Exposure-response modeling of liver fat imaging endpoints in non-alcoholic fatty liver disease populations administered ervogastat alone and co-administered with clesacostat

• Published in: CPT: pharmacometrics and systems pharmacology 2024-11
• Main Authors: Hughes, Jim H, Amin, Neeta B, Wojciechowski, Jessica, Vourvahis, Manoli
• Format: Article
• Language: English
• ISSN: 2163-8306; 2163-8306
• DOI: 10.1002/psp4.13275

Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis describe a collection of liver conditions characterized by the accumulation of liver fat. Despite biopsy being the reference standard for determining the severity of disease, non-invasive measures such as magnetic resonance imaging proton density fat fraction (MRI-PDFF) and FibroScan® controlled attenuation parameter (CAP™) can be used to understand longitudinal changes in steatosis. The aim of this work was to describe the exposure-response relationship of ervogastat with or without clesacostat on steatosis, through population pharmacokinetic/pharmacodynamic (PK/PD) modeling of both liver fat measurements simultaneously. Population pharmacokinetic and exposure-response models using individual predictions of average concentrations were used to describe ervogastat/clesacostat PKPD. Due to both liver fat endpoints being continuous-bounded outcomes on different scales, a dynamic transform-both-sides approach was used to link a common latent factor representing liver fat to each endpoint. Simultaneous modeling of both MRI-PDFF and CAP™ was successful with both measurements being adequately described by the model. The clinical trial simulation was able to adequately predict the results of a recent Phase 2 study, where subjects given ervogastat/clesacostat 300/10 mg BID for 6 weeks had a LS means and model-predicted median (95% confidence intervals) percent change from baseline MRI-PDFF of -45.8% and -45.6% (-61.6% to -31.8%), respectively. Simultaneous modeling of both MRI-PDFF and CAP™ was successful with both measurements being adequately described. By describing the underlying changes of steatosis with a latent variable, this model may be extended to describe biopsy results from future studies.