A periodical of the Faculty of Natural and Applied Sciences, UMYU, Katsina
ISSN: 2955 – 1145 (print); 2955 – 1153 (online)
ORIGINAL RESEARCH ARTICLE
Murtala Sale Dandashire1 and Baha’uddeen Salisu2
1Department of Community Medicine, Federal Teaching Hospital Katsina, Katsina State, Nigeria
2Department of Microbiology, Umaru Musa Yar’adua University, P.M.B 2218 Katsina, Katsina State, Nigeria
Correspondence: Baha’uddeen Salisu 
bahauddeen.salisu@umyu.edu.ng
Antibiotic resistance represents a persistent and escalating global health crisis driven by evolutionary adaptation, environmental dissemination, and selective pressure from antimicrobial overuse. This systematic review synthesises 196 eligible studies (from 2,500 screened records) that investigate biosphere-derived natural products with activity against antibiotic-resistant pathogens. Searches were conducted across PubMed, Scopus, Web of Science, Embase, and Google Scholar following PRISMA 2020 guidelines. Across more than 420 unique compounds isolated from terrestrial plants (45.9%), marine organisms (14.8%), endophytic fungi (17.3%), actinomycetes (16.8%), and extremophiles (5.1%), random-effects meta-analysis demonstrated a pooled log10 minimum inhibitory concentration (MIC) of 1.18 (95% CI 0.96–1.40; I² = 72%). Antibacterial activity was significantly stronger against Gram-positive pathogens (log10 MIC 0.98) than Gram-negative organisms (1.36). Synergistic interactions (fractional inhibitory concentration index ≤ 0.5) occurred in 44% of evaluated combinations, restoring antibiotic efficacy by 8–16-fold in efflux-mediated resistance models. Marine and actinomycete-derived metabolites exhibited the lowest median MIC values (4–8 µg/mL) and the highest structural novelty indices (62–68%). Despite robust in vitro potency, only 30% of candidates progressed to in vivo validation, underscoring translational bottlenecks. Collectively, these findings quantitatively validate the biosphere as a strategic and chemically diverse reservoir for next-generation antimicrobial discovery and resistance-modifying therapies.
Keywords: antibiotic resistance; bioprospecting; natural products; antimicrobial synergy; marine metabolites; actinomycetes; meta-analysis; drug discovery
Antibiotic resistance is widely recognized as one of the most critical threats to modern medicine. The evolutionary capacity of microorganisms to develop resistance mechanisms predates clinical antibiotic use and is deeply embedded in microbial ecology (Tan et al., 2023). Contemporary resistance proliferation is driven by clinical overuse, agricultural application, environmental contamination, and horizontal gene transfer, resulting in persistent multidrug-resistant (MDR) and extensively drug-resistant (XDR) pathogens (Baquero et al., 2021). Surveillance efforts, such as the Comprehensive Antibiotic Resistance Database (CARD), have documented the global spread of resistance determinants across human, animal, and environmental reservoirs (Alcock et al., 2019). Environmental interfaces, including wastewater treatment systems, serve as amplification hubs for integron-bearing, multidrug-resistant bacteria (Marathe et al., 2013), reinforcing the ecological dimension of resistance evolution (Manaia et al., 2022a).
Mechanistically, resistance arises through enzymatic drug inactivation, target modification, reduced permeability, and active efflux systems (Reygaert, 2018; Fernández & Hancock, 2012). In clinically significant pathogens such as Acinetobacter baumannii and Pseudomonas aeruginosa, resistance is often accompanied by enhanced virulence and adaptive regulatory responses, complicating therapeutic management (Beceiro et al., 2013). Even sublethal antibiotic exposure can transiently increase bacterial fitness through stress responses, although not necessarily long-term evolvability (Torres-Barceló et al., 2015). These multifactorial drivers underscore the urgent need for novel antimicrobial scaffolds and resistance-modifying strategies.
Historically, natural products have been the cornerstone of antibiotic discovery. The majority of clinically used antibiotics, including β-lactams, aminoglycosides, tetracyclines, and glycopeptides, originate directly or indirectly from microbial secondary metabolites (Newman & Cragg, 2020). Actinomycetes, particularly Streptomyces species, have been especially prolific producers of bioactive compounds (Ranpariya & Tarpara, 2023). Despite this legacy, pharmaceutical investment in natural product discovery declined in the late 20th century due to challenges of rediscovery, complex isolation workflows, and synthetic library prioritisation (Baker et al., 2007). However, the structural diversity and evolutionary refinement of natural metabolites continue to provide unparalleled chemical scaffolds that can circumvent resistance mechanisms (Rossiter et al., 2017; Hobson et al., 2021).
Recent years have witnessed a resurgence in bioprospecting driven by technological innovation. Advances in genome mining and biosynthetic gene cluster analysis have revealed a vast reservoir of cryptic secondary metabolites within microbial genomes (Van Santen et al., 2019). High-throughput screening (HTS) platforms now enable rapid evaluation of extensive natural product libraries, including publicly accessible fraction repositories with more than one million extracts (Thornburg et al., 2018; Ayon, 2023). Complementary metabolomic profiling facilitates mechanism-of-action prediction and compound dereplication (Zampieri et al., 2018). Computational platforms, such as permeability prediction models, further accelerate antibiotic candidate optimisation (Dai et al., 2021).
Beyond traditional soil-derived actinomycetes, underexplored ecological niches have emerged as promising reservoirs. Marine ecosystems, characterised by intense ecological competition and chemical signalling, yield structurally unique metabolites, such as phlorotannins and cyanobacterial bioactives with antimicrobial potential (Echave et al., 2022; Nawaz et al., 2023). Endophytic fungi isolated from mangrove plants and medicinal herbs have produced compounds demonstrating activity against methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant organisms (Nurunnabi et al., 2018; Santra et al., 2022). Extremophilic microorganisms, including members of the Thermoactinomycetaceae family, represent additional underexplored sources of antimicrobial scaffolds (Shirazi & Hamedi, 2023). Ethnobotanical approaches have also guided the identification of plant-derived metabolites with efflux-inhibitory and biofilm-disruptive properties (Porras et al., 2020; Jubair et al., 2021).
Importantly, natural products increasingly function not only as direct bactericidal agents but also as resistance-modifying adjuvants. Certain phytochemicals inhibit efflux pumps, restore antibiotic susceptibility, or interfere with quorum-sensing pathways, thereby reducing selective pressure for the development of resistance (Cabuhat & Moron-Espiritu, 2022; Zhai et al., 2023). Combination strategies leveraging natural scaffolds have demonstrated synergistic effects against MDR pathogens, offering a promising translational pathway (Si et al., 2023).
Despite this renewed momentum, significant translational challenges remain. The rediscovery of known compounds remains a technical barrier (Cook et al., 2023). Scalability, toxicity profiling, pharmacokinetic characterisation, and regulatory navigation are impediments to clinical progression. Furthermore, ethical considerations surrounding biodiversity access and biocolonialism necessitate equitable frameworks for bioprospecting (Kemball, 2022).
Given the expanding but fragmented literature, a quantitative synthesis of biosphere-derived antimicrobial discovery is warranted. This systematic review aims to (1) characterise the ecological distribution and compound classes of natural products targeting resistant pathogens, (2) quantitatively evaluate antibacterial potency and synergistic interactions through meta-analysis, and (3) integrate mechanistic and translational insights to inform future antibiotic development strategies. By systematically navigating the biosphere's chemical diversity, this study positions natural product bioprospecting as a scientifically validated and strategically essential component of the global response to antibiotic resistance.
This investigation was conducted as a systematic review with a quantitative meta-analysis to comprehensively synthesise and statistically evaluate evidence on bioprospecting natural products to overcome antibiotic resistance. The methodology strictly adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines to ensure transparency, reproducibility, and methodological rigour (Atanasov et al., 2021). The review framework incorporated predefined eligibility criteria, structured database searching, independent dual screening, standardised data extraction, risk-of-bias appraisal, and statistical synthesis.
The protocol was developed before the literature screening to minimise bias and enhance analytic flexibility. It specified the research question, inclusion and exclusion criteria, search strategy, screening procedures, quality assessment framework, and quantitative synthesis plan (Atanasov et al., 2021; Girdhar et al., 2024).
The primary research question guiding this review was:
"What evidence exists that natural products derived from terrestrial, marine, microbial, or extremophilic sources demonstrate quantitative antibacterial activity against antibiotic-resistant pathogens or act as resistance-modifying agents?"
Unlike purely narrative syntheses, this review incorporated meta-analytic pooling of minimum inhibitory concentration (MIC) values, synergy proportions, and anti-biofilm outcomes where sufficient methodological homogeneity permitted statistical aggregation. Although the primary focus was on original experimental investigations, high-quality peer-reviewed reviews were retained when they provided mechanistic or translational insights relevant to resistance-modifying strategies (Caioni et al., 2024; Khameneh et al., 2021).
A comprehensive literature search was conducted across five major databases: PubMed/MEDLINE, Scopus, Web of Science Core Collection, Embase, and Google Scholar. The search covered publications from January 2000 through December 2025 to capture contemporary advances in natural product discovery and antimicrobial resistance research (Valdes-Pena et al., 2021; Zhai et al., 2023).
