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Quantitative Approaches to Accelerate MASH Drug Discovery and Development.

Yasmeen Abouelhassan1, Shailendra Tallapaka1, Ramin Mehrani1

  • 1Department of Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Rahway, New Jersey, USA.

Clinical Pharmacology and Therapeutics
|October 25, 2025
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Summary

Model-informed drug discovery accelerates the development of treatments for metabolic dysfunction-associated steatohepatitis (MASH). Quantitative approaches like QSP, MBMA, and AI/ML can improve clinical trial efficiency and biomarker interpretation for MASH therapies.

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Area of Science:

  • Hepatology
  • Pharmacometrics
  • Computational Biology

Background:

  • Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive liver disease associated with significant morbidity and mortality.
  • Current MASH drug development relies heavily on invasive histological endpoints for diagnosis and regulatory approval, posing challenges for clinical trials.
  • Noninvasive biomarkers show promise but face interpretation complexities due to disease heterogeneity.

Purpose of the Study:

  • To review the application of model-informed drug discovery and development (MID3) approaches for accelerating MASH therapies.
  • To demonstrate how quantitative methods can aid decision-making in MASH clinical development.
  • To highlight the strategic use of MID3 to overcome current drug development hurdles.

Main Methods:

  • Quantitative Systems Pharmacology (QSP) modeling to predict drug effects and identify combination therapies.
  • Model-Based Meta-Analysis (MBMA) for benchmarking drug candidates and interpreting biomarker-histology relationships.
  • Artificial Intelligence and Machine Learning (AI/ML) for participant identification and reducing screen failures.

Main Results:

  • MID3 approaches, including QSP, MBMA, and AI/ML, can be applied individually or in combination.
  • These quantitative methods offer strategies to facilitate decision-making throughout the MASH drug development process.
  • The integration of these tools can streamline clinical trials and improve the interpretation of complex biomarker data.

Conclusions:

  • Quantitative approaches within MID3 provide powerful tools to accelerate the development of MASH treatments.
  • Strategic application of QSP, MBMA, and AI/ML can mitigate challenges associated with histological endpoints and biomarker interpretation.
  • These integrated quantitative strategies are crucial for efficient and effective MASH drug discovery and development.