Microbial Bioactives

1. Introduction

The rise of antimicrobial resistance (AMR) has, perhaps more than any other contemporary biomedical challenge, forced the scientific community to confront an uncomfortable reality: the therapeutic foundations built during the “golden age” of antibiotics are steadily eroding. What once seemed like a manageable evolutionary arms race has, over recent decades, accelerated into something far more complex and, at times, unsettlingly unpredictable. Current global estimates suggest that millions of deaths are associated with resistant infections each year, with a substantial proportion directly attributable to bacterial AMR (Brüssow, 2024). Clinically dominant pathogens—Escherichia coli, Klebsiella pneumoniae, and methicillin-resistant Staphylococcus aureus (MRSA), among others—continue to evolve resistance faster than new drugs can be developed, leaving clinicians with shrinking therapeutic options and increasingly uncertain outcomes (Lewis, 2020; Cardona et al., 2025).

Yet, if the clinical urgency is clear, the underlying scientific bottleneck is equally stark. Antibiotic discovery has, in many ways, slowed to a crawl. The vast majority of antibiotics still in use today trace their origins to discoveries made between the 1940s and 1980s, and despite advances in synthetic chemistry and high-throughput screening, genuinely novel scaffolds remain rare (Klahn & Brönstrup, 2017; Lewis, 2020). One might argue that the problem is not a lack of chemical ingenuity, but rather a failure to access the full diversity of natural microbial ecosystems—ecosystems that historically served as the richest source of antimicrobial compounds.

For decades, soil microbiota have been recognized as prolific producers of bioactive secondary metabolites, accounting for a substantial fraction of clinically relevant antibiotics (Berdy et al., 2017). However, conventional laboratory cultivation techniques have imposed a severe constraint: the vast majority of environmental microorganisms—often estimated at more than 99%—remain unculturable under standard conditions. This “great plate count anomaly” has effectively limited exploration to a narrow subset of microbial diversity, leading to repeated rediscovery of known compounds and diminishing returns in antibiotic screening efforts (Geers et al., 2022). It is within this context that the development of the isolation chip (iChip) begins to feel less like an incremental innovation and more like a conceptual shift.

The iChip technology, by enabling in situ cultivation of previously unculturable microorganisms, offers a way to bridge the gap between environmental diversity and laboratory accessibility (Berdy et al., 2017; Hidat et al., 2026). By isolating single bacterial cells in microchambers and allowing them to grow within their native environmental milieu through semi-permeable membranes, the iChip effectively recreates ecological conditions that are otherwise lost in vitro. This seemingly simple adjustment—cultivating microbes where they naturally thrive—has opened access to a vast reservoir of previously untapped biosynthetic potential.

The most widely cited outcome of this approach is the discovery of teixobactin, a structurally distinctive depsipeptide antibiotic isolated from Eleftheria terrae (Ling et al., 2015). Initially, teixobactin generated considerable excitement, not only because of its potent activity against Gram-positive pathogens—including MRSA, vancomycin-resistant enterococci (VRE), and Mycobacterium tuberculosis—but also because of its proposed “resistance-proof” mechanism. Rather than targeting proteins, which are prone to mutational escape, teixobactin binds to highly conserved cell wall precursors, specifically lipid II and lipid III, disrupting essential processes in peptidoglycan and teichoic acid biosynthesis (Chiorean et al., 2020; Shukla et al., 2022).

At first glance, this mechanism appears elegantly simple. By targeting molecular structures that bacteria cannot easily modify without compromising viability, teixobactin seemed to sidestep one of the central drivers of resistance evolution. Early studies, notably those by Ling et al. (2015), reported no detectable resistance under laboratory conditions, reinforcing the perception of teixobactin as a fundamentally new class of antibiotics (Piddock, 2015; McCarthy, 2019). And yet, as subsequent work has shown, the story is perhaps more nuanced than initially assumed.

Follow-up investigations have revealed that while resistance to teixobactin develops far more slowly than with conventional antibiotics, it is not entirely absent. Experimental evolution studies indicate that resistance can emerge under prolonged selective pressure, often accompanied by significant fitness costs (Lloyd et al., 2021). Moreover, regulatory systems such as CroRS in Enterococcus faecalis appear to mediate adaptive responses to cell envelope stress, suggesting that bacteria may exploit indirect pathways—tolerance rather than outright resistance—to survive teixobactin exposure (Darnell et al., 2019; Todd Rose et al., 2023). These findings complicate the earlier narrative of “resistance-proof” antibiotics and instead point toward a spectrum of adaptive strategies that warrant closer examination.

Parallel to these mechanistic insights, efforts to optimize teixobactin for clinical use have highlighted additional challenges. The molecule’s complex structure, including rare amino acid residues such as L-allo-enduracididine, poses significant obstacles for large-scale synthesis and cost-effective production (Iyer et al., 2019; Velkov & Karas, 2020). As a result, considerable attention has been directed toward the development of synthetic analogues—variants such as Arg10-teixobactin and L-Chg10-teixobactin—that aim to simplify synthesis while preserving antimicrobial potency (Ng et al., 2018; Jarkhi et al., 2022; Ramchuran et al., 2018). Encouragingly, many of these analogues retain strong activity against Gram-positive pathogens, although their resistance profiles and pharmacological properties vary in ways that are not yet fully understood.

Another limitation—perhaps less frequently emphasized but equally important—is the restricted spectrum of activity. Teixobactin and its derivatives are largely ineffective against Gram-negative bacteria, primarily due to the impermeability of the outer membrane. This has prompted exploration of combination therapies, including the use of membrane-disrupting agents such as colistin, to enhance drug uptake and extend antimicrobial coverage (Matos de Opitz & Sass, 2020). Whether such strategies can achieve clinically meaningful synergy without introducing additional toxicity remains an open question.

Beyond teixobactin, the iChip platform has facilitated the discovery of other promising compounds, such as darobactin, which targets the BamA protein involved in outer membrane biogenesis in Gram-negative bacteria (Prakasam et al., 2026). These findings collectively suggest that iChip-enabled discovery is not limited to a single breakthrough molecule but represents a broader paradigm shift in how antibiotics can be sourced from previously inaccessible microbial niches.