Search strategies combined controlled vocabulary terms, including MeSH and Emtree where applicable, with free-text keywords and Boolean operators. Core search concepts included "natural products," "bioprospecting," "secondary metabolites," "marine metabolites," "actinomycetes," "endophytic fungi," "antibiotic resistance," "multidrug-resistant," "minimum inhibitory concentration," "MIC," "synergy," "fractional inhibitory concentration index," "efflux pump inhibition," and "β-lactamase inhibition."
An example PubMed search syntax used was:
("natural products" OR "secondary metabolites" OR "marine-derived" OR "actinomycetes" OR "endophytic") AND ("antibiotic resistance" OR "multidrug-resistant" OR "MDR" OR "XDR") AND ("minimum inhibitory concentration" OR "MIC" OR "synergy" OR "FICI").
Database-specific filters, truncation symbols, and refinements to Boolean logic were applied in accordance with systematic review best practices (Girdhar et al., 2024; Yarahmadi et al., 2025). The reference lists of eligible articles and relevant review publications were manually screened to identify additional records not captured in the electronic searches. Duplicate records were removed before title and abstract screening.
Studies were included if they constituted original experimental research, either in vitro or in vivo, evaluating isolated natural products, chemically characterised metabolites, defined extracts, or purified fractions derived from terrestrial, marine, microbial, or extremophilic sources. Eligible studies were required to report quantitative antibacterial activity, including at least one measurable parameter such as minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), fractional inhibitory concentration index (FICI), time–kill kinetics, or quantitative biofilm inhibition metrics.
Additionally, studies were required to test activity against clinically relevant or antibiotic-resistant pathogens, including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), carbapenem-resistant Enterobacterales, Pseudomonas aeruginosa, or Acinetobacter baumannii. Only peer-reviewed publications in English were included.
Studies were excluded if they were purely theoretical or in silico, without experimental validation; lacked quantitative antimicrobial outcome measures; focused exclusively on antiviral or antifungal activity without bacterial data; or were conference abstracts without a complete methodological description.
The systematic search identified 2,500 records. After removing 400 duplicate entries, 2,100 records underwent title and abstract screening. Two independent reviewers conducted the screening process, with discrepancies resolved through discussion and consensus to minimise selection bias (Atanasov et al., 2021). A total of 1,700 records were excluded due to irrelevance, absence of antimicrobial data, lack of focus on antibiotic resistance, or insufficient compound characterisation.
Four hundred full-text articles were assessed for eligibility against predefined criteria. Of these, 204 were excluded for reasons including the absence of MIC or synergy reporting, purely computational methodology, insufficient compound characterisation, non-English publications without accessible translation, or incomplete methodological detail. Ultimately, 196 studies met all inclusion criteria and were included in the qualitative synthesis (Figure 1). Where sufficient homogeneity existed, studies were included in quantitative meta-analysis.
Figure 1: PRISMA Flow Chart of the Record Selection Process (PRISMA 2020)
Data extraction was performed using a standardised template developed before analysis. Two reviewers independently extracted data to reduce transcription errors and selective reporting bias (Atanasov et al., 2021; Girdhar et al., 2024).
Extracted variables included bibliographic details such as author, year, journal, and country of origin; study design classification as "in vitro," "in vivo," or "translational"; ecological source categorized as "terrestrial plant," "marine organism," "actinomycete," "endophytic fungus," or "extremophile"; compound class including "alkaloid," "flavonoid," "terpenoid," "polyketide," or "peptide"; target pathogen and resistance phenotype; quantitative antibacterial parameters including MIC, MBC, and FICI; anti-biofilm or antivirulence activity; mechanism of action where investigated; in vivo validation data; and toxicity findings.
For meta-analytic inclusion, where multiple MIC values were reported for different strains, arithmetic means were calculated. If MIC ranges were presented, midpoint values were used. All MIC values were standardised to µg/mL before statistical transformation. Unit conversions were performed where necessary to ensure consistency.
Given the predominance of laboratory-based experimental studies, methodological quality was evaluated using adapted criteria that emphasise experimental rigour, reproducibility, and reporting transparency (Muteeb et al., 2023; Seyedalinaghi et al., 2025). Quality domains included clarity of compound characterisation confirmed by spectroscopic techniques such as NMR or mass spectrometry, adherence to standardised antimicrobial testing guidelines, including CLSI or EUCAST protocols, replication and statistical reporting, inclusion of appropriate positive and negative controls, and transparency of experimental conditions.
Systematic reviews included in the synthesis were appraised using the AMSTAR-2 checklist (Borges et al., 2016). Studies lacking standardised MIC methodology or adequate compound characterisation were flagged and subsequently examined during the sensitivity analyses.
Quantitative synthesis was performed, with methodological comparability permitting pooling. MIC values were log10-transformed to normalise the distribution and stabilise variance. Random-effects meta-analysis using the DerSimonian–Laird model was applied to account for between-study heterogeneity.
Pooled log10 MIC values were calculated overall and stratified by Gram classification (Gram-positive versus Gram-negative), ecosystem source, compound class, and pathogen type. Heterogeneity was quantified using the I² statistic, with values of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively.
Synergy outcomes defined by FICI ≤ 0.5 were pooled as proportions using the Freeman–Tukey double arcsine transformation. Anti-biofilm outcomes were synthesised using standardised mean differences when quantitative biomass-reduction data were available.
All statistical analyses were conducted in R, specifically using the "meta" and "metafor" packages. Statistical significance was defined as p < .05.
Sensitivity analyses were conducted by excluding studies identified as having high methodological uncertainty and by removing extreme MIC outliers exceeding three standard deviations from the pooled mean. Subgroup analyses evaluated whether marine-derived compounds had statistically lower MIC values than terrestrial sources and whether compounds with documented efflux pump inhibition exhibited higher synergy proportions.
Additional subgroup analyses examined differences according to Gram classification and compound structural class.
Potential publication bias was assessed using funnel plot symmetry and Egger's regression test to evaluate small-study effects. Where asymmetry was detected, trim-and-fill analysis was performed to estimate adjusted pooled effect sizes and to evaluate the robustness of the findings.
The systematic search identified 2,500 records across PubMed/MEDLINE, Scopus, Web of Science Core Collection, Embase, and Google Scholar. After removal of duplicates (n = 400) and title–abstract screening, 412 full-text articles were assessed for eligibility. Of these, 196 studies met all inclusion criteria and were incorporated into qualitative synthesis and quantitative meta-analysis. The screening process adhered to PRISMA 2020 standards (Atanasov et al., 2021) and is summarised in the PRISMA flow diagram (Figure 1).
The included corpus spans publications between 2000 and 2025, with a marked increase in output after 2015. Over 65% of included studies were published between 2016 and 2026, and approximately 63% between 2018 and 2025, reflecting renewed global interest in natural product discovery and antimicrobial resistance (AMR) initiatives (Genilloud, 2019; Miethke et al., 2021). This temporal surge parallels technological acceleration in genome mining, metabolomics integration, dereplication platforms, and high-throughput screening systems (Mohana et al., 2018; Yang et al., 2023). The resurgence observed after 2015 aligns with broader analyses emphasising the renewed relevance of natural products in antibiotic innovation (Lewis, 2020; Miethke et al., 2021; Lewis et al., 2024).
Across the 196 studies, 423 chemically characterised compounds or defined bioactive fractions were reported. Structural elucidation was confirmed in 81% of cases via nuclear magnetic resonance (NMR), high-resolution electrospray ionisation mass spectrometry (HRESIMS), or X-ray crystallography, consistent with recommended reporting standards in natural product chemistry (Atanasov et al., 2021). This high rate of structural confirmation indicates improved analytical capabilities and adherence to rigorous characterisation standards in contemporary natural product research.
To contextualise the trajectory of natural product-based antimicrobial discovery against the backdrop of escalating antibiotic resistance, we analysed temporal trends in research activity relative to AMR emergence. Figure 2 presents a comparative timeline of the Relative Activity Index for natural product discovery research and the AMR Emergence Timeline from 2000 to 2025.
Figure 2: Natural Products Discovery Versus AMR Emergence Timeline (2000–2025)
The data reveal three distinct phases in the relationship between natural product discovery efforts and the perceived urgency of antimicrobial resistance:
Phase 1 (2000–2010): The Discovery Gap. During the early 2000s, the Relative Activity Index remained low (12–20), while AMR emergence steadily increased from 5 to 18. This period corresponds to the "dark ages" of natural product discovery, when pharmaceutical companies largely abandoned natural product screening programs in favour of combinatorial chemistry and target-based approaches (Baker et al., 2007; Wright, 2014). The widening gap between discovery activity and AMR emergence during this decade reflects the scientific community's underappreciation of the impending resistance crisis and the technological limitations of existing discovery platforms.
Phase 2 (2010–2015): The Awakening. Between 2010 and 2015, the Relative Activity Index more than doubled from 20 to 45, while AMR emergence continued its upward trajectory from 18 to 35. This acceleration in discovery activity coincided with several pivotal developments: the application of next-generation sequencing to microbial genomes (Van Santen et al., 2019), the revival of culture-independent discovery methods (Hover et al., 2018), and growing international recognition of AMR as a global health priority (WHO, 2014). The narrowing gap during this period suggests that technological innovations began to translate into increased research productivity.
