Review proposes a microbiome-aware oral formulation framework: excipient risk classification, nanocarrier design rules, and a tiered testing roadmap

The review reframes the gut microbiome as a formulation-relevant variable in oral drug delivery rather than only a host biology topic, and presents a bidirectional formulation–microbiome interaction map. Prior drug–microbiome studies often emphasized metabolism and response variability, excipient studies often focused on microbiota disruption or additive safety, nanocarrier studies often prioritized delivery performance, and colon-targeting studies often described microbial triggers; this review integrates these domains into a formulation-centered framework. It is a narrative review and authors' perspective synthesis drawing on cited mechanistic studies, personalized ex vivo human microbiome work, animal models, and in vitro models; the text states the four outputs are the authors' proposed, evidence-informed tools rather than established regulatory classifications or mandatory development standards.

It proposes an excipient–microbiome risk classification with six categories: microbiome-compatible, microbiome-active, microbiota-degradable, microbiome-disruptive or barrier-active, microbiome-supportive, and microbiome-rescued excipients. Category assignment rests on five criteria: directness of evidence, relevance of tested exposure to pharmaceutical use, consistency across studies or donors, demonstration of a microbial or barrier consequence, and the importance of that consequence for formulation performance or patient safety; classification is stated to be exposure and context dependent. Evidence quality is uneven: carboxymethylcellulose altered microbiota composition and fecal metabolomic profiles in healthy adults receiving 15 g/day for 11 days as a dietary emulsifier, which is human dietary controlled-exposure evidence; PEG400 evidence in mice suggests altered microbiota and metabolome; polysaccharide evidence is largely in vitro enzyme or fecal models and animal studies; Eudragit-type pH-dependent polymers lack established direct microbiome effects.

It proposes nanocarrier–microbiome interface design rules organized around properties rather than carrier names, treating size, surface charge, surface chemistry and coating, biodegradability, mucoadhesion, mucus penetration, enzyme responsiveness, and surfactant load as microbiome-interface variables. Conventional nanocarrier development evaluates particle size, polydispersity index, zeta potential, encapsulation efficiency, release kinetics, stability in simulated gastrointestinal media, permeability, and pharmacokinetic outcomes; the review does not replace these but adds a microbiome interface and requires properties to be matched to the intended delivery goal rather than optimized in a single direction. A systematic review of nanomaterial effects on gut microbiota reported that outcomes depend on size, dose, exposure duration, and functional groups; daily oral administration of representative Type I, Type II, and Type III lipid self-emulsifying formulations to rats for 21 days altered gut microbiota composition and diversity, with dysbiosis signatures correlating with jejunal pro-inflammatory cytokine expression and reduced plasma citrulline, a repeated-dose preclinical warning signal.

It proposes decision logic for microbiota-responsive oral delivery and a tiered testing roadmap that uses fermentation models, SHIME-like dynamic systems, gut-on-chip, organoids, and gnotobiotic or humanized microbiota animal models according to the specific formulation question. It stresses that a microbial trigger is only useful if sufficiently reliable, and notes that bacterial enzymes used for colon-specific delivery have been reported to decrease in active Crohn's disease, so microbiota-only release may become delayed or incomplete and combined pH-, time-, and microbiota-responsive designs may be more robust. Pectin-ethylcellulose-coated theophylline pellets showed microbially enhanced release in enzyme- and bacteria-containing in vitro models but the effect was not reproduced in healthy or microbiota-depleted pigs, with the discordance associated with low pectinase activity in porcine fecal samples, indicating that a mechanistic in vitro signal does not establish predictive in vivo performance.

The framework is intended for oral formulation research and development settings, especially drugs known or suspected to undergo microbial metabolism, high or repeated luminal excipient exposure, surfactant-rich systems, local intestinal nanocarriers, colon-targeted and microbiota-responsive release, and dysbiotic or vulnerable patient populations; the authors state the four outputs are proposed, evidence-informed tools rather than established regulatory classifications or mandatory development standards, and recommend defining candidate microbiome-related critical quality attributes and critical material attributes only when a plausible microbiome-facing mechanism exists.

The evidence base is uneven, and exposures, carrier compositions, treatment durations, and microbiome endpoints differ substantially across studies, limiting quantitative comparison and preventing direct extrapolation to routine pharmaceutical dosing; microbial composition and functional capacity vary with disease, diet, age, geography, intestinal transit, and antibiotic exposure, so no single donor or pooled inoculum can establish population-wide trigger reliability; fermentation models lack absorption and host feedback, epithelial and organoid models usually simplify microbial ecology, and animal models differ from humans in physiology, diet, transit, and microbiome function; the text notes AI-assisted formulation design and microbiome profiling remain constrained by heterogeneous datasets, high dimensionality, and limited external and prospective validation, and should be treated as future tools for risk ranking and hypothesis generation.