Despite these advances, the available literature remains fragmented. Studies often focus on individual compounds, specific bacterial strains, or isolated mechanistic aspects, making it difficult to draw generalizable conclusions about efficacy, binding interactions, and resistance dynamics. There is, therefore, a clear need for a systematic and quantitative synthesis of existing data—one that integrates antimicrobial potency metrics (e.g., MIC, MBC), molecular target interactions, and resistance evolution into a unified analytical framework.

This study is guided by a set of interrelated, and in some respects deliberately probing, hypothetical questions. To what extent do structural modifications in teixobactin analogues translate into measurable differences in antimicrobial efficacy across diverse clinical isolates? Are early signals of tolerance or low-level resistance—such as those mediated by regulatory systems like CroRS—indicative of longer-term evolutionary trajectories? And perhaps most intriguingly, can rational design or combination strategies extend the utility of iChip-derived compounds beyond their current spectrum without compromising their apparent resistance resilience?

In addressing these questions, the present work aims to move beyond descriptive review and toward a more integrative, quantitatively grounded understanding of iChip-derived antibiotics. Specifically, the objectives are: (i) to systematically compare antimicrobial efficacy across natural and synthetic variants; (ii) to analyze binding interactions with lipid targets and their relationship to activity; (iii) to evaluate resistance emergence and associated fitness costs; and (iv) to explore structure–activity relationships that may inform future drug design.

Ultimately, this systematic review and quantitative comparative synthesis seeks to do something slightly more ambitious than merely summarizing existing knowledge. It attempts to trace the contours of an emerging antibiotic paradigm—one rooted in ecological realism, molecular specificity, and, perhaps inevitably, a recognition that even the most promising discoveries come with their own complexities. In doing so, it hopes to contribute, even in a modest way, to the ongoing effort to realign antibiotic discovery with the evolving realities of microbial resistance.

2. Methodology

2.1 Study Design and Reporting Framework

This study was designed as a systematic review with quantitative comparative synthesis, integrating antimicrobial efficacy, target-binding interactions, and resistance-related outcomes of iChip-derived compounds and their analogues. The review was conducted in accordance with the PRISMA 2020 reporting framework, ensuring transparency, reproducibility, and structured reporting of the selection and synthesis process (PRISMA 2020; Page et al., 2021) (Figure 1). Although the study incorporated quantitative data extraction, a full meta-analysis model was applied only selectively, given the heterogeneity of endpoints. The overall methodological approach was informed by established principles in evidence synthesis and meta-analysis, including frameworks described by Introduction to Meta-Analysis and the Cochrane Handbook for Systematic Reviews of Interventions (Borenstein et al., 2009; Higgins et al., 2022).

2.2 Literature Search Strategy

A comprehensive literature search was conducted across major databases, including PubMed, Scopus, Web of Science, and Google Scholar, covering publications up to March 2026. Search terms included combinations of keywords such as “iChip,” “teixobactin,” “teixobactin analogues,” “darobactin,” “antimicrobial activity,” “MIC,” “binding affinity,” “lipid II,” “resistance,” and “synergy.” Boolean operators (AND/OR) were applied to refine search results, and backward citation tracking was performed to identify additional relevant studies.

This systematic search strategy was aligned with best practices in evidence synthesis, ensuring comprehensive coverage and minimizing selection bias (Higgins et al., 2022; Setu et al., 2025).

Figure 1: PRISMA 2020 Flow Diagram of Study Selection for Systematic Review and Quantitative Comparative Synthesis of iChip-Derived Antimicrobial Compounds. This flow diagram illustrating the study selection process. Of 770 records identified, 612 were screened and 88 full-text articles were assessed for eligibility. Ultimately, 16 studies were included in the qualitative and quantitative comparative synthesis.

2.3 Eligibility Criteria

Studies were included if they:

• Reported iChip-derived antibiotics or synthetic analogues
• Provided quantitative antimicrobial metrics (e.g., MIC, MBC, MBIC)
• Included binding affinity or mechanistic data
• Reported resistance evolution, tolerance, or synergy effects
• Contained primary experimental data (in vitro or in vivo)

Exclusion criteria included:

• Reviews, editorials, or non-primary studies
• Studies lacking extractable quantitative data
• Reports not relevant to antimicrobial efficacy or mechanism

Following screening and eligibility assessment, 16 studies were included for quantitative synthesis, consistent with systematic review practices in emerging biomedical domains (Amin et al., 2025).

2.4 Data Extraction and Standardization

Data were extracted using a structured template to ensure consistency across studies. Extracted variables included:

• compound identity
• pathogen or molecular target
• antimicrobial endpoints (MIC, MBC, MBIC)
• binding affinity (Kd values)
• pharmacodynamic parameters (e.g., PD₅₀, survival dose)
• resistance and synergy data

To enable cross-study comparison, data were standardized as follows:

• MIC values were converted to µg/mL
• Binding affinity values were normalized to nM
• Ranges were converted to representative values using midpoint or geometric mean approaches
• Threshold values (e.g., >256 µg/mL) were treated as right-censored observations
• Terminology and compound naming were harmonized

This structured transformation allowed the integration of heterogeneous datasets into a unified analytical framework, consistent with methodological standardization approaches in comparative biomedical synthesis (Borenstein et al., 2009; Setu et al., 2025).

2.5 Quantitative Synthesis and Analytical Approach

Due to substantial heterogeneity in study design, endpoints, and reporting formats, a random-effects meta-analysis model (DerSimonian & Laird, 1986) was considered but not broadly applied across all outcomes. Instead, a hybrid analytical framework was adopted.

2.5.1 Antimicrobial Activity Analysis

MIC data were aggregated across compound–pathogen pairs and visualized using a heatmap (Figure 2). Where multiple values were reported, representative values were derived using log-transformed aggregation to preserve scale consistency. This approach aligns with best practices for handling skewed biological data (Borenstein et al., 2009).

2.5.2 Target Binding Analysis

Binding affinity data (Kd) were analyzed across compound–target interactions and visualized using a second heatmap (Figure 3). These analyses enabled comparative assessment of molecular target engagement, particularly for lipid II and related cell wall precursors.

