Harnessing biological insights to accelerate drug discovery against ESKAPE pathogens

Tashi Palmo ab, Vishwani Jamwal ab, Diksha Kumari ab and Kuljit Singh *ab
aInfectious Diseases Division, CSIR-Indian Institute of Integrative Medicine, Jammu, 180001, India. E-mail: singhkuljit.iiim@csir.res.in
bAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India

Received 24th April 2025 , Accepted 30th November 2025

First published on 3rd December 2025


Abstract

The escalating global health crisis of antimicrobial resistance (AMR) is driven by the increasing prevalence of drug-resistant bacterial infections, particularly among ESKAPE pathogens. These multidrug-resistant bacteria pose a significant threat to public health, causing severe and fatal infections in healthcare settings. To combat AMR, a comprehensive understanding of the mechanisms of drug resistance employed by ESKAPE pathogens is crucial. These bacteria utilize various strategies, including drug inactivation, modification, and overexpression of antibiotic target sites, efflux pump overexpression, and porin protein reduction, to evade the effects of antibiotics. Addressing this urgent challenge requires a concerted effort to develop novel antimicrobial agents. Our review highlights the promising drug targets that can be exploited for therapeutic interventions. A preclinical roadmap is outlined, emphasizing the essentiality of various antibacterial susceptibility assays and studies to identify potent drug candidates. Furthermore, to broadly explore the associated pathogenesis, virulence, host immune responses, and therapeutics, various in vitro and in vivo infection models have been explored that pave the way for unraveling novel therapies against a wide spectrum of ESKAPE pathogens. Lastly, this review delves into the significant challenges faced by the research community in the drug discovery process and explores potential avenues to combat the growing threat of drug-resistant pathogens.


image file: d5md00358j-p1.tif

Kuljit Singh Tashi Palmo Vishwani Jamwal Diksha Kumari

Kuljit Singh leads a research group at the Infectious Diseases Division, CSIR-IIIM, Jammu, focused on combating bacterial and parasitic infections. Dr. Singh's research group at CSIR-IIIM investigates antimicrobial resistance (AMR) and pathogen survival mechanisms along with drug discovery efforts targeting global health challenges. Tashi Palmo and Vishwani Jamwal hold an M.Sc. in Zoology and are currently pursuing a Ph.D. at CSIR-IIIM Jammu. Both are passionate research scholars with an aim to explore novel chemical scaffolds targeting ESKAPE pathogens and translating cutting-edge science into impactful health solutions. Diksha Kumari, the senior-most Ph.D. student of the research group, works on identifying and developing small drug-like molecules against leishmaniasis through molecular biology and biochemical approaches.


1. Introduction

Antimicrobial resistance (AMR) has become one of the leading causes of the global public health crisis in the 21st century due to the increasing incidence of bacterial infections.1 This issue is further exacerbated due to the lack of novel antimicrobial agents, leaving behind limited treatment options to fight against resistant bacterial pathogens.2 AMR is often referred to as a “silent pandemic” which underscores that AMR spreads without being noticed, but if it is not addressed promptly, it has the potential to cause widespread destruction to global health.3 Previously, a worldwide assessment of AMR burden conducted in 2019 revealed that approximately 8.9 million deaths were caused due to bacterial infection per year. Out of which, 1.27 million mortalities were directly attributed to AMR, surpassing the toll of HIV and Malaria combined, while 4.95 million deaths were associated with AMR. It has been estimated that this number is expected to increase significantly, with an estimated death of 10 million annually by 2050.4 Also, by 2050, Asia is predicted to experience 4.7 million deaths directly by AMR.5 In India in 2019, AMR was directly responsible for an estimated 297[thin space (1/6-em)]000 deaths with a total of 1[thin space (1/6-em)]042[thin space (1/6-em)]500 deaths associated with AMR, according to IHME data.6 Furthermore, in the United States, AMR poses a significant health challenge, with over 2.8 million AMR infections occurring every year, and more than 35[thin space (1/6-em)]000 casualties are directly related to AMR.7

Recently, the World Health Organization (WHO) has updated the pathogen priority list to direct and accelerate research and development efforts for novel antibiotics against ESKAPE pathogens. The acronym ESKAPE belongs to the group of bacteria that includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species.8,9 Among them, A. baumannii, K. pneumoniae, and Enterobacter species have been categorized under the critical group, whereas E. faecium, S. aureus, and P. aeruginosa have been categorized under the high-priority group.10 These pathogens are responsible for causing serious life-threatening nosocomial and opportunistic infections, particularly affecting critically ill and immunocompromised patients in healthcare settings because they are generally categorized as multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR).11,12

In general, E. faecium is a Gram-positive opportunistic pathogen usually found in the human gastrointestinal tract and is responsible for various diseases such as endocarditis, neonatal meningitis, and bacteremia.12S. aureus is a versatile and opportunistic pathogen responsible for a variety of infections, ranging from mild skin and soft tissue infections to severe and potentially fatal conditions like infective endocarditis, bacteremia, and pneumonia.13K. pneumoniae is a Gram-negative bacterium from the Enterobacteriaceae family found in the mouth, skin, intestines, and lungs responsible for causing necrosis, inflammation, urinary tract infection, and hemorrhage.14,15A. baumannii is a Gram-negative bacterium, found in various environments and is associated with various infections such as respiratory infection and urinary tract infection, particularly in critically ill and immunocompromised patients.16,17P. aeruginosa is a Gram-negative, opportunistic pathogen capable of infecting various body sites and is particularly dangerous in healthcare settings due to its resistance to multiple antibiotics having a death rate of around 60%. It is a significant cause of chronic lung infections in cystic fibrosis patients.18,19Enterobacter species belong to a genus that consists of Gram-negative bacteria that mainly affect the lower gastrointestinal tracts, like the kidney, and the respiratory tract region, such as lungs, and are responsible for various diseases such as urinary tract infection, respiratory tract infection, sepsis, and meningitis.20

The ESKAPE pathogens adopt various strategies, including drug inactivation, target site modification, efflux pump overexpression, and reduction of porin protein, to achieve resistance against the most commonly used class of antibiotics.16,21 Therefore, it is imperative to bolster drug discovery efforts for the development of novel antimicrobial agents to address the rising global threat of AMR. In the present review, we have first briefly discussed the currently used chemotherapy and major mechanisms of drug resistance adopted by ESKAPE pathogens (Fig. 1). Further, emphasis is given to various drug targets in ESKAPE pathogens that can be exploited for the development of novel therapeutics. Next, we have focused on elaborating a preclinical roadmap in the field of antibacterial drug discovery to identify and characterize potent molecules using antibacterial studies, cytotoxicity assays, and in vivo infection models. Next, we shed light on the challenges faced in the drug discovery process and future perspectives to fight against the menace caused by drug-resistant pathogens.


image file: d5md00358j-f1.tif
Fig. 1 Diagrammatic illustration presenting the burden of antimicrobial resistance prompting the emergent need for the development of novel therapeutics. (A) Treatment of bacterial pathogens with a panel of antibiotics. (B) The emergence of different multi-drug resistant strains mechanisms. (C) Escaping strategies from the biocidal action of antibiotics. (D) The catastrophe of antimicrobial resistance raised the need for novel drugs. Further, the road map of preclinical drug evaluation has been presented. (E) Different screening techniques and cytotoxicity study of the identified agent. (F) Ex vivo and (G) in vivo assessment for a better understanding of the mechanism of action of the identified molecules. (H) Preclinical evaluation of the identified hits.

2. Existing chemotherapy

There are various therapeutic options available to control the catastrophe of bacterial infections caused by ESKAPE pathogens. Different antibiotics belonging to diverse classes exhibit different mechanisms of action against sensitive as well as resistant bacterial strains. In this section, we have discussed the panel of antibiotics that are effectively involved in ceasing the progression of infections in the host. Current chemotherapy includes cephalosporin, β-lactamase inhibitor, polymyxin, glycopeptides, quinolones, carbapenems, and tetracycline class of drugs to treat ESKAPE pathogen-related infections as discussed in Table 1.
Table 1 Existing treatment options against various ESKAPE pathogens
Pathogen Antibiotic class Antibiotics Optimized dose/treatment duration Advantages/disadvantages Ref.
IV – intravenous, q – every, PO – through mouth/orally, TBW – total body weight.
E. faecium Glycopeptide Vancomycin IV, 25–30 mg kg−1 followed by 15 mg kg−1 q8 h Associated with relapses or treatment failure due to the emergence of resistance 22–24
Lipopeptide Daptomycin IV, 6–8 mg kg−1 daily (non-vancomycin resistant)
IV, 9 mg kg−1 daily (vancomycin resistant)
Oxazolidinone Linezolid IV, 600 mg q12 h
Glycylcycline Tigecycline IV, 100 mg loading dose followed by 50 mg q12 h Side effects such as vomiting, nausea
Bacteriostatic with low blood concentrations
Lipoglycopeptide Telavancim IV, 10 mg kg−1 q24 h Renal impairment, GI, and taste disturbance
Oritavancim IV, 1200 mg once Nausea and hypersensitivity, and arm abscesses
Dalbavancim IV, 1000 mg (day 1) Red man syndrome is associated with rapid administration
Followed by IV, 500 mg (day 8)
Staphylococcus-related infections (including MRSA) Glycopeptide Vancomycin 15 to 22.5 mg kg−1 IV q12 h Slow bactericidal drug 25–27
Cephalosporin Cefazolin 2000 mg IV q8 h
Oxazolidinone Linezolid 600 mg PO q12 h Bacteriostatic, but oral availability (100%)
Quinolone Ciprofloxacin/levofloxacin + rifampin Ciprofloxacin-500 mg PO daily
Levofloxacin-500 mg PO daily
Rifampin-600 mg PO daily
K. pneumoniae (colistin-carbapenem-resistant) Semisynthetic glycylcycline Tigecycline IV, 100 mg (loading dose) then 50 mg q12 h Approved drug for treating serious abdominal and skin-related infections 28–30
200 mg (loading dose) following 100 mg q12 h
Carbapenem + β-lactamase inhibitor Meropenem/vaborbactam IV, 4 g q8 h (3 h infusion) Ineffective against metallo-β-lactamase and OXA-48-related lactamase
Siderophore cephalosporin Cefiderocol IV, multiple doses up to 2 g Mostly effective against MDR strains
A. baumannii Carbapenems Imipenem Imipenem-IV, 1 g q6 h Strong action against serious infections 16, 31, 32
Leads to an increasing dispersion of carbapenem resistance
Meropenem Meropenem-IV, 2 g q8 h
Tetracycline Minocycline IV/orally, 200 mg q 12 h Orally available and high success rate of IV minocycline
Tigecycline IV, 200 mg × 1 dose or 100 mg q12 h thereafter
Polymyxins Polymyxin B IV, 2.5 mg kg−1 TBW, 1.5 mg kg−1 TBW q12 h thereafter High skin penetration stability with less toxicity
Colistin IV, 300 mg, 360 mg divided q12 h thereafter High success rate, synergistic effect with vancomycin and linezolid
Low penetration power
P. aeruginosa Cephalosporin Ceftazidime 2 g q8 h Single-drug treatment may lead to the induction of β-lactamases 33–35
Cefoperazone 2 g q12 h
Penicillin Piperacillin–tazobactam 4.5 g q6 h
Quinolones Ciprofloxacin Oral, 400 mg q8 h, 750 mg q12 h Synergistic effect with aminoglycosides
Polymyxin Polymyxin B 15[thin space (1/6-em)]000–25[thin space (1/6-em)]000 units per kg q24 h Orally available, contraindicated in children below 16 years
Colistin 9 million (loading dose) followed by 4.5 million q12 h Low risk of resistance development, but with few toxic effects
Enterobacteriaceae (carbapenem-resistant) Cephalosporin + β-lactamase inhibitor Ceftazidime/avibactam 8 to 14 days Multiple drug requirements for stable and critically ill patients 36, 37
Ceftazidime/avibactam + aminoglycoside/colistin/fosfomycin
Cephalosporin + monobactam Ceftazidime/avibactam + aztreonam
Colistin + fosfomycin/tigecycline
Polymyxin


3. Mechanisms of drug resistance in ESKAPE pathogens

Bacteria develop various complex mechanisms to evade the effects of the antibacterial drugs. These include enzymatic degradation of antibiotics via hydrolysis or chemical modification, chemical alteration of target sites or modification of binding sites through genetic mutation or post-translational modification, reduced antibiotic accumulation through expression of various efflux pumps, and modification of porin protein. Overall, these strategies substantially reduced the susceptibility of bacteria to antibiotics. In this section, we have discussed various mechanisms underpinning the emerging threat of MDR in ESKAPE pathogens (Fig. 2 and Table 2).
image file: d5md00358j-f2.tif
Fig. 2 Pictorial representation of various underlying mechanisms facilitating drug resistance in ESKAPE pathogens. (1) Antibiotic inactivation, either by drug degradation or chemical modification, promotes the non-susceptibility of bacterial pathogens to drugs. (2) Down-regulation of porins leads to less intracellular drug accumulation. (3) Overexpression of efflux pumps promotes the efflux of the drug outside the cells, thereby decreasing drug accumulation inside the cells. (4) A decrease in cell permeability hinders drug penetration into the cells, which assists in decreasing drug susceptibility and therefore increasing antimicrobial resistance in bacterial pathogens.
Table 2 Depiction of various types and mechanisms of drug resistance in ESKAPE pathogens
Pathogen Antibiotic resistance Mechanism of resistance Type of resistance Resistance gene Gene products Ref.
E. faecium Vancomycin Modified peptidoglycan precursor that prevents its binding to vancomycin Modification of the drug target vanA, vanB, vanC D-Ala–D-Ala to D-Ala–D-Lac 83–85
Aminoglycoside Enzymatic modification of the antibiotics by aminoglycoside-modifying enzymes (AME) Antibiotics inactivation Aph(2″)-Ia-Aac(6′)Ie ant(6′)-Ia Aminoglycoside phosphoryltransferases, acetyltransferases, nucleotidyltransferases
Methylation alters ribosomal binding sites, reducing antibiotic interference with protein synthesis Modification of the drug target EfmM Ribosome-modifying methyltransferase
Lincosamides Actively transporting antibiotics out of bacterial cells Efflux pump MsrC, Lsa, VgaD Encode efflux pump belongs to the ABC-efflux pump
Daptomycin Alter the membrane composition to reduce antibiotic binding and penetration Reduced permeability liaFSR, cls, and yycFG Encoding cell-envelope homeostasis proteins (cardiolipin synthase)
S. aureus Methicillin Decreased binding affinity to penicillin-binding proteins (PBPs) Modification of drug targets mecA PBP2a 86, 87
Vancomycin Reduced affinity for vancomycin Van A D-Ala–D-Lac
β-Lactams Breakdown of the β-lactam ring through enzymatic action Antibiotic inactivation blaZ β-Lactamase
K. pneumoniae β-Lactams Enzymatic hydrolysis of the β-lactam ring Antibiotic inactivation bla OXA1, blaTEM, and blaSHV Extended-spectrum beta-lactamases (ESBLs) 88–90
Hydrolyze carbapenems, rendering them ineffective. bla OXA48, blaKPC, blaNDM, and blaVIM Carbapenemase
Aminoglycoside Prevents the binding of aminoglycoside antibiotics to the ribosome and deprives them of antibacterial activity AAC(3)-II and AAC(6′)-Ib 3-N-Aminoglycoside acetyltransferase aminoglycoside 6′-N-acetyltransferase
16S rRNA modification by methyltransferases enhances aminoglycoside resistance Modification of the drug target ArmA, RmtB, and RmtC 6S rRNA methylases
Fluoroquinolone resistance Overexpression of the AcrAB-TolC efflux pump actively expels various antibiotics Efflux pump Mutant overexpression of AcrR and RamR Encoded AcrAB–TolC efflux pump
Quinolones, fluoroquinolone Overexpression of the OqxAB efflux pump actively expels various antibiotics OqxAB gene Encoded OqxAB efflux pumps
A. baumannii Aminoglycosides Prevents the binding of aminoglycoside antibiotics to the ribosome and deprives them of antibacterial activity Antibiotic inactivation Aac(3)-I, aph(3′)-VI, and ant(3″)-I Aminoglycoside 3-N-acetyltransferase type I 16, 91, 92
Aminoglycoside 3′-O-phosphotransferase type VI
Aminoglycoside 3″-O-nucleotidyltransferase type I
Polymyxins (colistin) Alters lipid A in the LPS layer by adding 4-amino-L-arabinose or phosphoethanolamine, decreasing polymyxin binding Target site modification pmrC, pmrA, and pmrB Phosphoethanolamine (PEA) transferase
PmrA/PmrB TCS
Fluoroquinolones Reduce the binding affinity of fluoroquinolones to DNA gyrase and topoisomerase IV GyrA and parC DNA gyrase
Topoisomerase IV
P. aeruginosa Beta-lactam Reducing the concentration of antibiotics within the cell Antibiotic inactivation blaAmpC gene, mutations, or loss of OprD AmpC beta lactamases, OprD porin downregulated 93–95
Fluoroquinolones Overexpression of RND family efflux pumps, such as MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MexXY Efflux pump MexA, MexC, MexE and MexX, MexB, MexD, MexF and MexY, OprM, OprJ and OprN MexAB–OprM, MexCD–OprJ, MexEF–OprN, and MexXY–OprM efflux pump
Aminoglycosides (gentamicin, tobramycin, and amikacin) Enzymatic modification of the antibiotics by aminoglycoside-modifying enzymes (AME) Antibiotic inactivation Ant(2′)-Ia Aminoglycoside nucleotidyltransferases
Aac(6′)-Ib3 Aminoglycoside acetyltransferases
Aph(3′)-VIa Aminoglycoside phosphoryltransferases
Polymyxin Alters lipid A in the LPS layer by adding 4-amino-L-arabinose or phosphoethanolamine, decreasing polymyxin binding Target site modification PhoPQ Encodes lipopolysaccharide modification enzymes
E. coli Tetracyclines These efflux pumps expel tetracycline from the bacterial cell, thereby decreasing intracellular concentration Efflux pump tet(A) and tet(B) Encoded efflux pump belongs to the major facilitator superfamily 44, 96, 97
Rifampin Mutation modifies the rifampicin-binding site on RNA polymerase, thereby preventing the antibiotic from inhibiting transcription Alteration of target sites rpoB gene β-Subunit of RNA polymerase
Fluoroquinolones Reduce the binding affinity of fluoroquinolones to DNA gyrase and topoisomerase IV GyrA and GyrB, ParC and ParE DNA gyrase topoisomerase IV


3.1 Reduced intracellular drug penetration and accumulation

Efflux pumps are cytoplasmic proteins that expel intracellular cytotoxic content, including chemicals and antibiotics, out of the cell, preventing accumulation at intracellular target sites. This results in conferring antibacterial resistance to various biocide agents.16,38 Gram-positive bacteria usually have single polypeptide efflux pumps located on cytoplasmic membranes, whereas Gram-negative bacteria have tripartite efflux pumps.39 Efflux pump-mediated antibiotic resistance may be either due to overexpression of efflux pump proteins or base substitution mutation in the gene that codes for the efflux pump proteins. These are located on mobile genetic elements, contributing to multidrug resistance as well as increasing the survival rate of pathogens.40 So far six families of efflux pumps have been reported in ESKAPE pathogens, they consist of the ATP-binding cassette (ABC) family, the multidrug and toxin extrusion (MATE), the major facilitator superfamily (MFS), the resistance-nodulation cell division (RND) superfamily, the small multidrug resistance (SMR) family and the proteobacterial antimicrobial compound efflux (PACE) family.41 Among these families, RND-type efflux-mediated multidrug resistance is particularly concerning when it comes to AMR in Gram-negative bacteria. For instance, the P. aeruginosa chromosomally encoded MexAB–OprM efflux system has broad substrate specificity and when over-expressed, imparts aminoglycoside, fluoroquinolone, and β-lactam resistance.2 Similarly, AdeABC is an essential efflux pump in A. baumannii whose overexpression due to a single-step mutation in the two-component system AdeRS confers A. baumannii resistance to aminoglycosides, carbapenems, and fluoroquinolones.38 The higher expression level of the OqxAB efflux pump appears to reduce the susceptibility of clinical K. pneumoniae isolates to fluoroquinolone.42 In the same way, the expression of the NorA efflux pump, a member of MFS, is responsible for the development of efflux-mediated resistance to fluoroquinolone antibiotics in S. aureus.43

