Quantitative Approaches to Accelerate MASH Drug Discovery and Development

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.