2.5.4 Evidence Mapping

An evidence matrix (Figure 4) was developed to summarize the presence or absence of data across key endpoints (MIC, MBC, MBIC, in vivo efficacy, synergy, resistance). This approach provides a structured overview of evidence density and research gaps.

2.6 Heterogeneity and Bias Assessment

Between-study heterogeneity was assessed qualitatively and, where applicable, interpreted using conceptual frameworks such as the I² statistic (Higgins et al., 2003). However, due to the absence of variance estimates (e.g., standard deviations), formal heterogeneity quantification was limited. Potential publication bias was considered using principles derived from funnel plot asymmetry and small-study effects (Egger et al., 1997), although formal statistical testing was not feasible due to dataset constraints.

2.7 Resistance and Synergy Analysis

Resistance-related outcomes were synthesized based on:

• resistance frequency
• adaptive tolerance mechanisms
• regulatory pathways (e.g., CroRS system)
• fitness costs associated with resistance

Synergy effects were evaluated based on reported reductions in MIC and fractional inhibitory concentration indices (FICI), where available. These analyses were interpreted within a comparative rather than a pooled statistical framework.

2.8 Statistical Considerations

All quantitative analyses were conducted using structured datasets derived from extracted values. Log transformation was applied to MIC and Kd values to account for wide dynamic ranges. A full meta-analysis using random-effects modeling (DerSimonian & Laird, 1986) was not performed due to:

• heterogeneity in endpoints
• inconsistent reporting of variance
• limited overlap across studies

Instead, findings were interpreted using descriptive and comparative statistical approaches, consistent with methodological recommendations for heterogeneous datasets (Higgins et al., 2022; Borenstein et al., 2009).

2.9 Limitations of the Methodological Approach

The primary limitations include:

• variability in experimental conditions across studies
• inconsistent reporting of quantitative variability
• limited overlap between antimicrobial and binding datasets
• sparse resistance and in vivo data

These constraints necessitated a cautious interpretation of results and justified the use of a quantitative comparative synthesis rather than a full meta-analysis, a strategy increasingly adopted in complex biomedical reviews (Amin et al., 2025; Setu et al., 2025).

3. New Structural Templates for Antimicrobials: Revitalizing the Discovery Pipeline

3.1. Confronting Architectural Stagnation in Antibiotic Discovery

It is increasingly difficult to discuss modern infectious disease management without acknowledging a quiet but persistent erosion in our therapeutic confidence. Antimicrobial resistance (AMR), once framed as a looming concern, has now taken on a far more immediate and tangible form—one that, in many ways, challenges the very assumptions upon which contemporary medicine was built. Estimates suggest that in 2019 alone, approximately 1.27 million deaths were directly attributable to bacterial AMR, a figure that continues to rise with unsettling consistency (Murray et al., 2022). And yet, perhaps what is most concerning is not only the scale of the problem, but the relative stagnation of solutions.

For decades, antibiotic discovery has remained anchored to a limited set of chemical scaffolds—structures largely identified during the mid-20th century “golden age” of antibiotics (Piddock, 2015). While these scaffolds have undergone countless refinements, derivatizations, and optimizations, their underlying architectures have changed little. This has created a paradoxical situation: even as medicinal chemistry has advanced, many new antibiotics are, at their core, variations on familiar themes—rendering them susceptible to pre-existing resistance mechanisms (Lewis, 2020; Brüssow, 2024).

Part of this stagnation can be traced back to the overexploitation of easily culturable soil microbes. Early discovery efforts mined this resource extensively, often yielding diminishing returns as known compounds were rediscovered repeatedly. At the same time, synthetic “corporate libraries,” though vast, have not always translated effectively into antibacterial success, in part because many compounds fail to navigate the complex permeability barriers of bacterial cell envelopes (Lewis, 2020). This dual limitation—ecological and chemical—has prompted a renewed interest in what has often been referred to as “microbial dark matter,” the vast majority of microorganisms that remain inaccessible through conventional cultivation methods (Berdy et al., 2017; Geers et al., 2022).

3.2. Unlocking the Vault: The iChip and In Situ Cultivation

If the problem, at least in part, lies in accessibility, then it follows that the solution must begin with rethinking how microbes are brought into the laboratory. The “Great Plate Count Anomaly”—the observation that less than 1% of environmental microbes grow under standard laboratory conditions—has long constrained discovery efforts (Piddock, 2015). Many organisms rely on subtle ecological cues, symbiotic interactions, or nutrient profiles that are difficult, if not impossible, to replicate artificially. The isolation chip, or iChip, represents an elegant, almost deceptively simple response to this challenge. Rather than forcing microbes to adapt to laboratory conditions, the iChip allows them to grow within their native environment (Nichols et al., 2010). By isolating individual cells in microchambers enclosed by semipermeable membranes, the device permits the diffusion of nutrients and signaling molecules directly from the surrounding soil. In doing so, it effectively recreates the organism’s ecological niche—at least sufficiently to enable growth.

This approach has proven transformative. Not only does it enable the cultivation of previously “unculturable” species, but it also facilitates their gradual adaptation—or domestication—for subsequent laboratory study (Berdy et al., 2017). In a sense, the iChip does not merely expand the searchable chemical space; it redefines it. What was once inaccessible becomes, with some patience, experimentally tractable.

3.3. The Depsipeptide Paradigm: Teixobactin and Resistance-Evading Scaffolds

Among the discoveries enabled by this approach, teixobactin stands out—not only for its antimicrobial potency, but for the conceptual shift it represents. Isolated from Eleftheria terrae, teixobactin is an 11-residue cyclic depsipeptide with a structural complexity that immediately distinguishes it from many conventional antibiotics (Ling et al., 2015; Qi et al., 2022). Its architecture, characterized by multiple D-amino acids and the rare residue L-allo-enduracididine, confers both rigidity and high-affinity binding capabilities (Velkov & Karas, 2020). What makes teixobactin particularly compelling, however, is not just its structure, but its mechanism of action. Unlike most antibiotics, which target proteins susceptible to mutation, teixobactin binds to highly conserved lipid precursors—Lipid II and Lipid III—integral to cell wall biosynthesis (Homma et al., 2016; Shukla et al., 2022). By targeting the pyrophosphate-sugar moiety of these molecules, it effectively bypasses the conventional routes through which resistance typically emerges.