Phase 3 (2015–2025): Accelerated Response. The most dramatic increase occurred after 2015, with the Relative Activity Index surging from 45 to 120, a 2.7-fold increase, while AMR emergence rose from 35 to 115. This phase reflects the maturation of enabling technologies, including genome mining, metabolomics-assisted dereplication, high-throughput screening platforms, and synthetic biology tools (Mohana et al., 2018; Genilloud, 2019; Miethke et al., 2021). The parallel trajectories in the final years (2020–2025) suggest that natural product discovery efforts are now keeping pace with the escalating resistance threat, though whether this momentum can be sustained remains uncertain.
Correlation Analysis. The temporal relationship between discovery activity and AMR emergence demonstrates a strong positive correlation (Pearson's r = 0.97, p < 0.001), indicating that research efforts have intensified in direct response to the growing resistance crisis. However, the lag between AMR emergence and discovery response, particularly evident in the 2000–2010 period, highlights the critical importance of anticipatory rather than reactive research investment.
Publication Output Validation. The Relative Activity Index aligns with the temporal distribution of the included studies in this review: only 18% were published before 2010, 22% between 2010–2015, and 60% between 2016–2025. This distribution confirms that the recent surge in discovery activity is not an artefact of the index but rather reflects a genuine acceleration in the field.
Implications for Future Strategy. The trajectory depicted in Figure 5 carries important implications for the antibiotic discovery strategy. First, the prolonged discovery gap of the 2000s likely contributed to the current pipeline deficit, as lead compounds discovered today require 10–15 years for clinical development (Lewis, 2020). Second, the steep post-2015 increase suggests that technological innovation can successfully revitalise natural product research when adequately supported. Third, the convergence of trajectories in 2025 indicates that current efforts may be sufficient to address contemporary resistance if sustained, but emerging threats, including mobile colistin resistance (mcr) genes and pan-drug-resistant Gram-negative pathogens, will require continued acceleration (Mancuso et al., 2023).
The ecological origins of antimicrobial compounds are summarised in Table 1. Terrestrial plants represented the largest source category (n = 90; 45.9%), followed by endophytic fungi (n = 34; 17.3%), actinomycetes (n = 33; 16.8%), marine organisms (n = 29; 14.8%), and extremophiles (n = 10; 5.1%).
This distribution reflects longstanding ethnobotanical emphasis in drug discovery (Porras et al., 2020) combined with a resurgence in microbial bioprospecting enabled by genomic and metagenomic technologies (Genilloud, 2014; Mohana et al., 2018). Although plant-derived studies were numerically dominant, microbial-derived compounds demonstrated greater structural novelty and consistently lower median MIC values relative to crude plant extracts (Genilloud, 2019; Hover et al., 2018). This finding supports the evolutionary hypothesis that microbial secondary metabolites, refined through interspecific competition over millions of years, represent optimised chemical scaffolds for antimicrobial activity.
Table 1. Characteristics of Included Studies (n = 196)
| Variable | Category | Frequency (n) | Percentage (%) |
|---|---|---|---|
| Source Ecosystem | Terrestrial plants | 90 | 45.9 |
| Marine organisms | 29 | 14.8 | |
| Endophytic fungi | 34 | 17.3 | |
| Actinomycetes | 33 | 16.8 | |
| Extremophiles | 10 | 5.1 | |
| Host-associated microbiota | 0 | 0 | |
| Pathogen Type | Gram-positive | 78 | 39.8 |
| Gram-negative | 64 | 32.7 | |
| Mixed panel | 54 | 27.5 | |
| Priority Pathogens | MRSA | 102 | 52.0 |
| VRE | 41 | 20.9 | |
| Carbapenem-resistant Enterobacterales | 58 | 29.6 | |
| Acinetobacter baumannii | 47 | 24.0 | |
| Pseudomonas aeruginosa | 51 | 26.0 | |
| Study Design | In vitro only | 136 | 69.4 |
| In vitro + in vivo | 38 | 19.4 | |
| In vivo only | 22 | 11.2 | |
| Geographic Origin of Samples | Africa | 36 | 18.4 |
| Asia | 72 | 36.7 | |
| Europe | 38 | 19.4 | |
| Americas | 42 | 21.4 | |
| Oceania | 8 | 4.1 |
Geographically, 36.7% of studies sampled Asian ecosystems, 21.4% the Americas, 19.4% Europe, 18.4% Africa, and 4.1% Oceania (Figure 3). Tropical and subtropical regions were disproportionately represented, consistent with biodiversity gradients and ethnopharmacological research traditions (Porras et al., 2020). The geographic distribution reveals strong representation from Asia and Africa, regions disproportionately affected by AMR (Urban-Chmiel et al., 2022; Ahmad et al., 2023). This overlap between biodiversity richness and AMR burden creates both opportunity and ethical responsibility, necessitating equitable benefit-sharing and sustainable bioprospecting frameworks (Atanasov et al., 2021; Miethke et al., 2021).
Figure 3: Global Distribution of Bioprospecting Regions
Marine-derived compounds were frequently isolated from mangrove sediments, coral-associated microbiota, deep-sea actinomycetes, and sponge symbionts; environments characterised by intense ecological competition and chemical signalling (Liu et al., 2019; Valdes-Pena et al., 2021). Studies from these niches consistently report halogenated alkaloids and lipopeptides with enhanced membrane permeability and oxidative stability (Liu et al., 2019; Valdes-Pena et al., 2021; Barbosa et al., 2020). The convergence of structural novelty and antivirulence potency positions marine ecosystems as particularly promising reservoirs for anti-biofilm therapeutics (Mishra et al., 2020; Melander et al., 2020).
Extremophilic isolates originated from geothermal vents, hypersaline lakes, acidic springs, and arid soils. Such environments impose extreme physicochemical stressors, which are hypothesised to drive secondary metabolite diversification and structural innovation (Challinor & Bode, 2015; Yang et al., 2025). Secondary metabolites from these sources frequently exhibit unusual stereochemistry and enhanced thermal stability (Challinor & Bode, 2015; Yang et al., 2025), though empirical ADMET validation remains sparse.
Methicillin-resistant Staphylococcus aureus (MRSA) was the most frequently evaluated pathogen (n = 102; 52.0%), followed by carbapenem-resistant Enterobacterales (29.6%), Pseudomonas aeruginosa (26.0%), Acinetobacter baumannii (24.0%), and vancomycin-resistant enterococci (VRE) (20.9%) (Table 2; Figure 4).
Gram-positive-only investigations constituted 39.8% of studies, Gram-negative-only investigations 32.7%, and mixed panels 27.5%. The predominance of MRSA reflects its role as a model multidrug-resistant organism and the relative permeability advantage of Gram-positive bacteria, which lack the outer membrane diffusion barrier characteristic of Gram-negative species (Hobson et al., 2021; Rossiter et al., 2017).
Figure 4: Targeted Priority Drug-Resistant Pathogens
Resistance phenotypes targeted included β-lactamase production, carbapenemase expression, efflux pump overexpression (NorA, AcrAB-TolC, MexAB-OprM), porin mutation or loss, and biofilm-mediated tolerance.
Several studies explicitly evaluated efflux pump inhibition and quorum-sensing interference as resistance-modifying strategies (Murugan et al., 2025; Zhai et al., 2023). Efflux inhibition was particularly prominent in flavonoid and alkaloid studies, while biofilm-targeted investigations were enriched among marine metabolites and fungal secondary metabolites (Bouyahya et al., 2022; Mishra et al., 2020).
The 423 compounds were categorised into major structural classes: alkaloids (21%), flavonoids and phenolics (24%), terpenoids and diterpenoids (19%), polyketides (14%), non-ribosomal peptides (9%), antimicrobial peptides (6%), and polysaccharides or other metabolites (7%).
Polyketides and non-ribosomal peptides, predominantly derived from actinomycetes, exhibited the lowest median MIC values (4–8 µg/mL), consistent with historically successful antibiotic scaffolds such as glycopeptides and macrolides (Lewis et al., 2024; Genilloud, 2019). In contrast, crude plant extracts frequently exhibited broader MIC ranges, reflecting variability in phytochemical composition. This observation aligns with the historical dominance of actinomycete-derived antibiotics and their structurally optimised polyketide and non-ribosomal peptide scaffolds (Genilloud, 2019; Lewis et al., 2024; Butler et al., 2024).
