Serum bile acid change correlates with improvement in pruritus in patients with primary biliary cholangitis receiving linerixibat.

BACKGROUND & AIMS: Total serum bile acid (TSBA) levels are elevated in patients with primary biliary cholangitis (PBC) and may mediate cholestatic pruritus. Linerixibat, an ileal bile acid transporter inhibitor, improved pruritus in patients with PBC. We explored the relationship between linerixibat dose, TSBA concentration, and pruritus. METHODS: Data from Phase 1/2 trials were used to develop a population kinetic-pharmacodynamic model to characterize the linerixibat dose-TSBA relationship. Individual Bayesian parameter estimates for participants in the GLIMMER study were used to derive the area under the TSBA concentration curve over 24 h (AUC0-24). Time-matched post hoc estimates of AUC0-24 were correlated with pruritus reported on a 0-10 numerical rating scale. Baseline TSBA concentration was correlated with change from baseline (ΔBL) in monthly itch score (MIS). ΔBL in model-estimated TSBA AUC0-24 was correlated with time-matched ΔBL in weekly itch score (WIS) or MIS. RESULTS: Linerixibat dose dependently reduced TSBA AUC0-24, reaching steady state after 5 days. Baseline TSBA levels in GLIMMER did not correlate with ΔBL in MIS. ΔBL in TSBA AUC0-24 correlated with improved WIS over 12 weeks of treatment (r = 0.52, p < 0.0001). Of participants with a ≥30% decrease in TSBA AUC0-24, 60% were pruritus responders (≥2-point improvement in WIS from baseline). CONCLUSIONS: Linerixibat treatment leads to rapid, dose-dependent TSBA reductions. Baseline TSBA levels do not correlate with on-treatment pruritus change, suggesting they do not predict linerixibat response. Change in TSBA AUC0-24 correlates significantly with, and can be predictive of, pruritus improvement in patients with PBC.

Latent process model of the 6-minute walk test in Duchenne muscular dystrophy : A Bayesian approach to quantifying rare disease progression.

Duchenne muscular dystrophy (DMD) is a rare X-linked genetic pediatric disease characterized by a lack of functional dystrophin production in the body, resulting in muscle deterioration. Lower body muscle weakness progresses to non-ambulation typically by early teenage years, followed by upper body muscle deterioration and ultimately death by the late twenties. The objective of this study was to enhance the quantitative understanding of DMD disease progression through nonlinear mixed effects modeling of the population mean and variability of the 6-min walk test (6MWT) clinical endpoint. An indirect response model with a latent process was fit to digitized literature data using full Bayesian estimation. The modeling data set consisted of 22 healthy controls and 218 DMD patients from one interventional and four observational trials. The model reasonably described the central tendency and population variability of the 6MWT in healthy subjects and DMD patients. An exploratory categorical covariate analysis indicated that there was no apparent effect of corticosteroid administration on DMD disease progression. The population predicted 6MWT began to rise at 1.32 years of age, plateauing at 654 meters (m) at 17.2 years of age for the healthy population. The DMD trajectory reached a maximum of 411 m at 8.90 years before declining and falling below 1 m at age 18.0. The model has potential to be used as a Bayesian estimation and posterior simulation tool to make informed model-based drug development decisions that incorporate prior knowledge with new data.

Bayesian modeling and simulation to inform rare disease drug development early decision-making: Application to Duchenne muscular dystrophy.

Rare disease clinical trials are constrained to small sample sizes and may lack placebo-control, leading to challenges in drug development. This paper proposes a Bayesian model-based framework for early go/no-go decision making in rare disease drug development, using Duchenne muscular dystrophy (DMD) as an example. Early go/no-go decisions were based on projections of long-term functional outcomes from a Bayesian model-based analysis of short-term trial data informed by prior knowledge based on 6MWT natural history literature data in DMD patients. Frequentist hypothesis tests were also applied as a reference analysis method. A number of combinations of hypothetical trial designs, drug effects and cohort comparison methods were assessed. The proposed Bayesian model-based framework was superior to the frequentist method for making go/no-go decisions across all trial designs and cohort comparison methods in DMD. The average decision accuracy rates across all trial designs for the Bayesian and frequentist analysis methods were 45.8 and 8.98%, respectively. A decision accuracy rate of at least 50% was achieved for 42 and 7% of the trial designs under the Bayesian and frequentist analysis methods, respectively. The frequentist method was limited to the short-term trial data only, while the Bayesian methods were informed with both the short-term data and prior information. The specific results of the DMD case study were limited due to incomplete specification of individual-specific covariates in the natural history literature data and should be reevaluated using a full natural history dataset. These limitations aside, the framework presented provides a proof of concept for the utility of Bayesian model-based methods for decision making in rare disease trials.