There is, perhaps, something strikingly strategic in this approach. By focusing on molecular features that bacteria cannot easily alter without compromising viability, teixobactin appears to exploit an evolutionary constraint. Moreover, its activity is not limited to a single pathway. The antibiotic has been shown to exert a “two-pronged” effect—simultaneously inhibiting cell wall synthesis and inducing autolysin-mediated cell lysis (Shukla et al., 2022). Adding another layer of complexity, teixobactin molecules assemble into supramolecular β-sheet fibrils upon binding, disrupting membrane organization and further enhancing bactericidal activity (Shukla et al., 2022). It is perhaps this combination of structural specificity and cooperative assembly that underlies the initial observation that resistance could not be readily induced under laboratory conditions (Ling et al., 2015).

3.4. Diversifying the Arsenal: Clovibactin and Hypeptin

The discovery of teixobactin has, in many ways, validated the broader potential of exploring uncultured microbial diversity. Subsequent efforts have identified structurally related compounds that appear to operate within a similar conceptual framework. Clovibactin, for instance, shares the ability to bind conserved cell wall precursors, forming supramolecular assemblies that disrupt bacterial integrity (Shukla et al., 2023). While its binding geometry differs, the underlying principle—targeting immutable molecular features—remains consistent. Hypeptin, another emerging compound, reinforces this pattern. By binding Lipid I and Lipid II, it further demonstrates that the depsipeptide scaffold may represent a broader class of resistance-resilient antibiotics rather than a singular anomaly (Wirtz et al., 2021). These discoveries collectively suggest that the success of teixobactin is not isolated, but rather indicative of a more generalizable strategy for antibiotic design.

3.5. Targeting the Outer Barrier: Darobactin and Gram-Negative Precision

While depsipeptides have shown remarkable efficacy against Gram-positive bacteria, their limited activity against Gram-negative pathogens remains a significant constraint. The outer membrane of Gram-negative bacteria serves as a formidable barrier, preventing many antibiotics from reaching intracellular targets (Iyer et al., 2019; McCarthy, 2019). Darobactin offers a compelling solution to this problem. Discovered from Photorhabdus species, darobactin belongs to the RiPP (ribosomally synthesized and post-translationally modified peptide) family and exhibits a highly constrained three-dimensional structure (Imai et al., 2019; Prakasam et al., 2026). Rather than attempting to penetrate the outer membrane, darobactin targets BamA, an essential protein involved in outer membrane protein assembly.

This strategy is, in many respects, conceptually elegant. By binding to the lateral gate of BamA, darobactin disrupts membrane biogenesis at its source (Imai et al., 2019). And because BamA is surface-exposed, the antibiotic avoids the permeability barrier altogether. This not only enhances its efficacy but also suggests a broader design principle: that targeting accessible, essential processes may circumvent traditional limitations of antibiotic uptake.

3.6. Emerging Synthetic and Computational Blueprints

While natural products continue to inspire antibiotic discovery, it would be misleading to suggest that innovation is confined to ecological exploration. Increasingly, synthetic and computational approaches are contributing to the identification of novel antimicrobial templates. Zosurabalpin, for example, targets the LptB2FGC complex involved in lipopolysaccharide transport, effectively trapping LPS within the cell and inducing toxic accumulation (Cardona et al., 2025). Halicin, identified through deep learning approaches, operates through an entirely different mechanism—disrupting the proton motive force of bacterial membranes, thereby bypassing conventional resistance pathways. Similarly, griselimycin has re-emerged as a promising anti-tuberculosis agent through its interaction with the DnaN sliding clamp, while afabicin targets the FabI enzyme in fatty acid biosynthesis (Klahn & Brönstrup, 2017). These examples highlight a growing trend: the convergence of computational prediction, structural biology, and medicinal chemistry in the search for new antibiotics.

3.7. Refining the Template: Optimization and Synthetic Accessibility

Despite their promise, many natural product scaffolds present practical challenges, particularly in terms of synthesis and scalability. Teixobactin, with its rare amino acid components, exemplifies this difficulty (Iyer et al., 2019). To address this, researchers have developed simplified analogues that replace challenging residues with more accessible alternatives.

Interestingly, substitutions such as arginine or lysine for L-allo-enduracididine have been shown to retain significant antimicrobial activity, suggesting that certain structural features may be more flexible than initially assumed (Iyer et al., 2019). Other modifications, including alterations to the depsipeptide bond, have aimed to improve synthetic accessibility, albeit sometimes at the cost of reduced potency.

At the same time, combination strategies—such as pairing teixobactin analogues with membrane-disrupting agents—offer a pragmatic route to extending activity against Gram-negative bacteria. While these approaches are still evolving, they underscore the importance of adaptability in translating promising molecules into clinically viable therapies.

3.8. Emerging Frontiers in Antibiotic Discovery

Taken together, these developments suggest that antibiotic discovery is undergoing a subtle but meaningful transformation. The focus is shifting—from familiar protein targets to conserved molecular structures, from easily culturable organisms to microbial dark matter, and from isolated discovery efforts to integrated, interdisciplinary strategies (Brüssow, 2024; Prakasam et al., 2026).

There is, perhaps, reason for cautious optimism. The emergence of compounds like teixobactin and darobactin demonstrates that fundamentally new antibiotic classes are still within reach. And yet, the path from discovery to clinical application remains complex, requiring sustained collaboration across disciplines. If there is a unifying lesson, it may be this: that innovation in antibiotic discovery does not arise from a single breakthrough, but from a willingness to rethink assumptions—about where antibiotics come from, how they work, and how resistance evolves. In that sense, the exploration of microbial dark matter is not merely a technical advance; it is, in a broader sense, a conceptual one.

4. Results

4.1 Study Selection and Characteristics

The systematic search and screening process yielded a final dataset of 16 studies included for quantitative synthesis, as illustrated in Figure 1. The included studies spanned a diverse range of experimental designs, encompassing in vitro antimicrobial assays, mechanistic binding analyses, resistance evolution experiments, and in vivo infection models. Collectively, these studies reflect the evolving landscape of iChip-enabled antibiotic discovery and subsequent analogue development.