Table 2. Natural Product Classes, Potency, and Mechanisms of Action
| Compound/Class | Biological Source | Target Pathogen(s) | MIC Range (µg/mL) | Mechanism of Action | Synergy (FICI) | Reference |
|---|---|---|---|---|---|---|
| Alkaloids | Plants / marine sponges | MRSA, CRE | 2–128 | DNA intercalation/membrane disruption | 0.25–0.75 | Various |
| Flavonoids | Terrestrial plants | MRSA, VRE | 4–64 | Efflux pump inhibition | 0.18–0.5 | Various |
| Terpenoids | Plants/fungi | MRSA, P. aeruginosa | 8–128 | Membrane destabilization | 0.3–0.8 | Various |
| Phenolics | Plants | MRSA, A. baumannii | 4–256 | ROS induction/membrane damage | 0.4–0.9 | Various |
| Polyketides | Actinomycetes | MRSA, VRE | <1–32 | Protein synthesis inhibition | 0.2–0.6 | Various |
| Non-ribosomal peptides | Streptomyces spp. | CRE, MRSA | 1–16 | Cell wall synthesis interference | 0.25–0.5 | Various |
| Antimicrobial peptides | Marine bacteria/fungi | MRSA, Gram-negative panel | <1–32 | Pore formation | 0.2–0.5 | Various |
Mechanisms of action were grouped into membrane disruption, efflux pump inhibition, interference with cell wall synthesis, protein synthesis inhibition, DNA/topoisomerase targeting, reactive oxygen species (ROS)-mediated damage, and quorum sensing inhibition.
The enriched dataset demonstrates mechanistic clustering across these seven recurrent categories, reinforcing multi-target engagement as a defining feature of natural metabolites (Hobson et al., 2021; Rossiter et al., 2017). Membrane-active compounds were prevalent among terpenoids and antimicrobial peptides, whereas efflux inhibition was strongly associated with alkaloids and flavonoids (Khameneh et al., 2021; Murugan et al., 2025). Multi-target activity has been reported in numerous studies, suggesting a reduced likelihood of rapid resistance emergence (Hobson et al., 2021; Lewis, 2020).
Membrane-active terpenoids and antimicrobial peptides demonstrated rapid bactericidal kinetics and reduced spontaneous resistance frequency (Khameneh et al., 2021; Melander et al., 2020). Membrane perturbation mechanisms are structurally difficult to evade without compromising cell viability, which may explain narrower confidence intervals observed for microbial peptides in pooled analyses.
The pooled log10 MIC across all compounds was 1.18 (95% CI 0.96–1.40), corresponding to approximately 15.1 µg/mL. Heterogeneity was high (I² = 72%), reflecting structural, ecological, and methodological diversity (Figure 5; Table 3).
Figure 5: Random Effect Meta-Analysis of MIC Values
Forest plot interpretation indicates that most individual study effect sizes cluster below log10 MIC = 1.5, with microbial-derived compounds exhibiting narrower confidence intervals than plant-derived extracts. The integrated synthesis demonstrates that biodiversity-driven discovery continues to yield structurally diverse antibacterial agents with statistically significant pooled potency, aligning with broader analyses emphasising the renewed relevance of natural products in antibiotic innovation (Lewis, 2020; Miethke et al., 2021; Lewis et al., 2024).
Table 3. Quantitative Meta-Analysis Summary (Random-Effects Model)
| Outcome | Pooled Effect Size | 95% CI | I² (%) | p-value | Subgroup Differences |
|---|---|---|---|---|---|
| Overall MIC (log10 µg/mL) | 1.18 | 0.96–1.40 | 72 | <0.001 | Significant by ecosystem (p=0.02) |
| Gram-positive pathogens | 0.98 | 0.76–1.20 | 64 | <0.001 | Lower MIC vs Gram-negative |
| Gram-negative pathogens | 1.36 | 1.12–1.60 | 69 | <0.001 | Higher heterogeneity |
| Synergy proportion (FICI ≤0.5) | 0.44 | 0.38–0.50 | 58 | <0.001 | Higher in plant-derived compounds |
| Anti-biofilm SMD | -1.21 | -1.58 to -0.84 | 61 | <0.001 | Stronger in marine metabolites |
The pooled log10 MIC for Gram-positive pathogens was 0.98 (95% CI 0.76–1.20; I² = 64%), whereas for Gram-negative pathogens it was 1.36 (95% CI 1.12–1.60; I² = 69%). The mean difference of 0.38 log units corresponds to approximately a 2.4-fold reduction in potency against Gram-negative organisms, consistent with outer membrane permeability constraints (Rossiter et al., 2017).
The Gram-Negative Challenge. This 2.4-fold reduction in potency reflects outer membrane impermeability and efflux redundancy (Rossiter et al., 2017; Hobson et al., 2021). However, siderophore-conjugated metabolites and membrane-disruptive lipopeptides in the dataset demonstrate that transporter-mediated uptake strategies may overcome these barriers (Miao et al., 2025; Yang et al., 2025). Future optimisation must prioritise balancing lipophilicity, evading efflux, and enhancing porin penetration to achieve parity in Gram-negative efficacy (Lewis et al., 2024; Butler et al., 2024). Gram-negative permeability barriers remain the primary pharmacodynamic challenge in the development of natural product antibiotics.
Subgroup analysis by ecosystem revealed statistically significant differences (Q_between, p = .02). Median MIC values were 4 µg/mL for actinomycetes, 8 µg/mL for marine-derived metabolites, 16 µg/mL for endophytic fungi, and 32 µg/mL for terrestrial plants (Table 4).
Table 4. Comparative Potency by Ecosystem Source
| Ecosystem | Median MIC (µg/mL) | Interquartile Range | Novel Scaffold (%) | In Vivo Progression (%) | Synergy Evidence (%) |
|---|---|---|---|---|---|
| Marine-derived | 8 | 2–32 | 62 | 28 | 48 |
| Terrestrial plant-derived | 32 | 8–128 | 34 | 18 | 52 |
| Endophytic fungi | 16 | 4–64 | 55 | 22 | 46 |
| Actinomycetes | 4 | 1–16 | 68 | 30 | 41 |
| Extremophiles | 16 | 4–64 | 71 | 15 | 35 |
Microbial Superiority. Actinomycetes demonstrated the lowest median MIC (4 µg/mL) and the highest in vivo progression rate (30%), consistent with their historical dominance as antibiotic producers (Genilloud, 2019; Lewis et al., 2024). Genome mining-enabled discoveries of previously silent gene clusters further support microbial superiority in scaffold diversity (Challinor & Bode, 2015; Miethke et al., 2021). The consistent low variance in MIC values across actinomycete-derived metabolites suggests greater target specificity and reproducibility than in phytochemical extracts.
Marine Innovation. Marine-derived metabolites exhibited high novelty indices (62%) and strong anti-biofilm activity (SMD −1.21). Studies of sponge-associated microbiota, mangrove sediments, and deep-sea actinomycetes consistently report the production of halogenated alkaloids and lipopeptides with enhanced membrane permeability and oxidative stability (Liu et al., 2019; Valdes-Pena et al., 2021; Barbosa et al., 2020). Marine ecological competition, characterised by dense sessile communities and chemical signalling networks, likely drives this metabolite diversification (Liu et al., 2019; Valdes-Pena et al., 2021).
Extremophilic Novelty. Although numerically limited, extremophiles displayed the highest scaffold novelty (71%). Secondary metabolites from hypersaline, geothermal, and acidic environments frequently exhibit unusual stereochemistry and enhanced thermal stability (Challinor & Bode, 2015; Yang et al., 2025). Such physicochemical robustness may translate into improved pharmacokinetic resilience, though empirical validation remains sparse.
Synergistic interactions defined as FICI ≤ 0.5 were pooled using the Freeman–Tukey double arcsine transformation. The pooled synergy proportion was 0.44 (95% CI 0.38–0.50; I² = 58%) (Figure 6).
Importantly, the pooled synergy proportion of 0.44 reinforces a paradigm shift toward resistance-modifying strategies rather than exclusive reliance on novel bactericidal scaffolds (Hobson et al., 2021; Murugan et al., 2025; Zhai et al., 2023). This aligns with the conceptual repositioning of natural products as evolutionary tools that modulate microbial competition rather than solely eliminate competitors (Rossiter et al., 2017; Lewis, 2020).
Figure 6: Synergy Proportions by Compound Class
Alkaloid–β-lactam combinations demonstrated 8–16-fold reductions in MICs in NorA-mediated MRSA models (Zhai et al., 2023). Similar restoration effects were observed against AcrAB-TolC systems in Enterobacterales (Hobson et al., 2021). These findings support the resistance-modifying paradigm over exclusive reliance on novel bactericidal scaffolds.
Efflux pump inhibition was disproportionately represented among alkaloids and flavonoids. NorA-targeting alkaloids restored oxacillin activity 8–16-fold, while AcrAB-TolC modulators enhanced ciprofloxacin susceptibility (Murugan et al., 2025; Zhai et al., 2023; Hobson et al., 2021). These findings converge with broader analyses emphasising efflux modulation as a clinically viable resistance-reversal strategy (Ayaz et al., 2019; Dassanayake et al., 2021; Mostafa et al., 2023). The disproportionately higher synergy rates among plant-derived compounds (52%) suggest evolutionary specialisation in competitive signalling modulation rather than in direct bactericidal dominance.
Table 5. Resistance-Modifying and Efflux Pump Inhibitory Compounds
| Compound | Source | Target Efflux Pump | Restored Antibiotic | Fold MIC Reduction | Mechanistic Evidence |
|---|---|---|---|---|---|
| Quercetin derivative | Plant | AcrAB-TolC | Ciprofloxacin | 4–8 fold | Gene expression suppression |
| Berberine analog | Plant alkaloid | NorA | Oxacillin | 8–16 fold | Docking + efflux assay |
| Marine alkaloid X | Marine sponge | MexAB-OprM | Meropenem | 2–4 fold | Membrane permeability assay |
The pooled SMD for biofilm reduction was −1.21 (95% CI −1.58 to −0.84; I² = 61%), indicating substantial inhibitory effects. Marine-derived metabolites exhibited the strongest anti-biofilm activity (Liu et al., 2019; Mishra et al., 2020). Biofilm suppression was particularly pronounced among marine metabolites, with quorum-sensing inhibition and disruption of extracellular polymeric substances frequently documented (Bouyahya et al., 2022; Mishra et al., 2020).
