The empirical foundation for this view was laid by large-scale laboratory screens, most notably a 2018 study in which the team around Lisa Maier and Kiran Patil tested roughly 1,000 marketed drugs against representative strains of human gut bacteria and found that about a quarter inhibited at least one commensal microbe. Subsequent work extended the map beyond pharmaceuticals. Recent screens have shown that industrial and agricultural chemicals also suppress gut bacteria in vitro, and that some per- and polyfluoroalkyl substances, the persistent so-called forever chemicals, are actively bioaccumulated by human gut microbes. Even dietary xenobiotics, transformed by resident bacteria, can restructure microbial communities. The boundary between the pharmacy, the food supply and the chemical environment, the review makes clear, is biologically porous.

What distinguishes non-antibiotic antimicrobials from true antibiotics is not the fact of antibacterial activity but its spectrum and potency. Unlike antibiotics, which are typically active at low concentrations and target a broad range of organisms, non-antibiotic antimicrobials tend to act at higher doses and against a narrower set of microbes. Paradoxically, that narrow set often excludes the Enterobacteriaceae, the family containing many classic pathogens for which new antibiotics are urgently needed, while hitting beneficial commensals harder. This inverted selectivity means that everyday exposure to these compounds can quietly erode the protective, health-associated fraction of the microbiome while leaving opportunistic pathogens relatively unscathed, creating ecological vacancies that resistant or disease-associated organisms can exploit.

The molecular mechanisms underlying these effects are only beginning to be resolved, but several recurring strategies have emerged. Some non-antibiotics share drug targets with classical antibiotics or, more intriguingly, bind bacterial counterparts of the human proteins they were designed to hit. Others mimic essential metabolites, poisoning microbial enzymes through molecular masquerade, or hijack bacterial transport systems to gain entry into cells. Individual examples are illuminating: the artificial sweetener saccharin has been shown to disrupt bacterial cell envelope stability and interfere with DNA replication dynamics; nonsteroidal anti-inflammatory drugs target DNA replication; the antidiabetic drug acarbose impairs gut Bacteroides growth by inhibiting intracellular glucosidases; and the Parkinson’s drug entacapone disrupts gut microbial homeostasis through iron sequestration. Yet for the majority of non-antibiotic antimicrobials, the microbial targets remain unknown, a knowledge gap the authors identify as a central obstacle to rational risk assessment.

Crucially, the antimicrobial activity of these compounds cannot be understood in isolation from the host. Microbiome composition varies enormously between individuals, and a drug that devastates one person’s community may barely perturb another’s. Host conditions further modulate outcomes: intestinal pH, bile salt exposure, nutrient availability, body temperature and the community’s biogeography all alter both drug activity and microbial susceptibility. Proton-pump inhibitors, for example, appear to increase the risk of Clostridioides difficile infection primarily by altering gut pH rather than through direct effects on the microbiome, a reminder that host-conditioned mechanisms can masquerade as microbiome effects. Conversely, some drugs act on the host in ways that reshape the gut environment, as with corticosteroids that impair mucin production, or statins whose microbiome-linked effects on metabolic health are still being mapped.

The clinical consequences of these interactions are now measurable in populations. Population-level metagenomic studies have shown that medications leave distinct, reproducible signatures on the gut microbiome, and that drugs taken years before sampling can still act as hidden confounders in microbiome research. One 2025 study reported that non-antibiotics disrupt colonization resistance against enteropathogens, and a companion analysis identified medication-microbiome interactions that affect gut infection outcomes. At the same time, the microbiome can alter drug efficacy in both directions: gut bacterial tyrosine decarboxylases deplete levodopa in Parkinson’s disease, microbial metabolism of the anti-inflammatory mesalazine diminishes its benefit in inflammatory bowel disease, and microbiome-derived metabolites such as inosine and 3-indole-3-acetic acid modulate responses to cancer immunotherapy and chemotherapy. Drug, microbe and host form a three-body problem, the review argues, that medicine has traditionally treated as a two-body one.

