Quantitative Approaches to Accelerate MASH Drug Discovery and Development

Metabolic dysfunction-associated steatohepatitis (MASH) is a widespread liver condition that leads to cirrhosis, hepatic carcinoma, and increased mortality rates. In MASH, the diagnosis and accelerated approval of drugs are dependent on changes in histological endpoints; full approval requires lengthy outcomes trials. This requirement for biopsies complicates and constrains clinical development. While the use of noninvasive biomarkers to aid in diagnosis and prognosis is prevalent in MASH trials, interpretation of these data is not straightforward. Due to the complexity of the disease and heterogeneity of the patient population, a single biomarker may not fully capture the changes in the pathobiology with disease progression or with pharmacological interventions. No biomarker or collection of biomarkers can be used in lieu of histological evaluations for accelerated approval. These challenges may be mitigated in part through the application of quantitative approaches and model informed drug discovery and development (MID3). In this review, we demonstrate how MID3 approaches can be applied alone or together to facilitate decision making. For example, quantitative systems pharmacology (QSP) modeling can predict the physiological effects of MASH drugs and identify opportunities for combination therapy. Model-based meta-analysis (MBMA) can benchmark molecules in early development and aid in biomarker interpretation by establishing relationships between biomarkers and histological endpoints. Artificial intelligence and machine learning (AI/ML) methods can aid in the identification of participants that meet histological criteria and reduce screen failure rates. Together, these quantitative approaches can be used strategically to accelerate MASH drug development.