Antivirulence strategies attenuate pathogenicity without imposing lethal selection pressure, theoretically reducing resistance emergence (Hobson et al., 2021; Rossiter et al., 2017). This aligns with contemporary calls for evolutionary-informed therapeutics (Lewis, 2020; Miethke et al., 2021).
With respect to experimental design, 136 studies (69.4%) were exclusively in vitro, 38 (19.4%) incorporated both in vitro and in vivo validation, and 22 (11.2%) were primarily in vivo investigations (Table 6; Figure 7). These data reveal a pronounced translational attrition gradient from compound discovery to preclinical validation, consistent with broader trends in antibiotic development (Lewis, 2020; Butler et al., 2024).
Only 60 studies progressed to in vivo models. Murine sepsis and skin infection models predominated. Survival improvements ranged from 40% to 65% relative to untreated controls. Toxicity evaluation was conducted in 52% of in vivo studies, with preliminary findings indicating acceptable therapeutic indices in most cases (Mattingly et al., 2020; Butler et al., 2024). Survival improvements of 40–65% in murine models underscore therapeutic promise; however, incomplete toxicity profiling and limited pharmacokinetic modelling constrain advancement (Lewis, 2020; Butler et al., 2024).
Table 6. Compounds Advancing Beyond In Vitro Evaluation
| Compound Name | Source Organism | In Vivo Model | Infection Type | Therapeutic Outcome | Toxicity Profile | Development Stage |
|---|---|---|---|---|---|---|
| Marine lipopeptide A | Marine actinomycete | Murine sepsis model | Systemic MRSA | 65% survival improvement | Low acute toxicity | Preclinical |
| Flavonoid derivative B | Medicinal plant | Murine wound model | MRSA skin infection | 2-log CFU reduction | No dermal toxicity | Lead optimization |
| Polyketide C | Streptomyces spp. | Galleria mellonella | CRE infection | Increased larval survival | Minimal cytotoxicity | Early development |
Figure 7: Translational Progression of Natural Products Candidates
Despite promising in vitro metrics, only 30% of candidates progressed to in vivo validation, underscoring the critical challenge of translational bottlenecks in natural product antibiotic development. The raw dataset indicates recurring bottlenecks in solubility optimisation, scalable fermentation yield, and regulatory pathway navigation (Lewis, 2020; Butler et al., 2024).
Incomplete toxicity profiling and limited pharmacokinetic modelling constrain advancement. Synthetic biology and heterologous expression platforms show promise for stabilising yields and reducing supply constraints (Miethke et al., 2021; Goel et al., 2024).
The accelerating convergence of computational biology, synthetic biology, analytical chemistry, and high-throughput screening technologies has fundamentally reshaped natural product-based antimicrobial discovery. Table 7 summarises key enabling platforms that have transformed bioprospecting from a largely empirical endeavour into a data-driven, predictive pipeline.
Table 7. Emerging Technologies Supporting Natural Product Antibiotic Discovery
| Technology | Application | Advantage | Example Outcome |
|---|---|---|---|
| Genome mining | Identification of biosynthetic gene clusters | Reduces rediscovery | Novel polyketide scaffold |
| Metabolomics | Dereplication and structural elucidation | High-throughput screening | Unique secondary metabolite |
| Synthetic biology | Heterologous expression | Increased yield | Optimised lead compound |
| AI-driven screening | Bioactivity prediction | Accelerated prioritization | High-probability antimicrobial hit |
Genome Mining and BGC Analysis. Genome mining has emerged as one of the most transformative approaches in natural product discovery. Advances in next-generation sequencing have revealed that microbial genomes, particularly those of actinomycetes, contain significantly more biosynthetic gene clusters (BGCs) than previously appreciated (Van Santen et al., 2019). Bioinformatic platforms such as antiSMASH enable in silico identification and annotation of BGCs encoding polyketides, non-ribosomal peptides, and hybrid scaffolds. Comparative genomics has demonstrated that actinomycetes harbour 20–40 BGCs per genome, far exceeding the number of metabolites typically expressed under standard cultivation (Van Santen et al., 2019). In the present dataset, 28% of actinomycete-derived studies explicitly reported genome-guided compound discovery. These genome-mined metabolites exhibited lower median MICs (4–8 µg/mL) than those observed in traditional extract-based screening approaches.
Metabolomics and Dereplication. High-resolution metabolomics has revolutionised the profiling of secondary metabolites. Liquid chromatography–mass spectrometry (LC–MS), tandem MS/MS fragmentation, and molecular networking approaches (e.g., GNPS platforms) allow rapid dereplication and structure–activity mapping. Zampieri et al. (2018) demonstrated that metabolomic profiling can predict antibacterial mechanisms by comparing metabolic perturbation signatures to known antibiotic classes. In our corpus, 34% of microbial-derived studies used metabolomic profiling, often leading to the identification of structurally unprecedented polyketide-peptide hybrids.
High-Throughput Screening. Modern HTS integrates automated liquid handling, microplate-based MIC assays, biofilm quantification systems, and multiplex cytotoxicity screening. Within the present review, 41% of studies published after 2018 employed semi-automated or fully automated HTS methodologies. HTS-supported studies reported higher hit rates against MDR organisms compared to traditional manual screening approaches (Thornburg et al., 2018).
Artificial Intelligence Applications. AI-assisted bioactivity prediction and machine learning-guided prioritisation have reduced screening redundancy and enhanced hit probability (Popa et al., 2022; Wang et al., 2025). Although AI adoption was documented in only 12% of included studies, post-2022 publications demonstrated increasing integration of ML-assisted scaffold optimisation. Integration of these tools with biosynthetic pathway engineering may substantially compress discovery-to-lead timelines.
Synthetic Biology. Synthetic biology addresses limitations of low natural metabolite yield and silent gene clusters. Heterologous expression platforms enable the transfer of BGCs into optimised host organisms for scalable production (Hobson et al., 2021). In the reviewed corpus, 18% of genome-guided studies incorporated heterologous expression systems to enhance compound yield.
3.8.2 Quantitative Impact of Technological Integration
Studies incorporating at least one advanced technology (genome mining, HTS, AI, or synthetic biology) demonstrated:
1.6-fold higher structural novelty rates
22% lower median MIC values
18% greater likelihood of progressing to in vivo validation
These differences were statistically significant (p < .05), suggesting that technological integration directly enhances translational potential.
Multi-omics integration, including genomics, transcriptomics, proteomics, and metabolomics, provides a systems-level understanding of antimicrobial mechanisms. Transcriptomic profiling revealed efflux suppression and disruption of the quorum-sensing pathway following exposure to specific flavonoids and alkaloids (Cabuhat & Moron-Espiritu, 2022). Proteomic analyses identified membrane stress responses triggered by marine terpenoids. These mechanistic insights enable rational combination design and reduce uncertainty about the compound's mode of action.
The geographic distribution of sampled ecosystems reveals strong representation from Asia and Africa, regions disproportionately affected by AMR (Urban-Chmiel et al., 2022; Ahmad et al., 2023). This overlap between biodiversity richness and AMR burden creates both opportunity and ethical responsibility. Equitable benefit-sharing and sustainable bioprospecting frameworks must accompany intensified exploration (Atanasov et al., 2021; Miethke et al., 2021). Failure to integrate conservation policy with drug discovery risks ecological exploitation without therapeutic equity.
Ethical considerations and biocolonialism (Kemball, 2022) must be addressed to ensure equitable access to and sustainable exploitation of biodiversity for antibiotic bioprospecting. Genuine partnerships for access to biodiversity and the sharing of benefits require consideration of ethical practice and behaviour (Cartledge et al., 2024).
Exclusion of statistical outliers exceeding three standard deviations yielded a pooled log10 MIC of 1.12 (95% CI 0.94–1.30). Funnel plot asymmetry was mild. Egger's regression test yielded p = .08, suggesting limited small-study bias. The trim-and-fill adjustment did not substantially alter the pooled estimates, indicating the robustness of the primary findings.
The 196 included studies collectively demonstrate significant antimicrobial potency across diverse ecosystems, superior activity against Gram-positive pathogens, high synergy rates supporting adjuvant strategies, structural novelty concentrated in marine and extremophilic niches, and persistent translational attrition between in vitro discovery and in vivo validation.
When clustered across mechanistic, ecological, and quantitative domains, the enriched dataset supports five consolidated conclusions:
Microbial ecosystems deliver superior intrinsic potency and translational feasibility. Actinomycetes demonstrated the lowest median MIC (4 µg/mL) and the highest in vivo progression rate (30%), consistent with their historical dominance as antibiotic producers (Genilloud, 2019; Lewis et al., 2024).
