibacterial assays demonstrated that Staphylococcus aureus and Escherichia coli were resistant to BGMs@Cs-MIP and BGMs@Cs-NIP; however, the BGMs@Cs-MIP-DOX formulation exhibited a clear inhibition zone, confirming the antibacterial efficacy of the system. Cytotoxicity evaluation using MTT assay of MG63 cell-like osteoblast cells (bone-forming cells) displayed cell viability of more than 80% with BGMs@Cs-MIP and BGMs@Cs-NIP, which ensures the cytocompatibility of MIP and successful removal of unreacted acrylamide monomers that are known to have toxic and carcinogenic properties. Furthermore, the cytotoxicity of DOX can be avoided by encapsulation with MIP. Additionally, osteogenic differentiation assays demonstrated a significant increase in calcium deposition for MG63 osteoblast cells treated with BGMs@Cs-MIP-DOX compared to controls, confirming the bone-forming activity.


image file: d6tb00194g-f3.tif
Fig. 3 (a) Schematic illustration of MIP synthesis over BGMs. (b) FE-SEM images of BGMs, coated chitosan (Cs) BGMs, and BGMs@Cs-MIP. Different surface roughness based on the particle formation stage. Reproduced from ref. 85 with permission from the American Chemical Society, copyright (2024).

In another study focused on wound dressing development, MIPs were employed as drug carriers and incorporated into alginate, a naturally occurring polymer derived from seaweed commonly used in wound dressings. Kurczewska et al.87 loaded the antibiotic vancomycin onto MIPs and subsequently encapsulated the vancomycin-loaded MIPs within an alginate matrix. MIPs were synthesised by combining vancomycin with the monomers ethylene glycol dimethacrylate and methacrylic acid, followed by initiation of the polymerisation reaction using potassium persulfate. The resulting MIPs were mixed with the alginate dressing and dried for 48 hours. In vitro release studies showed that vancomycin release from the MIP-alginate matrix was slower than from the control (Fig. 4), while antibacterial testing demonstrated that the inhibition zones against streptococcal strains were larger for vancomycin-MIP-alginate than for vancomycin-MIP alone. These findings highlight the potential of MIPs as antibiotic carriers, offering both controlled drug release and enhanced antibacterial activity.


image file: d6tb00194g-f4.tif
Fig. 4 Vancomycin release profiles from the encapsulated MIP in comparison to the controls. Reproduced the ref. 87 with permission from Elsevier, copyright (2017).

MIPs exhibit promising characteristics that can effectively contribute to enhancing the performance of drug delivery systems, improving therapeutic outcomes, and optimising the pharmaceutical properties of drugs.

MIPs enhancing boron affinity to bacterial structures

Materials based on boronates exhibit a unique affinity for compounds and biomolecules containing cis-diol functionalities, such as nucleosides, saccharides, glycans, and glycoproteins, all of which are critical components of the cell surface. This distinctive property has been leveraged to develop a range of biological applications, including cell recognition97–99 and the creation of antibacterial and anticancer drugs.100–104 Prior research has integrated boronate affinity with MIPs for the selective recognition of various analytes: nucleosides in pharmaceutical preparations,105 glycoproteins, glycans, and monosaccharides,106 as well as for the highly specific recognition of glucosides,107 sialic acid,108 and the rapid and selective isolation of luteolin.109 Boronic acid moieties can bind to bacterial cell walls – specifically, to lipopolysaccharides in Gram-negative bacteria and peptidoglycans in Gram-positive bacteria.110 This characteristic has been exploited to augment the antibacterial efficacy of chloramphenicol via MIPs. For example, Gong et al.90 reported a novel approach to enhance chloramphenicol's antibacterial activity by synthesising MIPs through precipitation polymerisation. The synthesis involved the use of 3-(acrylamide)phenylboronic acid (APBA) and acrylamide monomers, with chloramphenicol serving as the template in an acetonitrile solvent. The incorporation of APBA introduced boronic acid groups into the MIP, enabling reversible boronate ester formation with bacterial cell wall components. The resulting MIP-loaded chloramphenicol demonstrated markedly improved antibacterial activity, with an IC50 of 0.6 µg mL−1 compared to 2 µg mL−1 for chloramphenicol alone. Moreover, the researchers observed a lower drug release at 37 °C (347 nm) relative to 20 °C (529 nm), establishing a clear correlation between particle size and drug release kinetics (Fig. 5).
image file: d6tb00194g-f5.tif
Fig. 5 Antibacterial effect of chloramphenicol (black) and chloramphenicol-loaded MIP2 (red) on E. coli (top) (A and B) and S. epidermidis (bottom) (C and D) at 37 °C (left) and 20 °C (right). Reproduced from ref. 90 with permission from John Wiley and Sons, copyright (2019).

Incorporation of boron-affinity functionality within MIPs significantly enhances their affinity for bacterial glycan structures while improving selectivity and stability under physiological conditions, ultimately leading to superior therapeutic system performance.

MIPs as photothermal antimicrobials

The selective detection of microorganisms through recognition of their characteristic biomarkers has emerged as an essential strategy in controlling pathogenic species such as bacteria, viruses, and fungi. Among the recently developed therapeutic approaches, photothermal therapy (PTT) has attracted considerable attention due to its high efficiency in various biomedical applications. PTT relies on the conversion of near-infrared (NIR) light into localised heat by nanomaterials situated within the disease microenvironment, thereby inducing damage to targeted cells.111,112 Polydopamine nanoparticles (PDA NPs) are known for their biocompatibility, strong photothermal efficiency, and ability to interact with amino-containing molecules.113 In a recent study, Zhang et al.91 introduced a molecular imprinting strategy that integrates boronic acid affinity toward the bacterial outer membrane component lipopolysaccharide (LPS) with the photothermal properties of PDA NPs.

This approach enabled the specific recognition of LPS on the Pseudomonas aeruginosa cell membrane, followed by its inactivation via photothermal heating. For the synthesis, PDA NPs were first generated through the self-polymerisation of dopamine. In practice, PDA NPs were prepared and collected, and the boronic acid functionality was introduced using 4-formylphenylboronic acid and sodium cyanoborohydride. The next step involved the imprinting of LPS as a template molecule. LPS was immobilised onto the FPBA-functionalized PDA surface in phosphate buffer, followed by washing to remove any unbound LPS.

Subsequently, dopamine was added to initiate polymerisation, producing an outer polydopamine shell around the immobilised template. After 3 h of polymerisation at room temperature, the resulting photothermal molecularly imprinted polymers (PMIPs) were obtained (Fig. 6a). The photothermal performance of the PMIPs was assessed by exposing them to NIR irradiation. Within 10 min, the temperature of the PMIP suspension increased to 58.1 °C, a threshold sufficient to induce bacterial death. Control experiments revealed negligible antibacterial effects when P. aeruginosa was treated with either NIR alone or PMIPs without irradiation. In contrast, NIR-activated PMIPs reduced bacterial survival to below 1%. Fluorescence microscopy with SYTO9/PI dual staining, together with SEM imaging, confirmed severe membrane damage and apoptosis in P. aeruginosa cells following treatment with NIR-irradiated PMIPs (Fig. 6b–d).


image file: d6tb00194g-f6.tif
Fig. 6 (a) Schematic illustration of the preparation of PMIP. Different experimental groups of P. aeruginosa were tested with and without NIR and PMIP. (b) Bacterial colony photographs showing the bacteria's survival. (c) Fluorescent images of live/dead-stained bacteria. (d) SEM images. Reproduced from ref. 91 from the American Chemical Society under the terms of the Creative Commons CC-BY 4.0 (2023).

Abiotic synthetic receptors are engineered materials designed to mimic the structure and function of natural biological receptors, enabling selective recognition and binding of target organisms. Despite their potential, the widespread application of abiotic synthetic receptors is limited by challenges related to synthesis complexity, cost, robustness, and adaptability to different environmental conditions. MIPs, as a class of synthetic receptors,114 offer a promising alternative for achieving high specificity and stability. To optimise MIPs for bacterial recognition and treatment, Shao et al.92 developed a rapid screening strategy to synthesise high-affinity, high-selectivity nano-MIPs. The screening was performed in a 96-well plate format, where monomers were combined at varying ratios in each row. The polymeric library was established from monomers comprising a variety of charged, neutral, and hydrophobic functional groups.

