Background: Liver fibrosis drives mortality in chronic liver disease, with effective and approved targeted therapies being an urgent unmet medical need. Natural polysaccharides are promising multitarget candidates, but a critical appraisal of the preclinical evidence for their translatability is lacking. Objective: This review systematically synthesizes the evidence on the efficacy, mechanisms, and methodological quality of preclinical studies investigating the antifibrotic potential of natural polysaccharides. Methods: Six databases were searched (inception to February 2025) for studies in experimental liver fibrosis models. The review followed PRISMA guidelines. Risk of bias and reporting quality were assessed using the SYRCLE (Systematic Review Centre for Laboratory Animal Experimentation) and ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines, respectively. Results: Eighty-eight studies on 44 polysaccharides were included. A major limitation was the predominant use of the carbon tetrachloride (CCl4) rat model (54.5%). Despite this, polysaccharides showed consistent efficacy: collagen deposition was suppressed in 92.0% of studies, and serum alanine/aspartate aminotransferase (ALT/AST) were reduced in 100%. Mechanistically, inhibition of the transforming growth factor-beta (TGF-β)/Smad pathway (implicated in 60.2% of studies) and modulation of the toll-like receptor 4 (TLR4)/nuclear factor kappa B (NF-κB) pathway (15.9%) were the most common findings. However, methodological quality was low, with unclear allocation concealment (92.0%) and absent blinding (86.4%) being pervasive issues. Conclusions: This review confirms that natural polysaccharides consistently attenuate experimental fibrosis by modulating key pathways like TGF-β/Smad. Our key contribution is highlighting a critical disconnect: demonstrated efficacy is undermined by poor methodological rigor and the use of simplistic models. This gap represents a major barrier to clinical translation. Advancing these promising agents requires prioritizing chemical standardization, employing more relevant disease models, and adhering to rigorous reporting standards.
Hu et al. (2026) studied this question.