A major concern with this meta-analysis lies in its foundational assumption that hepatoprotective efficacy of silymarin can be reliably quantified across fundamentally heterogeneous models of liver injury. The review aggregates data from preclinical studies employing a wide array of hepatotoxins, including CCl4, paracetamol, ethanol, iron, and thioacetamide, each of which triggers liver damage via distinct molecular pathways (e.g., CYP2E1-mediated free radical formation, mitochondrial glutathione depletion, lipid peroxidation, or ferroptosis-like mechanisms). These mechanisms not only differ in the nature and location of oxidative damage but also in their pharmacodynamic responsiveness to antioxidants or anti-inflammatory agents.
Pooling effect sizes across such diverse models, without stratified subgroup analyses based on the type of injurious agent or the redox-pathway profile involved, raises a fundamental issue: it conflates model-specific effects with generalizable pharmacological efficacy. For a compound like silymarin, whose activity is highly dependent on oxidative and inflammatory signaling context, this lack of mechanistic resolution undermines the interpretability of the summary effect.
Further, the absence of dose–response modeling or evaluation of treatment timing (pre-treatment vs. post-injury administration) weakens the translational value of the conclusions. The field has long recognized that antioxidant efficacy is timing- and context-dependent, particularly in the case of silymarin, whose bioavailability and tissue distribution vary across models.
Given these concerns, how can the reported aggregate hepatoprotective effect be considered robust or clinically informative without controlling for these mechanistic and experimental variables? At minimum, subgroup or meta-regression analyses based on toxin class and treatment regimen would be necessary to avoid misleading conclusions regarding the universality of silymarin’s hepatoprotective properties.