As summarized in Table 1, the included compounds represent both natural products and rationally designed analogues, with teixobactin serving as the central scaffold. The dataset includes natural teixobactin, multiple synthetic derivatives (e.g., Arg10-teixobactin, Lys10-teixobactin, L-Chg10-teixobactin), and structurally distinct compounds such as darobactin. These compounds predominantly target highly conserved bacterial structures, including lipid II and lipid III, or, in the case of darobactin, outer membrane proteins such as BamA (Ling et al., 2015; Chiorean et al., 2020; Prakasam et al., 2026). This diversity in compound origin and mechanism provided a suitable basis for comparative synthesis, although heterogeneity in experimental endpoints remained a defining feature of the dataset.

Table 1. Study Characteristics and Representative Lead Compounds from iChip-Derived and Synthetic Antibiotic Platforms. This table summarizes the principal iChip-derived antibiotics and their synthetic analogues, highlighting their origin, molecular targets, and primary research focus. Collectively, these compounds illustrate the transition from natural product discovery to rational analogue development, with emphasis on targeting conserved bacterial structures such as lipid II/III and outer membrane proteins (e.g., BamA).

4.2 Comparative Antimicrobial Efficacy Across Pathogens

Quantitative antimicrobial data extracted from the included studies revealed a clear pattern of strong activity against Gram-positive pathogens, contrasted with limited intrinsic activity against Gram-negative organisms. As detailed in Table 2, natural teixobactin demonstrated potent activity against Staphylococcus aureus (MRSA) with MIC values around 0.25 µg/mL, consistent with the original findings of Ling et al. (2015). Similarly, activity against Enterococcus faecalis (VRE) and Mycobacterium tuberculosis remained within sub-microgram ranges, reinforcing its broad Gram-positive efficacy (Ling et al., 2015; Qi et al., 2022). Notably, exceptionally low MIC values were observed for Clostridioides difficile (0.005 µg/mL) and Bacillus anthracis (as low as 0.02 µg/mL), indicating high potency against specific pathogens (Ling et al., 2015; Lawrence et al., 2025).

However, synthetic analogues exhibited variable potency, often with reduced activity relative to the native compound. For example, Arg10-teixobactin showed MIC values of approximately 2.0 µg/mL against MRSA, representing an order-of-magnitude decrease in potency (Iyer et al., 2019; Qi et al., 2022). Similar trends were observed for other analogues, including Lys10-teixobactin, which displayed MIC values in the range of 2–4 µg/mL (Ramchuran et al., 2018). The heatmap visualization in Figure 2 further illustrates these patterns, highlighting both compound-dependent and pathogen-dependent variability. While most compounds retained activity against Gram-positive organisms, Gram-negative bacteria such as Escherichia coli and Pseudomonas aeruginosa exhibited markedly higher MIC values, often exceeding 100 µg/mL or reported as >256 µg/mL (Ng et al., 2018; Chiorean et al., 2020). This disparity underscores the permeability barrier imposed by the outer membrane of Gram-negative bacteria.

Figure 2: Heatmap of compounds × pathogens × minimum inhibitory concentration (MIC) values for iChip-derived compounds and teixobactin analogs. This heatmap summarizes MIC values extracted from the structured workbook across compound-pathogen pairs. Values are shown in µg/mL and represent the available extracted study-level observations after restructuring of the uploaded datasets. Where multiple entries were available for the same compound-pathogen combination, the plotted value reflects an aggregated representative value derived from the structured table for visualization. Lower MIC values indicate stronger antimicrobial potency, whereas higher MIC values indicate reduced activity or conditional activity against the tested organism. Blank cells indicate that no corresponding MIC value was available in the current extracted dataset.

Table 2. Comparative Antimicrobial Efficacy of Teixobactin and Its Analogues (MIC Values, µg/mL). Minimum inhibitory concentration (MIC) values indicate the antimicrobial potency of teixobactin and its derivatives across a range of clinically relevant pathogens. Lower MIC values reflect higher potency. Notably, strong activity is observed against Gram-positive organisms, while Gram-negative pathogens exhibit reduced susceptibility unless membrane-permeabilizing agents are used.

4.3 Binding Affinity and Target Engagement Patterns

Binding affinity data, summarized in Table 6 and visualized in Figure 3, provided insight into the mechanistic basis of antimicrobial activity. Across the dataset, teixobactin and its analogues demonstrated preferential binding to lipid II and related cell wall precursors, consistent with previously reported mechanisms (Chiorean et al., 2020; Shukla et al., 2022). Natural teixobactin exhibited binding affinities in the low micromolar range (0.08–0.43 µM) for lipid II, reflecting strong target engagement. Interestingly, analogues displayed heterogeneous binding profiles, with some showing reduced affinity for Gram-positive lipid II but enhanced interaction with Gram-negative lipid II variants. For instance, Arg10-teixobactin demonstrated relatively weaker binding to Gram-positive lipid II (4.13 µM) but stronger binding to Gram-negative lipid II (0.06 µM), suggesting structural modifications may alter target specificity (Chiorean et al., 2020).

Despite these differences, the relationship between binding affinity and antimicrobial potency was not strictly linear. This exploratory plot illustrates this complexity, indicating that stronger binding does not always translate into improved antimicrobial efficacy, particularly in the context of Gram-negative organisms where permeability constraints dominate.

Figure 3: Heatmap of compounds × molecular targets × binding affinity (Kd) for teixobactin-related compounds. This heatmap presents binding affinity values compiled from the extracted dataset for available compound-target pairs, including Lipid II-related targets and undecaprenyl pyrophosphate where reported. Values are plotted as Kd in nM using the structured binding table derived from the uploaded workbook. Lower Kd values indicate stronger target binding affinity. Missing cells denote target-compound combinations for which no binding measurement was available in the extracted data. This figure is intended to visualize comparative target engagement patterns rather than infer a pooled mechanistic effect size.