Perhaps the most alarming dimension is the contribution of non-antibiotic antimicrobials to the spread of antibiotic resistance. Laboratory and animal studies have shown that antidepressants, antipsychotics, antiepileptic drugs and artificial sweeteners can all promote the horizontal transfer of antibiotic resistance genes, accelerate plasmid conjugation, induce mutagenesis or select for efflux-based resistance in Escherichia coli. Because these compounds are consumed chronically by billions of people and persist in the environment, they may exert a continuous, diffuse selection pressure that quietly amplifies the resistome in human guts and beyond, independent of any antibiotic prescription. The review frames this as a planetary-scale problem, noting that microbiomes across human, animal and environmental habitats are connected by gene flow, so resistance selected anywhere can eventually matter everywhere.

Dietary chemicals occupy a particularly contested middle ground in this landscape. Emulsifiers such as carboxymethylcellulose have been shown in controlled-feeding studies to damage the human gut microbiota and metabolome, while artificial sweeteners have been reported to induce glucose intolerance in mice and personalized, microbiome-dependent glucose responses in humans, though some trials of saccharin and sucralose in healthy adults have found no detectable microbiome effects at all. Pesticide exposure has been associated with altered gut microbiota and metabolites in observational studies of both the general population and occupationally exposed workers. This inconsistency, the authors suggest, reflects the context-dependence that pervades the entire field: identical exposures can yield different outcomes depending on a person’s baseline microbiome, diet, host physiology and co-exposures to other xenobiotics.

The translational implications are substantial. The authors call for integrated stewardship of all xenobiotics, not just antibiotics, across medicine, agriculture, industry and the environment. Practically, that means incorporating microbiome effects into drug development and safety testing, using new high-throughput anaerobic screening methods and defined microbial community models to identify antimicrobial activity before compounds reach patients, deploying chemical-genetic and proteomic tools to pin down microbial targets, and developing computational models of xenobiotic metabolism that can predict how individual microbiomes will respond to given drugs. For clinicians, the immediate lesson is more modest but no less important: non-antibiotic prescriptions are microbiome interventions, and polypharmacy should be recognized as a combinatorial, dose-dependent perturbation of the gut ecosystem.

What the review ultimately delivers is a reframing. Antimicrobial activity, once considered the defining property of a specialized class of drugs, is revealed to be a generic hazard of the chemical world that modern humans inhabit, arising because human and bacterial biochemistry share enough common machinery that molecules designed for one can wound the other. The benefits of these compounds, in treating psychiatric illness, diabetes, cancer and countless other conditions, are real and immense, and the authors are careful not to argue that non-antibiotic antimicrobials should be abandoned. The task, they conclude, is to move from unwitting to deliberate management: to know which compounds disturb the microbiome, through which mechanisms, in whom, and with what downstream consequences for infection risk, chronic disease and resistance, so that the hidden chemistry between our drugs, our food, our environment and our microbes can finally be conducted with open eyes.

Subject of Research: Antimicrobial effects of non-antibiotic compounds on the human gut microbiome and host health

Article Title: Drug–microbiome–host interactions: antimicrobial effects of non-antibiotic compounds

Article References: de la Cuesta-Zuluaga, J., Patil, K. R., & Maier, L. (2026). Drug–microbiome–host interactions: antimicrobial effects of non-antibiotic compounds. Nature Reviews Gastroenterology & Hepatology. https://doi.org/10.1038/s41575-026-01258-w

Image Credits: AI Generated

DOI: 10.1038/s41575-026-01258-w

Keywords: gut microbiome, non-antibiotic drugs, antimicrobial resistance, microbiome–drug interactions, artificial sweeteners, pesticides, colonization resistance, pharmacomicrobiomics, xenobiotics, dysbiosis, horizontal gene transfer, drug metabolism

Traditional experimental methods for assessing CYP450-mediated metabolism, though invaluable, are often constrained by high costs, extensive timelines, and limited scalability. These limitations hamper early-stage drug screening where rapid and accurate predictions are paramount. In response, computational approaches have evolved, moving from simplistic rule-based algorithms to sophisticated machine learning paradigms. Among these, graph-based models—particularly Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Graph Attention Networks (GATs)—have emerged as powerful instruments. By representing molecules and their interactions as graphs, these networks can harness structural and electronic nuances inherent in chemical and protein architectures, capturing the multifaceted enzyme-ligand interplay essential for metabolic prediction.