To prepare fluorescently labelled imprinted nanoparticles, the optimal monomer ratios, which exhibited the highest affinity constants (KD) towards LPS, namely 10% 1-vinylimidazole (IM), 10% N-[(3(dimethylamino)propyl]methylacrylamide, 30% N-tert-butylacrylamide (TBAm), 48% N-isopropylacrylamide (NIPAm), 2% N,N′-methylenebisacrylamide (BIS), and 2.5 mg of FITC, were selected for MIP synthesis. The prepolymerisation mixture included LPS template (2.5 mg), and the initiator ammonium persulfate (30 mg) dissolved in deionised water. Then, the polymerisation was performed at 65 °C for 1 h. Additionally, magnetic nanoparticles (10.4 mg) and gold nanorods (AuNRs) (1 mg) were incorporated to prepare magnetic imprinted nanoparticles (magnetic nano-MIPs) and AuNR@MIP composites with a similar optimal monomer ratio used to prepare the MIP under the same polymerisation conditions.

The resulting MIPs demonstrated remarkable performance. When cultured with E. coli, 95.3% of the bacteria were captured by magnetic nano-MIPs, compared to only 32.1% captured by non-imprinted magnetic nanoparticles (NIPs). In a more complex system, 97% of E. coli (105 cells per mL) in mouse whole blood were captured by magnetic nano-MIPs, whereas magnetic nano-NIPs captured only 62%, confirming the selectivity of the nano-MIPs in biological samples. The antimicrobial efficacy of the MIPs was further evaluated using photothermal treatment. Nano-MIPs encapsulated with AuNR@MIP were irradiated with a laser, resulting in highly efficient bacterial elimination. Compared with AuNR@NIPs, AuNR@MIPs demonstrated superior antimicrobial activity when co-cultured with mixed microbial populations, including E. coli, yeast, and Bacillus subtilis, highlighting their selectivity in complex bacterial environments (Fig. 7). These findings suggest that MIPs provide a cost-effective, stable, and highly selective alternative to conventional abiotic receptors, with significant potential for bacterial detection and treatment applications. However, this approach needs to be further evaluated in vivo in animal experiments.


image file: d6tb00194g-f7.tif
Fig. 7 (a) Schematic Illustration of the synthesis of MIP nanoparticles. (b) Photograph showing the PPT of E. coli, B. subtilis, and yeast incubated with AuNR@MIP and AuNR@NIP when treated with or without 808 nm laser irradiation. Reproduced from ref. 92 with permission from the American Chemical Society, copyrights (2023).

MIPs for blocking quorum-sensing molecules

Targeting the cellular mechanisms that sustain bacterial life offers an effective approach to suppressing bacterial growth. One of the most notable strategies employed by bacteria is quorum sensing (QS), a regulatory process through which gene expression is modulated in response to cell density. QS operates as a cell–cell signalling system that relies on the production, release, and detection of signalling molecules, known as autoinducers. These molecules accumulate in the surrounding environment during bacterial growth, and, in turn, promote biofilm formation and virulence, thereby enhancing bacterial survival. Biofilm development involves the adhesion of microorganisms to surfaces, followed by the formation of multicellular communities embedded within a protective extracellular matrix. Among the diverse classes of autoinducers, acyl-homoserine lactones (AHLs) are among the most extensively studied, as they readily diffuse across bacterial membranes and directly regulate gene expression.115–118 Previous studies have investigated the sequestration of autoinducers using MIPs as a strategy to reduce biofilm formation.72,119 In this context, Ma et al.73 synthesised porous monolithic MIPs at the micrometre scale, employing N-(3-oxododecanoyl)-L-homoserine lactone (3-oxo-C12AHL) as the template, to inhibit Pseudomonas aeruginosa biofilm formation via autoinducer sequestration. These MIPs were prepared through bulk polymerisation using itaconic acid (IA) or 2-hydroxyethyl methacrylate (HEMA) as functional monomers, in the presence of ethylene glycol dimethacrylate (EGDMA) as the cross-linker and dimethylformamide (DMF) as the porogen solvent. Different template/monomer/cross-linker ratios (1[thin space (1/6-em)]:[thin space (1/6-em)]6[thin space (1/6-em)]:[thin space (1/6-em)]25, 1[thin space (1/6-em)]:[thin space (1/6-em)]6[thin space (1/6-em)]:[thin space (1/6-em)]48, 1[thin space (1/6-em)]:[thin space (1/6-em)]8[thin space (1/6-em)]:[thin space (1/6-em)]25, and 1[thin space (1/6-em)]:[thin space (1/6-em)]8[thin space (1/6-em)]:[thin space (1/6-em)]48) were employed. Polymerisation was initiated with an azo initiator under UV irradiation (365 nm, 6 W, with a 10 cm distance between the light source and the reaction solution) for 12 hours at 4 °C to prevent template degradation (Fig. 8). This method is simpler, cheaper, and requires less effort than precipitation polymerisation and suspension polymerisation.120 To evaluate biofilm inhibition, a crystal violet assay was performed to quantify the total biomass, while a triphenyl tetrazolium chloride (TTC) assay was conducted to assess cell viability. The antibiofilm activity results were consistent with the equilibrium rebinding data: HEMA-based MIPs, which exhibited higher adsorption capacities than IA-based MIPs, demonstrated significant inhibition of biofilm formation (43.78–62.93%) compared with IA-MIPs and HEMA-NIPs.
image file: d6tb00194g-f8.tif
Fig. 8 (a) Schematic presentation of MIP development. Relative biofilm formation in the presence of (b) IA-MIPs and IA-NIPs and (c) HEMA-MIPs and HEMA-NIPs. Reproduced from ref. 73 with permission from the American Chemical Society, copyright (2018).

Nevertheless, several limitations in this study merit attention. Although IA-MIPs adsorbed 3-oxo-C12AHL, they failed to suppress either biofilm formation or cell viability.

Furthermore, despite the promising inhibitory effects, no statistically significant difference was observed between HEMA-MIPs and their corresponding non-imprinted polymers (HEMA-NIPs). This raises critical questions regarding the imprinting quality achieved through bulk polymerisation, the optimisation of monomer-to-template ratios, and polymerisation conditions, particularly in comparison with other studies that reported more pronounced affinity differences between MIPs and NIPs.72

In the same area, Piletska and colleagues, in two consecutive studies,72,121 explored the use of MIP-based microparticles to sequester AHL autoinducers and inhibit quorum-sensing pathways. However, their approach was limited by relatively low adsorption capacities. To address these shortcomings, López et al.,75 working with the same research group, integrated Mosbach's transition state analogue (TSA) strategy for catalytically active MIPs122 with the solid-phase synthesis protocol developed by Canfarotta et al.45 This combination enabled the preparation of nano-MIPs capable of recognising and degrading the Gram-negative quorum-sensing autoinducer N-L-hexanoyl homoserine lactone (C6-AHL).

To design the TSA template, 6-amino-N-(1,1-dioxidotetrahydro-thiophen-2-yl)hexanamide was synthesised. This involved the preparation of 2-azidotetrahydrothiophene from 1-oxidotetrahydrothiophene via the Pummerer reaction, followed by its reaction with [tert-butyl-6-((1,1-dioxidotetrahydro-thiophen-2-yl)amino)-6-oxohexyl]carbamate, which was derived from Boc-6-aminocaproic acid.123 The TSA template was then immobilised onto activated glass beads via its amino group, after silanisation with (3-aminopropyl)triethoxysilane (APTMS). Six different nano-MIPs were subsequently synthesised using three different functional monomers; methacrylic acid (MAA), itaconic acid (IA), or 2-(dimethylamino) ethyl methacrylate (DEAEM) as functional monomers, together with the cross-linkers trimethylolpropane trimethacrylate and ethylene glycol dimethacrylate. The polymerisation mixture was added to 30 g of glass beads bearing the immobilised template and initiated under UV light using the “living” photoiniferter N,N′-diethyl dithiocarbamic acid benzyl ester. Dynamic light scattering (DLS) confirmed that the resulting TSA-nano-MIPs had particle sizes ranging between 175 and 204 nm, which represents a notable improvement compared to earlier MIP microparticles.

The hydrolytic activity of TSA-nano-MIPs toward the lactone ring of C6-AHL was investigated using liquid chromatography-mass spectrometry (LC-MS) by monitoring the decline in C6-AHL concentration over time. After 2 hours of incubation with 500 ng ml−1 of C6-AHL in water, TSA-nano-MIPs at 1 mg ml−1 achieved a 41% reduction in C6-AHL concentration, whereas non-specific nano-MIPs and controls without nano-MIPs showed reductions of only 17% and 0%, respectively. These findings confirm the successful imprinting of the TSA template and demonstrate the catalytic recognition activity. After 20 hours, the C6-AHL concentrations were reduced by approximately 57%, 31%, and 16% with MAA-TSA-nano-MIPs, non-specific nano-MIPs, and in the absence of nano-MIPs, respectively. Importantly, this work demonstrates that nano-MIPs can function as molecular recognition and catalytic systems at picomolar levels, a concentration range directly relevant to quorum-sensing environments, where autoinducer levels typically occur at the nanomolar scale.124 Nevertheless, the translation of TSA-nano-MIPs into practical applications will require comprehensive assessments of their stability, functional performance, and scalability under biologically relevant conditions.