4.4 Resistance Profiles and Adaptive Responses

Resistance-related findings, summarized in Table 3, revealed a remarkably low frequency of resistance development for teixobactin-class compounds. Early studies reported resistance frequencies below 10⁻¹⁰ in S. aureus and B. anthracis, with no identifiable resistance genes (Ling et al., 2015; Lawrence et al., 2025). However, subsequent investigations introduced a more nuanced perspective. Experimental evolution studies demonstrated that resistance could emerge under prolonged selective pressure, albeit slowly and at a significant fitness cost (Lloyd et al., 2021). These findings suggest that while resistance is not easily acquired, it is not absent. Importantly, adaptive responses appeared to be mediated through tolerance mechanisms rather than classical resistance mutations. Activation of regulatory systems such as the CroRS two-component system in Enterococcus faecalis was shown to play a critical role in mediating cell envelope stress responses (Darnell et al., 2019; Todd Rose et al., 2023). This distinction between resistance and tolerance is particularly relevant, as it may influence long-term therapeutic outcomes.

Table 3. Resistance Profiles and Mechanistic Insights of Teixobactin-Class Antibiotics. This table outlines resistance dynamics and mechanistic features associated with teixobactin and its analogues. The findings highlight a resistance-resilient profile driven by targeting conserved lipid structures and demonstrate that adaptive responses are more often linked to tolerance mechanisms rather than classical resistance mutations.

4.5 In Vivo Efficacy and Translational Potential

In vivo data, presented in Table 4, provided evidence of the therapeutic potential of teixobactin and its analogues across multiple infection models. In murine septicaemia models, teixobactin exhibited a PD₅₀ of approximately 0.2 mg/kg, demonstrating significantly higher potency than vancomycin (Ling et al., 2015). Similarly, in rabbit models of inhalation anthrax, teixobactin achieved complete survival and pathogen clearance at doses of 1.0 mg/kg (Lawrence et al., 2025). Additional studies reported substantial reductions in bacterial load in murine thigh and lung infection models, with reductions reaching up to six log units (Ling et al., 2015). These findings collectively suggest that in vitro potency translates effectively into in vivo efficacy, although the number of available studies remains limited.

Table 4. In Vivo Efficacy of Teixobactin and Related Compounds Across Animal Models. In vivo studies demonstrate the therapeutic potential of teixobactin and its analogues across multiple infection models. Outcomes consistently show strong bactericidal activity, often surpassing standard-of-care antibiotics, thereby supporting translational relevance.

4.6 Synergy and Gram-Negative Potentiation

Given the limited intrinsic activity against Gram-negative bacteria, several studies explored synergistic strategies to enhance efficacy, as summarized in Table 5. These approaches primarily involved the use of membrane-disrupting agents to facilitate antibiotic entry. Notably, combinations of teixobactin with H-TriA1 resulted in up to a 125-fold reduction in MIC against Salmonella enterica (Chiorean et al., 2020). Even more pronounced effects were observed with certain analogues, achieving reductions exceeding 1000-fold. Similarly, co-administration with colistin restored activity against Pseudomonas aeruginosa, with fractional inhibitory concentration indices indicating synergistic interactions (Ng et al., 2018). These findings highlight the potential of combination therapies to overcome permeability barriers, although clinical applicability remains to be established.

Table 5. Synergistic Interactions and Gram-Negative Potentiation Strategies. This table summarizes synergistic strategies used to enhance the activity of teixobactin-class compounds against Gram-negative bacteria. Membrane-disrupting agents facilitate antibiotic entry, significantly reducing MIC values and restoring antimicrobial activity.

4.7 Integrated Evidence Landscape

The overall distribution of evidence across endpoints is summarized in Figure 4. This evidence matrix reveals a high density of MIC data, reflecting the central role of antimicrobial potency in the literature. In contrast, data on MBIC, in vivo efficacy, and resistance mechanisms were comparatively sparse. This uneven distribution underscores a key limitation of the current evidence base: while antimicrobial activity is well-characterized, mechanistic, resistance, and translational data remain underrepresented. This imbalance has important implications for the interpretation of efficacy and long-term therapeutic potential.

Figure 4. Evidence matrix summarizing endpoint coverage across iChip-derived compounds. This evidence matrix summarizes the presence or absence of extracted evidence across six endpoint domains: MIC, MBC, MBIC, in vivo efficacy, synergy, and resistance-related findings. A marked cell indicates that at least one relevant extracted record was available for that compound-endpoint pair in the structured workbook, while an unmarked cell indicates no extracted evidence in the present dataset. This figure is designed to show evidence coverage and reporting density across compounds rather than magnitude of effect.

4.8 Forest Plot Interpretation of Antimicrobial Efficacy

To further explore the distribution of antimicrobial potency across studies, a forest plot was constructed for log-transformed MIC values of teixobactin and its analogues against Staphylococcus aureus (MRSA) (Figure 5). This organism was selected due to the relatively higher availability of comparable data across studies.

The forest plot reveals a clear clustering of low MIC values for natural teixobactin, with most estimates falling within a narrow range around 0.25 µg/mL, consistent with previously reported high potency (Ling et al., 2015; Qi et al., 2022). In contrast, synthetic analogues such as Arg10-teixobactin and Lys10-teixobactin demonstrate right-shifted distributions, reflecting higher MIC values and reduced potency (Iyer et al., 2019; Ramchuran et al., 2018). Importantly, the dispersion of estimates across studies suggests moderate to high between-study variability, particularly among analogues. This variability likely reflects differences in experimental conditions, strain selection, and assay methodologies rather than purely intrinsic differences in compound efficacy.

Although a formal pooled effect size was not calculated due to the absence of variance measures (e.g., standard deviations), the visual distribution of effect sizes aligns with principles described in Introduction to Meta-Analysis, where forest plots can still provide meaningful comparative insights in heterogeneous datasets. From a conceptual standpoint, the observed pattern supports the hypothesis that structural modifications in teixobactin analogues often result in diminished antimicrobial potency, although some analogues retain clinically relevant activity. The relatively tight clustering for the parent compound further reinforces its consistency across studies.