Focusing on five pivotal CYP isoforms—CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4—current research exploits graph-based techniques to disentangle their distinct metabolic roles and substrate specificities. These isoforms account for the majority of xenobiotic metabolism, rendering their accurate modeling critical. Graph-based deep learning frameworks analyze molecular graphs to predict not only binding affinities but also the metabolic rates and potential toxicities with enhanced granularity. This method surpasses traditional descriptor-based models by directly encoding atom-level connectivity and bond relationships, leading to more robust and generalizable ADMET predictions.

Incorporating multi-task learning represents a significant leap in model sophistication, allowing simultaneous prediction of various pharmacokinetic parameters across multiple CYP450 isoforms. This approach trains a single model to understand shared and isoform-specific features concurrently, thereby improving predictive power and reducing overfitting risks. Additionally, attention mechanisms embedded within GATs have dramatically enhanced interpretability by selectively focusing on crucial molecular substructures influencing enzyme interactions. Such insights shine a light on biochemical determinants driving metabolism, aiding medicinal chemists in rational drug design and optimization.

Parallel to these advancements, the integration of explainable AI (XAI) techniques addresses a critical bottleneck in deploying machine learning models in pharmacology: transparency. By elucidating model decision pathways, XAI bridges the gap between computational predictions and experimental validation, fostering trust and facilitating hypothesis generation. Researchers can now pinpoint which molecular features most significantly impact CYP450 metabolism, enabling targeted modifications to ameliorate adverse effects or enhance bioavailability.

However, despite these breakthroughs, several challenges persist. Dataset variability, stemming from heterogeneous experimental conditions and limited high-quality metabolic data, poses considerable hurdles to model generalization. Furthermore, extrapolating predictions to novel chemical spaces remains an open problem, as models often struggle with out-of-distribution compounds that defy learned patterns. Addressing these issues demands concerted efforts to curate expansive, standardized datasets and advance transfer learning methodologies capable of adapting to emerging chemical entities.

Scalability also represents a frontier for future research. While current graph-based models deliver impressive accuracy, their computational demands can impede application in high-throughput screening pipelines. Optimizing algorithmic efficiency, leveraging advanced hardware acceleration, and developing lightweight model variants will be essential to translate these tools into routine pharmaceutical workflows. Moreover, real-time experimental validation, integrated with in silico predictions, could establish feedback loops to continuously refine model fidelity and accelerate drug candidate evaluation.

Another promising trajectory lies in deepening our understanding of enzyme-specific interactions at atomic resolutions. Beyond static representations, incorporating dynamic conformational changes and allosteric effects within graph architectures could unravel further layers of metabolic complexity. Such integration necessitates interdisciplinary collaboration, melding computational chemistry, structural biology, and machine learning to engineer comprehensive predictive frameworks.

The confluence of these technological and scientific advances signals a transformative era for ADMET prediction. Graph-based models, empowered by multi-task learning, attention mechanisms, and explainable AI, are redefining the landscape of drug metabolism studies. Their capacity to simulate complex biochemical interactions with aesthetic precision offers hope for reducing late-stage drug attrition, minimizing adverse drug reactions, and ushering in personalized medicine paradigms rooted in metabolic profiling.

In essence, the evolution from traditional assays to sophisticated graph neural architectures not only augments predictive accuracy but also democratizes access to metabolic insights across the pharmaceutical industry. As datasets expand and computational methods mature, such models promise to become indispensable tools that bridge the gap from molecular design to clinical success. This synergy of bioinformatics and enzymology heralds a future where drug development is faster, safer, and more ingenious.

As researchers continue to tackle existing limitations and harness emerging opportunities, the field marches toward a holistic understanding of drug metabolism. By embracing graph-based approaches, the scientific community is poised to unlock new frontiers in pharmacokinetics, ultimately enhancing therapeutic outcomes and safeguarding patient health on a global scale.

Subject of Research: Cytochrome P450 (CYP450) enzyme-mediated metabolism and ADMET prediction using graph-based computational models.

Article Title: Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions

Article References:
Abdelwahab, A.A., Elattar, M.A. & Fawzi, S.A. Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.
BioMed Eng OnLine 24, 93 (2025). https://doi.org/10.1186/s12938-025-01412-6

Image Credits: AI Generated

DOI: https://doi.org/10.1186/s12938-025-01412-6