Beyond quorum sensing, bacteria employ another defence strategies to survive adverse environmental stresses such as nutrient deprivation and other harsh conditions. In these circumstances, bacteria produce signalling molecules known as alarmone nucleotides, namely guanosine 5′-diphosphate 3′-diphosphate (ppGpp) and guanosine 5′-triphosphate 3′-diphosphate (pppGpp), collectively referred to as (p)ppGpp. These small molecules play a central role in the stringent response, a global regulatory mechanism that enables bacteria to endure unfavourable conditions and maintain survival. Under normal conditions, cellular growth proceeds steadily, and alarmone levels remain low. However, under stress, their levels increase via synthetase activity, allowing cells to adjust by slowing growth to conserve nutrients and energy, while simultaneously enhancing antibiotic resistance and the expression of virulence factors.125–127

To assess whether nano-MIPs could interfere with this adaptive response, Chen et al.93 developed molecularly imprinted nanoparticles using guanosine-5′-diphosphate disodium salt (ppG) as a template. The template was immobilised onto silanised glass beads, and nano-MIPs were synthesised through a solid-phase protocol employing N-isopropylacrylamide (NIPAm, 1.6 mmol), N-(3-aminopropyl) methacrylamide hydrochloride (APM, 0.2 mmol), N-tert-butylacrylamide (TBAm, 0.1 mmol), and N,N′-methylenebisacrylamide (Bis, 0.1 mmol) as a cross-linker. Polymerisation was carried out in 48 mL of water and initiated by potassium persulfate (KPS, 18 mg in 500 µL water) and tetramethylethylenediamine (TEMED, 1.3 µL).45 The synthesised nano-MIPs demonstrated excellent biocompatibility. When incubated with sheep red blood cells (RBCs), the haemolysis rate was below 5%, while cytotoxicity assays with Madin–Darby canine kidney (MDCK) cells indicated cell viability above 89%, confirming their safety for biological applications. The antimicrobial activity of these nano-MIPs was then evaluated against both Gram-negative bacteria (E. coli and kanamycin-resistant E. coli) and Gram-positive bacteria (S. aureus and MRSA). Their ability to capture stress-induced signalling molecules was tested following stimulation either by UV radiation (254 nm) or antibiotics; oxytetracycline hydrochloride (OTC), or Kanamycin sulfate (KS).

Notably, the combination of nano-MIPs with antibiotics; demonstrated greater inhibitory efficiency compared to the full dose of the antibiotic alone, with the results showing a clear inhibition rate-concentration dependency. The therapeutic potential of the nano-MIPs was further validated in vivo using a wound infection model in male Balb/c mice (6–7 weeks old, 18–20 g). Twelve mice were inflicted with 8 × 8 mm dorsal wounds inoculated with E. coli (10 µL, 1.0 × 108 CFU mL−1). After 24 hours, the animals were divided into four groups: negative control (saline 0.9%), nano-MIPs, positive control (oxytetracycline [OTC], 10 µL, 1.0 mg mL−1), and a combination therapy (OTC 5 µL, 0.1 mg mL−1 + nano-MIPs 5 µL, 0.05 mg mL−1). After six days of treatment, wound shrinkage was 65% and 68% in the OTC and OTC + nano-MIP groups, respectively, whereas only 31% and 30% shrinkage was observed in the negative control and nano-MIP-only groups. Bacterial colony counts from harvested wound tissues further confirmed that the lowest bacterial loads were observed in the OTC and OTC + nano-MIP groups. Overall, this study demonstrated that nano-MIPs can enhance the antibacterial effects of conventional antibiotics, enabling the same therapeutic outcomes with only half the effective drug dose. This highlights their potential role in reducing antibiotic consumption while maintaining efficacy.

Other strategies using MIPs to fight bacteria

Cryogel-based polymers, owing to their highly porous architecture, mechanical stability, and capacity to act as biomolecule carriers, have attracted considerable attention for antimicrobial applications.128 Employing a molecular imprinting technology (MIT) approach, Gür et al.94 synthesised a lysozyme-imprinted cryogel membrane (MIP-Lyz) designed for antibacterial activity against both Gram-positive and Gram-negative bacteria. Lysozyme (Lyz) is a small, naturally occurring globular protein129 with well-established antibacterial activity through hydrolysis of the β-1,4 glycosidic linkages in the peptidoglycan layer of Gram-positive bacteria.130 Its effect against Gram-negative bacteria is comparatively limited due to the protective outer membrane;131,132 however, Lyz can destabilise this barrier in the presence of divalent metal ions, such as Cu2+, which act as membrane-permeabilising cofactors. To exploit this mechanism, the authors employed N-methacryloyl-(L)-histidine methyl ester (MAH) complexed with Cu2+ to improve the interaction between MAH and lysozyme.

The cryogel was prepared using 2-hydroxyethyl methacrylate (HEMA) as a functional monomer, N,N′-methylene bisacrylamide (MBAAm) as the cross-linker, and ammonium persulfate (APS) with tetramethylethylenediamine (TEMED) as initiators. Polymerisation was carried out between two glass plates at −12 °C for 24 hours, resulting in a lysozyme-imprinted cryogel membrane. Antibacterial evaluation demonstrated that the MIP-Lyz cryogel produced distinct zones of inhibition against both Staphylococcus aureus (Gram-positive) and Escherichia coli (Gram-negative). Interestingly, the cryogel exhibited stronger and more rapid activity against E. coli than S. aureus, highlighting its potential to overcome the outer membrane barrier of Gram-negative bacteria. In contrast, the corresponding non-imprinted cryogel (NIP) showed a negligible antibacterial effect, confirming that the activity arose from specific lysozyme adsorption and release.

Further assays revealed that antibacterial performance was concentration-dependent: higher lysozyme loading in the MIP-cryogel correlated with enhanced inhibition, reflecting a sustained release of Lyz from the cryogel matrix. The cytotoxicity of both MIP and NIP cryogel membranes was evaluated using the mouse fibroblast cell line L929. After 24 hours of incubation, cell viability assays confirmed that the synthesised cryogels were non-cytotoxic, indicating their biocompatibility and suitability for biomedical applications. Collectively, these findings demonstrate that lysozyme-imprinted cryogels provide a promising platform for controlled antimicrobial delivery, with particular efficacy against Gram-negative pathogens.

Nanofibers are widely recognised for their intrinsic advantages, including large surface area, high porosity, and low production cost, which collectively provide excellent drug-loading capacity.133 However, conventional nanofiber systems face challenges in achieving controlled drug release, a limitation that can be mitigated through polymer grafting strategies.134,135 Among these systems, bacterial cellulose (BC) nanofibers, biosynthesised by Acetobacter xylinum, have emerged as highly effective drug carriers due to their biocompatibility, mechanical strength, and efficient loading properties.136,137

To combine the molecular recognition ability of MIPs with the carrier advantages of nanofibers, Tamahakar et al.89 developed a controlled drug loading and release platform by grafting MIP microparticles onto BC nanofibers through in situ polymerisation. In their approach, methacrylic acid (MAA) was employed as the functional monomer, N,N′-methylene bisacrylamide (MBAAm) as the cross-linker, and gentamicin sulfate, a broad-spectrum antibiotic, as the template, and 2,2 azobis(2-methylpropionitrile) as the initiator. BC nanofibers were extracted from A. xylinum (strain 10245). Three MIP formulations were prepared using a fixed ratio of MAA (1 mmol) and MBAAm (1 mmol), with varying gentamicin concentrations (0.20, 0.10, and 0.05 mmol).

Drug loading and release experiments showed that the imprinting method was successful, with results strongly influenced by the amount of gentamicin used during polymerisation. MIPs prepared with higher gentamicin concentrations were able to load more of the drug. In terms of release, the MIPs provided a slower and more controlled profile compared to non-imprinted polymers (NIPs). While NIPs released almost all (98%) of their gentamicin within just 8 hours, the release from MIPs was more gradual: MIP1 (the highest gentamicin concentration) released about 97% in 48 hours, MIP2 released around 80%, and MIP3 about 60% in the same period. This difference is explained by the formation of recognition cavities during polymerisation, which regulate how much drug can be loaded and how slowly it is released, depending on the template concentration used. The antibacterial activity of the MIP–nanofiber composites was assessed against Gram-negative E. coli and Gram-positive S. aureus. The inhibition zones produced by MIP1, MIP2, and MIP3 were 11.0, 9.5, and 7.5 mm for E. coli, and 14.5, 13.5, and 12.5 mm for S. aureus, respectively. These results confirmed concentration-dependent antibacterial activity, with higher template loading translating into more effective inhibition. Overall, the findings highlight MIP-functionalised BC nanofibers as a promising therapeutic platform, offering sustained antibiotic release and effective antibacterial activity, with performance adjustable through the drug concentration applied during polymerisation.

