Introduction

Primary biliary cholangitis (PBC) is a rare, chronic, autoimmune cholestatic liver disease. Cholestatic pruritus (itch) affects up to 80% of patients with PBC. Pruritus can be debilitating and significantly impairs quality of life (QoL). Severe pruritus can lead to QoL similar to patients with advanced Parkinson’s disease.¹ Bile acids (BA) are an important pathophysiological mediator of cholestatic pruritus; patients with PBC have higher systemic levels of BA. The ileal bile acid transporter (IBAT) reabsorbs most BA excreted into the small intestine and is thus an attractive target for pharmacological modulation to improve pruritus in PBC. Linerixibat, a selective small-molecule inhibitor of IBAT, reduced circulating BA levels and improved itch in patients with PBC.²

Aims & Methods

Here, we analyse the relationship between linerixibat dose and change in total serum BA (TSBA) over time and explore the correlation between change in TSBA and change in itch. Data from Phase 1/2 studies of healthy volunteers or patients with PBC were used to develop a population dose-pharmacodynamic (k-PD) model to characterise the linerixibat dose-TSBA relationship. Simulations were performed to explore the effect of linerixibat dose and regimen on daily TSBA profiles. Modelling was used to derive the area under the TSBA concentration-curve (AUC_0-24) for participants in GLIMMER, a Phase 2b study of linerixibat in patients with PBC and moderate to severe pruritus (NCT02966834).² These post hoc estimates of AUC_0-24 were correlated with change in weekly itch score (previously referred to as mean worst daily itch) and monthly itch score (MIS).² Itch was reported on a 0–10 numerical rating scale (NRS). In GLIMMER, 4 weeks single-blind placebo (baseline = Week 4) was followed by a randomised, double-blind 12-week treatment period with linerixibat or placebo (to Week 16), and a further 4 weeks single-blind placebo (to Week 20).² Itch responders were defined as having a ≥2 point improvement in itch score from baseline.

Results

The final population k-PD model successfully described the effect of linerixibat on TSBA in PBC. Linerixibat treatment resulted in a rapid dose-dependent decrease in TSBA AUC_0-24; the reduction in TSBA AUC_0-24 was apparent after a single dose. Baseline TSBA concentrations did not correlate with change from baseline in MIS at Week 16 (r=−0.13, p=0.14). At Week 16, there was a moderate correlation between change in TSBA AUC_0-24 and change in MIS from baseline (r=0.27, p=0.002), which dissipated during the placebo washout period (Week 20; r=0.011, p=0.91). Change in TSBA AUC_0-24 significantly correlated with improvement in weekly itch score from baseline over the 12-week treatment period (r=0.52, p<0.0001). A ≥30% decrease in TSBA AUC_0-24 was associated with 64% of participants having an itch response (≥2 point improvement in MIS at Week 16).

Conclusion

Linerixibat treatment leads to rapid and dose-dependent reductions in TSBA, a mediator with a causative role in cholestatic pruritus. Baseline TSBA levels do not correlate with on-treatment change in NRS itch score, suggesting they do not predict linerixibat response. Change in TSBA over the double-blind treatment period correlates significantly with, and can be predictive of, improvement in itch in patients with PBC. The ongoing Phase 3 GLISTEN study (NCT04950127) will further evaluate the efficacy and safety of linerixibat, facilitating further validation of the k-PD model.
Funding: GSK (BAT114985, BSC116511, 205808, BAT117213, 201000). A modified version of this abstract was presented at EASL 2023, held June 21–24.

References

1. Smith HT, et al. J Hepatol 2022;77(S1):S327.
2. Levy C, et al. Clin Gastroenterol Hepatol 2022;S1542-3565(22)01021-7.

Disclosure

FC, SM, LC, JF, PJFT, MMM and BS are employees of, and hold shares in GSK. EK has received grant/research support from GSK and holds stocks in Janssen and Johnson & Johnson.

Introduction

Chronic liver disease is a major global health concerns, contributing to over 1.4 million annual deaths worldwide¹. Its primary fatal outcomes, hepatic complications^2,3, arise from liver cirrhosis, hepatocellular carcinoma and hepatic decompensation. The early, reversible stages of chronic liver disease are frequently asymptomatic and overlooked, leading to delayed diagnoses and irreversible complications that treatment options are limited.^4,5. Therefore, reliable tools to identify whether a person will develop hepatic complications in the future have become a public health priority, enabling early diagnosis and timely intervention in high-risk populations.

Aims & Methods

This study aimed to employ machine learning to develop proteomics-based models for the early identification of hepatic complications. In this study, 37,178 adults without hepatic complications at baseline were analyzed from a prospective cohort from United Kingdom, with plasma levels of 2,736 proteins measured using Olink technology. Cox regression was used to identify significant proteins associated with incident hepatic complications. Models based on importance-top-ranking proteins were constructed using LightGBM learners of Automated Machine Learning (AutoML) and internal validated through 5-fold cross validation in the derivation set (n = 37,178). Independent validation was then conducted in a geographically distinct UKB cohort (n = 15,767).

Results

Over a median follow-up of 13.8 years, 338 participants developed hepatic complications. Cox regression identified 593 proteins significantly associated with incident hepatic complications. Among them, GDF15, PROC, GGT1, ANXA10, IL18, MFAP4, COL4A1, CDCP1 and IL6 were ranked highest in protein importance ordering. The protein model showed a considerable predictive accuracy for hepatic complications under different time scenarios in the UKB validation set: all-year (area under the curve [AUC] = 0.89), within 5 years (AUC = 0.88), within 10 years (AUC = 0.91), over 10 years (AUC = 0.81), and achieved superior predictive performance compared to models with demographic predictors, laboratory indicators, polygenic risk score (PRS) and traditional risk score models (Protein 0.89 vs. Demographics 0.83, Laboratory 0.85, PRS 0.49; Fibrosis-4 index [FIB-4] 0.76, Aspartate aminotransferase to platelet ratio index [APRI] 0.78; all P < 0.05). Combined with clinical data and PRS, the predictor performance is further enhanced: all-year (AUC = 0.91), within 5 years (AUC = 0.91), within 10 years (AUC = 0.90), over 10 years (AUC = 0.83). Individuals in the high-risk group stratified by the protein model were 16.27 times more likely to develop hepatic complications.

Conclusion

Our study constructed a novel protein model for hepatic complications risk stratification and could be used to predict individuals who will develop hepatic complications up to 16 years in advance in the general population.

References

1. Tham EKJ, Tan DJH, Danpanichkul P, et al. The Global Burden of Cirrhosis and Other Chronic Liver Diseases in 2021. Liver Int 2025;45:e70001.
2. Åberg F, Asteljoki J, Männistö V, et al. Combined use of the CLivD score and FIB-4 for prediction of liver-related outcomes in the population. Hepatol Baltim Md 2024;80:163–172.
3. Innes H, Morling JR, Buch S, et al. Performance of routine risk scores for predicting cirrhosis-related morbidity in the community. J Hepatol 2022;77:365–376.
4. Karlsen TH, Sheron N, Zelber-Sagi S, et al. The EASL–Lancet Liver Commission: protecting the next generation of Europeans against liver disease complications and premature mortality. The Lancet 2022;399:61–116.
5. Serra-Burriel M, Juanola A, Serra-Burriel F, et al. Development, validation, and prognostic evaluation of a risk score for long-term liver-related outcomes in the general population: a multicohort study. Lancet Lond Engl 2023;402:988–996.