Figure 5: Forest plot of antimicrobial efficacy (log-transformed MIC values) of teixobactin and its analogues against Staphylococcus aureus (MRSA). This forest plot summarizes study-level MIC values (log-transformed) for teixobactin and selected analogues against Staphylococcus aureus (MRSA). Each point represents an individual study estimate, with horizontal lines indicating variability where available. Due to limited reporting of variance measures, pooled effect estimates were not calculated, and the plot is presented for comparative visualization only.

4.9 Funnel Plot Interpretation and Publication Bias Assessment

To assess potential publication bias and small-study effects, a funnel plot was generated using available MIC data (Figure 6). The plot displays study-level estimates plotted against a measure of study precision, allowing visual inspection of asymmetry. The resulting funnel plot demonstrates partial asymmetry, with a tendency toward clustering of studies reporting lower MIC values (i.e., higher potency). This pattern may suggest the presence of publication bias, where studies reporting strong antimicrobial activity are more likely to be published. However, alternative explanations must be considered.

Given the relatively small number of studies and the heterogeneity of experimental designs, the observed asymmetry may also reflect:

• variability in assay sensitivity
• selective reporting of specific endpoints
• differences in strain selection or experimental conditions

According to the framework proposed by Bias in meta-analysis detected by a simple graphical test, funnel plot asymmetry should be interpreted cautiously, particularly in datasets with limited statistical power. In the present analysis, formal regression-based tests (e.g., Egger’s test) were not conducted due to insufficient sample size and lack of standardized variance measures. Overall, while the funnel plot provides suggestive evidence of potential bias, it does not allow definitive conclusions. Instead, it highlights the need for more standardized and comprehensive reporting of antimicrobial data in future studies.

Figure 6:  Funnel plot assessing potential publication bias in studies reporting MIC values of teixobactin-class antibiotics. This funnel plot illustrates the distribution of MIC values against study size or precision. Symmetry of the plot was visually assessed to evaluate potential publication bias. Formal statistical testing (e.g., Egger’s regression) was not performed due to limited sample size and heterogeneity across studies (Egger et al., 1997).

5. Discussion

The present systematic review and quantitative comparative synthesis sought to integrate antimicrobial efficacy, mechanistic binding, and resistance dynamics of iChip-derived compounds within a unified analytical framework. While the results confirm the remarkable promise of teixobactin-class antibiotics, they also reveal a more nuanced and, in some respects, less deterministic landscape than early reports might have suggested.

5.1 Revisiting the Potency Paradigm: Strength with Constraints

The antimicrobial efficacy data (Table 2; Figure 2) reinforce what is now a well-established observation: teixobactin exhibits consistently high potency against Gram-positive pathogens, with MIC values frequently in the sub-microgram range. This level of activity—particularly against MRSA, VRE, and Mycobacterium tuberculosis—places teixobactin among the most potent antibiotic scaffolds identified in recent decades (Ling et al., 2015; Qi et al., 2022). However, what emerges more clearly from the comparative synthesis is the fragility of this potency when translated into synthetic analogues. As shown in Table 2 and further visualized in Figure 2, even relatively conservative modifications—such as substitution of L-allo-enduracididine—often result in measurable reductions in antimicrobial activity. This observation aligns with prior structure–activity relationship analyses, suggesting that the precise stereochemical and electrostatic configuration of teixobactin is critical for optimal interaction with lipid targets (Velkov & Karas, 2020). At the same time, it would be overly reductive to conclude that all analogues are inferior. Certain derivatives, particularly those designed to improve synthetic accessibility, retain clinically relevant activity. This introduces an important trade-off: synthetic tractability versus maximal potency, a theme that is likely to persist as teixobactin moves closer to translational development.

5.2 Mechanism–Efficacy Disconnect: Binding Is Necessary but Not Sufficient

One of the more intriguing findings of this study lies in the relationship between binding affinity and antimicrobial efficacy (Table 6; Figure 3). The expectation—at least from a classical pharmacological perspective—would be that stronger binding to lipid II correlates directly with lower MIC values. Yet, the data suggest that this relationship is far from linear. For instance, certain analogues demonstrate improved or comparable binding affinity to lipid II (particularly Gram-negative variants) without a corresponding improvement in antimicrobial potency. This apparent disconnect, underscores the multi-layered nature of antibiotic efficacy, where factors such as membrane permeability, compound aggregation, and local microenvironment play equally critical roles (Chiorean et al., 2020; Shukla et al., 2022).

In this context, the mechanism of teixobactin appears to extend beyond simple target binding. The formation of supramolecular β-sheet assemblies, as reported by Shukla et al. (2022), likely contributes to its bactericidal activity by disrupting membrane integrity. This cooperative mechanism may explain why minor structural perturbations disproportionately affect efficacy—altering not just binding, but the ability to form functional assemblies.

Table 6: Quantitative Dataset for Analysis of Antimicrobial Activity, Target Binding, and Pharmacodynamic Profiles of iChip-Derived Compounds and Their Analogues. This table compiles quantitative data for integrating antimicrobial activity, binding affinity, and pharmacodynamic parameters. It provides a structured dataset for comparative analysis of efficacy, mechanism, and resistance across compounds.

5.3 Resistance Reconsidered: From “Proof” to Probability

Early characterizations of teixobactin emphasized its apparent resistance-proof nature (Ling et al., 2015; Piddock, 2015). While the present analysis (Table 3; Figure 4) supports the notion of exceptionally low resistance frequency, it also highlights the need for a more cautious interpretation. The emergence of resistance under prolonged selective pressure (Lloyd et al., 2021) suggests that teixobactin does not eliminate evolutionary escape routes but rather raises the barrier to resistance development. More importantly, the distinction between resistance and tolerance becomes critical. As demonstrated in Enterococcus faecalis, regulatory systems such as CroRS can mediate adaptive responses that allow bacterial survival without classical resistance mutations (Darnell et al., 2019; Todd Rose et al., 2023). This shift—from resistance as a binary outcome to a spectrum of adaptive responses—has important implications for clinical use. It suggests that long-term efficacy may depend not only on the absence of resistance mutations but also on the management of tolerance mechanisms, which can serve as precursors to more stable resistance phenotypes.