Nanofiber-based scaffolds in combination with gentamicin-imprinted polymers have also been explored for antibacterial applications, particularly in wound healing. Wound dressings play a critical role in skin regeneration, as wounded tissue provides a favourable environment for colonisation and proliferation of a wide range of microorganisms. Among various biomaterials, polyvinyl alcohol (PVA) and gelatin are frequently employed in tissue engineering due to their biocompatibility and biodegradability.138,139 Building on these properties, Koudehi and Ziaseresht88 developed MIP-based wound dressing by fabricating gentamicin-imprinted PVA/gelatin nanofibers through electrospinning. The polymerisation involved a cross-linking reaction between the hydroxyl groups of PVA and the aldehyde groups of glutaraldehyde, catalysed by hydrochloric acid.140 Following polymerisation, the gentamicin template was removed via Soxhlet extraction, and the MIPs were subsequently reloaded with the drug. Drug release studies revealed a clear difference between MIP- and non-imprinted polymer (NIP)-based nanofibers. While approximately 85% of gentamicin was released from the NIP system within the first 8 hours, the PVA/gelatin/MIP nanofibers exhibited a significantly slower and more controlled release profile, with only 50% release after 40 hours and complete (98%) release after 110 hours. Importantly, the MIP nanofibers demonstrated no cytotoxicity against fibroblast cells, confirming their biocompatibility. The therapeutic efficacy was further validated in vivo using a rat skin wound model. Full-thickness excision wounds (8 mm) were treated with PVA/gelatin/MIP nanofibers, PVA/gelatin nanofibers, or left untreated (control). After 14 days, wounds treated with MIP nanofibers exhibited complete skin regeneration, whereas those treated with non-imprinted nanofibers or left untreated showed only partial healing, with wound sizes reduced to approximately 5 mm and 2 mm, respectively. Taken together, this study exemplifies the broader potential of integrating molecular imprinting with nanofiber-based scaffolds to design multifunctional wound dressings that combine controlled antibiotic release, selective recognition, and tissue compatibility.

In a similar area, Acne vulgaris is a common dermatological condition associated with dysbiosis of the skin microbiota, particularly the overgrowth of acne-associated bacteria, and is clinically characterised by pimples, comedones (whiteheads and blackheads), and chronic inflammation of the sebaceous follicles.141 Conventional treatment strategies rely on the administration of topical and oral antibiotics, among which clindamycin (Cln) is one of the most widely prescribed topical agents. However, challenges such as limited drug penetration, reduced bioavailability, and the emergence of resistance often compromise therapeutic outcomes. To address these limitations, Elhabal et al.96 designed a nano-MIP-based drug delivery system aimed at enhancing the antibacterial activity of clindamycin. In their work, Cln-MIPs were synthesised via precipitation polymerisation at 60 °C using methacrylic acid (MAA, 4 mmol) as the functional monomer, ethylene glycol dimethacrylate (EGDMA, 20 mmol) as the cross-linker, methanol as the porogenic solvent, and AIBN as the radical initiator. The resulting Cln-MIPs were subsequently incorporated into polyurethane nanofiber scaffolds (PUNFs) at a loading of 14% using electrospinning. Successful deposition of Cln-MIPs was confirmed by measurable increases in both scaffold thickness (from 0.18 mm to 0.28 mm) and weight (from 22.65 mg to 31.54 mg). In vivo evaluations demonstrated that Cln-MIP-loaded PUNFs were non-toxic and exhibited superior therapeutic efficacy compared with Cln-MIPs alone or clindamycin controls. Specifically, Cln-MIP-PUNFs markedly reduced redness and inflammation in infected rat ears, while also achieving a significant reduction in S. aureus colonization – from ∼1 × 108 CFU mL−1 in untreated controls to 3.9 × 102 CFU mL−1 in the treated group (Fig. 9). By comparison, positive (clindamycin) controls resulted in 9.2 × 103 CFU mL−1, highlighting the enhanced antibacterial performance of the MIP-functionalised nanofiber system. This innovative approach underscores the potential of integrating nano-sized MIP carriers with nanofiber scaffolds to improve topical antibiotic delivery. By enhancing drug permeability, prolonging release, and reducing bacterial load, such systems provide compelling evidence for the therapeutic applicability of MIP-based platforms in treating skin infections such as acne vulgaris.


image file: d6tb00194g-f9.tif
Fig. 9 Antibacterial activity of clindamycin and Clin-MIP polyurethane nanofibers against S. aureus (ATCC 6538) for different time intervals. Reproduced from ref. 96 under the terms of the Creative Commons CC BY license (MDPI) (2024).

Surface-imprinted polymers represent a versatile strategy for bacterial recognition and control, offering the ability to combine selective targeting with antimicrobial functionality. Gong et al.95 introduced an innovative approach by fabricating silver nanoparticle (AgNP)-embedded surface-imprinted polymer beads (Ag-BIBs) through an oil-in-water pickering emulsion polymerisation method. Silver nanoparticles were incorporated owing to their broad-spectrum antibacterial properties,142 which have a well-known bactericidal effect by disrupting the cell membrane structure and functions in Gram-negative bacteria. In this study, E. coli (TG1, rod-shaped, Gram-negative) and S. epidermidis (PCI 1200, spherical, Gram-positive) were employed as templates to prepare bacteria-imprinted beads (BIBs). The Ag-BIBs were fabricated by combining a water phase containing bacteria pre-coated with an acrylate-functionalised polyethyleneimine prepolymer with an oil phase consisting of Ag nanoparticles, trimethylolpropane trimethacrylate (TRIM), divinylbenzene (DVB), and the initiation mixture of benzoyl peroxide (BPO) and N,N-dimethylaniline (DMA) in hexane (Fig. 10a). Binding assays demonstrated that the optimised Ag-BIBs exhibited pronounced bacterial recognition and capture efficiency, achieving up to 50% binding of E. coli and 60% of S. epidermidis in PBS, and as high as 90% for both species in LB medium.


image file: d6tb00194g-f10.tif
Fig. 10 (a) Schematic illustration of Ag-MIPs. Growth curves of E. coli (b) and S. epidermidis (c) in L.B. medium in the presence of bacteria-imprinted polymers with and without Ag loading. Reproduced From ref. 95 from the American Chemical Society under the terms of the Creative Commons CC-BY 4.0 (2023).

Antibacterial testing further confirmed that the captured bacteria displayed markedly impaired growth in LB in the presence of Ag-BIBs. This bactericidal activity was attributed to the dual action of the imprinted beads: (i) selective recognition and capture of the bacterial targets, and (ii) the subsequent release of Ag nanoparticles embedded within the polymer matrix, which disrupt bacterial membranes. Interestingly, the release of Ag+ ions into aqueous solution remained below 11% of the total Ag content, indicating that the antibacterial effect was largely governed by an Ag-BIB surface-mediated contact mechanism rather than extensive ion leaching into the medium. This feature is particularly advantageous as it minimises unnecessary silver release, thereby reducing potential environmental and cytotoxic risks while maintaining strong antibacterial efficacy.

Promising bacterial proteins as recognisable biomarkers to MIPs for future works

In this section, we discuss the potential of MIPs to target bacterial proteins that play a pivotal role in microbial survival and resistance. Bacterial persistence and drug resistance are mediated by diverse biochemical mechanisms, many of which rely on proteins essential for growth and adaptation. A notable example is β-lactamases, a broad family of bacterial enzymes responsible for conferring resistance to β-lactam antibiotics (e.g., penicillins, cephalosporins, and carbapenems) by hydrolysing the β-lactam ring and thereby abolishing its inhibitory effect on cell wall synthesis. These enzymes are widely expressed in clinically relevant pathogens, including Pseudomonas aeruginosa, Klebsiella pneumoniae, and E. coli.143,144

Li et al.78 successfully demonstrated the feasibility of the approach, employing β-lactamase as a template for the preparation of stimuli-responsive imprinted hydrogels via free-radical polymerisation of low-cost monomers. When tested against methicillin-resistant Staphylococcus aureus (MRSA), the hydrogel reduced bacterial viability by approximately 80%, attributed to the binding of MIPs to β-lactamase and inhibition of its hydrolytic activity. Such findings highlight the feasibility of employing MIPs to neutralise resistance-related enzymes.