Porins, a class of proteins found on the outer membrane of Gram-negative bacteria, are involved in the formation of channels that allow movement of hydrophilic substances such as antibiotics to the inner compartment cell.44 Decreased expression of porin protein or mutational effects at the genetic level leads to reduced membrane permeability, resulting in low accumulation of drugs inside the cell and thus decreasing drug susceptibility to numerous antibiotics.45 It has been observed that loss of OprD porin in P. aeruginosa is associated with the acquisition of carbapenem tolerance. Likewise, the reduction of ompk35 and ompk36 porin proteins along with the production of enzyme (AmpC β-lactamase) in MDR K. pneumoniae strains exhibits less sensitivity to β-lactams such as cephalosporins and carbapenems.46 Similarly, carbapenem resistance is correlated with the lack of CarO expression due to disruption of a gene by ISAba10 or ISAba825 insertion in A. baumannii.47

3.2 Antibiotics inactivation or alteration

Other major mechanisms that facilitate antibiotic resistance are enzyme-dependent drug inactivation and antibiotic inactivation. This can be achieved either by hydrolysis of the core structure or alteration of the antibiotic by employing chemical modification.48,49
3.2.1 Inactivation of antibiotics by hydrolysis. ESKAPE pathogens evolve antibiotic resistance mechanisms by the production of hydrolytic enzymes that cause irreversible inactivation of antibiotics.11 One of the most extensively studied enzymes concerning antibiotic resistance is β-lactamases, which have broad-spectrum hydrolytic ability, causing degradation of the β-lactam ring found in a broad range of drugs such as penicillin, cephalosporin, cephamycin, monobactam, and carbapenems.50 Currently, more than 2600 different β-lactamases are identified. Among them, the most common types of β-lactamases are penicillinase, cephalosporinase, broad-spectrum β-lactamases, metallo-β-lactamases, extended-spectrum β-lactamases, and carbapenemases, which are responsible for inactivation of drugs, exacerbating antibiotic resistance in MDR bacteria.51
3.2.2 Inactivation of antibiotics by chemical modifications. Various target site chemical modifications in antibiotics, such as the transfer of acyl, phosphate, nucleotidyl, and ribitoyl moieties, prevent the efficient binding of drugs to their target.52,53 These chemical modifications are mediated by a large and diverse family of enzymes.54 An excellent illustration of the impact of the group transfer mechanism is provided by aminoglycosides. Aminoglycosides are broad-spectrum antibiotics that act by occupying the A-site of the ribosome, which blocks the binding of aminoacyl–tRNA and hampers protein synthesis.48 Bacterial pathogens acquire a high level of resistance to aminoglycoside antibiotics via the production of aminoglycoside-modifying enzymes, which covalently catalyze the modification of hydroxyl and an amino group of aminoglycosides, thus reducing the antibacterial potency of these drugs. There are three main classes of aminoglycoside-modifying enzymes: acetyltransferase, phosphotransferase, and nucleotidyltransferase.55 Among these three enzymes, acetyltransferases are the largest class which is involved in the transfer of an acetyl group from acetyl–CoA to an amine of aminoglycosides antibiotics. Phosphotransferase catalyzes the phosphorylation of the hydroxy group and nucleotidyltransferase is involved in the transfer of adenine to the hydroxyl group.44 Romanowska and group also reported that all three categories of aminoglycoside-modifying enzymes bind to aminoglycoside antibiotics as they mimic the target environment of the ribosomal binding cleft.56 Similarly, rifamycin, a well-known drug, inhibits transcription of bacterial RNA by interacting with the β-subunit of the RNA polymerase. The acquisition of rifamycin-modifying enzymes causes hydroxylation of antibiotics, thereby reducing the bactericidal activity of the antibiotics.44

3.3 Modification of drug targets

Modifications of drug targets enable bacteria to protect themselves from the biocidal action of antimicrobial agents. Such modifications include genetic mutations that encode the target protein, chemical alteration of the target (binding) sites, and substitution of the original target.57,58 One of the classical examples of point mutation-mediated AMR is the development of fluoroquinolone resistance. Fluoroquinolone is a class of antibiotics that interfere with bacterial DNA replication by targeting DNA gyrase and topoisomerase IV encoded by gyrA–gyrB and parC–parE, respectively. It has been found that specific gene mutations within the quinolone-resistance-determining region in the gyrA subunit and parC subunit of topoisomerases lead to the development of fluoroquinolone resistance in most of the bacterial pathogens.59,60 Glycopeptide antibiotics are specifically used against Gram-positive pathogens. It inhibits the synthesis of bacterial cell walls by preventing the cross-linking of the peptidoglycan layer by targeting the acyl-D-alanyl–D-alanine (acyl D-Alan–D-Ala) residue of the peptidoglycan precursor. However, the replacement of the dipeptide sequence from D-Ala–D-Ala to D-Ala–D-Lac or D-Ala–D-Ser due to the complex gene cluster (Van-A, Van-B, Van-D, Van-C, Van-E, and Van-G) in the case of E. faecium and E. faecalis leads to a decrease in the affinity of glycopeptides (vancomycin and teicoplanin) to the peptidoglycan precursor.61 Likewise, another method of resistance development against aminoglycosides is mediated by target site modification. The methylation of the A-site of 16S rRNA by 16S rRNA methyltransferases decreases the binding affinity of aminoglycosides to the ribosome, which increases the insusceptibility of the drug against bacterial pathogens.44,62 β-Lactam antibiotics exert their antibacterial activity by binding to penicillin-binding proteins (PBPs), which are important for the synthesis of the bacterial cell wall. However, MRSA evades this action through the expression of the mecA gene, located on the staphylococcal cassette chromosome mec (SCCmec). The mecA gene encodes a modified PBP known as PBP2a, which has a significantly decreased affinity for β-lactam antibiotics, rendering them ineffective against MRSA. Another very common strategy adopted by the bacterial cell is the overexpression of drug targets. Dihydrofolate reductase (DHFR) is the most essential enzyme required for bacterial growth and cell division. This enzyme catalyses the conversion of dihydrofolate to tetrahydrofolate, a crucial cofactor in amino acid and nucleotide biosynthesis.63 The antibiotic (trimethoprim) targets DHFR and prevents the conversion of dihydrofolate to tetrahydrofolate, thereby preventing amino acid and nucleotide biosynthesis. This subsequently leads to reduced bacterial growth.64 However, the emergence of trimethoprim resistance takes place due to overexpression of the folA gene encoding the DHFR enzyme. This increases the level of DHFR in bacterial cells and reduces trimethoprim efficacy, thereby decreasing the bacterial susceptibility to the drug.65

Among the above-mentioned mechanism types, enzymatic inactivation is the most serious concern. In particular, production of β-lactamases, including extended-spectrum β-lactamases (ESBLs), Amp C, and carbapenemases (KPC, OXA, NDM), inactivates a broad range of β-lactam antibiotics (including cephalosporins and carbapenems), which are often considered as a last resort of antibiotics. Moreover, genes responsible for the production of these enzymes are located on mobile genetic elements like plasmids, which promote their rapid dispersion among different bacterial strains. This further instigates the risk of antibacterial resistance, which significantly limits the treatment options and contributes to the rise in nosocomial infections. This enzymatic inactivation-based resistance is the most prevalent and clinically challenging, and needs to be addressed by inventing new drugs having a novel mechanism of action.62,66

3.4 Biofilm formation

Biofilm is a complex bacterial community attached to a biotic and abiotic surface, embedded in a self-produced extracellular polymeric (EPS) matrix, made up of polysaccharides, lipids, proteins, and extracellular DNA. The composition of ECM polysaccharides differs between different bacterial species and even varies between different isolates belonging to the same species. The ECM helps in nutrient sequestration, adhesion, and acts as a protective shield to bacterial cells from the host immune defense system, toxins, and antibiotics.67,68 Biofilm formation involves a change in the mode of growth from a planktonic organism (free-swimming) to a sessile lifestyle that is resistant to antimicrobial agents and harsh environmental conditions; hence, eradication of sessile cells or biofilm from living hosts becomes more challenging than planktonic cells.69

For biofilm formation, there are three essential steps. The first step is attachment, which is mediated by multiple structures and factors that include fimbriae, pilli, flagella, capsule, specialized proteins, surface charges, adhesion matrix molecules, roughness, topography, and hydrophobicity. This assists in the attachment of motile planktonic cells to the surface under the influence of a variety of environmental signals. At this stage, bacteria are still sensitive to antibacterial agents.70,71 The second step is micro-colony formation, to multilayered cell bunches, and at this stage biofilm expresses maximum resistance to antibiotics. The last step is cell detachment, which can be either active or passive. Active detachment is mediated by the enzymatic degradation of the biofilm matrix or by quorum sensing. Passive detachment is due to external forces such as human intervention, scraping, and fluid shear.11,72

The underlying mechanism behind the drug resistance mediated by biofilm is multifactorial. Biofilm regulation is controlled by specific genes that are directly related to AMR. It has been observed that bacterial cells still acquire the characteristics of antibiotic resistance even when the bacterial cells in the biofilm layer shift into planktonic cells.71,73 Various factors that contribute to providing biofilm-related resistance against antibiotics are discussed below.

(a) Slow growth rate and reduced metabolic state. Biofilm's architecture and complex landscape generate fluctuating gradients of oxygen, nutrients, pH, waste products, and signaling molecules. This gives rise to physiological heterogeneity where each cell exhibits a unique metabolic rate (aerobic, microaerobic, and fermentative bacteria) and dissimilar growth rate (slow, dormant, and persister cells). This heterogeneity gives rise to a phenomenon of drug indifference in which slow-growing or non-growing bacterial cells are less susceptible to the biocidal action of drugs. Therefore, the uneven availability of resources leads to differences in the physiological state of the bacterial cells sequestered in biofilm, which significantly contributes to drug resistance.68,71
(b) Biofilm matrix – a physical obstacle. It has been studied that the biofilm matrix acts as a barricade for the penetration of antibiotics. The poor permeability of the drug limits the exposure of the drug to bacterial cells, which contributes to the insusceptibility of the drug to bacterial cells.73
(c) Adaptive mechanism and stress response. The persistence of biofilm under harsh environments is closely associated with adaptive resistance and stress response. In exposure to mechanical stress, antibiotic exposure, and chemical agents, biofilms possess an excellent ability to elicit SOS response as well as oxidative stress pathways. These adaptive mechanisms lead to the induction of genetic manipulations and a repair system that heighten resistance development and promote the existence of a bacterial community within biofilm under extreme conditions.71 It has been found that the cooperative role of a metabolically heterogeneous community in biofilm is largely associated with the resilience property against stressful conditions. Metabolically inactive cells are highly stress-tolerant and often unsusceptible to drug treatment.74,75 Moreover, stressors also induce metabolic changes in biofilm, which often assist in protecting against harsh conditions. Stress-mediated metabolic shift has been observed in the case of biofilm. An interesting study conducted by Booth and group76 demonstrated that in response to a stressor such as copper metal, cells within the biofilm layer displayed a shift in metabolic pathways related to exopolysaccharide synthesis, providing a shield against extreme conditions. The characteristic change observed in exopolysaccharide metabolism marked the induction of a protective response in the presence of stressors.
(d) Horizontal gene transfer. Horizontal gene transfer (HGT) is mainly facilitated by five principle mechanisms: conjugation, transduction, transformation, membrane vesicles behaving as DNA reservoirs, and nanotubes for cell-to-cell communication. It has been studied that the biofilm layer forms a conducive environment for lateral transfer of genes at a maximum rate as compared to planktonic cells. It is due to high cell density, the presence of plasmids, transposons, and increased competency. Also, biofilm helps in the transfer of MDR plasmid, an AMR gene which leads to genetic recombination and variation, and therefore promotes antibiotic resistance.77,78
(e) Persister cells and overexpression of the efflux pump. Persister cells are specialized cells belonging to a discrete group of inactive and highly drug-resistant bacterial cells. Their cell density increases with an increase in drug exposure and therefore marks the essentiality of their existence in conferring biofilm viability even in the presence of a high antibiotic load.71 On the other hand, various molecular processes such as quorum sensing (QS) and efflux pumps mediate biofilm-related resistance. QS is referred as cell to cell communication. This process utilizes signaling molecules called autoinducers, allowing bacteria to detect cell density and regulate resistance-related genes and biofilm establishment.79,80 It was found that in the case of P. aeruginosa, the QS system modulates the expression of various efflux pumps that include MexCD-OprJ, MexAB-OprM, and MexEF-OprN. These pumps are well known for actively pumping out various drugs such as fluoroquinolones, β-lactams, and aminoglycosides. Therefore, in this way, the QS system and its regulation in efflux pump expression participate in promoting multidrug resistance in biofilm against antibiotics.81,82

4. Potential drug targets in ESKAPE pathogens

Various biosynthetic pathways, including cell wall biosynthesis (peptidoglycan, teichoic acid, and lipopolysaccharide biosynthesis), DNA synthesis, and the cell division process, offer several drug targets that can be exploited for the development of novel therapeutic agents.98 Most clinically administered antibiotics target these crucial pathways, which result in cessation of bacterial growth and hence progression of infection.

4.1 Cell wall biosynthesis

The bacterial cell wall is a complex, mesh-like structure that serves two purposes: to maintain the bacteria in their appropriate shape and to prevent them from bursting due to high osmotic pressure.99 This unique structural feature, absent in human counterparts, has been the focus of extensive research in recent decades. Its essential role in bacterial survival makes it a promising target for the development of new antibiotics to overcome drug-resistant infections.
4.1.1 Teichoic acid in Gram-positive bacteria. Teichoic acid is an anionic glycopolymer acting as a structural component of peptidoglycan and the cell envelope of Gram-positive bacteria such as S. aureus and Enterococci. Teichoic acid is mainly divided into two major classes: wall teichoic acid, which is a glycerol phosphate surface polymer covalently attached to peptidoglycan, and lipoteichoic acid, which is a membrane-bound glycerol or ribityl phosphate attached to the plasma membrane and extends from the surface of the cell to the peptidoglycan layer.100,101 Both wall teichoic acid and lipoteichoic acid are essential for the survival of Gram-positive bacteria as they participate in various bacterial biological processes such as bacterial growth, biofilm formation, cell wall physiology, membrane homeostasis, cell division, and virulence which makes the teichoic acid synthesis pathway an attractive antibacterial target against Gram-positive bacteria.102,103 Lipoteichoic acid synthase plays an essential role in the cell wall biosynthesis of Gram-positive bacteria. It is primarily required for lipoteichoic acid backbone synthesis, which is further involved in the regulation of the bacterial cell division initiator protein FtsZ.104 It has been investigated that a mutation in LTA synthase (LtaS) in Gram-positive bacteria resulted in delocalization of cell division protein FtsZ, resulting in growth arrest, and abnormalities in the cell envelope and cell division.104,105 Previously, Richter and group106 identified 2-oxo-2-(5-phenyl-1,3,4-oxadiazol-2-ylamino) ethyl 2-naphtho[2,1-b]furan-1-ylacetate (compound A) as an LTA synthesis inhibitor with a reported MIC of 5.34 μg mL−1 against S. aureus. Similarly, Naclerio and group107 reported N-(1,3,4-oxadiazol-2-yl) benzamides (compound B) as a potent antibacterial target which selectively inhibited the synthesis of LTA with an excellent MIC of 0.25 μg mL−1 against MRSA. These studies presented the idea for designing novel target-based inhibitors to combat Gram-positive related bacterial pathogens.
4.1.2 Peptidoglycan. Peptidoglycan (PG) is an essential component of the bacterial cell wall, offering structural integrity and characteristic shape to the bacterial cell.108 It is vital for the viability of a bacterial cell. Importantly, it is not found in human cells, making it an excellent target for antibiotic therapy. In addition to its structural function, PG is also critical for growth and cell division. Hence, disruption of PG can cause rapid death of bacterial cells, enhancing the effectiveness of antibiotics in the treatment of nosocomial infections.109 PG comprises repeated units of N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc) residues.110 It is critical for bacterial survival as it assists in retaining shape, maintaining internal turgor pressure, homeostasis, and structural integrity of the bacterial cell wall.111 The synthesis of peptidoglycan precursors lipid I and lipid II depends on the group of enzymes called the Mur family of enzymes, or Mur ligases. These enzymes catalyze diverse steps in the biosynthesis of peptidoglycan, and the inhibition of these enzymes leads to impairment of cell wall synthesis, making them desirable targets for antibacterial agents. Various inhibitors of Mur enzymes have been explored as potential target-based inhibitors.105 For example, MurG catalyzes the transfer of GLcNc from UDP-GlcNAc to lipid I and forms lipid II (key precursor of peptidoglycan). Earlier, Mann and group112 identified the steroid-like compound Murgocil (compound C) as a peptidoglycan inhibitor with a reported MIC of 4 μg mL−1 against S. aureus. Similarly, MraY, another crucial enzyme, catalyzes the synthesis of lipid I. A group of uridine-containing natural products, such as uridyl peptide-based antibiotics including compounds such as muramycin, tunicamycin, mureidomycin, liposidomycin, and capuramycin, inhibits the synthesis of lipid I by blocking the action of MraY enzymes, thereby blocking the synthesis of lipid II, which subsequently disrupts peptidoglycan synthesis.
4.1.3 Lipopolysaccharide (LPS). One of the most important virulence factors produced at the time of Gram-negative infection is LPS, which forms a highly protective permeability barrier that obstructs the entry of harmful molecules into Gram-negative bacteria.113 LPS consists of three parts: i) lipid A, a glucosamine-based phospholipid that forms the outermost layer of the cell envelope in the majority of Gram-negative bacteria, and it plays an important role in the integrity of the outer-membrane permeability barrier, ii) core oligosaccharides which attach to lipid A and contribute to maintaining the integrity of the outer membrane, and iii) O-antigen polysaccharides.114 It has been observed that E. coli mutants that are deficient in lipid A are not able to survive when treated with broad-spectrum antibiotics.115 Since lipid A is crucial for the growth and integrity of the majority of Gram-negative bacteria, a cascade of enzymes is involved in the biosynthesis of lipid A, such as LpxA, LpxC, LpxD, LpxH, LpxB, LpxK, KdtA, LpxL, and LpxM. Out of these enzymes, the most advanced programs targeting lipid A are the inhibition of LpxC due to the following reasons. i) The reaction catalyzed by LpxC is irreversible and is a committed step in the biosynthesis of Lipid A. ii) LpxC is important for the survival of most Gram-negative bacteria. iii) LpxA is highly conserved to several pathogenic bacteria and lacks a human homologue.113,116 Since the biosynthesis and transport of LPS are crucial to the viability and virulence of most Gram-negative bacteria, thus, LPS serves as an attractive target for the discovery of antibiotics.117 Scientists from Merck Research Laboratories were the first to characterize LpxC inhibitors in 1996.118 So far, there are only two LpxC inhibitors that reached clinal trials such as ACHN-975 and RC-01.119 ACHN-975 (compound D) was the first LpxC inhibitor to go through clinical trials against Gram-negative bacteria showing outstanding preclinical results with a MIC value of 2 μg mL−1 against P. aeruginosa but unfortunately, the phase I clinical trials were discontinued after a year because of inflammation problems.114,120 Similarly, Kurasaki and group121 synthesized ten oxazolidone-based inhibitors and determined their potential as LpxC inhibitors. It has been observed that oxazolidinones bearing a fluoride group as a functional group (compound E) displayed excellent antibacterial activity against wild-type E. coli and K. pneumoniae with MIC values ranging from 0.004 to 0.063 μg mL−1.

