Combining Drug Targets Could Finally Crack the MASH Treatment Code
Single drugs only partially treat fatty liver disease. New combination therapy strategies may deliver deeper, more durable results.
Summary
Metabolic dysfunction-associated steatohepatitis (MASH) is a complex liver disease tied to obesity and insulin resistance. Single-drug therapies targeting one pathway often yield incomplete improvements in liver fibrosis and metabolic health. This review from a leading international team examines the rationale for combining agents such as GLP-1 receptor agonists, thyroid hormone receptor-beta agonists, ACC inhibitors, and FGF-21 analogues. These pairings target complementary disease mechanisms simultaneously, potentially achieving greater fibrosis regression, better metabolic outcomes, and reduced side effects compared to monotherapy. The authors provide a framework for clinicians to select and implement individualized combination regimens for patients living with MASH.
Detailed Summary
MASH is rapidly becoming one of the most prevalent liver diseases globally, driven by rising rates of obesity, type 2 diabetes, and metabolic syndrome. Unlike simpler conditions, MASH involves multiple interacting pathological pathways — lipotoxicity, inflammation, oxidative stress, and fibrogenesis — making single-target drugs inherently limited in scope.
This expert review, published in Gut and authored by a multinational team of hepatologists, synthesizes current knowledge on multitargeted combination strategies for MASH treatment. The authors assess drug classes including GLP-1 receptor agonists, thyroid hormone receptor-beta (THR-β) agonists, acetyl-CoA carboxylase (ACC) inhibitors, peroxisome proliferator-activated receptor (PPAR) agonists, fibroblast growth factor-21 (FGF-21) analogues, and diacylglycerol O-acyltransferase 2 (DGAT2) inhibitors.
Key findings suggest that liver-directed combinations — such as THR-β agonists paired with ACC inhibitors or PPAR agonists — can more effectively target histological features of MASH. Meanwhile, regimens combining systemic metabolic agents (e.g., GLP-1 receptor agonists with FGF-21 analogues or THR-β agonists) show promise for improving body weight, insulin sensitivity, and lipid profiles beyond what either drug achieves alone. Certain pairings, like DGAT2 inhibitors with ACC inhibitors, may also reduce treatment-related adverse events through complementary mechanisms.
The review emphasizes that thoughtful drug pairing is not merely additive but potentially synergistic, addressing different nodes in MASH pathogenesis simultaneously. The authors offer a practical framework for patient-centered implementation, accounting for comorbidities and tolerability.
Important caveats apply: this is a narrative review based largely on early-phase clinical data and mechanistic rationale. Long-term efficacy and safety data from large randomized trials for most combinations remain limited, and regulatory approvals are still evolving.
Key Findings
- Monotherapy for MASH yields only partial histological and metabolic improvements, motivating combination strategies.
- THR-β agonists combined with ACC inhibitors or PPAR agonists may improve liver fibrosis more effectively than either alone.
- GLP-1 receptor agonists paired with FGF-21 analogues or THR-β agonists show enhanced systemic metabolic benefits.
- DGAT2 inhibitor plus ACC inhibitor pairings may reduce adverse effects by leveraging complementary mechanisms.
- A patient-centered framework integrating comorbidity profiles is proposed for individualized combination therapy selection.
Methodology
This is a narrative expert review published in Gut, synthesizing mechanistic rationales and emerging clinical trial evidence for combination therapies in MASH. It does not present original clinical trial data but integrates findings from existing studies and trials. The authorship includes leading international hepatology researchers with extensive MASH expertise.
Study Limitations
The review is narrative rather than a systematic meta-analysis, introducing potential selection bias in evidence cited. Most combination therapy data derive from early-phase or mechanistic studies, lacking large-scale long-term randomized controlled trial confirmation. Several authors disclosed extensive industry relationships, which may influence interpretation of emerging evidence.
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