In Gram-negative bacteria, the outer membrane acts as a protective barrier and organises key cellular functions. Outer membrane proteins (OMPs) with β-barrel structures, such as porins and other channels, facilitate the transport of ions, nutrients, and waste products, which is essential for bacterial metabolism and survival (Table 2 and Fig. 11). MIPs can be designed to specifically recognise these proteins,145 and disrupt their functions, including permeability, structural integrity, virulence, and molecular transport.

Table 2 Examples of bacterial outer membrane structures
Outer membrane proteins Function Structure Ref.
OmpF porin Allows diffusion of small hydrophilic molecules such as nutrients and waste products across the outer bacterial membrane β-Barrel 146 and 147
LamB porin Facilitates the diffusion of maltose and maltodextrin into the cell β-Barrel 148 and 149
BtuB outer membrane protein Transporter that permits the high-affinity binding and transport of vitamin B12 β-Barrel protein 150 and 151
TolC outer membrane protein Channel for Type I secretion systems (T1SS) β-Barrel protein 152
LptD/LptE outer membrane proteins Translocates LPS across the outer membrane to the outer leaflet β-Barrel protein complex 153 and 154
BamA β-barrel assembly machinery protein Assembly and insertion of outer membrane proteins (OMPs) and maintaining membrane integrity β-Barrel protein 155 and 156



image file: d6tb00194g-f11.tif
Fig. 11 Structure of some outer membrane proteins (https://www.rcsb.org/).

Advanced approaches such as solid-phase imprinting45 allow the use of purified proteins as templates to produce highly selective nano-MIPs. Similarly, snapshot imprinting157,158 enables the identification of key peptide sequences that contribute to protein stability, which are then used as templates to generate nano-MIPs capable of recognising the full protein within the cell.46

In a recent study, snapshot imprinting was employed to map the surface proteome of lung cancer cells as a strategy for biomarker discovery. E. Piletska et al.159 selectively captured peptides from proteins exposed on the surface of lung cancer cells using snapshot imprinting. By polymerising directly on live cells, the nanoMIPs enriched surface-accessible proteins, which were then identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The study demonstrates a novel approach for profiling the cell surface proteome, providing potential biomarkers and targets for future cancer diagnostics and therapies.

Magumba et al.160 employed a snapshot imprinting approach using MIPs to investigate differences in the cell-surface proteomes of three lung cancer cell lines (A549, H460, and H522) compared with a non-cancerous bronchial epithelial cell line (BEAS-2B). Proteomic analysis using LC-MS/MS identified hundreds of differentially expressed proteins (DEPs). Among these, the MIP-based approach enabled the identification of five key proteins (NPM1, TOP2A, EZH2, PRKDC, and HNRNPK) which are associated with lung cancer and may represent potential diagnostic biomarkers or therapeutic targets. These findings demonstrate the potential of nanoMIP-based snapshot imprinting as an alternative approach for identifying protein targets for diagnostic and therapeutic applications.

Snapshot imprinting can be exploited to systematically map outer membrane proteins (OMPs) and identify the most effective peptide sequences as templates for the synthesis of nano-MIPs via molecular imprinting. By precisely targeting these key protein sequences, the resulting nano-MIPs have the potential to selectively bind OMPs, thereby interfering with critical bacterial functions. This interference may compromise membrane permeability, disrupt structural integrity, attenuate virulence, and hinder the transport of essential molecules in coordination with inner membrane systems. Such a targeted approach offers a promising strategy for developing next-generation antibacterial materials with high specificity and efficacy.

Conclusion

This review describes recent advancements in the application of MIPs as antimicrobial agents, emphasising synthetic methodologies, therapeutic outcomes, and their broader implications for mitigating antimicrobial resistance (AMR). Current research demonstrates significant progress in engineering MIPs with high specificity and binding affinity for diverse bacterial targets. Notably, MIPs have proven effective as localised delivery vehicles for infections of the eye, skin, bone, and dental tissues. Furthermore, specialised MIPs have enhanced the efficacy of chloramphenicol through boron affinity and, when integrated with photothermal therapy, have achieved selective bacterial eradication by targeting lipopolysaccharides (LPS) to convert infrared energy into localised heat.

Beyond drug delivery, MIPs serve as robust abiotic receptors for sequestering LPS and inhibiting critical signalling molecules, such as quorum-sensing factors and alarmone nucleotides, thereby disrupting bacterial growth and intercellular communication. Structural integration with cryogels and nanofibers has further optimised these platforms by improving drug loading capacity and enabling sustained-release profiles. However, a more comprehensive characterisation of template–monomer complexes remains essential to elucidate the formation and spatial distribution of binding sites fully.

Future research must focus on the fundamental interactions between MIPs and bacterial biomarkers at the molecular level to clarify underlying binding mechanisms. The evidence suggests that outer membrane proteins in Gram-negative bacteria and β-lactamases in Gram-positive bacteria are prime templates for developing nano- and micro-MIPs. By targeting these vital structures, MIPs offer a transformative approach to bacterial neutralisation, providing a promising trajectory for innovative therapeutic and diagnostic solutions in the global fight against microbial resistance.

Conflicts of interest

There are no conflicts to declare.

Data availability

No primary research results, software or code have been included, and no new data were generated or analysed as part of this review.

Acknowledgements

The authors express gratitude for the doctoral scholarship from the Higher Committee of Education and Development in Iraq (HCED) awarded to A. Ibrahim.