4.2 DNA synthesis

The synthesis of DNA is crucial for the viability of bacterial cells. Various important enzymes, such as DNA gyrase and DNA topoisomerases, which participate in the synthesis of DNA, present potential therapeutic targets for the development of antibacterial agents. Bacterial DNA gyrase and DNA topoisomerases IV are highly conserved type II topoisomerases responsible for regulating the topological state of DNA during replication, transcription, repair, and decantation.122 Both enzymes are homologous functional heterotetramers, with DNA gyrase composed of two GyrA and two GyrB subunits, and topo IV consists of two ParC and ParE subunits. GyrB and ParE subunits are ATPase domains supplying energy for enzymatic-mediated ATP hydrolysis.123 During replication, DNA gyrase modulates the superhelical state of the bacterial genome by removing positive supercoils that accumulate ahead of replication forks and introducing negative supercoils in relaxed DNA. On the other hand, topo IV is responsible for the decatenation or unlinking of daughter chromatids.124 Both of these enzymes display significant structural and sequence differences as compared to the human topoisomerase, making them an attractive target for antibiotic therapy.125 Moreover, inhibition of these enzymes disrupts an essential cellular process in bacteria, which consequently leads to bacterial cell death.123 For several decades, these enzymes have been significantly employed as a desirable molecular target of quinolone and fluoroquinolone antibiotics.126 Fluoroquinolones mainly act by binding to the GyrA and ParC subunits, resulting in the generation of a ternary drug–enzyme–DNA complex that inserts breaks in double-stranded DNA and prevents the replication and transcription of DNA. This results in apoptosis of bacterial cells.127,128 Another major class of drugs–fluoroquinolones, often referred to as novel bacterial topoisomerase inhibitors (NBTIs), were identified, which were structurally unique and possessed distinct mechanisms of action against bacterial type II topoisomerases.126,129 Kokot and group130 carried out a structural optimization strategy to synthesize new antibacterial molecules having topoisomerase inhibitory activity. It was found that p-halogenated phenyl right-hand side fragments led to increased antibacterial potency with better DNA gyrase and topo IV targeting activity. The study revealed that the electron-withdrawing nature of phenyl moieties sufficiently increased the antibacterial potency of the identified molecules, indicating that the topoisomerase inhibition potential was purely based upon hydrogen bonding. In the optimized series of molecules, N-(4-bromo-3-fluorobenzyl)-1-(2-(6-methoxy-1,5-naphthyridin-4-yl)ethyl)piperidin-4-amine (compound F), N-(3-fluoro-4-iodobenzyl)-1-(2-(6-methoxy-1,5-naphthyridin-4-yl)ethyl)piperidin-4-amine (compound G), and N-(4-bromo-3,5-difluorobenzyl)-1-(2-(6-methoxy-1,5-naphthyridin-4-yl)ethyl)piperidin-4-amine (compound H) exhibited remarkable antibacterial activity with reported MIC values ranging from 0.004 to 32 μg mL−1 against bacterial strains. Also, these compounds showed nanomolar enzyme inhibition against DNA gyrase and topo IV of S. aureus and E. coli. Among all, the highest potency was observed in compound H with a MIC range of 0.004 to 8 μg mL−1 against a panel of bacterial strains along with high topoisomerase inhibitory activity. Recently, gepotidacin has been reported as the most successful candidate of NBTIs which is currently in phase III clinical trials for the treatment of uncomplicated urinary tract infections and it has shown strong antibacterial efficacy against many other bacterial pathogens including E. coli, MRSA, etc.131

4.3 Cell division

In recent years, cell division in bacteria has been considered an appealing new target pathway for the design and development of novel antibacterial therapeutic agents. Cell division in bacteria is initiated by the formation of a macromolecular protein complex divisome.132 The gathering of the complex at the site of cell division relies on the formation of a Z-ring or protofilament by FtsZ, which provides a basis for the attachment of other cell division proteins.133 Any disruption in the assembly of protofilaments results in a detrimental impact on bacterial cell division.134 FtsZ is a GTP-dependent prokaryotic cell division protein that is identified as a structural homolog of the eukaryotic cytoskeletal tubulin and has emerged as an attractive target for new antibacterial drug discovery.135In vitro studies revealed that during the process of bacterial cell division, FtsZ undergoes GTP-dependent polymerization, which forms a cytokinetic ring, also known as the Z-ring, the middle of cells that promotes the recruitment and organization of other cell division proteins. This leads to septum formation, which enables the division of cells into two daughter cells.136 Since FtsZ plays a critical role in Z ring formation, it thereby promotes cell division. Also, FtsZ absence in human cells makes it a highly promising and selective target for antibiotic development.135 Previously, Andreu and group137 revealed that 3-[(6-chloro[1,3]thiazolo[5,4-b]pyridin-2-yl)methoxy]-2,6-difluorobenzamide (compound I) derived from 3-methoxy benzamide has been reported to be a potent antibacterial agent as it exerts inhibitory activity via disruption of FtsZ function in S. aureus with a MIC value of 1.0 μg mL−1. Similarly, Stokes and group139 further identified the most advanced derivative of 3-methoxybenzamide, designated as a derivative of 3-methoxybenzamide with a substituted phenyl bromo-oxazole moiety (compound J) that inhibits bacterial cells with an average MIC value of 0.12 μg mL−1, along with demonstrating considerable effectivity in the murine model of systemic S. aureus infection. It also has been observed that compound J inhibited the S. aureus strain having G196A mutation in FtsZ which was resistant to PC190723. Margalit and group139 screened chemical libraries against FtsZ GTPase and identified five small molecules called Zantrins (FtsZ guanosine triphosphatase inhibitors) that inhibited E. coli FtsZ GTPase activity which resulted in the disruption of the cytokinetic ring and induced bacterial lethality with an IC50 range between 4 and 25 μM. Among them, Zantrin-Z5 (compound K) was the most effective with an IC50 of 4 μM. Chemical structures of potent hits (compound A to K) and detailed description pertaining to target-based studies are depicted in Fig. 3 and Table 3, respectively.
image file: d5md00358j-f3.tif
Fig. 3 Chemical structures of reported molecules showing potential antimicrobial activities.
Table 3 Potential molecules with antibacterial effects identified through a target-based approach
Code Class Target organism (s) Model (s) Assay (s) Outcomes Ref.
Compound A Oxadiazoles S. aureus In vitro and in vivo High-throughput screening, immunoblotting, and electron microscopy Compound A blocked phosphatidylglycerol binding to LtaS and inhibited LTA synthesis in S. aureus 106
Compound B Benzamides MRSA In vitro MIC assay, target-based inhibition studies Selectively inhibited LTA synthesis with an excellent MIC of 0.25 μg mL−1 against MRSA 107
Compound C Pyridine MRSA In vitro MurG-His6 in vitro assay, fluorescence microscopy, mutation and enzyme inhibition studies Compound C disrupts the synthesis of PG by inhibiting the MurG enzyme 112
Compound D Benzamide P. aeruginosa In vitro and in vivo LpxC enzyme inhibition assay, spontaneous-mutation-frequency studies, lung infection model Compound D binds to the active site of LpxC, inhibiting its enzymatic activity and thereby blocking lipid A production 119
Compound E Oxazolidinone E. coli and K. pneumoniae In vitro MIC determination, enzyme inhibition studies It inhibits the enzymatic activity of LpxC and demonstrates strong antibacterial activity 121
Compound F–H Piperidine S. aureus, MRSA, and E. coli In vitro Determination of DNA gyrase, topoisomerase IV inhibitory activity, and metabolic activity assay. Correlation of inhibitory and antibacterial activity Dual-targeted DNA gyrase and topoisomerase IV activities 130
Compound I Benzamide S. aureus In vitro GTPase and sedimentation assays, electron microscopy Compound I inhibits FtsZ activity by blocking the GTPase activity 137
Compound J Benzamide S. aureus, MRSA In vitro and in vivo Cell division assays Chemical and plasma stability assays Pharmacokinetics and thigh infection model Compound I interferes with FtsZ activity, thereby blocking cytokinesis 138
Compound K Benzofuran E. coli In vitro FtsZ expression and purification, GTPase and sedimentation assays, electron and immuno-fluorescence microscopy Zantrins disrupt FtsZ ring assembly, leading to bacterial cell death 139


5. Preclinical roadmap in the field of antibacterial drug discovery

The alarming threat of AMR has pushed the existing chemotherapy on the back foot. This has increased the demand for novel therapeutics to combat the burden of drug-resistant pathogens. In an attempt to do this, the scientific community is deeply engaged in exploring various antibacterial leads bearing excellent biocidal properties with remarkable biocompatibility. The inspection of plant-based and synthetic sources has been a critical area of research to combat the increasing menace of antimicrobial resistance. Alongside, various innovative susceptibility techniques and studies have been designed to examine the preclinical suitability of novel molecules that are crucial for advancing the drug development pipeline and addressing AMR. In this section, we have highlighted various decisive steps of the drug discovery pipeline that include antibacterial susceptibility techniques, toxicity profiling, ex vivo studies, and in vivo studies for ensuring the drug-like potential of the novel candidates (Fig. 4).
image file: d5md00358j-f4.tif
Fig. 4 Schematic representation of the preclinical roadmap in the field of antibacterial drug discovery.

5.1 Antibacterial screening approaches

5.1.1 Diffusion-based antimicrobial susceptibility assays. Diffusion methods are widely employed techniques to examine the antimicrobial activities of test compounds.140 Depending upon the diffusion of antimicrobial agents into the adjoining agar medium, it has been categorized into different types as mentioned below.

The agar disk diffusion or Kirby–Bauer test method is one of the most common techniques employed in different microbiological laboratories for evaluating the antibacterial activity of test compounds. This is based on the principle of evaluating the susceptibility of antibacterial agents against the bacteria grown in an agar medium.141 Briefly, the test pathogen is cultured by placing it into an appropriate medium. Upon reaching an appropriate growth phase, the bacterial suspension is allowed to spread uniformly on the agar plate surface. Then, filter paper discs (about 6 mm in diameter) containing an antibacterial agent at the desired concentration are placed on that agar surface. After that, the agar plate is incubated at a particular temperature for a specific time period to enable diffusion of a test compound into the surrounding agar medium (Fig. 5A). The diffusion of the test (antimicrobial) agent into the agar leads to the inhibition of bacterial growth in the specific area which is indicated by the zone of inhibition (ZOI) in millimeters. Recently, Qonitah and group142 investigated the antibacterial activity of red ginger extract against S. aureus using agar the disk diffusion or Kirby–Bauer method. It was observed that at a 100% concentration of red ginger extract, the inhibition zones exhibited a marked increase, ranging from 14.2 mm to 16.9 mm. A similar technique was also implemented by Chew and group143 to evaluate the antibacterial activity of Bauhinia kockiana flower extracts against clinical isolates of MRSA. Their finding revealed that this extract exhibited a ZOI of 11.3 ± 0.7 mm against MRSA. These studies highlighted the agar disk diffusion method as an efficient method to explore the antimicrobial activity of numerous agents. This tool provides qualitative analysis by determining the resistance phenotypic profile (susceptible, intermediate, or resistant) of the tested pathogen. This profiling assists clinicians in selecting appropriate treatment tailored to individual patients' requirements.144,145


image file: d5md00358j-f5.tif
Fig. 5 Diagrammatic representation illustrating different antimicrobial susceptibility techniques: (A) Agar diffusion method that involves susceptibility testing of a variety of antibacterial agents against a single pathogen using a disc immersed in a testing agent. (B) The broth dilution test provides a quantitative analysis of the testing agent by determining the MIC of that agent against the tested pathogen by measuring the turbidity of the sample. (C) The agar dilution test is accredited as a gold standard method that involves a series of agar plates having gradient concentrations of test agents against test pathogens.

Agar well diffusion is another technique used for assessing the antimicrobial susceptibility of plant or microbial extracts. Similar to the protocol employed in the disk diffusion method, the surface of the agar plate is inoculated with microbial inoculums, and then specific sizes (usually 6 to 8 mm) of wells are created with pipette tips or sterile cork borer. After this, 20–100 μL of antibacterial agents at the desired concentration are added to the respective wells. After this, plates are incubated under cultivation conditions for an appropriate time period to enable the infusion of test molecules into the surrounding agar medium. Then, the antibacterial activity of the test compound is evaluated by measuring the ZOI (the same as mentioned above). In this context, Karvani and group146 employed the agar well diffusion method to evaluate the antibacterial activity of ZnO nanoparticles against S. aureus and E. coli. The nanoparticles displayed ZOI values of 19 mm and 29 mm against S. aureus and E. coli at a concentration of 10 mg mL−1, indicating significant antibacterial potency. Similarly, Ahmad and group147 also utilized a similar technique to evaluate the antibacterial potency against various bacterial pathogens, marking the significance of this tool in the easy identification of antibacterial hits.

The agar plug diffusion technique is employed to study the antagonism between microorganisms145,148 and the protocol used in this method is similar to the disk diffusion method with minor differences. In this method, the culture of desired strains is grown by tight streaks on the agar plate. Microbial cells release chemicals that diffuse into agar media upon proliferation. After that, an agar plot or cylinder is punched out by using a sterile cork borer and placed on the surface of fresh agar plates containing test microorganisms and allowing diffusion of microbial-secreted molecules from the plug to the agar medium. Then, the antimicrobial activity of the microbial-secreted substance is determined by measuring the diameter of the inhibition zone around the agar plug. Recently, Dhevi and group149 evaluated the antibacterial activity of the ethyl acetate extract of Fusarium oxysporum using the agar plug diffusion technique. The tested extract exhibited inhibitory activity at a concentration of 100 μg mL−1 against MRSA and vancomycin-resistant Enterococcus (VRE), producing ZOI values of 22 ± 0.05 mm and 24 ± 0.05 mm, respectively. A similar approach was adopted by Sriragavi and group150 to assess the antimicrobial potency of actinobacterial strains, which exhibited a ZOI of 19 ± 1.44 mm against S. aureus.

The agar diffusion assay offers many benefits which make it the most widely used method. It is an easy-to-use method that requires fewer specialized tools, making it affordable for the scientific community with limited resources. This method allows the determination of an enormous number of antimicrobial agents against a single microorganism, and it has also been validated and accepted as a standard method by the Clinical and Laboratory Standards Institute (CLSI).144 However, at the same time, this method offers several disadvantages as it doesn't provide accurate quantitative measurements. Also, the diameter of the ZOI is affected by various factors such as humidity, pH, and temperature that influence the penetration of the test compound into agar, which affects determining the actual efficacy of the testing agents.

5.1.2 Dilution methods for MIC determination. The dilution method is a highly versatile and sensitive technique for the evaluation of the MIC of the test compound. It provides high accuracy in measuring particular inhibitory concentrations of the antimicrobial agent within a medium. Depending upon the type of medium used, the dilution method is broadly categorized into two major classes.

The broth dilution method is one of the most preferred techniques used for the determination of the antimicrobial activity of the testing agent. In this method, liquid broth is used for the growth of bacterial culture. Based on the volume of the reaction mixture, the broth dilution assay is subdivided into microdilution and macrodilution methods.144 The procedure involves the two-fold dilution of different concentrations of the antimicrobial agent in a series of tubes with higher volumes (macro dilution) or wells of microtiter plates with smaller volumes (microdilution) (Fig. 5B). Each tube or well is inoculated with the microbial suspension adjusted to 0.5 McFarland's scale. These samples are incubated under suitable conditions based on the nature of the testing pathogen. After appropriate incubation, the turbidity or microbial growth is determined by using a microplate reader or spectrophotometer.144,145 This method provides a quantitative measure of the antibacterial potency of the tested agent. The minimum concentration that inhibits the visible growth of the pathogens is defined as the minimum inhibitory concentration (MIC). It is generally expressed in μM, μg mL−1, or mg mL−1.

Previously, Hamad and group151 evaluated the antimicrobial potency of polyheterocyclic compounds against MRSA using the broth microdilution method. The compounds demonstrated significant activity, with MIC values ranging from 3.125 to 6.25 μg mL−1, indicating their potential as effective anti-MRSA agents. Similarly, Hijazi and group152 assessed the antimicrobial efficiency of Ga(III) compounds against ESKAPE pathogens. The findings revealed that the compounds demonstrated an MIC value of ≤32 μm against S. aureus and A. baumannii. Furthermore, Fleeman and group153 conducted an elaborate study in which a scaffold-ranking library was utilized to screen 37 different libraries against ESKAPE pathogens. Further, five lead compounds were identified exhibiting excellent antibacterial activity with MIC values of <2 μM. These potent hits exhibited a molecular weight in the range of 566.03 ± 23.33, which was higher than the inactive set of compounds, presenting a molecular weight of 421.32 ± 55.46. This study demonstrated effective determination of the MIC values of tested agents in terms of micromoles. This micromole-based activity determination provided the idea of the molar concentration of the tested agent instead of mass, which allows real-time comparison of the antibacterial potential of different biological molecules having varied molecular weights. This study also reflects the crucial role of the broth dilution assay in exploring a huge library in a speedy and efficient way. This method is most suitable for determining the MIC value of the tested agent against a specific pathogen with high precision, which overcomes the limitation of the agar diffusion method. This is a highly flexible and cost-effective strategy.154–156 However, this technique faces a few downsides, such as it is a highly tedious process in terms of the preparation of serial broth tubes having different concentrations of antimicrobial agents.157 Some other disadvantages of the macro dilution method include error risk in the preparation of antimicrobial agents, a labor-intensive process, the requirement of various reagents, and space.158 Also, this method is not preferred for antimicrobial susceptibility testing of fastidious or slow-growing bacterial species.159 Despite all these limitations, this method is still more reliable for antimicrobial evaluation and is most recommended as a standardized antimicrobial testing method.