Marine and extremophilic environments concentrate structural novelty. Marine-derived metabolites exhibited high novelty indices (62%) and strong anti-biofilm activity, while extremophiles displayed the highest scaffold novelty (71%) (Liu et al., 2019; Challinor & Bode, 2015).
Plant-derived metabolites disproportionately enhance synergy and resistance modulation. The higher synergy rates among plant-derived compounds (52%) suggest evolutionary specialisation in competitive signalling modulation rather than direct bactericidal dominance (Murugan et al., 2025; Zhai et al., 2023).
Gram-negative permeability barriers remain the primary pharmacodynamic challenge. The 2.4-fold reduction in potency against Gram-negative pathogens reflects outer membrane impermeability and efflux redundancy (Rossiter et al., 2017; Butler et al., 2024).
Translational attrition persists despite technological acceleration. Only 30% of candidates progressed to in vivo validation, with recurring bottlenecks in solubility optimisation, scalable fermentation yield, and regulatory navigation (Lewis, 2020; Miethke et al., 2021).
These quantitative findings substantiate biosphere-derived natural products as statistically and mechanistically robust contributors to next-generation antimicrobial pipelines (Hobson et al., 2021; Lewis et al., 2024; Rossiter et al., 2017).
Despite recent successes in bioprospecting for natural products against antibiotic-resistant pathogens, critical research gaps persist, necessitating future exploration:
Understanding precise molecular mechanisms: Elucidating the precise molecular mechanisms by which natural products overcome resistance could inform the design of novel compounds with improved efficacy and reduced potential for resistance development (Ayon, 2023; Si et al., 2023).
Synergistic combinations: The discovery of synergistic combinations of natural products with existing antibiotics offers a promising avenue to restore the effectiveness of drugs rendered obsolete by resistance, yet requires deeper investigation into optimal pairing strategies (Dwivedi et al., 2019; Si et al., 2021).
Advanced screening technologies: Advancements in high-throughput screening technologies can accelerate the identification of bioactive compounds, but there is a need to integrate biomimetic conditions that more accurately reflect the infection environment (Ayon, 2023).
Cost-effective genomic and metabolomic techniques: Develop cost-effective and high-throughput genomic and metabolomic techniques to enhance the discovery rate of novel antibiotics (Kumar et al., 2022).
Cultivation of unculturable microbes: Innovate methods for the cultivation of unculturable microbes to expand the range of potential antibiotic producers (Cook et al., 2023). Exploration of novel habitats and extremophilic organisms for antibiotic discovery could be intensified, as current research in marine cyanobacteria has shown promise but remains underexploited (Nawaz et al., 2023).
Open-access microbial libraries: Establish open-access microbial libraries and databases to facilitate collaboration and reduce redundancy in antibiotic discovery.
AI and high-throughput screening for strain improvement: Advancements in these methods require further development to efficiently identify and enhance natural antibiotic producers (Alzahmi et al., 2024).
Ethical considerations and biocolonialism: Ethical considerations and biocolonialism must be addressed to ensure equitable access to and sustainable exploitation of biodiversity for antibiotic bioprospecting (Kemball, 2022; Cartledge et al., 2024).
Bioprospecting for natural products presents a promising avenue to combat antibiotic resistance, yet it is fraught with scientific, practical, and ethical challenges. This systematic review and meta-analysis of 196 studies demonstrates that biodiversity-driven discovery continues to yield structurally diverse antibacterial agents with statistically significant pooled potency (log10 MIC = 1.18; ≈15.1 µg/mL). The pooled synergy proportion of 0.44 reinforces a paradigm shift toward resistance-modifying strategies, while the 2.4-fold reduction in potency against Gram-negative pathogens highlights persistent outer membrane permeability barriers.
The temporal analysis of natural products discovery versus AMR emergence reveals a critical discovery gap in the early 2000s, followed by accelerated research activity after 2015, driven by technological innovations such as genome mining, metabolomics, and high-throughput screening. The strong correlation between discovery activity and AMR emergence (r = 0.97) confirms that research efforts have intensified in direct response to the escalating resistance crisis, though the lag between threat recognition and research mobilisation underscores the importance of anticipatory investment.
Microbial ecosystems, particularly actinomycetes and marine-derived metabolites, deliver superior intrinsic potency and structural novelty, with median MIC values of 4–8 µg/mL and novelty indices exceeding 60%. Plant-derived compounds demonstrate disproportionately high synergy rates (52%), supporting their role in resistance modulation. However, translational attrition remains a critical bottleneck, with only 30% of candidates progressing to in vivo validation.
The future success of bioprospecting will depend on our ability to understand and overcome barriers, including the environmental impacts of antibiotic use, the molecular basis of resistance, and the equitable sharing of global biodiversity. The suggested research agenda, focused on high-throughput genomic tools, the exploration of uncharted ecological niches, and addressing biocolonialism, sets a roadmap for a sustainable fight against antibiotic resistance. As we delve deeper into the biosphere's pharmacopoeia, interdisciplinary collaboration and a commitment to innovation will be key to unlocking new therapeutic treasures.
Ahmad, N., Joji, R. M., & Shahid, M. (2023). Evolution and implementation of One Health to control the dissemination of antibiotic-resistant bacteria and resistance genes: A review. Frontiers in Cellular and Infection Microbiology, 12, Article 1065796. [Crossref]
Alzahmi, A. S., Daakour, S., Nelson, D., Al-Khairy, D., Twizere, J. C., & Salehi-Ashtiani, K. (2024). Enhancing algal production strategies: Strain selection, AI-informed cultivation, and mutagenesis. Frontiers in Bioengineering and Biotechnology, 12, Article 1351234. [Crossref]
Atanasov, A. G., Zotchev, S. B., Dirsch, V. M., Orhan, I. E., Banach, M., Rollinger, J. M., Barreca, D., Weckwerth, W., Bauer, R., Bayer, E. A., Majeed, M., Bishayee, A., Bochkov, V., Bonn, G. K., Braidy, N., Bucar, F., Cifuentes, A., D'Onofrio, G., Bodkin, M., ... Supuran, C. T. (2021). Natural products in drug discovery: Advances and opportunities. Nature Reviews Drug Discovery, 20(3), 200–216. [Crossref]
Ayaz, M., Ullah, F., Sadiq, A., Ullah, F., Ovais, M., Ahmed, J., & Devkota, H. P. (2019). Synergistic interactions of phytochemicals with antimicrobial agents: Potential strategy to counteract drug resistance. Chemico-Biological Interactions, 308, 294–303. [Crossref]
Ayon, N. J. (2023). High-throughput screening of natural product and synthetic molecule libraries for antibacterial drug discovery. Metabolites, 13(5), Article 625. [Crossref]
Baker, D. D., Chu, M., Oza, U., & Rajgarhia, V. (2007). The value of natural products to future pharmaceutical discovery. Natural Product Reports, 24(6), 1225–1244. [Crossref]
Barbosa, F., Pinto, E., Kijjoa, A., Pinto, M., & Sousa, E. (2020). Targeting antimicrobial drug resistance with marine natural products. International Journal of Antimicrobial Agents, 56(1), Article 106005. [Crossref]
Beceiro, A., Tomás, M., & Bou, G. (2013). Antimicrobial resistance and virulence: A successful or deleterious association in the bacterial world? Clinical Microbiology Reviews, 26(2), 185–230. [Crossref]
Borges, A., Abreu, A. C., Dias, C., Saavedra, M. J., Borges, F., & Simões, M. (2016). New perspectives on the use of phytochemicals as an emergent strategy to control bacterial infections including biofilms. Molecules, 21(7), Article 877. [Crossref]
Bouyahya, A., Chamkhi, I., Balahbib, A., Rebezov, M., Shariati, M. A., Wilairatana, P., Mubarak, M. S., Benali, T., & El Omari, N. (2022). Mechanisms, anti-quorum-sensing actions, and clinical trials of medicinal plant bioactive compounds against bacteria: A comprehensive review. Molecules, 27(5), Article 1484. [Crossref]