Notes and references

  1. R. C. Founou, L. L. Founou and S. Y. Essack, PLoS One, 2017, 12, e0189621 CrossRef PubMed.
  2. F. Prestinaci, P. Pezzotti and A. Pantosti, Pathog. Glob. Health, 2015, 109, 309–318 CrossRef PubMed.
  3. K. W. K. Tang, B. C. Millar and J. E. Moore, Br. J. Biomed. Sci., 2023, 80, 11387 CrossRef PubMed.
  4. P. Dadgostar, Infect. Drug Resist., 2019, 3903–3910 CrossRef CAS PubMed.
  5. A. Tačić, V. Nikolić, L. Nikolić and I. Savić, Adv. Technol., 2017, 6, 58–71 CrossRef.
  6. K. M. Krause, A. W. Serio, T. R. Kane and L. E. Connolly, Cold Spring Harbor Perspect. Med., 2016, 6, a027029 CrossRef PubMed.
  7. T. H. Grossman, Cold Spring Harbor Perspect. Med., 2016, 6, a025387 CrossRef PubMed.
  8. M. Strieker and M. A. Marahiel, ChemBioChem, 2009, 10, 607–616 CrossRef CAS PubMed.
  9. C. Foti, A. Piperno, A. Scala and O. Giuffrè, Molecules, 2021, 26, 4280 CrossRef CAS PubMed.
  10. M. S. Butler, K. A. Hansford, M. A. Blaskovich, R. Halai and M. A. Cooper, J. Antibiot., 2014, 67, 631–644 CrossRef CAS PubMed.
  11. S. Reissier and V. Cattoir, Expert Rev. Anti-Infect. Ther., 2021, 19, 587–599 CrossRef CAS PubMed.
  12. N. Vázquez-Laslop and A. S. Mankin, Trends Biochem. Sci., 2018, 43, 668–684 CrossRef PubMed.
  13. J. Spížek and T. Řezanka, Biochem. Pharmacol., 2017, 133, 20–28 CrossRef PubMed.
  14. M. I. Hutchings, A. W. Truman and B. Wilkinson, Curr. Opin. Microbiol., 2019, 51, 72–80 CrossRef CAS PubMed.
  15. N. G. Bush, I. Diez-Santos, L. R. Abbott and A. Maxwell, Molecules, 2020, 25, 5662 CrossRef CAS PubMed.
  16. G. L. Armstrong, L. A. Conn and R. W. Pinner, JAMA, 1999, 281, 61–66 CrossRef CAS PubMed.
  17. G.-Y. Liu, D. Yu, M.-M. Fan, X. Zhang, Z.-Y. Jin, C. Tang and X.-F. Liu, Mil. Med. Res., 2024, 11, 7 Search PubMed.
  18. M. Vaara, Front. Microbiol., 2019, 10, 1689 CrossRef PubMed.
  19. W. H. Organisation.
  20. F. Malouin and L. Bryan, Antimicrob. Agents Chemother., 1986, 30, 1–5 CrossRef CAS PubMed.
  21. D. Sun, K. Jeannot, Y. Xiao and C. W. Knapp, Front. Microbiol., 2019, 10, 1933 CrossRef PubMed.
  22. K. Tang and H. Zhao, Infect. Drug Resist., 2023, 811–820 CrossRef CAS PubMed.
  23. G. Zhou, Q. Wang, Y. Wang, X. Wen, H. Peng, R. Peng, Q. Shi, X. Xie and L. Li, Microorganisms, 2023, 11, 1690 CrossRef CAS PubMed.
  24. A. Farra, S. Islam, A. Strålfors, M. Sörberg and B. Wretlind, Int. J. Antimicrob. Agents, 2008, 31, 427–433 CrossRef CAS PubMed.
  25. Z.-l Fang, L.-y Zhang, Y.-m Huang, Y. Qing, K.-y Cao, G.-b Tian and X. Huang, Infect., Genet. Evol., 2014, 21, 124–128 CrossRef CAS PubMed.
  26. M. Webber and L. Piddock, J. Antimicrob. Chemother., 2003, 51, 9–11 CrossRef CAS PubMed.
  27. B. Marquez, Biochimie, 2005, 87, 1137–1147 CrossRef CAS PubMed.
  28. J. M. Munita and C. A. Arias, Vir. Mech. Bact. Pathog., 2016, 481–511 CAS.
  29. J. Lin, K. Nishino, M. C. Roberts, M. Tolmasky, R. I. Aminov and L. Zhang, Front. Microbiol., 2015, 6, 34 Search PubMed.
  30. T. M. Uddin, A. J. Chakraborty, A. Khusro, B. R. M. Zidan, S. Mitra, T. B. Emran, K. Dhama, M. K. H. Ripon, M. Gajdács and M. U. K. Sahibzada, J. Infect. Publ. Health, 2021, 14, 1750–1766 CrossRef PubMed.
  31. Y. Ma, F. Cai, Y. Li, J. Chen, F. Han and W. Lin, Bioact. Mater., 2020, 5, 732–743 Search PubMed.
  32. O. C. Farokhzad and R. Langer, ACS Nano, 2009, 3, 16–20 CrossRef CAS PubMed.
  33. M. A. Beach, U. Nayanathara, Y. Gao, C. Zhang, Y. Xiong, Y. Wang and G. K. Such, Chem. Rev., 2024, 124, 5505–5616 CrossRef CAS PubMed.
  34. H. Bhardwaj, S. Sarthi and R. K. Jangde, Pharm. Nanotechnol., 2025 DOI:10.2174/0122117385366838250314110525.
  35. N. Zhang, N. Zhang, Y. Xu, Z. Li, C. Yan, K. Mei, M. Ding, S. Ding, P. Guan and L. Qian, Macromol. Rapid Commun., 2019, 40, 1900096 CrossRef PubMed.
  36. M. Włoch and J. Datta, Comprehensive analytical chemistry, Elsevier, 2019, vol. 86, pp. 17–40 Search PubMed.
  37. K. Flavin and M. Resmini, Anal. Bioanal. Chem., 2009, 393, 437–444 CrossRef CAS PubMed.
  38. A. G. Mayes and M. J. Whitcombe, Adv. Drug Delivery Rev., 2005, 57, 1742–1778 CrossRef CAS PubMed.
  39. K. Yoshimatsu, K. Reimhult, A. Krozer, K. Mosbach, K. Sode and L. Ye, Anal. Chim. Acta, 2007, 584, 112–121 CrossRef CAS PubMed.
  40. J. Wang, P. A. Cormack, D. C. Sherrington and E. Khoshdel, Angew. Chem., 2003, 115, 5494–5496 CrossRef.
  41. G. Zhao, J. Liu, M. Liu, X. Han, Y. Peng, X. Tian, J. Liu and S. Zhang, Appl. Sci., 2020, 10, 2868 CrossRef CAS.
  42. J. Yang, Y. Li, J. Wang, X. Sun, R. Cao, H. Sun, C. Huang and J. Chen, Anal. Chim. Acta, 2015, 872, 35–45 CrossRef CAS PubMed.
  43. L. Wan, Z. Chen, C. Huang and X. Shen, TrAC, Trends Anal. Chem., 2017, 95, 110–121 CrossRef CAS.
  44. Q. Liu, J. Wan and X. Cao, Process Biochem., 2018, 70, 168–178 CrossRef CAS.
  45. F. Canfarotta, A. Poma, A. Guerreiro and S. Piletsky, Nat. Protoc., 2016, 11, 443–455 CrossRef CAS PubMed.
  46. S. S. Piletsky, A. Garcia Cruz, E. Piletska, S. A. Piletsky, E. O. Aboagye and A. C. Spivey, Polymers, 2022, 14, 1595 CrossRef CAS PubMed.
  47. P. T. Vallano and V. T. Remcho, J. Chromatogr. A, 2000, 887, 125–135 CrossRef CAS PubMed.
  48. M. Ovezova, F. Yılmaz, I. Göktürk, K. Ç. Güler and A. Denizli, J. Pharm. Biomed. Anal. Open, 2024, 100038 CrossRef.
  49. J. Sarvutiene, U. Prentice, S. Ramanavicius and A. Ramanavicius, Biotechnol. Adv., 2024, 71, 108318 CrossRef CAS PubMed.
  50. A. Bossi, F. Bonini, A. Turner and S. Piletsky, Biosens. Bioelectron., 2007, 22, 1131–1137 CrossRef CAS PubMed.
  51. S. Scorrano, L. Mergola, R. Del Sole and G. Vasapollo, Int. J. Mol. Sci., 2011, 12, 1735–1743 CrossRef CAS PubMed.
  52. Y. Zhang, Q. Wang, X. Zhao, Y. Ma, H. Zhang and G. Pan, Molecules, 2023, 28, 918 CrossRef CAS PubMed.
  53. H. Zhang, Adv. Mater., 2020, 32, 1806328 CrossRef CAS PubMed.
  54. L. Longo and G. Vasapollo, Mini-Rev. Org. Chem., 2008, 5, 163–170 CrossRef CAS.
  55. V. Pichon and F. Chapuis-Hugon, Anal. Chim. Acta, 2008, 622, 48–61 CrossRef CAS PubMed.
  56. F. Tamayo, J. Casillas and A. Martin-Esteban, Anal. Bioanal. Chem., 2005, 381, 1234–1240 CrossRef CAS PubMed.
  57. F. Puoci, G. Cirillo, M. Curcio, F. Iemma, U. Spizzirri and N. Picci, Anal. Chim. Acta, 2007, 593, 164–170 CrossRef CAS PubMed.
  58. C. Baggiani, L. Anfossi and C. Giovannoli, Anal. Chim. Acta, 2007, 591, 29–39 CrossRef CAS PubMed.