The agar dilution method is the standard method where agar medium is employed for the determination of an antimicrobial agent. This method involves the preparation of a series of agar plates having gradient concentrations of antimicrobial agents (Fig. 5C). Each plate is inoculated with a standardized amount of the test pathogen. Following the incubation, the plates are analyzed for visible bacterial growth.144 This method is most reliable for determining antimicrobial susceptibility due to its accuracy and consistency. It is considered as the gold standard for antimicrobial testing. This method is usually preferred over broth dilution when multiple pathogenic strains are being tested against a single compound/extract, or when the colored tested agent obscures the bacterial growth detection in broth.145 Additionally, it offers the advantage of testing multiple bacteria simultaneously on the same set of plates. However, the short shelf-life of agar plates associated with bacterial contamination limits the use of this strategy.157

5.1.3 Resazurin-based cell viability assay. The resazurin assay is a traditionally used colorimetric test designed to determine the metabolic activity of living cells. This method works on the principle of the reduction of resazurin (a blue-colored non-fluorescent dye) to resorufin (a pink-colored fluorescent dye), which displays the presence or absence of viable microorganisms.160,161 The amount of resorufin generated is directly related to the metabolic activity of the cell, and it can be calculated by measuring the fluorescence of the treated sample (Fig. 6). In the field of microbiology, this method plays a crucial role in the determination of the growth of bacteria and antimicrobial susceptibility testing. It generally follows the basics of the microdilution method. The desired concentration of antimicrobial agents is dispensed into the 96-well microtiter plate along with selected organisms prepared in suitable liquid broth.162 The microplate is incubated at appropriate temperatures for the growth of microorganisms in the presence of antimicrobial compounds. Based on the experimental conditions, the optimized final concentration of resazurin solution is added to each well. Subsequently, the final mixture is allowed to incubate for 3–4 h for color transformation. The blue-to-pink color indicates the presence of viable cells, whereas the blue color indicates bacterial growth inhibition.160,163 This method gives a direct, quantifiable measure of the metabolic state of a bacterial cell after treatment with a testing agent, just like the ATP bioluminescence assay.163 For instance, Yildirim and group164 recently analyzed the antibacterial activity of standard antibiotic colistin against clinical isolates of E. coli and A. baumannii using the resazurin-based cell viability assay. Their results showed MIC values of 4 μg mL−1 against E. coli and 2 μg mL−1 against A. baumannii. Similarly, Jia and group165 applied a similar method to determine the MIC value of colistin against 253 clinical isolates, including A. baumannii, P. aeruginosa, K. pneumoniae, and E. coli. The routine-based screening studies performed using the resazurin assay highlight the essentiality of this technique in drug discovery. This assay offers various benefits such as high sensitivity, specificity, and robustness.162 It allows the detection of microbial growth within microtiter plates, making it more sensitive as compared to the traditional optical density method,166 and it also allows simultaneous screening of multiple antimicrobial agents, which makes it a highly efficient and cost-effective method for large-scale studies.144 However, this method also has certain limitations since the reduction of resazurin is associated with the consumption of oxygen, which makes this assay ideal for aerobic or microaerophilic microorganisms but less suitable for strictly anaerobic organisms, and resazurin can be toxic to some cells.167,168
image file: d5md00358j-f6.tif
Fig. 6 Representation of the cell titer-blue assay, a colorimetric test that detects the metabolic activity of living cells. The pathogen is added in the presence of a gradient concentration of the test compound in a microtiter plate. The blue-colored resazurin (blue) conversion to resorufin (pink) indicates no activity, whereas the blue color indicates the antibacterial activity of the testing agent.
5.1.4 Time–kill kinetics analysis. It provides critical information about the dynamic interaction between bacterial strains and antibacterial agents. This test is employed to determine the concentration as well as the time-dependent bactericidal effect of identified hits.145,156 This test has been standardized by CLSI, in which each molecule at various concentrations is added to a broth culture medium having bacterial culture. The periodic spotting of the sample on an agar plate, followed by colony counting, estimates the determination of the bactericidal effect of the tested antimicrobial agent.141 The colony formation in response to a specific drug at different times and concentration gradients provides relevant information related to the dosage regimen and optimum duration for dose exposure. Uninterrupted monitoring of the inhibition activity of specific molecules provides detailed information related to killing dynamics at specific time intervals. Also, the combined effect of multiple drugs can be examined through this technique. However, this assay faces a few limitations. Undoubtedly, this is a tedious, time-consuming approach that may not mimic the exact scenario of in vivo models.144 The advantages and disadvantages of various antibacterial susceptibility methods are discussed in Table 4.
Table 4 Pros and cons of different antibacterial susceptibility methods
Methods Advantages Disadvantages Ref.
Diffusion-based antimicrobial susceptibility test Simple and cost-effective. Allows screening of huge chemical libraries in less time. Validated and accepted as a standard method by CLSI Lacks precise quantitative data. Susceptible to external factors. Cannot detect all forms of resistance. It does not provide accurate quantitative measurements 140, 144, 145
Broth dilution method Highly flexible and cost-effective strategy. Provides a precise, quantitative measurement of an antimicrobial potency Risk of error in the preparation of antimicrobial agents. Labour-intensive process, the requirement of various reagents, and space. This method is not preferred for susceptibility testing of fastidious or slow-growing bacterial species 154, 155, 159
Agar dilution method Most reliable for determining antimicrobial susceptibility due to its accuracy and consistency. This method is preferred over broth dilution when testing many strains or when colored agents hinder broth-based detection Tedious and labour-intensive. The short shelf-life of agar plates associated with bacterial contamination limits the use of this strategy. Risk of contamination 154–157
Resazurin-based cell viability assay This method gives a direct, quantifiable measure of the metabolic state of a bacterial cell after treatment with a testing agent. Offers various benefits such as high sensitivity, specificity, and robustness. It also allows simultaneous screening of multiple antimicrobial agents, which makes it a highly efficient and cost-effective method for large-scale studies Primarily suitable for aerobic or microaerophilic microorganisms. Resazurin reduction depends on oxygen availability. Resazurin may be toxic to some cells. Required optimised concentration of dye for accuracy 160, 161, 174
Time-kill kinetics analysis Enables continuous monitoring of bacterial growth over time. Distinguishes between bactericidal and bacteriostatic effects Time-consuming and labour-intensive. Required frequent sampling. Does not reveal detailed mechanisms of microbial killing 144, 145, 156
ATP bioluminescence assay Quantifies intracellular ATP. Rapid and accurate luminescence provides immediate results. Widely used for antibacterial, antimycobacterial, and antifungal agents Requires specialized reagents and instrumentation (luminometer). False results are possible due to quenching of emitted light by certain test substances or media components 145, 169, 170, 172
Flow cytometry analysis Rapid and high-throughput screening. Analyses thousands of cells per second. Distinguishes between viable, injured, and dead cells in a population. Effective for antimicrobial susceptibility testing using fluorescent dyes (e.g., propidium iodide) Requires expensive, specialized equipment (flow cytometer, lasers, detectors). Time-consuming sample preparation and staining protocols. Fluorescent dyes may be toxic or interfere with pathogens 144, 173


5.1.5 ATP bioluminescence assay. Adenosine triphosphate (ATP) is the energy currency of the living cell. Its quantification is majorly employed to assess the effect of biocidal molecules upon bacterial metabolism and cell viability.169,170 By detecting intracellular ATP concentration, one can easily decipher how the potent molecule hijacks the energy house and induces bacterial cell death. This assay is widely used for drug screening techniques as well as cytotoxicity studies. In brief, ATP production is detected in the presence of the luciferase enzyme, which converts D-luciferin into oxyluciferin that releases a luminescent effect that is detected by a luminometer.141 The amount of luminescence produced is directly proportional to ATP abundance in metabolically active bacterial cells, which serves as a reliable marker for the estimation of viable cells. This assay provides diverse applications, including in situ assessment of biofilm impact.171 Moreover, it has been used by various groups for antimycobacterial, antifungal, and mold testing.145,172 This is a highly sensitive method for detecting even nominal changes in the viability of microorganisms. Also, the continuous monitoring allows researchers to understand various changes occurring after treatment with high precision. However, this method is limited to transformed microorganisms producing luciferase enzymes. This is a labor-intensive process and cannot be employed for routine screening. Also, the quenching of light produced can generate false measurements.144
5.1.6 Flow cytometry analysis. Flow cytometry works on the principle of the application of lasers for easy detection and measurement of physicochemical properties of cells passing through a fluid stream. This test allows speedy examination of various parameters such as granularity, cell size, and fluorescence. Concerning antimicrobial action, it is used for studying membrane integrity, metabolic state, and cell viability.144,173 This approach offers antibacterial susceptibility testing by detecting damaged cells using dye staining methods. An intercalating, fluorescent dye, propidium iodide, is commonly used as a DNA staining dye and carboxyfluorescein diacetate dye for esterase activity measurement. The detection of lysed cells and subpopulations of dead, injured, and viable cells can be performed by this method.145 It allows the study of thousands of cells simultaneously within seconds with high accuracy and sensitivity. However, the requirement of specialized equipment demands high costs in specific research environments. Extensive early investment and training are needed for calibration, assay setup, and data interpretation. This technique is widely employed for examining planktonic cells, whereas the study of adherent cells requires supplemental adaptations and considerations.144

5.2 Cytotoxicity evaluation of the identified hits in the host cells

Toxicity profiling of a drug is a crucial parameter in the field of pharmacology that is primarily responsible for the identification of one-third of drugs. It is a leading contributor to a cost-ineffective aspect of drug development. The toxicity evaluation involves a cell viability assay that is used to determine the number and proportion of live host cells in the specific population.174–176 MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide) assay is the most common and reliable reagent that is used for determining cellular metabolic activity.160,174,177 It is a mono-tetrazolium salt consisting of a quaternary tetrazole ring core (positively charged). In this assay, the activity of NAD(P)H-dependent oxidoreductases is detected, which indicates the number of living cells. The reduction of MTT causes cleavage of the tetrazole core and leads to the formation of water-insoluble purple formazan crystals, which represent a number of live cells. These crystals are quantified by using a microplate reader in terms of optical density at 570 nm.178–180 XTT (2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-5-[(phenylamino)carbonyl]-2-H-tetrazo-lium hydroxide is an extension of the MTT assay, which provides the advantage of the formation of water-soluble formazan crystals. The enzymes involved in this assay are mitochondrial succinoxidase, flavoprotein oxidases, and the cytochrome P450 system.

The sulforhodamine B (SRB) assay is widely employed for cytotoxicity-based screening by quantifying cellular protein content. This assay works on the principle of binding SRB to the protein-rich part of the fixed cells. SRB consists of a pink-colored sulfonic-rich aminoxanthene dye that binds basic amino acids. The amount of dye released from the stained cells is directly proportional to the quantity of cell mass present.181 This method offers a variety of advantages, such as a large sample size testing, less time-consuming, a requirement for cheap reagents, and simple equipment. All these striking advantages present SRB as an efficient colorimetric assay for cytotoxicity studies.182

5.3 Ex vivo bactericidal assays

In vitro models are generally used for conducting antimicrobial susceptibility screening as this model is easy to perform, less expensive, and responsive to high-throughput designs and automation. However, it fails to recapitulate the complexity of the bacterial environment within the human host. The ex vivo system plays a crucial role in closing the knowledge gap between the in vitro and in vivo models. This model involves the use of tissue extracted from animals or humans, preserving the three-dimensional structure of host tissues.183
5.3.1 Ex vivo lung model. Ex vivo lung models have been developed to investigate the behavioral pattern associated with bacterial pathogens, such as their growth, virulence factors, quorum-sensing activity, and the pathogenesis of tissue damage involved in cystic fibrosis.184 It uses tissue derived from various sources such as rodents, pigs, and human donors.184,185 Among them, tissues derived from pigs are a more preferred option than humans, as porcine lungs are structurally similar to human lungs, which makes them a good candidate for investigating the behaviors of lungs and diseases more closely.183
5.3.2 Ex vivo skin model. Ex vivo skin models involve the use of skin extracted from pigs and human donors to examine bacterial infections, particularly focusing on wound infections. These models are cost-effective, readily accessible, and an attractive substitute to replicate the physiological condition of human skin. This enables the scientific community to explore the complex mechanisms involved in the biofilm establishment in human skin. This method is conducted by obtaining pig skin from the animal butcher, making it sterile using chlorine gas, and then inoculating S. aureus and P. aeruginosa onto sterile pig skin and allowing it to produce the most realistic biofilm. The persister cells present in the biofilm layer exhibit outstanding antibiotic resistance, and also the pattern of bacterial penetration into the dermal matrix is similar to that of human wound infection.186

5.4 In vivo models

Animal models play a crucial role in our understanding of ESKAPE pathogens and the development of effective treatments. These models allow researchers to study the complex interactions between these bacteria and the host immune system in a controlled environment. By mimicking human diseased conditions, animal models provide valuable insights into the mechanisms of infection, the progression of disease, and the efficacy of potential therapies.187 The various animal models employed for investigating the in vivo potential of the antibacterial susceptible agents are discussed below and represented in Fig. 7 and 8.
image file: d5md00358j-f7.tif
Fig. 7 Various animal models utilized for understanding and studying bacterial infections. Invertebrate models like C. elegans, silkworms, G. mellonella, and Drosophila offer cost-effective and ethically sound platforms for initial screening. It offers valuable insights into the mechanisms of bacterial infection, identifies novel drug targets, and screens for potential therapeutic compounds.

image file: d5md00358j-f8.tif
Fig. 8 Vertebrate models such as zebrafish, mice, and rats are widely used to study complex host–pathogen interactions and evaluate drug efficacy. Rabbits and pigs provide insights into specific infections and physiological responses. Further, non-human primate models are more complex and expensive, and offer the closest approximation to human physiology.
5.4.1 Invertebrate infection model. Caenorhabditis elegans, a non-parasitic, free-living nematode, has emerged as a powerful model organism for understanding and assessing in vivo bacterial infections.188,189 Since its earliest applications in developmental biology research, C. elegans has become a versatile model organism for elucidating the molecular and cellular mechanisms underlying infectious diseases. The expanding acceptance of C. elegans as a desirable model is attributed to its compact size (∼1 mm), simple anatomy, easy maintenance, short lifespan, fully characterized genome (∼100 Mb), and conserved biological processes.190 Moreover, the transparent body of the worm facilitates real-time visualization of cellular processes and the tracking of fluorescently labeled bacterial and host genes throughout experimental processes.189 The utilization of C. elegans as a whole animal model not only allows a preliminary assessment of drug toxicity but also evaluates the antimicrobial potency in worms treated with potent compounds. Further, the rapid cultivation of a large population of worms for screening larger molecule libraries considerably abridged any of the ethical restraints that are otherwise highlighted in the case of vertebrates.191 For instance, a study evaluating the antibacterial efficacy of SPI009 against P. aeruginosa highlighted the utility of the C. elegans model in the identification and clinical assessment of novel antibacterial candidates. The in vivo assessment revealed more than 70% survival rate when administered alone as well as in combination with ciprofloxacin.192 Interestingly, the C. elegans infection model also permits the identification of compounds that may not be directly involved in targeting the pathogen's susceptibility but obstruct its virulence machinery or elevate host pathogenic response due to highly conserved molecular mechanisms.193,194 While C. elegans offers a robust platform for studying bacterial infections, its inability to thrive at mammalian body temperature, i.e., 37 °C, reduces the range of pathogens under investigation. Additionally, the efficient detoxification system of C. elegans might limit the identification of drugs that target host defense mechanisms. Nevertheless, C. elegans remains a valuable tool for drug discovery and should continue to be explored.

Silkworm larvae, with their molecular similarities to mammals, offer another unique platform for studying ESKAPE pathogens. By introducing infection into silkworms, researchers can investigate virulence mechanisms, evaluate antimicrobials, and study host–pathogen interactions.195,196 While silkworms lack an adaptive immune system, their innate immune response shares similarities with mammals, enabling the study of host defense mechanisms against ESKAPE pathogens.197 Despite their limitations, silkworms provide a valuable bridge between in vitro studies and complex mammalian models, accelerating the discovery and development of novel antimicrobial therapies to combat the growing threat of ESKAPE pathogens.198,199

Another invertebrate model is Galleria mellonella larvae, which is an easy-to-maintain insect used as food for reptiles and fish. They are increasingly employed as model organisms for studying human pathogens like S. aureus, Cryptococcus neoformans, and A. baumannii,200 unveiling the strategies to combat the escalating AMR burden. This model offers advantages like ease of use, cost-effectiveness, and the ability to study various therapeutic strategies, including drug combinations, peptide therapies, and natural compounds. By providing insights into infection processes and drug efficacy, wax worm models contribute to the development of new treatments to combat antibiotic resistance.187

For several decades, Drosophila melanogaster, a fruit fly, has been significant in the field of genetics for exploring fundamental biological processes and human diseases. Interestingly, due to the rapid life cycle, cost-effectiveness, genetic tractability, and conserved immune system, this model organism is considered an ideal tool for investigating the molecular mechanisms of bacterial pathogenic proteins and thus, studying various infectious diseases.200 In relevance to this context, Lee and group201 assessed the crucial aspects of the polymicrobial interaction in vivo, with P. aeruginosa and S. aureus infected D. melanogaster, and noted several interesting phenotypes associated with virulent attenuated mutants and wild-types, highlighting the use of D. melanogaster in understanding bacterial pathogenesis and host response. The Drosophila model can be directly infected with bacterial pathogens using needle puncture, or the flies can be fed with the bacterial culture. Alternatively, ectopically overexpressing virulence factors using genetic techniques is beneficial for understanding host–pathogen interactions.187 Altogether, these generated models can be leveraged to identify potential drug targets and screen for effective treatments against various infectious diseases, along with exploring biofilm formation and other host–pathogen responses.

5.4.2 Mice model. Murine models have become indispensable tools in the fight against a wide spectrum of infectious diseases, including ESKAPE infections.202 These models have been instrumental in elucidating the intricate mechanisms underlying host immune responses to complex bacterial infections along with evaluating the efficacy of novel antimicrobial agents.203,204 By employing genetically engineered mouse models, including inbred strains, gene knockouts, and transgenic lines, researchers have been able to decipher the specific roles of immune cell subsets and signaling pathways in immunological interventions against bacterial pathogens.205,206 Since each of the strains offers distinct advantages in exploring various facets such as susceptibility, immune response, and efficacy of potential therapeutics, thus multiple murine models have been employed to investigate microbial pathogenesis.

For instance, a study employing a cecal ligation and puncture (CLP) model in mice investigated the efficacy of a non-conventional treatment strategy combining celecoxib and antibiotics against multi-microbial sepsis caused by ESKAPE pathogens. In this experiment, mice were anesthetized, and a 2 cm midline incision was made through the linea alba. The cecum was identified, ligated with sterile 3–0 silk, and punctured twice with a 20-gauge needle. To ensure successful puncture and induction of septicemia, a small amount of cecal content was extruded. The cecum was then repositioned within the abdomen, and the incision was immediately closed without suturing. Notably, the murine models demonstrated a prominent reduction in bacteremia along with down-regulated inflammatory markers like COX-2 and NF-kB.207 In the case of the intratracheal infection model of K. pneumoniae, a standard dose is directly instilled into the trachea through minor surgery of anesthetized mice, whereas, in the case of the intraperitoneal infection model, the inoculum is injected into the peritoneal cavity of mice.14 Several different mouse strains have been tested using this inoculation model, including BALB/c, C57BL/6J, CD-1, Swiss, ICR, C3H/HeN, C3H/HeJ (parental toll-like receptor 4-deficient strain), MF1, and Kunming mice.208 However, the choice of mouse model and strain depends on the specific research question and the desired outcome. It is important to consider factors such as the virulence, the desired severity of infection, and the specific endpoints to be measured when selecting a model.