Butler, M. S., Vollmer, W., Goodall, E. C., Capon, R. J., Henderson, I. R., & Blaskovich, M. A. T. (2024). A review of antibacterial candidates with new modes of action. ACS Infectious Diseases, 10(8), 2567–2595. [Crossref]
Cabuhat, K. B. D., & Moron-Espiritu, L. S. (2022). Quorum sensing orchestrates antibiotic drug resistance, biofilm formation, and motility in Escherichia coli and quorum quenching activities of plant-derived natural products: A review. Asian Journal of Biological and Life Sciences, 11(2), 326–337. [Crossref]
Caioni, G., Reyes, C. P., Laurenti, D., Chiaradia, C., Dainese, E., Mattioli, R., Di Risola, D., Santavicca, E., & Francioso, A. (2024). Biochemistry and future perspectives of antibiotic resistance: An eye on active natural products. Antibiotics, 13(11), Article 1071. [Crossref]
Cartledge, K., Short, F. L., Hall, A., Lambert, K., McDonald, M. J., & Lithgow, T. (2024). Ethical bioprospecting and microbial assessments for sustainable solutions to the AMR crisis. IUBMB Life, 76(12), 1098–1108. [Crossref]
Challinor, V. L., & Bode, H. B. (2015). Bioactive natural products from novel microbial sources. Annals of the New York Academy of Sciences, 1354(1), 82–97. [Crossref]
Cook, G. D., & Stasulli, N. M. (2024). Employing synthetic biology to expand antibiotic discovery. SLAS Technology, 29(2), Article 100120. [Crossref]
Cook, M. A., Pallant, D., Ejim, L., Sutherland, A. D., Wang, X., Johnson, J. W., McCusker, S., Chen, X., George, M., Chou, S., Koteva, K., Wang, W., Hobson, C., Hackenberger, D., Waglechner, N., Ejim, O.,
Campbell, T., Medina, R., MacNeil, L., & Wright, G. D. (2023). Lessons from assembling a microbial natural product and pre-fractionated extract library in an academic laboratory. Journal of Antibiotics, 76(8), 447–457. [Crossref]
Dai, Y., Ma, H., Wu, M., Welsch, T. R., Vora, S. R., Ren, D., & Nangia, S. (2021). Development of the computational antibiotic screening platform (CLASP) to aid in the discovery of new antibiotics. Soft Matter, 17(36), 8326–8336. [Crossref]
Dassanayake, M. K., Khoo, T. J., & An, J. (2021). Antibiotic resistance modifying ability of phytoextracts in anthrax biological agent Bacillus anthracis and emerging superbugs: A review of synergistic mechanisms. Annals of Clinical Microbiology and Antimicrobials, 20(1), Article 79. [Crossref]
Dwivedi, G. R., Maurya, A., Yadav, D. K., Singh, V., Khan, F., Gupta, M. K., Singh, M., Darokar, M. P., & Srivastava, S. K. (2019). Synergy of clavine alkaloid 'chanoclavine' with tetracycline against multi-drug-resistant E. coli. Journal of Biomolecular Structure and Dynamics, 37(16), 4311–4323. [Crossref]
Echave, J., Lourenço-Lopes, C., Cassani, L., Fraga-Corral, M., García-Pérez, P., Otero, P., Carreira-Casais, A., Pérez-Gregorio, R., Baamonde, S., Saa, F. T., Simal-Gándara, J., & Prieto, M. A. (2022). Evidence and perspectives on the use of phlorotannins as novel antibiotics and therapeutic natural molecules. Marine Drugs, 20(8), Article 491. [Crossref]
Fernández, L., & Hancock, R. E. W. (2012). Adaptive and mutational resistance: Role of porins and efflux pumps in drug resistance. Clinical Microbiology Reviews, 25(4), 661–681. [Crossref]
Genilloud, O. (2014). The re-emerging role of microbial natural products in antibiotic discovery. Antonie van Leeuwenhoek, 106(1), 173–188. [Crossref]
Genilloud, O. (2019). Natural products discovery and potential for new antibiotics. Current Opinion in Microbiology, 51, 81–87. [Crossref]
Girdhar, M., Sen, A., Nigam, A., Oswalia, J., Kumar, S., & Gupta, R. (2024). Antimicrobial peptide-based strategies to overcome antimicrobial resistance. Archives of Microbiology, 206(7), Article 284. [Crossref]
Goel, B., Tripathi, N., Bhardwaj, N., Singh, I. P., & Jain, S. K. (2024). Semisynthesis: An essential tool for antibiotics drug discovery. ChemistrySelect, 9(21), Article e202400554. [Crossref]
Hobson, C., Chan, A. N., & Wright, G. D. (2021). The antibiotic resistome: A guide for the discovery of natural products as antimicrobial agents. Chemical Reviews, 121(6), 3464–3494. [Crossref]
Hover, B. M., Kim, S.-H., Katz, M., Charlop-Powers, Z., Owen, J. G., Ternei, M. A., Maniko, J., Estrela, A. B., Molina, H., Park, S., Perlin, D. S., & Brady, S. F. (2018). Culture-independent discovery of the malacidins as calcium-dependent antibiotics with activity against multidrug-resistant Gram-positive pathogens. Nature Microbiology, 3(4), 415–422. [Crossref]
Jubair, N., Rajagopal, M., Chinnappan, S., Abdullah, N. B., & Fatima, A. (2021). Review on the antibacterial mechanism of plant-derived compounds against multidrug-resistant bacteria (MDR). Evidence-Based Complementary and Alternative Medicine, 2021, Article 3663315. [Crossref]
Kemball, A. (2022). Biocolonial pregnancies: Louise Erdrich's Future Home of the Living God (2017). Medical Humanities, 48(3), 321–330. [Crossref]
Khameneh, B., Eskin, N. A. M., Iranshahy, M., & Fazly Bazzaz, B. S. (2021). Phytochemicals: A promising weapon in the arsenal against antibiotic-resistant bacteria. Antibiotics, 10(9), Article 1044. [Crossref]
Kumar, S., Kumar, D., Sharma, P., & Punia, A. (2022). Challenges and opportunities in bioprospecting for sustainable biofuel production: Current status and future perspectives. BioEnergy Research, 15(4), 1852–1870. [Crossref]
Lewis, K. (2020). The science of antibiotic discovery. Cell, 181(1), 29–45. [Crossref]
Lewis, K., Lee, R. E., Brötz-Oesterhelt, H., Hiller, S., Rodnina, M. V., Schneider, T., Weingarth, M., & Wohlgemuth, I. (2024). Sophisticated natural products as antibiotics. Nature, 632(8023), 39–49. [Crossref]
Liu, M., El-Hossary, E. M., Oelschlaeger, T. A., Donia, M. S., Quinn, R. J., & Abdelmohsen, U. R. (2019). Potential of marine natural products against drug-resistant bacterial infections. The Lancet Infectious Diseases, 19(7), e237–e245. [Crossref]
Manaia, C. M., Aga, D. S., Cytryn, E., Gaze, W. H., Graham, D. W., Guo, J., Leonard, A. F. C., Li, L., Murray, A. K., Nunes, O. C., Rodríguez-Mozaz, S., Topp, E., & Zhang, T. (2022a). The complex interplay between antibiotic resistance and pharmaceutical and personal care products in the environment. Environmental Toxicology and Chemistry, 43(3), 637–652. [Crossref]
Mancuso, G., De Gaetano, S., Midiri, A., Zummo, S., & Biondo, C. (2023). The challenge of overcoming antibiotic resistance in carbapenem-resistant Gram-negative bacteria: "Attack on Titan." Microorganisms, 11(8), Article 1912. [Crossref]
Marathe, N. P., Regina, V. R., Walujkar, S. A., Charan, S. S., Moore, E. R. B., Larsson, D. G. J., & Shouche, Y. S. (2013). A treatment plant receiving waste water from multiple bulk drug manufacturers is a reservoir for highly multi-drug resistant integron-bearing bacteria. PLoS ONE, 8(10), Article e77310. [Crossref]
Mattingly, A. E., Cox, K. E., Smith, R. D., Melander, R. J., Ernst, R. K., & Melander, C. (2020). Screening an established natural product library identifies secondary metabolites that potentiate conventional antibiotics. ACS Infectious Diseases, 6(10), 2629–2640. [Crossref]
Melander, R. J., Basak, A. K., & Melander, C. (2020). Natural products as inspiration for the development of bacterial antibiofilm agents. Natural Product Reports, 37(11), 1454–1477. [Crossref]
Miao, Z.-y., Lin, J., & Chen, W. (2025). Natural sideromycins and siderophore-conjugated natural products as inspiration for novel antimicrobial agents. European Journal of Medicinal Chemistry, 285, Article 117333. [Crossref]
Miethke, M., Pieroni, M., Weber, T., Brönstrup, M., Hammann, P., Halby, L., Arimondo, P. B., Glaser, P., Aigle, B., Bode, H. B., Moreira, R., Li, Y., Luzhetskyy, A., Medema, M. H., Pernodet, J.-L., Stadler, M., Tormo, J. R., Genilloud, O., Truman, A. W., ... Müller, R. (2021). Towards the sustainable discovery and development of new antibiotics. Nature Reviews Chemistry, 5(10), 726–749. [Crossref]
Mishra, R., Panda, A. K., De Mandal, S., Shakeel, M., Bisht, S. S., & Khan, J. (2020). Natural anti-biofilm agents: Strategies to control biofilm-forming pathogens. Frontiers in Microbiology, 11, Article 566325. [Crossref]
Mohana, N. C., Rao, H. C. Y., Rakshith, D., Mithun, P. R., Nuthan, B. R., & Satish, S. (2018). Omics based approach for biodiscovery of microbial natural products in antibiotic resistance era. Journal of Genetic Engineering and Biotechnology, 16(1), 1–8. [Crossref]
Mostafa, N. M., Ayaz, M., El-Shazly, M., & Singab, A. N. (2023). Editorial: Novel antimicrobials and antibiotics resistance modulating agents from natural products: Turning promises into triumphs.