  59. S. A. Piletsky, N. W. Turner and P. Laitenberger, Med. Eng. Phys., 2006, 28, 971–977 CrossRef PubMed.
  60. W. Li and S. Li, Oligomers-Polymer Composites-Molecular Imprinting, Springer, 2007, pp. 191–210 Search PubMed.
  61. B. H. Abd, Molecularly Imprinted Polymers for Drug Delivery, University of Leicester, 2018 Search PubMed.
  62. L. Longo and G. Vasapollo, Met.-Based Drugs, 2008, 5, 163–170 CAS.
  63. Y. Ge and A. P. Turner, Chem. – Eur. J., 2009, 15, 8100–8107 CrossRef CAS PubMed.
  64. B. Tse Sum Bui, T. Auroy and K. Haupt, Angew. Chem., Int. Ed., 2022, 61, e202106493 CrossRef CAS PubMed.
  65. A. Aherne, C. Alexander, M. Payne, N. Perez and E. Vulfson, J. Am. Chem. Soc., 1996, 118, 8771–8772 CrossRef CAS.
  66. K. Ren and R. N. Zare, ACS Nano, 2012, 6, 4314–4318 CrossRef CAS PubMed.
  67. H. Bao, B. Yang, X. Zhang, L. Lei and Z. Li, Chem. Commun., 2017, 53, 2319–2322 RSC.
  68. Y. Long, Z. Li, Q. Bi, C. Deng, Z. Chen, S. Bhattachayya and C. Li, Int. J. Pharm., 2016, 502, 232–241 CrossRef CAS PubMed.
  69. M. Abdin, Z. Altintas and I. Tothill, Biosens. Bioelectron., 2015, 67, 177–183 CrossRef CAS PubMed.
  70. K.-I. Ogawa, M. Hyuga, T. Okada and N. Minoura, Biosens. Bioelectron., 2012, 38, 215–219 CrossRef CAS PubMed.
  71. R. Sulc, G. Szekely, S. Shinde, C. Wierzbicka, F. Vilela, D. Bauer and B. Sellergren, Sci. Rep., 2017, 7, 44299 CrossRef CAS PubMed.
  72. E. V. Piletska, G. Stavroulakis, L. D. Larcombe, M. J. Whitcombe, A. Sharma, S. Primrose, G. K. Robinson and S. A. Piletsky, Biomacromolecules, 2011, 12, 1067–1071 CrossRef CAS PubMed.
  73. L. Ma, S. Feng, C. S. D. L. Fuente-Nunez, R. E. Hancock and X. Lu, ACS Appl. Mater. interfaces, 2018, 10, 18450–18457 CrossRef CAS PubMed.
  74. S. Fa and Y. Zhao, Bioorg. Med. Chem. Lett., 2019, 29, 978–981 CrossRef CAS PubMed.
  75. J. G. Lopez, E. Piletska, M. Whitcombe, J. Czulak and S. Piletsky, Chem. Commun., 2019, 55, 2664–2667 RSC.
  76. J. de Dieu Habimana, J. Ji, F. Pi, E. Karangwa, J. Sun, W. Guo, F. Cui, J. Shao, C. Ntakirutimana and X. Sun, Anal. Chim. Acta, 2018, 1031, 134–144 CrossRef PubMed.
  77. A. Motib, A. Guerreiro, F. Al-Bayati, E. Piletska, I. Manzoor, S. Shafeeq, A. Kadam, O. Kuipers, L. Hiller and T. Cowen, Angew. Chem., 2017, 129, 16782–16785 CrossRef.
  78. W. Li, K. Dong, J. Ren and X. Qu, Angew. Chem., 2016, 128, 8181–8185 CrossRef.
  79. M. A. R. Khan, A. R. A. Cardoso, M. G. F. Sales, S. Merino, J. M. Tomás, F. X. Rius and J. Riu, Sens. Actuators, B, 2017, 244, 732–741 CrossRef CAS.
  80. X. Xue, J. Pan, H. Xie, J. Wang and S. Zhang, React. Funct. Polym., 2009, 69, 159–164 CrossRef CAS.
  81. Z. Wu, J. Hou, Y. Wang, M. Chai, Y. Xiong, W. Lu and J. Pan, Int. J. Pharm., 2015, 496, 1006–1014 CrossRef CAS PubMed.
  82. N. Malakooti, C. Alexander and C. Alvarez-Lorenzo, J. Pharm. Sci., 2015, 104, 3386–3394 CrossRef CAS PubMed.
  83. S. Kioomars, S. Heidari, B. Malaekeh-Nikouei, M. Shayani Rad, B. Khameneh and S. A. Mohajeri, Pharm. Dev. Technol., 2017, 22, 122–129 CrossRef CAS PubMed.
  84. V. Galván-Romero, F. Gonzalez-Salazar, K. Vargas-Berrones, L. E. Alcantara-Quintana, F. Martinez-Gutierrez, S. Zarazua-Guzman and R. Flores-Ramírez, Eur. J. Pharm. Biopharm., 2024, 195, 114178 CrossRef PubMed.
  85. R. Khademi and M. Kharaziha, ACS Appl. Mater. Interfaces, 2024, 16(25), 31966–31982 CrossRef CAS.
  86. Q. Hu, X. Chen, N. Zhao and Y. Li, Mater. Lett., 2013, 106, 452–455 CrossRef CAS.
  87. J. Kurczewska, M. Cegłowski, P. Pecyna, M. Ratajczak, M. Gajęcka and G. Schroeder, Mater. Lett., 2017, 201, 46–49 CrossRef CAS.
  88. M. Foroutan Koudehi and R. Zibaseresht, Mater. Technol., 2020, 35, 21–30 CrossRef CAS.
  89. E. Tamahkar, M. Bakhshpour and A. Denizli, J. Biomater. Sci., Polym. Ed., 2019, 30, 450–461 CrossRef CAS PubMed.
  90. H. Gong, W. Liu, M. Carlquist and L. Ye, ChemBioChem, 2019, 20, 2991–2995 CrossRef CAS PubMed.
  91. Q. Zhang, M. Zhang, Z. Huang, Y. Sun and L. Ye, ACS Appl. Polym. Mater., 2023, 5, 3055–3064 CrossRef CAS.
  92. S. Shao, S. Gao, Y. Li and Y. Lv, ACS Appl. Mater. Interfaces, 2023, 15, 16408–16419 CrossRef CAS PubMed.
  93. Y. Chen, Z. Zhang, Y. Chen, S. Zhou, Q. Deng and S. Wang, J. Mater. Chem. B, 2022, 10, 9438–9445 RSC.
  94. S. Diken Gür, M. Bakhshpour, N. Bereli and A. Denizli, J. Biomater. Sci., Polym. Ed., 2021, 32, 1024–1039 CrossRef PubMed.
  95. H. Gong, S. Hajizadeh, W. Liu and L. Ye, ACS Appl. Bio Mater., 2021, 4, 2829–2838 CrossRef CAS PubMed.
  96. S. F. Elhabal, R. Abdelmonem, R. M. El Nashar, M. F. M. Elrefai, A. M. E. Hamdan, N. A. Safwat, M. S. Shoela, F. E. Hassan, A. Rizk and S. L. Kabil, Pharmaceutics, 2024, 16, 947 CrossRef CAS PubMed.
  97. A. Mikagi, K. Manita, A. Yoyasu, Y. Tsuchido, N. Kanzawa, T. Hashimoto and T. Hayashita, Molecules, 2021, 27, 256 CrossRef PubMed.
  98. M. Jiang, A. N. Chattopadhyay, C. H. Li, Y. Geng, D. C. Luther, R. Huang and V. M. Rotello, Chem. Sci., 2022, 13, 12899–12905 RSC.
  99. M. Santucci, F. Spyrakis, S. Cross, A. Quotadamo, D. Farina, D. Tondi, F. De Luca, J.-D. Docquier, A. I. Prieto and C. Ibacache, Sci. Rep., 2017, 7, 17716 CrossRef PubMed.
  100. G. S. Weston, J. Blázquez, F. Baquero and B. K. Shoichet, J. Med. Chem., 1998, 41, 4577–4586 CrossRef CAS PubMed.
  101. P. C. Trippier and C. McGuigan, MedChemComm, 2010, 1, 183–198 RSC.
  102. A. F. Halbus, T. S. Horozov and V. N. Paunov, ACS Appl. Mater. Interfaces, 2019, 11, 12232–12243 CrossRef CAS PubMed.
  103. H. M. U. Abid, M. Hanif, K. Mahmood, M. Aziz, G. Abbas and H. Latif, ACS Omega, 2022, 7, 24415–24422 CrossRef CAS PubMed.
  104. A. Galstyan, R. Schiller and U. Dobrindt, Angew. Chem., Int. Ed., 2017, 56, 10362–10366 CrossRef CAS PubMed.
  105. Y. Hu, W. Huang, Y. Tong, Q. Xia and M. Tian, New J. Chem., 2017, 41, 7133–7141 RSC.
  106. R. Xing, S. Wang, Z. Bie, H. He and Z. Liu, Nat. Protoc., 2017, 12, 964–987 CrossRef CAS PubMed.
  107. M. Peng, H. Xiang, X. Hu, S. Shi and X. Chen, J. Chromatogr. A, 2016, 1474, 8–13 CrossRef CAS.
  108. X. Chen, Y. Liu, M. Zhong, J. Yang, Z. Lin and Y. Liang, Anal. Sci., 2023, 39, 13–22 CrossRef CAS PubMed.
  109. L.-x Ding, Y.-q Wang, X. Sun, Z.-q Jiang, X.-y Wang, Y.-f Zhou and X.-y Hou, Anal. Methods, 2023, 15, 925–936 RSC.