5.4.3 Rat model. Rat models are frequently favored for understanding bacterial infection pathology due to their physiological similarity to humans, ease of handling, complex immune system, and versatility. Their larger size, compared to mice, allows for more intricate surgical procedures and better mimics human organ size and function. Many different rat models are utilized for evaluation according to the type of clinical manifestation, such as pneumonia, skin and soft tissue infections, endocarditis, osteomyelitis, and mastitis.209

The earliest animal model of chronic pulmonary infection also utilized rats, where P. aeruginosa bacteria embedded in agar beads were intratracheally inoculated. This model successfully induced a persistent infection, with P. aeruginosa detectable for 35 days. Notably, the infected rat lungs displayed pathological features similar to human P. aeruginosa pneumonia, including goblet cell hyperplasia, necrosis, and acute and chronic inflammation.18 To understand the molecular insights of virulence proteins in S. aureus-related pneumonia infection, an established rat model suggests that fibronectin-binding protein-mediated internalization of alveolar epithelial cells was not a virulence mechanism in a rat model of pneumonia.210 Similarly, Sprague–Dawley (SD) rats and Wistar rats were utilized to assess the efficacy of iclaprim against pulmonary infections, in which the bacterial suspension was delivered using intratracheal inoculation and an endotracheal tube, respectively.211,212

Further, rat models of endocarditis closely mimic human native valve endocarditis, making them valuable tools for studying the role of specific S. aureus virulence factors and evaluating antibiotic treatment dosages.213 Similarly, rats are commonly used to study mastitis due to their relatively large papillary ducts and mammary glands, which facilitate inoculation without specialized equipment.214 In the case of K. pneumoniae infected pneumonia models, SD rats are generally preferred, where infection is either induced by tracheal instillation or by an intranasal 22-gauge catheter. These models were used to evaluate the prophylactic and therapeutic efficacy of a traditional Chinese medicine formulation against lung infections.14 In summary, rat models offer a robust platform for investigating the pathogenesis of ESKAPE infections and for evaluating the efficacy of novel therapeutic interventions.

5.4.4 Rabbit and pig models. Rabbits have been established as valuable animal models in the study of ESKAPE infections, particularly in the context of phage therapy research. Their susceptibility to infection with ESKAPE pathogens, physiological similarities to humans, ease of handling, and well-established research techniques make them suitable for investigating the efficacy and safety of phage therapy against these antibiotic-resistant pathogens.215 For instance, white New Zealand rabbits were inoculated with biofilm-producing strains of K. pneumoniae or P. aeruginosa with an endotracheal tube in the trachea of the rabbits, coupled with a hyperthermia device to induce a fever state. Notably, the study found that while temporary 42 °C pulses significantly inhibited biofilm formation, complete eradication was lacking.216 Similarly, to understand wound healing in rabbit wounds infected with P. aeruginosa type III secretion system (T3SS) and alginate biosynthesis mutants, adult female New Zealand white rabbits were given a 6 mm deep wound into the perichondrium on the ventral surface. The results suggested that a combined approach targeting both the T3SS and alginate biosynthesis pathways, as demonstrated by the double gene knockout, exhibited superior efficacy in mitigating P. aeruginosa virulence and its detrimental impact on wound healing compared to individual gene disruptions.217 Another relevant finding of the study on optimizing the rabbit model of S. aureus (USA300) skin and soft tissue infections (SSTIs) highlighted the role of the host response to the infection.218

The choice of pig models for perusing ESKAPE infections is attributed to the fact that they share physiological similarities with humans, particularly in terms of skin, lung, and gastrointestinal systems, making them suitable for modeling various infectious diseases. Pigs offer advantages like large body size, enabling the study of systemic infections, and the ability to model chronic infections.219 In the cure of experimental osteomyelitis caused by S. aureus, a pig model was formed by gunshot fracture, and a ballistic wound was created at the right tibia with immediate contamination by S. aureus. The observations showed that intramuscular administration of benzylpenicillin and flucloxacillin led to a significant reduction of osteomyelitis cases in the treated groups in comparison to the infected group. Another notable example is the porcine model that was developed against A. baumannii causing SSTIs by creating multiple 12 mm diameter wounds in the skin overlying the cervical-thoracic dorsum. The model paves the way for assessing the efficacy of novel antibacterial agents in a preclinical setting and unveiling the underlying host–pathogenic response.220 Although both rabbit and pig models allow for the study of complex infections and therapeutic interventions, their practical limitations, such as high maintenance, limited physiological similarity in the case of rabbits, and ethical considerations, limit their applicability. Ultimately, the selection of an appropriate model should be guided by the specific research question and the need for a model that accurately reflects human physiology.

5.4.5 Non-human primates. Non-human primates (NHPs) are often used as models for studying bacterial infections due to their physiological and genetic similarities to humans.187 Rhesus macaques (Macaca mulatta) are one of the most commonly used NHP models due to their availability, similarity in the immune system, ease of handling, and well-established husbandry practices.221,222 They can be used to study a wide range of bacterial infections, including those caused by Mycobacterium tuberculosis, Salmonella typhimurium, and E. coli.223,224 Cynomolgus macaques (Macaca fascicularis) exhibit more genetic diversity than rhesus macaques, making them a valuable model for studying inter-individual variability in response to infection. Their smaller size compared to rhesus macaques can be advantageous for certain studies, particularly those requiring fewer animals or limited resources.222 They are susceptible to a wide range of bacterial infections, including those caused by K. pneumoniae, Shigella flexneri, S. enterica, and M. tuberculosis.14,224 Other NHP models include African green monkeys (Chlorocebus aethiops), baboons (Papio cynocephalus), and common marmosets (Callithrix jacchus) used for studying specific aspects of bacterial infections, particularly those related to mucosal immunity.224 The use of NHPs in research raises ethical concerns, and it is crucial to ensure that all research involving NHPs is conducted in accordance with strict ethical guidelines and regulations. NHP models are relatively expensive to maintain and use, limiting their accessibility for many researchers. NHPs require specialized housing and care, which can be challenging and time-consuming.225
5.4.6 Other relevant animal models. While the above-mentioned animal models offer valuable insights into antimicrobial pathogenesis and host–pathogenic immune response, other animal models, such as zebrafish, Canine animals, goat, sheep, etc., have also been employed in bacterial infection research.219 Zebrafish, with their high degree of genetic conservation with humans and transparent embryos, offer a powerful model system for understanding vertebrate development and genetics. This unique characteristic allows for direct observation of bacterial infection processes, providing insights into host–pathogen interactions and potential therapeutic targets.226,227 Likewise, canine models (dogs, wolves, foxes) have more physiological similarities with humans and have a larger size that allows for invasive procedures and sampling techniques, facilitating the investigation of disease progression and treatment efficacy. However, ethical considerations, cost, and individual variability must be carefully considered when utilizing these models in research. Overall, developing animal models for studying ESKAPE bacteria is a complex process that requires careful consideration of various factors to ensure the relevance and applicability of the research findings to human diseases.

6. Challenges in the drug discovery process

The discovery of antibiotics has dramatically transformed healthcare settings by effectively overcoming infections caused by bacteria. However, the increasing incidence of bacterial resistance to existing antibiotics has made an urgent need for novel antibacterial agents. Despite this pressing need, the antibiotics development pipeline remains insufficient to combat the menace caused by AMR, as several factors challenge the development of new drugs.

6.1 Financial and regulatory hurdles

The main problem behind the declining development of novel antibacterial agents is not only a scientific challenge but also economic reasons. Between the 1930s and 1960s, significant advancements in antibiotic production were achieved as there were extensive public and private investments in the research and development of antibiotics, which were motivated by the urgent requirement for effective treatments.228 However, by the early 1980s, private investment in antimicrobial research was reduced due to shifts in priorities of pharmaceutical companies towards more profitable areas such as cancer and other lifestyle diseases.228,229 For instance, the average cost for the development of a cancer drug was around $640 million, and the median revenue of the first four standards years post-approval was $1.7 billion. However, developing a new antibiotic has an estimated cost of $1.5 billion, but revenue was very low, with an average of only $46 million per year after approval.230,231 That is why the majority of pharmaceutical companies withdraw their investment from antibiotic research and development.232,233 This decline was further exacerbated by declining public research efforts due to the political shift toward privatization and market-driven approaches.228 The extensive approval process remains the foremost bottleneck in the journey of drug discovery and development. The antibacterial drugs that have been approved by the Food and Drugs Administration (FDA) and European Medicines Agency (EMA) include meropenem–vaborbactam and delafloxacin. These drugs were approved in 2017. Another set of drugs, such as eravacycline, plazomicin, omadacycline, and rifamycin, was approved in 2018. Drugs, including cefiderocol, lefamulin, and imipenem-cilastatin-relebactam, were approved in 2019. Various other drugs such as delpazolid, contezolid, nafithromycin, afabicin, brilacidin, murepavadin, and ridinilazole are currently in clinical phase II and III trials.2,234 Although the regulatory procedures are critical for ensuring efficacy as well as the safety profile of the lead molecule, there is significant procedural discrepancy among regulatory agencies in terms of the selection of patients, definitions of clinical endpoints, and rules regarding expedited approvals, which makes the antibiotic approval process more complex, time-consuming, and expensive.232,235 These extended delays lead to the unavailability of an effective treatment alternative, which could serve as a life-saving drug during an emergent need.

6.2 Compound penetration and retention in Gram-negative pathogens

Between the 1990s and 2000s, extensive antibiotic discovery efforts revealed several high-potency inhibitors targeting essential bacterial genes. However, many compounds failed to inhibit bacterial growth despite exhibiting promising target inhibition. These continuous failing efforts are due to the complex and sophisticated double membrane structure possessed by the Gram-negative bacteria, imposing a major hurdle in the entry of drug-like compounds. The restricted compound permeation, along with a poor influx system, is responsible for the failure of the majority of hit compounds against Gram-negative bacteria.
6.2.1 Physicochemical barriers and porin selectivity governing compound entry. One of the major obstacles in antibacterial drug discovery against Gram-negative pathogens is the compound permeation barrier posed by the cumulative action of both the outer membrane and multidrug efflux systems.236 The outer membrane of Gram-negative bacteria has a very complex architecture with phospholipids, lipopolysaccharides, and selective porins that combine to act as an asymmetrical lipid bilayer barrier, inducing rigorous physicochemical constraints on compound entry.237 On the other hand, porins constitute a vital diffusion channel in the outer membrane of Gram-negative bacteria, enabling passive movement of small hydrophilic molecules with low molecular weight. Since their inner surface contains hydrophilic and charged residues, the channel is selective for polar solutes such as ions, amino acids, and sugars.238 This selective behaviour exhibited by porins was previously studied by Alcaraz and group239 which explained the molecular basis of selectivity of OmpF porin, a common diffusion porin present in E. coli. The constriction zone of these porins contains an asymmetric distribution of charged residues. This generated a strong electrostatic field, which regulates the preference of the porin channel towards cations or anions. Further, it was observed that OmpF increases selectivity for cations as compared to anions under low ionic environmental conditions. The observed findings highlight that the orientation of the porin channel and ionic strength control the selectivity of the porin protein towards small molecules. Furthermore, it was also observed that porin expression levels fluctuate between species and are frequently downregulated in clinical isolates, which further restrains diffusion pathways.240 Also, porin mutations result in alteration of structural confirmation, which narrows down the central diameter or alters the channel electrostatics (electrical field), thereby decreasing the rate of permeation of antibiotics such as carbapenems in E. coli.241 In the case of P. aeruginosa and A. baumannii species, which do not exhibit typical E. coli-type trimeric porins, they primarily produce slow porins, such as OprF-like channels, of which only a small proportion forms open diffusion pores. Consequently, their outer membrane permeability is significantly lower than E. coli. For instance, the permeability of cephalothin and cephaloridine in A. baumannii is 100-fold lower than in E. coli K-12, and deletion of the ompAab gene further decreases permeability by two- to three-fold.242 In this way, limited membrane permeability and porin selectivity influence the intracellular drug accumulation of the hit molecules.
6.2.2 Efflux–influx balance and intracellular drug levels. The intracellular antibiotics accumulation depends upon the dynamic balance between influx via outer membrane porin (passive uptake) and active extrusion by multidrug efflux pumps. Porin-mediated uptake is generally slow and strongly dependent on molecular size, polarity, and charge,238 whereas efflux systems such as AcrAB-TolC and MexAB-OprM complexes can remove structurally diverse compounds at rates far exceeding passive diffusion.243 Recently, Goff and group244 demonstrated the crucial balance between influx and efflux by screening 13[thin space (1/6-em)]056 diverse small molecules with two isogenic E. coli strains, a wild-type and an ArcAB-TolC deleted mutant, using LC/MS-based quantification. The experimental observations indicated that around 60% of the compounds failed to accumulate in the wild-type strain, where efflux pumps remain active. This study underscored that active TolC-dependent efflux counterbalances the influx of compounds and prevents their intracellular exposure. Previously, Westfall and group245 developed a kinetic model to understand the accumulation of compounds in Gram-negative bacteria by integrating both passive influx and active efflux. This model underscores that intracellular drug accumulation is regulated by two parameters, i.e. the efflux constant that indicates the potency of active extrusion and the barrier constant which reflects the permeability of the outer membrane. The interaction between these two parameters created a divergence in the kinetics of drug accumulation that was controlled by the barrier constant. When the barrier constant is below 1, i.e., the rate of diffusion via the outer membrane is high, and efflux becomes saturated enabling intracellular accumulation. However, when this constant rises above 1, the rate of influx could not surpass the rate of efflux leading to low intracellular drug levels that fail to reach the therapeutic concentration necessary for antibacterial efficiency.

Furthermore, the most promising strategy for addressing challenges of bacterial resistance lies in the design of chameleonic antibacterial drugs.247 Chameleonic antibacterial agents are those that adopt unique conformations depending on their environment. In an aqueous environment (like inside the cell), they adopt a polar conformation with an exposed backbone that allows hydrogen bonding with a biological target or water. On the other hand, they assume a less polar conformation within the membrane in which most of the polar groups are concealed, facilitating their intracellular accumulation.247 For example, A- and B-type conformers of rifamycin exhibit a hydrophilic nature due to an open ansa-bridge, while the C-type conformer is more lipophilic in nature because of the presence of a “closed” ansa-bridge structure. Dynamic equilibrium between A- and C-type conformers of rifamycin enables them to adapt to the heterogeneous composition of bacterial cell membranes, which is particularly essential for penetrating Gram-negative bacterial strains.246,248 Cyclosporin acts as a prototypical example of a chameleonic compound, displaying conformational flexibility that enables it to bypass permeability barriers.249 This adaptive chameleonic approach provides a new direction to address the threat caused by resistant bacterial pathogens.

6.2.3 Emerging rules of permeation and design strategies for improved uptake. The wide gap in our understanding of the restriction of compound accumulation creates hurdles in the rational development of novel antibacterial agents against Gram-negative bacteria.250 However, substantial efforts have been made toward establishing a guideline for antibiotic design, particularly to improve permeability and activity against Gram-negative bacteria. Such guidelines have been successfully applied in converting Gram-positive antibiotics to broad-spectrum agents in some cases.251 In recent studies, Richter and group252 investigated the accumulation properties of more than 180 small molecules in E. coli which led to the creation of a crucial set of guidelines called eNTRy rules which specify that compounds are more likely to accumulate if they possess three key parameters such as non-sterically encumbered ionisable nitrogen (specially primary amine), comparably high rigidity (≤5 rotatable bonds) and low three-dimensionality (globularity ≤ 0.25). The application of these eNTRy rules has been successful in converting deoxynybomycin, a Gram-positive specific antibiotic, into a broad-spectrum antibiotic by the addition of a primary amine.

In addition to the eNTRy rules, the recent work revealed that the Gram-negative pathogens use multiple, species-specific mechanisms that determine the accumulation of antibiotics. For example, P. aeruginosa lacks general diffusion porins and depends on the self-promoted uptake pathways. This was experimentally validated by Ude and group.253 In this study, a specific strain was constructed by deleting 40 identified outer membrane porins of P. aeruginosa. It was found that most of the hydrophilic nutrients and almost all clinically relevant antimicrobial agents penetrate directly through the bilayer membrane. Porins, on the other hand, remain vital for the uptake of carboxylate-rich compounds (citrate and succinate). Overall, the study emphasizes that even in the absence of the 40 porin proteins, bacteria still grew and absorbed maximum available nutrients, which highlights that the lipid membrane remains a mainstay for the penetration of molecules. This study suggests that a new antimicrobial agent should be designed for better penetration across the membrane rather than exclusive dependency upon porins. In this connection, recently Geddes and group254 assessed whole cell accumulation of 345 diverse molecules in P. aeruginosa and E. coli. Computational studies revealed that discrete properties such as hydrogen bond donor surface area, positive polar surface charge, and formal charge play a decisive role in P. aeruginosa accumulation. The study demonstrated that the classical E. coli eNTRy rule does not apply to P. aeruginosa. In contrast, the two other important physiochemical parameters, such as an appropriate positive polar surface area (Q_VSA_PPOS ≥ 80) and/or a positive formal charge (FC ≥ 0.98), as well as sufficient hydrogen bond donor surface area (HBDSA ≥ 23), are more likely to accumulate in P. aeruginosa. This rule is called the P. aeruginosa self-promoted Entry rule (PASsagE rule).255 Overall, these studies have attempted to guide new therapeutic agents for drug discovery against Gram-negative bacterial pathogens.

6.3 Differences in the physiological state of the pathogen in vitro and in vivo

Similarly, one of the significant challenges in the development of antibacterial drugs is the discrepancy between successful antibacterial results in in vitro models and limited success in in vivo models. For example, many drugs display strong antimicrobial activity in laboratory settings but fail to effectively treat infections in actual clinical trials.256 This inconsistency is due to a lack of understanding of the physio-pathological conditions of bacteria within infection sites, where bacteria grow in a complex microenvironment that is not accurately mimicked in a laboratory setting.183,257 Hence, a deeper understanding of bacterial metabolism, physio-pathological, and diversity within bacterial populations is needed for better translation of laboratory findings to clinical outcomes.

Further, the continuous and rapid dispersion of antibiotic resistance results in treatment failure, demanding an urgent need for new treatment alternatives. To tackle this hurdle of AMR, there is a persistent need for the replenishment of existing antibiotics with new drugs.258 Some conventional methods that can be adopted to avoid MDR include identification of microbial extract (glycopeptides), semi-synthetic molecules having novel mechanisms of action, or through ab initio drug discovery routes that can circumvent the increasing episodes of resistance.259 The successful implementation of these traditional methods demands time as well as resources, which is another major hurdle in the process of drug discovery.

6.4 Challenges in using antibacterial susceptibility testing standards

EUCAST and CLSI guidelines are mainly designed for an in vitro antimicrobial susceptibility tests, which mainly target molecules exhibiting hydrophilic properties. These assays are best fit for synthetic molecules having chemical origin. However, in the case of natural products, there is a mixture of molecules with different solubility parameters. The lipophilic characteristics displayed by the constituents of plant extracts limit the versatility of these susceptibility tests.260,261 Also, the lack of an established minimum inhibitory concentration for a plant-derived compound reflects the major limitations of these susceptibility assays. In the case of conventional antibiotics, there is a standard MIC range (0.01 to 10 μg mL−1) defining the efficiency of the antibiotics. On the other hand, there is no clear-cut demarcation of MIC values for natural compounds. An investigation conducted by Ríos and group262 proposed the antimicrobial activity with <100 μg mL−1 and <10 μg mL−1 for plant extract and isolated compounds, respectively. Another study performed by Taguri and group263 proposed different MIC ranges of crude extract or pure compound with <400 μg mL−1 for strong antimicrobial agents and >800 μg mL−1 for weak agents. This lack of dedicated guidelines and inconsistency in MIC breakpoints for natural products complicates the comparison of results and decreases the reliability of the antibacterial activity of naturally derived compounds.