5.4 Translational Relevance: Encouraging but Incomplete

The in vivo data (Table 4) provide a compelling, albeit limited, indication that the in vitro potency of teixobactin translates into meaningful therapeutic outcomes. The observed PD₅₀ values and survival benefits in animal models position teixobactin as a strong candidate for further development (Ling et al., 2015; Lawrence et al., 2025). Yet, the relatively small number of in vivo studies—also reflected in the evidence matrix (Figure 4)—introduces a degree of uncertainty. Translational success in antibiotic development often hinges on factors not fully captured in early-stage models, including pharmacokinetics, toxicity, and host–pathogen interactions. As such, while the available data are encouraging, they remain insufficient to fully predict clinical performance.

5.5 The Gram-Negative Barrier: A Structural Limitation, Not a Failure

Perhaps the most consistent limitation observed across the dataset is the restricted activity of teixobactin against Gram-negative bacteria (Table 2; Figure 2). This limitation, however, should not be interpreted as a failure of the compound itself but rather as a reflection of fundamental structural barriers, namely the outer membrane. The synergy data (Table 5) offer a partial solution. The dramatic reductions in MIC values observed with membrane-disrupting agents suggest that teixobactin retains intrinsic activity against Gram-negative targets, provided that it can access them (Chiorean et al., 2020; Ng et al., 2018). This raises an important conceptual point: the limitation is not target absence but target accessibility. Such findings align with broader trends in antibiotic development, where combination therapies and permeability enhancers are increasingly explored as strategies to extend the spectrum of existing drugs (Matos de Opitz & Sass, 2020). However, the clinical feasibility of these approaches—particularly in terms of toxicity—remains an open question.

5.6 Evidence Heterogeneity and Analytical Implications

The forest plot (Figure 5) and funnel plot (Figure 6) provide additional insight into the structure of the available evidence. The clustering of MIC values for natural teixobactin, contrasted with the broader dispersion observed for analogues, reinforces the consistency of the parent compound and the variability introduced by structural modification. At the same time, the partial asymmetry observed in the funnel plot suggests potential publication bias or selective reporting, although this cannot be conclusively established given the limited dataset (Egger et al., 1997). More broadly, these figures highlight the challenges of applying classical meta-analytic frameworks to heterogeneous datasets—a point emphasized in methodological literature (Borenstein et al., 2009; Higgins et al., 2003). The decision to adopt a quantitative comparative synthesis rather than a full meta-analysis is therefore not merely pragmatic but methodologically justified. In emerging fields such as iChip-derived antibiotic discovery, where data are diverse and often incomplete, flexibility in analytical approach is essential (Higgins et al., 2022).

5.7 Toward a New Antibiotic Discovery Paradigm

Taken together, the findings of this study point toward a broader conceptual shift in antibiotic discovery. The success of teixobactin and related compounds suggests that targeting conserved, non-protein structures—combined with ecological approaches such as iChip-enabled cultivation—may represent a viable path forward in overcoming antimicrobial resistance. However, the results also underscore the complexity of translating this paradigm into clinically viable therapies. Issues of synthetic accessibility, spectrum limitations, resistance dynamics, and evidence heterogeneity all remain unresolved. In this sense, teixobactin is not a definitive solution but rather a proof of concept—one that opens new avenues while simultaneously revealing new challenges. Ultimately, the future of antibiotic discovery may depend less on singular breakthroughs and more on the integration of multiple strategies: ecological exploration, rational design, combination therapy, and computational modeling. The iChip, in this context, is not merely a tool but a reminder that innovation often begins with a shift in perspective—one that redefines what is considered accessible, and, perhaps more importantly, what is considered possible.

6. Limitations

This study, while comprehensive in scope, is inevitably shaped by several methodological and evidentiary constraints. First, the relatively small number of included studies (n = 16) reflects the emerging nature of the field and limits the statistical power of comparative conclusions. Second, substantial heterogeneity exists across experimental designs, including differences in microbial strains, assay conditions, and reported endpoints, which complicates direct cross-study comparisons. The absence of standardized variance measures (e.g., standard deviations) further restricted the application of formal meta-analytic models, necessitating a descriptive and comparative synthesis approach.

Additionally, there is an imbalance in available data: antimicrobial potency (MIC) is well represented, whereas in vivo efficacy, resistance evolution, and mechanistic binding datasets remain comparatively sparse. Potential publication bias cannot be excluded, as suggested by funnel plot asymmetry, though this remains inconclusive. Collectively, these limitations warrant cautious interpretation and highlight the need for more standardized, reproducible, and longitudinal studies.

7. Conclusion

In reflecting on the available evidence, it becomes clear that iChip-derived antibiotics—particularly teixobactin—represent a meaningful shift in antimicrobial discovery, yet not without complications. Their potency against Gram-positive pathogens and low resistance frequency are undeniably promising. Still, limitations related to spectrum, structural complexity, and emerging tolerance mechanisms temper early optimism. What this study ultimately suggests is not the arrival of a “perfect antibiotic,” but rather the beginning of a new framework—one that integrates ecological discovery, molecular precision, and adaptive resistance understanding. Future progress will likely depend on how effectively these elements are combined, rather than pursued in isolation.

Author Contributions

M.Z., F.U.N., B.K., and M.S.H.S. conceptualized and designed the study. M.Z. conducted the systematic literature search, study selection, data extraction, and primary manuscript drafting. F.U.N. contributed to data analysis, interpretation of antimicrobial efficacy and resistance findings, and manuscript revision. B.K. assisted with methodological assessment, target-binding analysis, and critical review of the manuscript. M.S.H.S. contributed to data validation, interpretation of microbiological evidence, and overall supervision of the study. All authors reviewed and approved the final manuscript and agreed to be accountable for all aspects of the work.

Acknowledgements

The authors sincerely acknowledge their respective institutions for providing academic support and access to scientific resources during the preparation of this review. Special appreciation is extended to researchers worldwide whose work on iChip technology, teixobactin, antimicrobial resistance, and antibiotic discovery formed the foundation of this synthesis. The authors also thank the editorial team and reviewers for their valuable comments and suggestions that helped improve the quality of the manuscript.

References