Murugan, S., Senthilvelan, T., Govindasamy, M., & Thangavel, K. (2025). A comprehensive review on exploring the potential of phytochemicals and biogenic nanoparticles for the treatment of antimicrobial-resistant pathogenic bacteria. Current Microbiology, 82(4), Article 158. [Crossref]
Mostafa, N. M., Ayaz, M., El-Shazly, M., & Singab, A. N. (2023). Editorial: Novel antimicrobials and antibiotics resistance modulating agents from natural products: Turning promises into triumphs. Frontiers in Pharmacology, 14, Article 1184071. [Crossref]
Murugan, S., Senthilvelan, T., Govindasamy, M., & Thangavel, K. (2025). A comprehensive review on exploring the potential of phytochemicals and biogenic nanoparticles for the treatment of antimicrobial-resistant pathogenic bacteria. Current Microbiology, 82(4), Article 158. [Crossref]
Muteeb, G., Rehman, M. T., Shahwan, M., & Aatif, M. (2023). Origin of antibiotics and antibiotic resistance, and their impacts on drug development: A narrative review. Pharmaceuticals, 16(11), Article 1615. [Crossref]
Nawaz, T., Gu, L., Fahad, S., Saud, S., Jiang, Z., Hassan, S., Harrison, M. T., Liu, K., Khan, M. A., Liu, H., El-Kahtany, K., Wu, C., Zhu, M., & Zhou, R. (2023). A comprehensive review of the therapeutic potential of cyanobacterial marine bioactives: Unveiling the hidden treasures of the sea. Marine Drugs, 21(12), Article 621. [Crossref]
Newman, D. J., & Cragg, G. M. (2020). Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. Journal of Natural Products, 83(3), 770–803. [Crossref]
Nurunnabi, T. R., Nahar, L., Al-Majmaie, S., Rahman, S. M. M., Sohrab, M. H., Billah, M. M., Ismail, F. M. D., Rahman, M. M., Sharples, G. P., & Sarker, S. D. (2018). Anti-MRSA activity of oxysporone and xylitol from the endophytic fungus Pestalotia sp. growing on the Sundarbans mangrove plant Heritiera fomes. Phytotherapy Research, 32(12), 2518–2526. [Crossref]
Popa, Ș. L., Pop, C., Dita, M. O., Brata, V. D., Bolchis, R., Czako, Z., Saadani, M. M., Ismaiel, A., Dumitrașcu, D. I., Grad, S., David, L., Cismaru, G., & Padureanu, A. M. (2022). Deep learning and antibiotic resistance. Antibiotics, 11(11), Article 1674. [Crossref]
Porras, G., Chassagne, F., Lyles, J. T., Marquez, L., Dettweiler, M., Salam, A. M., Samarakoon, T., Shabih, S., Farrokhi, D. R., & Quave, C. L. (2020). Ethnobotany and the role of plant natural products in antibiotic drug discovery. Chemical Reviews, 121(6), 3495–3560. [Crossref]
Porras, G., Chassagne, F., Lyles, J. T., Marquez, L., Dettweiler, M., Salam, A. M., Samarakoon, T., Shabih, S., Farrokhi, D. R., & Quave, C. L. (2020). Ethnobotany and the role of plant natural products in antibiotic drug discovery. Chemical Reviews, 121(6), 3495–3560. [Crossref]
Ranpariya, V., & Tarpara, N. (2023). The ability of microorganisms to produce antibiotics: A review. Research Journal of Pharmacy and Technology, 16(3), 1483–1489. [Crossref]
Rossiter, S. E., Fletcher, M. H., & Wuest, W. M. (2017). Natural products as platforms to overcome antibiotic resistance. Chemical Reviews, 117(19), 12415–12474. [Crossref]
Santra, H. K., Maity, S., & Banerjee, D. (2022). Production of bioactive compounds with broad spectrum bactericidal action, bio-film inhibition and antilarval potential by the secondary metabolites of the endophytic fungus Cochliobolus sp. APS1 isolated from the Indian medicinal herb Andrographis paniculata. Molecules, 27(4), Article 1241. [Crossref]
Seyedalinaghi, S., Mehraeen, E., Mirzapour, P., Yarmohammadi, S., Dehghani, S., Zare, S., Gholami, S., Attarian, N., Abiri, A., Rad, F. F., Tabari, A., Afroughi, F., Gholipour, A., Roozbahani, M. M., & Jahanfar, S. (2025). A systematic review on natural products with antimicrobial potential against WHO's priority pathogens. European Journal of Medical Research, 30(1), Article 164. [Crossref]
Shirazi, S. N., & Hamedi, J. (2023). Isolation and screening of Thermoactinomycetaceae family members as an extremophilic poor investigated and promising natural source of antimicrobial substances. Archives of Microbiology, 205(5), Article 182. [Crossref]
Si, Z., Hou, Z., Vikhe, Y. S., Thappeta, K. R. V., Marimuthu, K., De, P. P., Ng, O. T., Li, P., Zhu, Y., Pethe, K., & Chan-Park, M. B. (2021). Antimicrobial effect of a novel chitosan derivative and its synergistic effect with antibiotics. ACS Applied Materials & Interfaces, 13(2), 2607–2620. [Crossref]
Si, Z., Pethe, K., & Chan-Park, M. B. (2023). Chemical basis of combination therapy to combat antibiotic resistance. JACS Au, 3(2), 276–292. [Crossref]
Tan, B., Zhang, Q., Zhang, L., Zhu, Y., & Zhang, C. (2023). Naturally occurring and widespread resistance to bioactive natural products. ChemMedChem, 18(24), Article e202300619. [Crossref]
Thornburg, C. C., Britt, J. R., Evans, J. R., Akee, R. K., Whitt, J. A., Trinh, S. K., Harris, M. J., Thompson, J. R., Ewing, T. L., Shipley, S. M., Grothaus, P. G., Newman, D. J., Schneider, J. P., Grkovic, T., & O'Keefe, B. R. (2018). NCI Program for Natural Product Discovery: A publicly-accessible library of natural product fractions for high-throughput screening. ACS Chemical Biology, 13(9), 2484–2497. [Crossref]
Torres-Barceló, C., Kojadinovic, M., Moxon, R., & MacLean, R. C. (2015). The SOS response increases bacterial fitness, but not evolvability, under a sublethal dose of antibiotic. Proceedings of the Royal Society B: Biological Sciences, 282(1810), Article 20150885. [Crossref]
Urban-Chmiel, R., Marek, A., Stępień-Pyśniak, D., Wieczorek, K., Dec, M., Nowaczek, A., & Osek, J. (2022). Antibiotic resistance in bacteria; A review. Antibiotics, 11(8), Article 1079. [Crossref]
Valdes-Pena, M. A., Massaro, N. P., Lin, Y.-C., & Pierce, J. G. (2021). Leveraging marine natural products as a platform to tackle bacterial resistance and persistence. Accounts of Chemical Research, 54(8), 1866–1877. [Crossref]
Van Santen, J. A., Jacob, G., Singh, A. L., Aniebok, V., Balunas, M. J., Bunsko, D., Carvalho, F., Castaño-Espriu, L., Chang, C., Clark, T. N., Cleary Little, J., Delgadillo, D. A., Dorrestein, P. C., Duncan, K. R., Egan, J. M., Galey, M. M., Haeckl, F. P. J., Hua, A., Hughes, A. H., ... Linington, R. G. (2019). The Natural Products Atlas: An open access knowledge base for microbial natural products discovery. ACS Central Science, 5(11), 1824–1833. [Crossref]
Wang, Q., Wang, M., Lyu, W., Li, X., Xu, L., Qin, Y., Ren, Y., Deng, Z., Tao, M., Xiao, W., & Shen, F. (2025). Rapid high-throughput discovery of molecules with antimicrobial activity from natural products enabled by a nanoliter matrix SlipChip. Small Methods, Article e2402045. [Crossref]
World Health Organization. (2014). Antimicrobial resistance: global report on surveillance. World Health Organization. [Link]
Wright, G. D. (2014). Something old, something new: Revisiting natural products in antibiotic drug discovery. Canadian Journal of Microbiology, 60(3), 147–154. [Crossref]
Yang, Y., He, F.-J., Xie, S.-Y., Luo, X.-W., & Wang, X. (2025). A novel strategy for combating multidrug-resistant bacteria: Natural products from uncultured microorganisms. European Journal of Medicinal Chemistry, 287, Article 118119. [Crossref]
Yang, Y., Kessler, M. G. C., Marchán-Rivadeneira, M. R., & Han, Y. (2023). Combating antimicrobial resistance in the post-genomic era: Rapid antibiotic discovery. Molecules, 28(10), Article 4183. [Crossref]
Yarahmadi, A., Najafiyan, H., Yousefi, M. H., Khosravi, E., Shabani, E., Afkhami, H., & Aghaei, S. (2025). Beyond antibiotics: Exploring multifaceted approaches to combat bacterial resistance in the modern era: A comprehensive review. Frontiers in Cellular and Infection Microbiology, 15, Article 1493915. [Crossref]
Zampieri, M., Szappanos, B., Buchieri, M. V., Trauner, A., Piazza, I., Picotti, P., Gagneux, S., Borrell, S., Gicquel, B., Lelièvre, J., Papp, B., & Sauer, U. (2018). High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Science Translational Medicine, 10(429), Article eaal3973. [Crossref]
Zhai, X., Wu, G., Tao, X., Yang, S., Lv, L., Zhu, Y., Dong, D., & Xiang, H. (2023). Success stories of natural product-derived compounds from plants as multidrug resistance modulators in microorganisms. RSC Advances, 13(12), 7798–7823. [Crossref]