7. Conclusion

Presently, bacterial infections pose an alarming threat to global health, associated with high morbidity and mortality worldwide. This devastation is mainly due to the progressive dispersion of antimicrobial resistance, rendering existing treatment alternatives ineffective. Recognizing this urgent threat, antibacterial research is experiencing remarkable advancements as scientists worldwide dedicate their efforts to pioneering innovative technologies that can expedite the discovery and development of novel drugs. The present review highlights the intricate mechanisms underlying AMR and promising drug targets for future therapeutic interventions. Furthermore, the various efficient and cost-effective antibacterial susceptibility techniques that are aimed at exploring and identifying novel lead molecules are also presented in detail. Different antimicrobial susceptibility techniques standardized by CLSI and EUCAT provided a breakthrough in the field of drug discovery. Also, the roadmap to preclinical evaluation for antibacterial drugs has been discussed. Furthermore, to bridge between in vitro and in vivo settings, the common ex vivo models that mimic the host's internal environment have been explained. The review also emphasizes the significance of various in vivo animal models in gaining a comprehensive understanding of the identified antimicrobial agents. Also, the discussion of inevitable challenges that majorly affect the success rate of the novel drug would play a crucial role in addressing the menace of AMR. By providing a clear and comprehensive roadmap that spans from ground-level antimicrobial screening to detailed preclinical evaluation, this review aims to empower researchers and accelerate the journey of discovering and developing urgently needed antibacterial agents with enhanced accuracy and more robust, translatable outcomes in the ongoing battle against the escalating threat of antimicrobial resistance.

8. Future perspectives

AMR has now become one of the looming threats that has been recognized as a global crisis in the present world. The emergence of antimicrobial resistance has been paralleled by the vanishing of drug discovery. The currently available chemotherapy is becoming ineffective in preventing the rapid growth of bacterial infection. This is due to the various mechanisms exhibited by pathogens to escape from the bactericidal action of antibiotics. The increasing efforts in the research and development of drugs are driven by the urgent need to address antimicrobial resistance and its consequences for global public health. In this endeavor, current attempts are being made to establish effective as well as modern techniques that can effectively end up providing novel drug mimetic molecules. Conventional screening methods like agar diffusion and broth dilution are considered the gold standard in the determination of antimicrobial susceptibility tests. However, these methods are associated with a few downsides, such as being highly laborious, less sensitive, and time-consuming. Hence, integration of cutting-edge technologies such as high-throughput screening, advanced microscopy, and artificial intelligence-driven predictive models are expected to advance the landscape of antimicrobial screening approaches. It will also provide an exceptional understanding of the mechanism of bacterial action, identification of drug targets, and structural properties of antimicrobial molecules. Also, the ex vivo model that has been highlighted provides crucial information about host–pathogen interactions, like metabolism and systemic immune responses, but further advancement in ex vivo models, like 3D tissue cultures and organoids, can stimulate the natural environment better than traditional 2D cultures. Further, in vivo models help in understanding the pharmacokinetics, pharmacodynamics, host–pathogen interaction, and drug efficacy, but there is a limitation in the translation of animal models to humans due to differences in the nature of the species. Moreover, emerging therapies such as phage therapy, probiotics, aptamers, monoclonal antibodies, and CRISPR-Cas hold significant potential for overcoming the limitations of conventional antibiotics, reducing the spread of resistance, and enhancing the process of the drug discovery pipeline.

9. Article highlights

• The World Health Organization (WHO) warns that AMR could cause 10 million deaths annually by 2050, emphasizing the urgent need for new solutions against a wide range of ESKAPE pathogens.

• Bacteria have evolved sophisticated ways to resist antibiotics, including drug inactivation, target site modification, overexpression, and efflux pump overexpression. Understanding these mechanisms is key to developing new strategies to combat the increasing threat of antibiotic resistance.

• Researchers are exploring new drug targets by focusing on essential bacterial processes like cell wall synthesis, DNA replication, and cell division. Compounds that inhibit key proteins in these pathways are being critically investigated.

• Various in vitro techniques, such as diffusion and dilution methods, dye-based assays, etc., are used to screen various potential drug candidates. This is followed by ex vivo and in vivo models using invertebrates and vertebrates to gain valuable insights into host–pathogen interactions and treatment efficacy.

• Developing new antibiotics faces significant obstacles, including financial constraints, regulatory hurdles, and the inherent complexity of bacterial pathogens. A notable challenge is the discrepancy between results from in vitro and in vivo studies.

• The future of antimicrobial therapy is promising, with emerging strategies like phage therapy, probiotics, aptamers, monoclonal antibodies, and CRISPR-Cas systems poised to revolutionize the fight against multidrug-resistant bacteria.

• To speed up the discovery and development of new antibiotics, it is crucial to address the challenges associated with screening protocols and bridge the gap between in vitro and in vivo studies, complemented by a better understanding of bacterial physiology and drug efficacy.

• To mitigate the impending global health crisis of antimicrobial resistance (AMR), this review, overall, highlights promising drug targets and screening approaches to accelerate the discovery and development of novel antibacterial drugs.

Author contributions

TP, VJ, and DK – literature search and formal analysis, writing – original draft, preparation of figures and tables. KS – conceptualization, writing – original draft, review & editing, supervision. All authors have read and approved the final version of the manuscript.

Conflicts of interest

The authors confirm that this article has no conflict of interest.

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

Tashi Palmo (UGC-SRF), Vishwani Jamwal (UGC-SRF), and Diksha Kumari (UGC-SRF) are highly thankful to the University Grants Commission (UGC), New Delhi, for fellowship assistance. Kuljit Singh acknowledges the Council of Scientific and Industrial Research (CSIR), New Delhi, for granting an R&D Seed Fund (RDSF), Grant number-CSPS24/RDSF/IIIM/IHP24/03 under the CSIR Special Projects Scheme (CSPS-2024). The institutional manuscript communication number is CSIR-IIIM/IPR/00838.