  110. S. Saito, T. L. Massie, T. Maeda, H. Nakazumi and C. L. Colyer, Anal. Chem., 2012, 84, 2452–2458 CrossRef CAS PubMed.
  111. A. C. V. Doughty, A. R. Hoover, E. Layton, C. K. Murray, E. W. Howard and W. R. Chen, Materials, 2019, 12, 779 CrossRef CAS PubMed.
  112. L. Zhao, X. Zhang, X. Wang, X. Guan, W. Zhang and J. Ma, J. Nanobiotechnol., 2021, 19, 1–15 Search PubMed.
  113. H. Li, D. Yin, W. Li, Q. Tang, L. Zou and Q. Peng, Colloids Surf., B, 2021, 199, 111502 CrossRef CAS PubMed.
  114. E. M. Peck and B. D. Smith, in Synthetic Receptors for Biomolecules: Design Principles and Applications, ed. B. Smith, The Royal Society of Chemistry, 2015 Search PubMed.
  115. V. C. Kalia, Biotechnol. Adv., 2013, 31, 224–245 CrossRef CAS PubMed.
  116. K. Papenfort and B. L. Bassler, Nat. Rev. Microbiol., 2016, 14, 576–588 CrossRef CAS PubMed.
  117. T. Defoirdt, G. Brackman and T. Coenye, Trends Microbiol., 2013, 21, 619–624 CrossRef CAS PubMed.
  118. A. V. Samrot, A. A. Mohamed, E. Faradjeva, L. S. Jie, C. H. Sze, A. Arif, T. C. Sean, E. N. Michael, C. Y. Mun and N. X. Qi, Medicina, 2021, 57, 839 CrossRef PubMed.
  119. E. Cavaleiro, A. S. Duarte, A. C. Esteves, A. Correia, M. J. Whitcombe, E. V. Piletska, S. A. Piletsky and I. Chianella, Macromol. Biosci., 2015, 15, 647–656 CrossRef CAS PubMed.
  120. J. Ashley, M.-A. Shahbazi, K. Kant, V. A. Chidambara, A. Wolff, D. D. Bang and Y. Sun, Biosens. Bioelectron., 2017, 91, 606–615 CrossRef CAS PubMed.
  121. E. V. Piletska, G. Stavroulakis, K. Karim, M. J. Whitcombe, I. Chianella, A. Sharma, K. E. Eboigbodin, G. K. Robinson and S. A. Piletsky, Biomacromolecules, 2010, 11, 975–980 CrossRef CAS PubMed.
  122. D. K. Robinson and K. Mosbach, J. Chem. Soc., Chem. Commun., 1989, 969–970 RSC.
  123. E. V. P. J. Garcia Lopez, M. J. Whitcombe, J. Czulak and S. A. Piletsky, R. Soc. Chem., 2019, 55(18), 2664–2667 Search PubMed.
  124. T. Long, K. C. Tu, Y. Wang, P. Mehta, N. P. Ong, B. L. Bassler and N. S. Wingreen, PLoS Biol., 2009, 7, e1000068 CrossRef PubMed.
  125. J. K. Hobbs and A. B. Boraston, ACS Infect. Dis., 2019, 5, 1505–1517 CrossRef CAS PubMed.
  126. V. Hauryliuk, G. C. Atkinson, K. S. Murakami, T. Tenson and K. Gerdes, Nat. Rev. Microbiol., 2015, 13, 298–309 CrossRef CAS PubMed.
  127. A. O. Gaca, C. Colomer-Winter and J. A. Lemos, J. Bacteriol., 2015, 197, 1146–1156 CrossRef CAS PubMed.
  128. K. Çetin, S. Aslıyüce, N. Idil and A. Denizli, J. Biomater. Sci., Polym. Ed., 2021, 32, 189–204 CrossRef PubMed.
  129. S. A. Ragland and A. K. Criss, PLoS Pathog., 2017, 13, e1006512 CrossRef PubMed.
  130. N. Khorshidian, E. Khanniri, M. R. Koushki, S. Sohrabvandi and M. Yousefi, Front. Nutr., 2022, 9, 833618 CrossRef PubMed.
  131. L. Aminlari, M. Mohammadi Hashemi and M. Aminlari, J. Food Sci., 2014, 79, R1077–R1090 CrossRef CAS PubMed.
  132. B. Masschalck and C. W. Michiels, Crit. Rev. Microbiol., 2003, 29, 191–214 CrossRef CAS PubMed.
  133. B. Singh, K. Kim and M.-H. Park, Nanomaterials, 2021, 11, 3411 CrossRef CAS PubMed.
  134. Y. Chen, X. Mu and F. Wang, Polym. Sci., Ser. A, 2018, 60, 311–321 CrossRef CAS.
  135. P. S. Gungor-Ozkerim, T. Balkan, G. T. Kose, A. S. Sarac and F. N. Kok, J. Biomed. Mater. Res., Part A, 2014, 102, 1897–1908 CrossRef PubMed.
  136. A. J. Silvestre, C. S. Freire and C. P. Neto, Expert Opin. Drug Delivery, 2014, 11, 1113–1124 CrossRef CAS.
  137. Y. Pötzinger, D. Kralisch and D. Fischer, Ther. Delivery, 2017, 8, 753–761 CrossRef PubMed.
  138. T. Li, M. Sun and S. Wu, Nanomaterials, 2022, 12, 784 CrossRef CAS PubMed.
  139. M. F. Koudehi and S. M. Pourmortazavi, Electroanalysis, 2018, 30, 2302–2310 CrossRef CAS.
  140. C. Tang, C. D. Saquing, J. R. Harding and S. A. Khan, Macromolecules, 2010, 43, 630–637 CrossRef CAS.
  141. H. C. Williams, R. P. Dellavalle and S. Garner, Lancet, 2012, 379, 361–372 CrossRef PubMed.
  142. H. Gong, K. Zhang, C. Dicko, L. Bülow and L. Ye, ACS Appl. Nano Mater., 2019, 2, 1655–1663 CrossRef CAS.
  143. R. A. Bonomo, Cold Spring Harbor Perspect. Med., 2017, 7, a025239 CrossRef PubMed.
  144. D. Meneksedag, A. Dogan, P. Kanlikilicer and E. Ozkirimli, Comput. Biol. Chem., 2013, 43, 1–10 CrossRef CAS PubMed.
  145. Y. He and Z. Lin, J. Mater. Chem. B, 2022, 10, 6571–6589 RSC.
  146. G. Kefala, C. Ahn, M. Krupa, L. Esquivies, I. Maslennikov, W. Kwiatkowski and S. Choe, Protein Sci., 2010, 19, 1117–1125 CrossRef CAS PubMed.
  147. K.-L. Lou, N. Saint, A. Prilipov, G. Rummel, S. A. Benson, J. P. Rosenbusch and T. Schirmer, J. Biol. Chem., 1996, 271, 20669–20675 CrossRef CAS PubMed.
  148. A. Charbit, Front. Biosci., 2003, 8, s265–s274 CrossRef CAS PubMed.
  149. T. Schirmer, T. A. Keller, Y.-F. Wang and J. P. Rosenbusch, Science, 1995, 267, 512–514 CrossRef CAS PubMed.
  150. C. Balusek and J. C. Gumbart, Biophys. J., 2016, 111, 1409–1417 CrossRef CAS PubMed.
  151. T. Pieńko and J. Trylska, PLoS Comput. Biol., 2020, 16, e1008024 CrossRef PubMed.
  152. V. Koronakis, FEBS Lett., 2003, 555, 66–71 CrossRef CAS PubMed.
  153. E. Freinkman, S.-S. Chng and D. Kahne, Proc. Natl. Acad. Sci. U. S. A., 2011, 108, 2486–2491 CrossRef CAS.
  154. K. Moehle, H. Kocherla, B. Bacsa, S. Jurt, K. Zerbe, J. A. Robinson and O. Zerbe, Biochemistry, 2016, 55, 2936–2943 CrossRef CAS PubMed.
  155. M. T. Doyle and H. D. Bernstein, Nat. Commun., 2019, 10, 3358 CrossRef PubMed.
  156. R. Albrecht, M. Schütz, P. Oberhettinger, M. Faulstich, I. Bermejo, T. Rudel, K. Diederichs and K. Zeth, Acta Crystallogr. Sect. D: Biol. Crystallogr., 2014, 70, 1779–1789 CrossRef CAS PubMed.
  157. S. S. Piletsky, E. Piletska, M. Poblocka, S. Macip, D. J. Jones, M. Braga, T. H. Cao, R. Singh, A. C. Spivey and E. O. Aboagye, Nano Today, 2021, 41, 101304 CrossRef CAS.
  158. E. Piletska, D. Thompson, R. Jones, A. G. Cruz, M. Poblocka, F. Canfarotta, R. Norman, S. Macip, D. J. Jones and S. Piletsky, Nanoscale Adv., 2022, 4, 5304–5311 RSC.
  159. E. Piletska, K. Magumba, L. Joseph, A. G. Cruz, R. Norman, R. Singh, A. F. Tabasso, D. J. Jones, S. Macip and S. Piletsky, RSC Adv., 2022, 12, 17747–17754 RSC.
  160. K. Magumba, E. Piletska, T. H. Cao, D. Jones, S. Macip and S. Piletsky, Polymers, 2026, 18, 281 CrossRef CAS PubMed.

This journal is © The Royal Society of Chemistry 2026
Click here to see how this site uses Cookies. View our privacy policy here.