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With the introduction of direct-acting antivirals (DAAs) for hepatitis C virus (HCV) infection, drug-drug interactions (DDIs) emerged as significant challenge. Since then, HCV therapy and the infected population have rapidly changed. So far, very limited data are available regarding the clinical relevance of DDIs when using most modern DAA regimens. We aimed to assess how the importance of DDIs has evolved over time.
Direct-acting antiviral agents (DAAs) for the treatment of hepatitis C (HCV) can be associated with drug-drug interactions (DDIs) with concomitant medications. The practical clinical implications of such DDIs are poorly understood. We assessed the clinical impact of possible pharmacokinetic (PK) interactions between simeprevir and frequently prescribed concomitant medications.
Treatment with nucleos(t)ide analogues (NAs) may be stopped after 1-3 years of hepatitis B virus DNA suppression in hepatitis B e antigen (HBeAg)-negative patients according to Asian Pacific Association for the Study of Liver and European Association for the Study of Liver guidelines. However, virological relapse (VR) occurs in most patients. We aimed to analyze soluble immune markers (SIMs) and use machine learning to identify SIM combinations as predictor for early VR after NA discontinuation. A validation cohort was used to verify the predictive power of the SIM combination. In a post hoc analysis of a prospective, multicenter therapeutic vaccination trial (ABX-203, NCT02249988), hepatitis B surface antigen, hepatitis B core antigen, and 47 SIMs were repeatedly determined before NA was stopped. Forty-three HBeAg-negative patients were included. To detect the highest predictive constellation of host and viral markers, a supervised machine learning approach was used. Data were validated in a different cohort of 49 patients treated with entecavir. VR (hepatitis B virus DNA ≥ 2,000 IU/mL) occurred in 27 patients. The predictive value for VR of single SIMs at the time of NA stop was best for interleukin (IL)-2, IL-17, and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5) with a maximum area under the curve of 0.65. Hepatitis B core antigen had a higher predictive power than hepatitis B surface antigen but lower than the SIMs. A supervised machine-learning algorithm allowed a remarkable improvement of early relapse prediction in patients treated with entecavir. The combination of IL-2, monokine induced by interferon γ (MIG)/chemokine (C-C motif) ligand 9 (CCL9), RANTES/CCL5, stem cell factor (SCF), and TNF-related apoptosis-inducing ligand (TRAIL) was reliable in predicting VR (0.89; 95% confidence interval: 0.5-1.0) and showed viable results in the validation cohort (0.63; 0.1-0.99). Host immune markers such as SIMs appear to be underestimated in guiding treatment cessation in HBeAg-negative patients. Machine learning can help find predictive SIM patterns that allow a precise identification of patients particularly suitable for NA cessation.
The HCV replication cycle is tightly associated with host lipid metabolism: Lipoprotein receptors SR-B1 and LDLr promote entry of HCV, replication is associated with the formation of lipid-rich membranous organelles and infectious particle assembly highjacks the very‑low-density lipoprotein (VLDL) secretory pathway. Hence, medications that interfere with the lipid metabolism of the cell, such as statins, may affect HCV infection. Here, we study the interplay between lipoprotein receptors, lipid homeostasis, and HCV infection by genetic and pharmacological interventions. We found that individual ablation of the lipoprotein receptors SR‑B1 and LDLr did not drastically affect HCV entry, replication, or infection, but double lipoprotein receptor knock-outs significantly reduced HCV infection. Furthermore, we could show that this effect was neither due to altered expression of additional HCV entry factors nor caused by changes in cellular cholesterol content. Strikingly, whereas lipid‑lowering drugs such as simvastatin or fenofibrate did not affect HCV entry or infection of immortalized hepatoma cells expressing SR-B1 and/or LDLr or primary human hepatocytes, ablation of these receptors rendered cells more susceptible to these drugs. Finally, we observed no significant differences between statin users and control groups with regards to HCV viral load in a cohort of HCV infected patients before and during HCV antiviral treatment. Interestingly, statin treatment, which blocks the mevalonate pathway leading to decreased cholesterol levels, was associated with mild but appreciable lower levels of liver damage markers before HCV therapy. Overall, our findings confirm the role of lipid homeostasis in HCV infection and highlight the importance of the mevalonate pathway in the HCV replication cycle.
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