References

  1. K. W. K. Tang, B. C. Millar and J. E. Moore, Br. J. Biomed. Sci., 2023, 80, 11387 CrossRef PubMed .
  2. D. M. De Oliveira, B. M. Forde, T. J. Kidd, P. N. Harris, M. A. Schembri, S. A. Beatson, D. L. Paterson and M. J. Walker, Clin. Microbiol. Rev., 2020, 33(3), e00181-19 CrossRef PubMed .
  3. R. C. Founou, A. J. Blocker, M. Noubom, C. Tsayem, S. P. Choukem, M. V. Dongen and L. L. Founou, Future Sci. OA, 2021, 7, Fso736 CrossRef CAS PubMed .
  4. J. O'Neill, Review On Antimicrobial Resistance, 2018, pp. 1–84 Search PubMed.
  5. V. P. Singh, D. Jha, B. U. Rehman, V. S. Dhayal, M. S. Dhar and N. Sharma, J. Agric. Food Res., 2024, 15, 100973 CAS .
  6. IHME, Institute for Health Metrics and Evaluation, 2020, pp. 1–4 Search PubMed.
  7. S. S. Kadri, Crit. Care Med., 2020, 48, 939–945 CrossRef PubMed .
  8. S. Kalpana, W. Y. Lin, Y. C. Wang, Y. Fu, A. Lakshmi and H. Y. Wang, Diagnostics, 2023, 13, 1014 CrossRef CAS PubMed .
  9. V. Koul, A. Sharma, D. Kumari, V. Jamwal, T. Palmo and K. Singh, Arch. Microbiol., 2025, 207, 18 CrossRef CAS PubMed .
  10. WHO, Global technical consultation report on proposed terminology for pathogens that transmit through the air, World Health Organization, Geneva, Switzerland, 2024 Search PubMed.
  11. S. Santajit and N. Indrawattana, Biomed Res. Int., 2016, 2016, 2475067 Search PubMed .
  12. S. K. Panda, S. Buroni, S. S. Swain, A. Bonacorsi, E. A. da Fonseca Amorim, M. Kulshrestha, L. C. N. da Silva and V. Tiwari, Front. Microbiol., 2022, 13, 1029098 CrossRef PubMed .
  13. S. Y. Tong, J. S. Davis, E. Eichenberger, T. L. Holland and V. G. Fowler, Jr., Clin. Microbiol. Rev., 2015, 28, 603–661 CrossRef CAS PubMed .
  14. L. Assoni, A. J. M. Couto, B. Vieira, B. Milani, A. S. Lima, T. R. Converso and M. Darrieux, Front. Microbiol., 2024, 15, 1367422 CrossRef PubMed .
  15. Y. Li, S. Kumar and L. Zhang, Infect. Drug Resist., 2024, 17, 1107–1119 CrossRef CAS PubMed .
  16. V. Jamwal, T. Palmo and K. Singh, RSC Med. Chem., 2024, 15, 3925–3949 RSC .
  17. K. Novović and B. Jovčić, J. Antibiot., 2023, 12, 516 CrossRef PubMed .
  18. S. J. Wood, T. M. Kuzel and S. H. Shafikhani, Cell, 2023, 12, 199 CrossRef CAS PubMed .
  19. L. R. Mulcahy, V. M. Isabella and K. Lewis, Microb. Ecol., 2014, 68, 1–12 CrossRef CAS PubMed .
  20. L. Tzouvelekis, A. Markogiannakis, E. Piperaki, M. Souli and G. Daikos, Clin. Microbiol. Infect., 2014, 20, 862–872 CrossRef CAS PubMed .
  21. K. Singh, S. Ahlawat, D. Kumari, U. Matlani, M. Singh, T. Kaur and A. Rao, in Biomedical Applications and Toxicity of Nanomaterials, ed. P. V. Mohanan and S. Kappalli, Springer Nature Singapore, Singapore, 2023, pp. 425–458,  DOI:10.1007/978-981-19-7834-0_17 .
  22. E. Rosselli Del Turco, M. Bartoletti, A. Dahl, C. Cervera and J. M. Pericàs, Clin. Microbiol. Infect., 2021, 27, 364–371 CrossRef CAS PubMed .
  23. T. O'Driscoll and C. W. Crank, Infect. Drug Resist., 2015, 8, 217–230 Search PubMed .
  24. I. S. Reinseth, K. V. Ovchinnikov, H. H. Tønnesen, H. Carlsen and D. B. Diep, Probiotics Antimicrob. Proteins, 2020, 12, 1203–1217 CrossRef PubMed .
  25. Y. Golan, Clin. Infect. Dis., 2019, 68, S206–S212 CrossRef CAS PubMed .
  26. B. Maddiboyina, H. Roy, M. Ramaiah, C. N. Sarvesh, S. H. Kosuru, R. K. Nakkala and B. S. Nayak, Bull. Natl. Res. Cent., 2023, 47, 95 CrossRef .
  27. D. L. Stevens, A. L. Bisno, H. F. Chambers, E. P. Dellinger, E. J. Goldstein, S. L. Gorbach, J. V. Hirschmann, S. L. Kaplan, J. G. Montoya and J. C. Wade, Clin. Infect. Dis., 2014, 59, e10–e52 CrossRef PubMed .
  28. N. Petrosillo, F. Taglietti and G. Granata, J. Clin. Med., 2019, 8, 934 CrossRef CAS PubMed .
  29. T. Karampatakis, K. Tsergouli and P. Behzadi, Antibiotics, 2023, 12, 234 CrossRef CAS PubMed .
  30. S. N. Başaran and L. Öksüz, Arch. Microbiol., 2025, 207, 110 CrossRef PubMed .
  31. J. Garnacho-Montero, R. Amaya-Villar, C. Ferrándiz-Millón, A. Díaz-Martín, J. M. López-Sánchez and A. Gutiérrez-Pizarraya, Expert Rev. Anti-infect. Ther., 2015, 13, 769–777 CrossRef CAS PubMed .
  32. A. Michalopoulos and M. E. Falagas, Expert Opin. Pharmacother., 2010, 11, 779–788 CrossRef CAS PubMed .
  33. D. Ibrahim, J. F. Jabbour and S. S. Kanj, Curr. Opin. Infect. Dis., 2020, 33, 464–473 CrossRef CAS PubMed .
  34. F. Cosentino, P. Viale and M. Giannella, Curr. Opin. Infect. Dis., 2023, 36, 564–571 CrossRef PubMed .
  35. M. Pina-Sánchez, M. Rua and J. L. Del Pozo, Rev. Esp. Quimioter., 2023, 36(Suppl 1), 54–58 CrossRef PubMed .
  36. C. C. Sheu, Y. T. Chang, S. Y. Lin, Y. H. Chen and P. R. Hsueh, Front. Microbiol., 2019, 10, 80 CrossRef PubMed .
  37. K. Tompkins and D. van Duin, Eur. J. Clin. Microbiol. Infect. Dis., 2021, 40, 2053–2068 CrossRef CAS PubMed .
  38. I. G. Auda, I. M. A. Salman and J. G. Odah, Gene Rep., 2020, 20, 100666 CrossRef CAS .
  39. N. Farhat, A. Ali, R. A. Bonomo and A. U. Khan, Drug Discovery Today, 2020, 25, 2307–2316 CrossRef CAS PubMed .
  40. L. J. Piddock, Clin. Microbiol. Rev., 2006, 19, 382–402 CrossRef CAS PubMed .
  41. D. Du, X. Wang-Kan, A. Neuberger, H. W. Van Veen, K. M. Pos, L. J. Piddock and B. F. Luisi, Nat. Rev. Microbiol., 2018, 16, 523–539 CrossRef CAS PubMed .
  42. F. Amereh, M. R. Arabestani and L. Shokoohizadeh, Iran. J. Basic Med. Sci., 2023, 26, 93–98 Search PubMed .
  43. D. Palazzotti, T. Felicetti, S. Sabatini, S. Moro, M. L. Barreca, M. Sturlese and A. Astolfi, J. Chem. Inf. Model., 2023, 63, 4875–4887 CrossRef CAS PubMed .
  44. R. Jadimurthy, S. B. Mayegowda, S. C. Nayak, C. D. Mohan and K. Rangappa, Biotechnol. Rep., 2022, 34, e00728 CrossRef CAS PubMed .
  45. A. Petchiappan and D. Chatterji, ACS Omega, 2017, 2, 7400–7409 CrossRef CAS PubMed .
  46. V. Kumar, P. Sun, J. Vamathevan, Y. Li, K. Ingraham, L. Palmer, J. Huang and J. R. Brown, Antimicrob. Agents Chemother., 2011, 55, 4267–4276 CrossRef CAS PubMed .
  47. X. Z. Li, P. Plésiat and H. Nikaido, Clin. Microbiol. Rev., 2015, 28, 337–418 CrossRef PubMed .
  48. A. J. Baylay, L. J. V. Piddock and M. A. Webber, in Bacterial Resistance to Antibiotics – From Molecules to Man, 2019, pp. 1–26 Search PubMed .
  49. A. Gauba and K. M. Rahman, Antibiotics, 2023, 12, 1590 CrossRef CAS PubMed .
  50. K. Bush and P. A. Bradford, Nat. Rev. Microbiol., 2019, 17, 295–306 CrossRef CAS PubMed .
  51. T. Naas, S. Oueslati, R. A. Bonnin, M. L. Dabos, A. Zavala, L. Dortet, P. Retailleau and B. Iorga, J. Enzyme Inhib. Med. Chem., 2017, 32, 917–919 CrossRef CAS PubMed .
  52. J. M. Blair, M. A. Webber, A. J. Baylay, D. O. Ogbolu and L. J. Piddock, Nat. Rev. Microbiol., 2015, 13, 42–51 CrossRef CAS PubMed .
  53. E. M. Darby, E. Trampari, P. Siasat, M. S. Gaya, I. Alav, M. A. Webber and J. M. Blair, Nat. Rev. Microbiol., 2023, 21, 280–295 CrossRef CAS PubMed .
  54. G. D. Wright, Adv. Drug Delivery Rev., 2005, 57, 1451–1470 CrossRef CAS PubMed .
  55. M. S. Ramirez and M. E. Tolmasky, Drug Resistance Updates, 2010, 13, 151–171 CrossRef CAS PubMed .
  56. J. Romanowska, N. Reuter and J. Trylska, Proteins., 2013, 81, 63–80 CrossRef CAS PubMed .
  57. J. M. Munita and C. A. Arias, Microbiol. Spectrum, 2016, 4, 1–24 CAS .
  58. M. A. Abushaheen, A. J. Fatani, M. Alosaimi, W. Mansy, M. George, S. Acharya, S. Rathod, D. D. Divakar, C. Jhugroo and S. Vellappally, Dis. Mon., 2020, 66, 100971 CrossRef PubMed .
  59. J. Ruiz, J. Antimicrob. Chemother., 2003, 51, 1109–1117 CrossRef CAS PubMed .
  60. S. Correia, P. Poeta, M. Hébraud, J. L. Capelo and G. Igrejas, J. Med. Microbiol., 2017, 66, 551–559 CrossRef CAS PubMed .
  61. A. Giedraitienė, A. Vitkauskienė, R. Naginienė and A. Pavilonis, Medicina, 2011, 47, 19 CrossRef .
  62. A. M. Egorov, M. M. Ulyashova and M. Y. Rubtsova, Acta Naturae, 2018, 10, 33–48 CrossRef CAS PubMed .
  63. D. Kneis, C. Lemay-St-Denis, S. Cellier-Goetghebeur, A. X. Elena, T. U. Berendonk, J. N. Pelletier and S. Heß, ISME J., 2023, 17, 1455–1466 CrossRef CAS PubMed .
  64. A. Wróbel, D. Maliszewski, M. Baradyn and D. Drozdowska, Molecules, 2020, 25, 116 CrossRef PubMed .
  65. C. Jena, S. Chinnaraj, S. Deolankar and N. Matange, eLife, 2025, 13, RP99785 CrossRef PubMed .
  66. R. A. Bonomo, Cold Spring Harbor Perspect. Med., 2017, 7, a025239 CrossRef PubMed .
  67. H. Flemming, J. Wingender, U. Szewzyk, P. Steinberg, S. Rice and S. Kjelleberg, Nat. Rev. Microbiol., 2016, 14, 563–575 CrossRef CAS PubMed .
  68. C. Uruén, G. Chopo-Escuin, J. Tommassen, R. C. Mainar-Jaime and J. Arenas, Antibiotics, 2021, 10, 3 CrossRef PubMed .
  69. L. K. Vestby, T. Grønseth, R. Simm and L. L. Nesse, Antibiotics, 2020, 9, 59 CrossRef CAS PubMed .
  70. M. Kostakioti, M. Hadjifrangiskou and S. J. Hultgren, Cold Spring Harbor Perspect. Med., 2013, 3, a010306 Search PubMed .
  71. A. Almatroudi, Biology, 2025, 14, 165 CrossRef CAS PubMed .
  72. N. Høiby, T. Bjarnsholt, M. Givskov, S. Molin and O. Ciofu, Int. J. Antimicrob. Agents, 2010, 35, 322–332 CrossRef PubMed .
  73. S. Patra, S. Saha, R. Singh, N. Tomar and P. Gulati, Microb. Pathog., 2025, 198, 107155 CrossRef CAS PubMed .
  74. R. V. Joshi, C. Gunawan and R. Mann, Front. Microbiol., 2021, 12, 635432 CrossRef PubMed .
  75. C. R. Evans, C. P. Kempes, A. Price-Whelan and L. E. P. Dietrich, Trends Microbiol., 2020, 28, 732–743 CrossRef CAS PubMed .
  76. S. C. Booth, M. L. Workentine, J. Wen, R. Shaykhutdinov, H. J. Vogel, H. Ceri, R. J. Turner and A. M. Weljie, J. Proteome Res., 2011, 10, 3190–3199 CrossRef CAS PubMed .
  77. H. Y. Liu, E. L. Prentice and M. A. Webber, NPJ Antimicrob. Resist., 2024, 2, 27 CrossRef PubMed .
  78. D. Sharma, L. Misba and A. U. Khan, Antimicrob. Resist. Infect. Control, 2019, 8, 76 CrossRef PubMed .
  79. D. N. McBrayer, C. D. Cameron and Y. Tal-Gan, Org. Biomol. Chem., 2020, 18, 7273–7290 RSC .
  80. X. Zhao, Z. Yu and T. Ding, Microorganisms, 2020, 8, 425 CrossRef CAS PubMed .
  81. R. Amieva, T. Gil-Gil, J. L. Martínez and M. Alcalde-Rico, Int. J. Mol. Sci., 2022, 23, 7492 CrossRef CAS PubMed .
  82. P. Rezaie, M. Pourhajibagher, N. Chiniforush, N. Hosseini and A. Bahador, J. Lasers Med. Sci., 2018, 9, 161–167 CrossRef PubMed .
  83. W. R. Miller, J. M. Munita and C. A. Arias, Expert Rev. Anti-infect. Ther., 2014, 12, 1221–1236 CrossRef CAS PubMed .
  84. M. Galimand, E. Schmitt, M. Panvert, B. Desmolaize, S. Douthwaite, Y. Mechulam and P. Courvalin, RNA, 2011, 17, 251–262 CrossRef CAS PubMed .
  85. W. Zeng, L. Feng, C. Qian, T. Chen, S. Wang, Y. Zhang, X. Zheng, L. Wang, S. Liu, T. Zhou and Y. Sun, Front. Microbiol., 2022, 13, 815600 CrossRef PubMed .
  86. S. Unni, T. J. Siddiqui and S. Bidaisee, Cureus, 2021, 13, e18925 Search PubMed .
  87. F. D. Lowy, J. Clin. Invest., 2003, 111, 1265–1273 CrossRef CAS PubMed .
  88. D. Mohammadpour, M. Y. Memar, H. E. Leylabadlo, A. Ghotaslou and R. Ghotaslou, Microbe, 2025, 6, 100246 CrossRef .
  89. Y. Li, S. Kumar and L. Zhang, Infect. Drug Resist., 2024, 17, 1107–1119 CrossRef CAS PubMed .
  90. B. C. Haldorsen, G. S. Simonsen, A. Sundsfjord and O. Samuelsen, Diagn. Microbiol. Infect. Dis., 2014, 78, 66–69 CrossRef CAS PubMed .
  91. I. Kyriakidis, E. Vasileiou, Z. D. Pana and A. Tragiannidis, Pathogens, 2021, 10, 373 CrossRef CAS PubMed .
  92. S. Roy, S. Chatterjee, A. Bhattacharjee, P. Chattopadhyay, B. Saha, S. Dutta and S. Basu, Front. Microbiol., 2021, 12, 602724 CrossRef PubMed .
  93. H. R. Goli, M. R. Nahaei, M. A. Rezaee, A. Hasani, H. S. Kafil, M. Aghazadeh, M. Nikbakht and Y. Khalili, J. Infect. Public Health, 2018, 11, 364–372 CrossRef PubMed .
  94. A. Elfadadny, R. F. Ragab, M. AlHarbi, F. Badshah, E. Ibáñez-Arancibia, A. Farag, A. O. Hendawy, P. R. De Los Ríos-Escalante, M. Aboubakr, S. A. Zakai and W. M. Nageeb, Front. Microbiol., 2024, 15, 1374466 CrossRef CAS PubMed .
  95. A. Thacharodi and I. L. Lamont, Antibiotics, 2022, 11, 884 CrossRef CAS PubMed .
  96. N. Skrzypczak and P. Przybylski, Nat. Prod. Rep., 2022, 39, 1653–1677 RSC .
  97. N. Skrzypczak and P. Przybylski, Nat. Prod. Rep., 2022, 39, 1678–1704 RSC .
  98. G. D. Wright, in Antibiotic Resistance, ed. A. R. M. Coates, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 13–30,  DOI:10.1007/978-3-642-28951-4_2 .
  99. E. M. Halawa, M. Fadel, M. W. Al-Rabia, A. Behairy, N. A. Nouh, M. Abdo, R. Olga, L. Fericean, A. M. Atwa, M. El-Nablaway and A. Abdeen, Front. Pharmacol., 2023, 14, 1305294 CrossRef CAS PubMed .
  100. J. G. Swoboda, J. Campbell, T. C. Meredith and S. Walker, ChemBioChem, 2010, 11, 35–45 CrossRef CAS PubMed .
  101. M. Rajagopal and S. Walker, Curr. Top. Microbiol. Immunol., 2017, 404, 1–44 CAS .
  102. X. Chee Wezen, A. Chandran, R. S. Eapen, E. Waters, L. Bricio-Moreno, T. Tosi, S. Dolan, C. Millership, A. Kadioglu, A. Gründling, L. S. Itzhaki, M. Welch and T. Rahman, J. Chem. Inf. Model., 2022, 62, 2586–2599 CrossRef CAS PubMed .
  103. M. G. Percy and A. Gründling, Annu. Rev. Microbiol., 2014, 68, 81–100 CrossRef CAS PubMed .
  104. N. T. Reichmann, C. Piçarra Cassona, J. M. Monteiro, A. L. Bottomley, R. M. Corrigan, S. J. Foster, M. G. Pinho and A. Gründling, Mol. Microbiol., 2014, 92, 273–286 CrossRef CAS PubMed .
  105. G. A. Naclerio and H. O. Sintim, Future Med. Chem., 2020, 12, 1253–1279 CrossRef CAS PubMed .
  106. S. G. Richter, D. Elli, H. K. Kim, A. P. Hendrickx, J. A. Sorg, O. Schneewind and D. Missiakas, Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 3531–3536 CrossRef CAS PubMed .
  107. G. A. Naclerio, C. W. Karanja, C. Opoku-Temeng and H. O. Sintim, ChemMedChem, 2019, 14, 1000–1004 CrossRef CAS PubMed .
  108. S. Kumar, A. Mollo, D. Kahne and N. Ruiz, Chem. Rev., 2022, 122, 8884–8910 CrossRef CAS PubMed .
  109. A. Gautam, R. Vyas and R. Tewari, Crit. Rev. Biotechnol., 2011, 31, 295–336 CrossRef CAS PubMed .
  110. B. Al-Dabbagh, D. Mengin-Lecreulx and A. Bouhss, J. Bacteriol., 2008, 190, 7141–7146 CrossRef CAS PubMed .
  111. D. P. M. Sethuvel, Y. D. Bakthavatchalam, M. Karthik, M. Irulappan, R. Shrivastava, H. Periasamy and B. Veeraraghavan, Infect. Dis. Ther., 2023, 12, 829–841 CrossRef PubMed .
  112. P. A. Mann, A. Müller, L. Xiao, P. M. Pereira, C. Yang, S. Ho Lee, H. Wang, J. Trzeciak, J. Schneeweis, M. M. Dos Santos, N. Murgolo, X. She, C. Gill, C. J. Balibar, M. Labroli, J. Su, A. Flattery, B. Sherborne, R. Maier, C. M. Tan, T. Black, K. Onder, S. Kargman, F. J. Monsma, Jr., M. G. Pinho, T. Schneider and T. Roemer, ACS Chem. Biol., 2013, 8, 2442–2451 CrossRef CAS PubMed .
  113. S. Kumar Pal and S. Kumar, Int. J. Biol. Macromol., 2023, 234, 122960 CrossRef CAS PubMed .
  114. S. Amudala, Sumit and I. S. Aidhen, Carbohydr. Res., 2024, 537, 109057 CrossRef CAS PubMed .
  115. S. Gronow and H. Brade, J. Endotoxin Res., 2001, 7, 3–23 CAS .
  116. W. Han, X. Ma, C. J. Balibar, C. M. Baxter Rath, B. Benton, A. Bermingham, F. Casey, B. Chie-Leon, M. K. Cho, A. O. Frank, A. Frommlet, C. M. Ho, P. S. Lee, M. Li, A. Lingel, S. Ma, H. Merritt, E. Ornelas, G. De Pascale, R. Prathapam, K. R. Prosen, D. Rasper, A. Ruzin, W. S. Sawyer, J. Shaul, X. Shen, S. Shia, M. Steffek, S. Subramanian, J. Vo, F. Wang, C. Wartchow and T. Uehara, J. Am. Chem. Soc., 2020, 142, 4445–4455 CrossRef CAS PubMed .
  117. K. P. Romano and D. T. Hung, Biochim. Biophys. Acta, Mol. Cell Res., 2023, 1870, 119407 CrossRef CAS PubMed .
  118. H. R. Onishi, B. A. Pelak, L. S. Gerckens, L. L. Silver, F. M. Kahan, M. H. Chen, A. A. Patchett, S. M. Galloway, S. A. Hyland, M. S. Anderson and C. R. Raetz, Science, 1996, 274, 980–982 CrossRef CAS PubMed .
  119. Z. Niu, P. Lei, Y. Wang, J. Wang, J. Yang and J. Zhang, Eur. J. Med. Chem., 2023, 253, 115326 CrossRef CAS PubMed .
  120. K. M. Krause, C. M. Haglund, C. Hebner, A. W. Serio, G. Lee, V. Nieto, F. Cohen, T. R. Kane, T. D. Machajewski, D. Hildebrandt, C. Pillar, M. Thwaites, D. Hall, L. Miesel, M. Hackel, A. Burek, L. D. Andrews, E. Armstrong, L. Swem, A. Jubb and R. T. Cirz, Antimicrob. Agents Chemother., 2019, 63, e00977-19 CrossRef PubMed .
  121. H. Kurasaki, K. Tsuda, M. Shinoyama, N. Takaya, Y. Yamaguchi, R. Kishii, K. Iwase, N. Ando, M. Nomura and Y. Kohno, ACS Med. Chem. Lett., 2016, 7, 623–628 CrossRef CAS PubMed .
  122. N. D'Atanasio, A. Capezzone de Joannon, L. Di Sante, G. Mangano, R. Ombrato, M. Vitiello, C. Bartella, G. Magarò, F. Prati, C. Milanese, C. Vignaroli, F. P. Di Giorgio and S. Tongiani, PLoS One, 2020, 15, e0228509 CrossRef PubMed .
  123. M. Zorman, M. Hrast Rambaher, M. Kokot, N. Minovski and M. Anderluh, Eur. J. Pharm. Sci., 2024, 192, 106632 CrossRef CAS PubMed .
  124. T. Khan, K. Sankhe, V. Suvarna, A. Sherje, K. Patel and B. Dravyakar, Biomed. Pharmacother., 2018, 103, 923–938 CrossRef CAS PubMed .
  125. S. A. Leyn, J. E. Kent, J. E. Zlamal, M. L. Elane, M. Vercruysse and A. L. Osterman, NPJ Antimicrob. Resist., 2024, 2, 5 CrossRef PubMed .
  126. M. T. Black, T. Stachyra, D. Platel, A. M. Girard, M. Claudon, J. M. Bruneau and C. Miossec, Antimicrob. Agents Chemother., 2008, 52, 3339–3349 CrossRef CAS PubMed .
  127. J. J. Champoux, Annu. Rev. Biochem., 2001, 70, 369–413 CrossRef CAS PubMed .
  128. J. C. Wang, Nat. Rev. Mol. Cell Biol., 2002, 3, 430–440 CrossRef CAS PubMed .
  129. B. D. Bax, P. F. Chan, D. S. Eggleston, A. Fosberry, D. R. Gentry, F. Gorrec, I. Giordano, M. M. Hann, A. Hennessy, M. Hibbs, J. Huang, E. Jones, J. Jones, K. K. Brown, C. J. Lewis, E. W. May, M. R. Saunders, O. Singh, C. E. Spitzfaden, C. Shen, A. Shillings, A. J. Theobald, A. Wohlkonig, N. D. Pearson and M. N. Gwynn, Nature, 2010, 466, 935–940 CrossRef PubMed .
  130. M. Kokot, M. Weiss, I. Zdovc, M. Hrast, M. Anderluh and N. Minovski, ACS Med. Chem. Lett., 2021, 12, 1478–1485 CrossRef CAS PubMed .
  131. E. G. Gibson, B. Bax, P. F. Chan and N. Osheroff, ACS Infect. Dis., 2019, 5, 570–581 CrossRef CAS PubMed .
  132. T. den Blaauwen, L. W. Hamoen and P. A. Levin, Curr. Opin. Microbiol., 2017, 36, 85–94 CrossRef CAS PubMed .
  133. K. D. Whitley, C. Jukes, N. Tregidgo, E. Karinou, P. Almada, Y. Cesbron, R. Henriques, C. Dekker and S. Holden, Nat. Commun., 2021, 12, 2448 CrossRef CAS PubMed .
  134. S. Roy Chowdhury, R. Saha, T. Koley, F. Naz, S. Sharma, M. I. Khan, M. Kumar, P. Kaur and A. S. Ethayathulla, J. Biomol. Struct. Dyn., 2024, 1–13,  DOI:10.1080/07391102.2024.2304675 .
  135. M. Ur Rahman, P. Wang, N. Wang and Y. Chen, Bosnian J. Basic Med. Sci., 2020, 20, 310–318 CAS .
  136. M. Kaul, L. Mark, Y. Zhang, A. K. Parhi, Y. L. Lyu, J. Pawlak, S. Saravolatz, L. D. Saravolatz, M. P. Weinstein, E. J. LaVoie and D. S. Pilch, Antimicrob. Agents Chemother., 2015, 59, 4845–4855 CrossRef CAS PubMed .
  137. J. M. Andreu, C. Schaffner-Barbero, S. Huecas, D. Alonso, M. L. Lopez-Rodriguez, L. B. Ruiz-Avila, R. Núñez-Ramírez, O. Llorca and A. J. Martín-Galiano, J. Biol. Chem., 2010, 285, 14239–14246 CrossRef CAS PubMed .
  138. N. R. Stokes, N. Baker, J. M. Bennett, J. Berry, I. Collins, L. G. Czaplewski, A. Logan, R. Macdonald, L. Macleod, H. Peasley, J. P. Mitchell, N. Nayal, A. Yadav, A. Srivastava and D. J. Haydon, Antimicrob. Agents Chemother., 2013, 57, 317–325 CrossRef CAS PubMed .
  139. D. N. Margalit, L. Romberg, R. B. Mets, A. M. Hebert, T. J. Mitchison, M. W. Kirschner and D. RayChaudhuri, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 11821–11826 CrossRef CAS PubMed .
  140. M. Moradi, S. A. Kousheh, R. Razavi, Y. Rasouli, M. Ghorbani, E. Divsalar, H. Tajik, J. T. Guimarães and S. A. Ibrahim, Trends Food Sci. Technol., 2021, 111, 595–609 CrossRef CAS .
  141. O. M. Lage, M. C. Ramos, R. Calisto, E. Almeida, V. Vasconcelos and F. Vicente, Mar. Drugs, 2018, 16, 279 CrossRef PubMed .
  142. S. Qonitah, N. A. Mappaware, M. W. Harahap, I. Royani and N. Fattah, Gema Lingkungan Kesehatan, 2025, 23, 279–285 CrossRef .
  143. Y. L. Chew, A. M. Mahadi, K. M. Wong and J. K. Goh, BMC Complementary Altern. Med., 2018, 18, 70 CrossRef PubMed .
  144. T. J. Hossain, Eur. J. Microbiol. Immunol., 2024, 14, 97–115 CAS .
  145. M. Balouiri, M. Sadiki and S. K. Ibnsouda, J. Pharm. Anal., 2016, 6, 71–79 CrossRef PubMed .
  146. Z. Emami-Karvani and P. Chehrazi, Afr. J. Microbiol. Res., 2011, 5, 1368–1373 CAS .
  147. I. Ahmad, M. Y. Alshahrani, S. Wahab, A. I. Al-Harbi, N. Nisar, Y. Alraey, A. Alqahtani, M. A. Mir, S. Irfan and M. Saeed, J. King Saud Univ. Sci., 2022, 34, 102110 CrossRef .
  148. A. Rütten, T. Kirchner and E. M. Musiol-Kroll, Pharmaceuticals, 2022, 15, 1302 CrossRef PubMed .
  149. V. S. R. Dhevi and S. Arunachalam, Saudi J. Biol. Sci., 2024, 31, 103937 CrossRef PubMed .
  150. G. Sriragavi, M. Sangeetha, M. Santhakumar, E. Lokesh, M. Nithyalakshmi, C. A. Saleel and R. Balagurunathan, ACS Omega, 2023, 8, 36333–36343 CrossRef CAS PubMed .
  151. M. Hamad, F. Al-Marzooq, V. Srinivasulu, H. A. Omar, A. Sulaiman, D. M. Zaher, G. Orive and T. H. Al-Tel, Front. Microbiol., 2022, 13, 823394 CrossRef PubMed .
  152. S. Hijazi, D. Visaggio, M. Pirolo, E. Frangipani, L. Bernstein and P. Visca, Front. Cell. Infect. Microbiol., 2018, 8, 316 CrossRef PubMed .
  153. R. Fleeman, T. M. LaVoi, R. G. Santos, A. Morales, A. Nefzi, G. S. Welmaker, J. L. Medina-Franco, M. A. Giulianotti, R. A. Houghten and L. N. Shaw, J. Med. Chem., 2015, 58, 3340–3355 CrossRef CAS PubMed .
  154. N. P. Varela, R. Friendship, C. Dewey and A. Valdivieso, Can. J. Vet. Res., 2008, 72, 168–174 CAS .
  155. A. R. Khezripour, D. Souri, H. Tavafi and M. Ghabooli, Measurement, 2019, 148, 106939 CrossRef .
  156. A. Tabassum, D. Kumari, H. B. Bhore, T. Palmo, I. Venkatesan, J. Samanta, A. K. Katare, K. Singh and Y. P. Bharitkar, Bioorg. Chem., 2025, 154, 108087 CrossRef CAS PubMed .
  157. J. Intra, C. Sarto, S. Mazzola, C. Fania, N. Tiberti and P. Brambilla, Mycopathologia, 2019, 184, 517–523 CrossRef CAS PubMed .
  158. A. Gehrt, J. Peter, P. A. Pizzo and T. J. Walsh, J. Clin. Microbiol., 1995, 33, 1302–1307 CrossRef CAS PubMed .
  159. J. M. Wilkinson, in Modern Phytomedicine, 2006, pp. 157–171 Search PubMed .
  160. B. Gopu, P. Kour, R. Pandian and K. Singh, Int. Immunopharmacol., 2023, 114, 109591 CrossRef CAS PubMed .
  161. D. Kumari, P. Kour, C. P. Singh, R. Choudhary, S. M. Ali, S. Bhayye, Y. P. Bharitkar and K. Singh, Int. J. Biol. Macromol., 2024, 269, 132034 CrossRef CAS PubMed .
  162. S. D. Sarker, L. Nahar and Y. Kumarasamy, Methods, 2007, 42, 321–324 CrossRef CAS PubMed .
  163. M. Elshikh, S. Ahmed, S. Funston, P. Dunlop, M. McGaw, R. Marchant and I. M. Banat, Biotechnol. Lett., 2016, 38, 1015–1019 CrossRef CAS PubMed .
  164. K. Yildirim, E. Simsek, O. Kocak, S. Bozkurt, O. Koyuncu Ozyurt and A. Y. Coban, Laboratory Medicine, 2024, 55, 380–385 CrossRef PubMed .
  165. H. Jia, R. Fang, J. Lin, X. Tian, Y. Zhao, L. Chen, J. Cao and T. Zhou, BMC Microbiol., 2020, 20, 7 CrossRef CAS PubMed .
  166. P. Costa, A. Gomes, M. Braz, C. Pereira and A. Almeida, Antibiotics, 2021, 10, 974 CrossRef CAS PubMed .
  167. B. S. Erikstein, H. R. Hagland, J. Nikolaisen, M. Kulawiec, K. K. Singh, B. T. Gjertsen and K. J. Tronstad, J. Cell. Biochem., 2010, 111, 574–584 CrossRef CAS PubMed .
  168. R. T. Pace and K. J. Burg, Cytotechnology, 2015, 67, 13–17 CrossRef CAS PubMed .
  169. D. Rani, D. Kumari, A. Bhushan, V. Jamwal, B. A. Lone, G. Lakhanpal, A. Nargotra, K. Singh and P. Gupta, J. Mol. Struct., 2024, 1308, 138105 CrossRef CAS .
  170. D. Kumari, V. Jamwal, A. Singh, S. K. Singh, S. Mujwar, M. Y. Ansari and K. Singh, Acta Trop., 2024, 258, 107338 CrossRef CAS PubMed .
  171. A. Bozorg, I. D. Gates and A. Sen, J. Microbiol. Methods, 2015, 109, 84–92 CrossRef CAS PubMed .
  172. S. Finger, C. Wiegand, H. J. Buschmann and U. C. Hipler, Int. J. Pharm., 2013, 452, 188–193 CrossRef CAS PubMed .
  173. D. Kumari, J. Kour, A. Kumar, M. Sangral, S. K. Singh, S. D. Sawant and K. Singh, Bioorg. Chem., 2025, 164, 108804 CrossRef CAS PubMed .
  174. P. Kour, P. Saha, S. Bhattacharya, D. Kumari, A. Debnath, A. Roy, D. K. Sharma, D. Mukherjee and K. Singh, RSC Med. Chem., 2023, 14, 2100–2114 RSC .
  175. E. V. Sazonova, M. S. Chesnokov, B. Zhivotovsky and G. S. Kopeina, Cell Death Discovery, 2022, 8, 417 CrossRef PubMed .
  176. D. Kumari, V. Jamwal, T. Palmo, A. Kumar and K. Singh, World J. Microbiol. Biotechnol., 2025, 41, 247 CrossRef CAS PubMed .
  177. D. Kumari, H. Kaur, T. Palmo, A. Nargotra and K. Singh, Nat. Prod. Res., 2024, 1–7,  DOI:10.1080/14786419.2024.2392749 .
  178. J. C. Stockert, R. W. Horobin, L. L. Colombo and A. Blázquez-Castro, Acta Histochem., 2018, 120, 159–167 CrossRef CAS PubMed .
  179. M. Ghasemi, T. Turnbull, S. Sebastian and I. Kempson, Int. J. Mol. Sci., 2021, 22, 12827 CrossRef CAS PubMed .
  180. D. Kumari, T. Palmo, S. Mujwar and K. Singh, Acta Trop., 2024, 260, 107473 CrossRef CAS PubMed .
  181. P. Houghton, R. Fang, I. Techatanawat, G. Steventon, P. J. Hylands and C. C. Lee, Methods, 2007, 42, 377–387 CrossRef CAS PubMed .
  182. V. Vichai and K. Kirtikara, Nat. Protoc., 2006, 1, 1112–1116 CrossRef CAS PubMed .
  183. D. Shi, G. Mi, M. Wang and T. J. Webster, Biomaterials, 2019, 198, 228–249 CrossRef CAS PubMed .
  184. F. Harrison, A. Muruli, S. Higgins and S. P. Diggle, Infect. Immun., 2014, 82, 3312–3323 CrossRef PubMed .
  185. J. Jäger, S. Marwitz, J. Tiefenau, J. Rasch, O. Shevchuk, C. Kugler, T. Goldmann and M. Steinert, Infect. Immun., 2014, 82, 275–285 CrossRef PubMed .
  186. Q. Yang, C. Larose, A. C. Della Porta, G. S. Schultz and D. J. Gibson, Int. Wound J., 2017, 14, 408–413 CrossRef PubMed .
  187. H. Yu, Y. Xu, S. Imani, Z. Zhao, S. Ullah and Q. Wang, ACS Infect. Dis., 2024, 10, 2336–2355 CrossRef CAS PubMed .
  188. P. Manohar, B. Loh, N. Elangovan, A. Loganathan, R. Nachimuthu and S. Leptihn, Microbiol. Spectrum, 2022, 10, e0139321 CrossRef PubMed .
  189. A. V. Revtovich, E. Tjahjono, K. V. Singh, B. M. Hanson, B. E. Murray and N. V. Kirienko, Front. Cell. Infect. Microbiol., 2021, 11, 667327 CrossRef CAS PubMed .
  190. Y. Wang, K. Guo, Q. Wang, G. Zhong, W. Zhang, Y. Jiang, X. Mao, X. Li and Z. Huang, Crit. Rev. Food Sci. Nutr., 2024, 64, 3167–3185 CrossRef PubMed .
  191. C. Kong, S. A. Eng, M. P. Lim and S. Nathan, Front. Microbiol., 2016, 7, 1956 Search PubMed .
  192. V. Defraine, L. Verstraete, F. Van Bambeke, A. Anantharajah, E. M. Townsend, G. Ramage, R. Corbau, A. Marchand, P. Chaltin, M. Fauvart and J. Michiels, Front. Microbiol., 2017, 8, 2585 CrossRef PubMed .
  193. K. M. Balla and E. R. Troemel, Cell. Microbiol., 2013, 15, 1313–1322 CrossRef CAS PubMed .
  194. G. Hajdú, C. Szathmári and C. Sőti, Int. J. Mol. Sci., 2024, 25, 7034 CrossRef PubMed .
  195. A. Montali, F. Berini, A. Saviane, S. Cappellozza, F. Marinelli and G. Tettamanti, Insects, 2022, 13, 748 CrossRef PubMed .
  196. M. H. Khan and K. Ramalingam, Biocatal. Agric. Biotechnol., 2019, 18, 101025 CrossRef .
  197. M. A. Cutuli, G. Petronio Petronio, F. Vergalito, I. Magnifico, L. Pietrangelo, N. Venditti and R. Di Marco, Virulence, 2019, 10, 527–541 CrossRef CAS PubMed .
  198. S. Panthee, A. Paudel, H. Hamamoto and K. Sekimizu, Front. Microbiol., 2017, 8, 373 Search PubMed .
  199. C. Hu and W. Yang, Folia Microbiol., 2023, 68, 703–739 CrossRef CAS PubMed .
  200. C. Kaito, K. Murakami, L. Imai and K. Furuta, Microbiol. Immunol., 2020, 64, 585–592 CrossRef CAS PubMed .
  201. Y. J. Lee, H. J. Jang, I. Y. Chung and Y. H. Cho, J. Microbiol., 2018, 56, 534–541 CrossRef PubMed .
  202. H. M. S. Goh, M. H. A. Yong, K. K. L. Chong and K. A. Kline, Virulence, 2017, 8, 1525–1562 CrossRef PubMed .
  203. M. Cieślik, N. Bagińska, A. Górski and E. Jończyk-Matysiak, Microorganisms, 2021, 9, 206 CrossRef PubMed .
  204. U. Waack, E. A. Weinstein and J. J. Farley, Antimicrob. Agents Chemother., 2020, 64, e02242-19 CrossRef PubMed .
  205. D. Lebeaux, A. Chauhan, O. Rendueles and C. Beloin, Pathogens, 2013, 2, 288–356 CrossRef PubMed .
  206. R. J. Law, L. Gur-Arie, I. Rosenshine and B. B. Finlay, Cold Spring Harbor Perspect. Med., 2013, 3, a009977 Search PubMed .
  207. M. Annamanedi, G. Y. N. Varma, K. Anuradha and A. M. Kalle, Front. Microbiol., 2017, 8, 805 CrossRef PubMed .
  208. M. K. Herzog, M. Cazzaniga, A. Peters, N. Shayya, L. Beldi, S. Hapfelmeier, M. M. Heimesaat, S. Bereswill, G. Frankel, C. G. M. Gahan and W. D. Hardt, Gut Microbes, 2023, 15, 2172667 CrossRef PubMed .
  209. H. Liang, Y. Wang, F. Liu, G. Duan, J. Long, Y. Jin, S. Chen and H. Yang, Pathogens, 2024, 13, 434 CrossRef CAS PubMed .
  210. M. C. McElroy, D. J. Cain, C. Tyrrell, T. J. Foster and C. Haslett, Infect. Immun., 2002, 70, 3865–3873 CrossRef CAS PubMed .
  211. D. B. Huang, I. Morrissey, T. Murphy, S. Hawser and M. H. Wilcox, Eur. J. Clin. Microbiol. Infect. Dis., 2018, 37, 673–678 CrossRef CAS PubMed .
  212. J. Prazak, L. G. Valente, M. Iten, L. Federer, D. Grandgirard, S. Soto, G. Resch, S. L. Leib, S. M. Jakob, M. Haenggi, D. R. Cameron and Y. A. Que, J. Infect. Dis., 2022, 225, 1452–1459 CrossRef CAS PubMed .
  213. W. A. Hady, A. S. Bayer and Y. Q. Xiong, J. Visualized Exp., 2012, e3863,  DOI:10.3791/3863 .
  214. Y. M. Zhu, J. F. Miao, H. J. Fan, S. X. Zou and W. H. Chen, Int. Immunopharmacol., 2007, 7, 435–443 CrossRef CAS PubMed .
  215. M. Beganovic, M. K. Luther, L. B. Rice, C. A. Arias, M. J. Rybak and K. L. LaPlante, Clin. Infect. Dis., 2018, 67, 303–309 CrossRef CAS PubMed .
  216. M. Palau, E. Muñoz, N. Larrosa, X. Gomis, E. Márquez, O. Len, B. Almirante and J. Gavaldà, Microbiol. Spectrum, 2023, 11, e0280722 CrossRef PubMed .
  217. S. L. R. Karna, J. Q. Nguyen, S. J. Evani, L. W. Qian, P. Chen, J. J. Abercrombie, E. A. Sebastian, A. B. Fourcaudot and K. P. Leung, Microb. Pathog., 2020, 147, 104254 CrossRef CAS PubMed .
  218. N. Malachowa, W. McGuinness, S. D. Kobayashi, A. R. Porter, C. Shaia, J. Lovaglio, B. Smith, V. Rungelrath, G. Saturday, D. P. Scott, F. Falugi, D. Missiakas, O. Schneewind and F. R. DeLeo, Microbiol. Spectrum, 2022, 10, e0271621 CrossRef PubMed .
  219. K. M. Roux, L. H. Cobb, M. A. Seitz and L. B. Priddy, Anim. Models Exp. Med., 2021, 4, 59–70 CrossRef PubMed .
  220. D. V. Zurawski, C. C. Black, Y. A. Alamneh, L. Biggemann, J. Banerjee, M. G. Thompson, M. C. Wise, C. L. Honnold, R. K. Kim, C. Paranavitana, J. P. Shearer, S. D. Tyner and S. T. Demons, Adv. Wound Care, 2019, 8, 14–27 CrossRef PubMed .
  221. H. A. Simmons, Vet. Pathol., 2016, 53, 399–416 CrossRef CAS PubMed .
  222. M. B. Gardner and P. A. Luciw, ILAR J., 2008, 49, 220–255 CrossRef CAS PubMed .
  223. L. Chen, K. E. Welty-Wolf and B. D. Kraft, Lab. Anim., 2019, 48, 57–65 CrossRef PubMed .
  224. B. A. Hart, W. M. Bogers, K. G. Haanstra, F. A. Verreck and C. H. Kocken, Eur. J. Pharmacol., 2015, 759, 69–83 CrossRef PubMed .
  225. J. Lemaitre, T. Naninck, B. Delache, J. Creppy, P. Huber, M. Holzapfel, C. Bouillier, V. Contreras, F. Martinon, N. Kahlaoui, Q. Pascal, S. Tricot, F. Ducancel, L. Vecellio, R. Le Grand and P. Maisonnasse, Mol. Immunol., 2021, 135, 147–164 CrossRef CAS PubMed .
  226. B. Busse, J. L. Galloway, R. S. Gray, M. P. Harris and R. Y. Kwon, J. Orthop. Res., 2020, 38, 925–936 CrossRef PubMed .
  227. V. Torraca and S. Mostowy, Trends Cell Biol., 2018, 28, 143–156 CrossRef CAS PubMed .
  228. D. M. Klug, F. I. M. Idiris, M. A. T. Blaskovich, F. von Delft, C. G. Dowson, C. Kirchhelle, A. P. Roberts, A. C. Singer and M. H. Todd, Wellcome Open Res., 2021, 6, 146 Search PubMed .
  229. C. Gradmann, Med. Hist., 2016, 60, 155–180 CrossRef PubMed .
  230. B. Plackett, Nature, 2020, 586, S50–S52 CrossRef CAS .
  231. B. Skender, Humanit. Soc. Sci. Commun., 2024, 11, 1069 CrossRef .
  232. M. Anderson, D. Panteli, R. van Kessel, G. Ljungqvist, F. Colombo and E. Mossialos, Lancet Reg. Health Eur., 2023, 33, 100705 CrossRef PubMed .
  233. K. Iskandar, J. Murugaiyan, D. Hammoudi Halat, S. E. Hage, V. Chibabhai, S. Adukkadukkam, C. Roques, L. Molinier, P. Salameh and M. Van Dongen, Antibiotics, 2022, 11, 182 CrossRef CAS PubMed .
  234. M. García-Castro, F. Sarabia, A. Díaz-Morilla and J. M. López-Romero, Explor. Drug Sci., 2023, 1, 180–209 Search PubMed .
  235. S. Stern, S. Chorzelski, L. Franken, S. Völler, H. Rentmeister and B. Grosch, Follow-Up Report for the German Guard Initiative, The Boston Consulting Group, Boston, MA, 2017 Search PubMed.
  236. H. I. Zgurskaya and V. V. Rybenkov, Ann. N. Y. Acad. Sci., 2020, 1459, 5–18 CrossRef PubMed .
  237. R. Shrivastava and S. S. Chng, J. Biol. Chem., 2019, 294, 14175–14184 CrossRef CAS PubMed .
  238. D. Saxena, R. Maitra, R. Bormon, M. Czekanska, J. Meiers, A. Titz, S. Verma and S. Chopra, NPJ Antimicrob. Resist., 2023, 1, 17 CrossRef PubMed .
  239. A. Alcaraz, E. M. Nestorovich, M. Aguilella-Arzo, V. M. Aguilella and S. M. Bezrukov, Biophys. J., 2004, 87, 943–957 CrossRef CAS PubMed .
  240. C. Elías-López, M. Muñoz-Rosa, J. Guzmán-Puche, E. Pérez-Nadales, E. Chicano-Galvez and L. Martínez-Martínez, Ann. Clin. Microbiol. Antimicrob., 2024, 23, 103 CrossRef PubMed .
  241. H. Bajaj, M. A. Scorciapino, L. Moynié, M. G. Page, J. H. Naismith, M. Ceccarelli and M. Winterhalter, J. Biol. Chem., 2016, 291, 2837–2847 CrossRef CAS PubMed .
  242. X. Z. Li, P. Plésiat and H. Nikaido, Clin. Microbiol. Rev., 2015, 28, 337–418 CrossRef PubMed .
  243. D. Gurvic, A. G. Leach and U. Zachariae, J. Med. Chem., 2022, 65, 6088–6099 CrossRef CAS PubMed .
  244. F. Le Goff, J. Hazemann, L. Christen, G. Bourquin, G. Pierlot, R. Lange, P. Panchaud, C. Zumbrunn, O. Peter, G. Rueedi and D. Ritz, Sci. Rep., 2025, 15, 25431 CrossRef CAS PubMed .
  245. D. A. Westfall, G. Krishnamoorthy, D. Wolloscheck, R. Sarkar, H. I. Zgurskaya and V. V. Rybenkov, PLoS One, 2017, 12, e0184671 CrossRef PubMed .
  246. K. Pyta, A. Janas, N. Skrzypczak, W. Schilf, B. Wicher, M. Gdaniec, F. Bartl and P. Przybylski, ACS Infect. Dis., 2019, 5, 1754–1763 CrossRef CAS PubMed .
  247. T. A. Ramelot, J. Palmer, G. T. Montelione and G. Bhardwaj, Curr. Opin. Struct. Biol., 2023, 80, 102603 CrossRef CAS PubMed .
  248. M. D. Surette, P. Spanogiannopoulos and G. D. Wright, Acc. Chem. Res., 2021, 54, 2065–2075 CrossRef CAS PubMed .
  249. C. K. Wang, J. E. Swedberg, P. J. Harvey, Q. Kaas and D. J. Craik, J. Phys. Chem. B, 2018, 122, 2261–2276 CrossRef CAS PubMed .
  250. K. A. Muñoz and P. J. Hergenrother, Acc. Chem. Res., 2021, 54, 1322–1333 CrossRef PubMed .
  251. C. Maher and K. A. Hassan, mBio, 2023, 14, e0120523 CrossRef PubMed .
  252. M. F. Richter and P. J. Hergenrother, Ann. N. Y. Acad. Sci., 2019, 1435, 18–38 CrossRef CAS PubMed .
  253. J. Ude, V. Tripathi, J. M. Buyck, S. Söderholm, O. Cunrath, J. Fanous, B. Claudi, A. Egli, C. Schleberger, S. Hiller and D. Bumann, Proc. Natl. Acad. Sci. U. S. A., 2021, 118, e2107644118 CrossRef CAS PubMed .
  254. E. J. Geddes, M. K. Gugger, A. Garcia, M. G. Chavez, M. R. Lee, S. J. Perlmutter, C. Bieniossek, L. Guasch and P. J. Hergenrother, Nature, 2023, 624, 145–153 CrossRef CAS PubMed .
  255. B. N. Cain and P. J. Hergenrother, Clin. Transl. Med., 2024, 14, e1600 CrossRef CAS PubMed .
  256. S. H. Kopf, A. L. Sessions, E. S. Cowley, C. Reyes, L. Van Sambeek, Y. Hu, V. J. Orphan, R. Kato and D. K. Newman, Proc. Natl. Acad. Sci. U. S. A., 2016, 113, E110–E116 CrossRef CAS PubMed .
  257. M. Bergkessel, B. Forte and I. H. Gilbert, ACS Infect. Dis., 2023, 9, 2062–2071 CrossRef CAS PubMed .
  258. D. Chinemerem Nwobodo, M. C. Ugwu, C. Oliseloke Anie, M. T. S. Al-Ouqaili, J. Chinedu Ikem, U. Victor Chigozie and M. Saki, J. Clin. Lab. Anal., 2022, 36, e24655 CrossRef PubMed .
  259. D. Altarac, M. Gutch, J. Mueller, M. Ronsheim, R. Tommasi and M. Perros, Drug Discovery Today, 2021, 26, 2084–2089 CrossRef CAS PubMed .
  260. R. Gonzalez-Pastor, S. E. Carrera-Pacheco, J. Zúñiga-Miranda, C. Rodríguez-Pólit, A. Mayorga-Ramos, L. P. Guamán and C. Barba-Ostria, Molecules, 2023, 28, 1068 CrossRef CAS PubMed .
  261. J. B. Tan and Y. Y. Lim, Food Chem., 2015, 172, 814–822 CrossRef CAS PubMed .
  262. J. L. Ríos and M. C. Recio, J. Ethnopharmacol., 2005, 100, 80–84 CrossRef PubMed .
  263. T. Taguri, T. Tanaka and I. Kouno, Biol. Pharm. Bull., 2006, 29, 2226–2235 CrossRef CAS PubMed .

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