Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for treating hepatitis C infection

Hepatitis C: The Story of a Long Journey through First, Second, and Third Generation NS3/4A Peptidomimetic Inhibitors. What Did We Learn?

Hepatitis C viral (HCV) infection is the leading cause of liver failure and still represents a global health burden. Over the past decade, great advancements made HCV curable, and sustained viral remission significantly improved to more than 98%. Historical treatment with pegylated interferon alpha and ribavirin has been displaced by combinations of direct-acting antivirals. These regimens include drugs targeting different stages of the HCV life cycle. However, the emergence of viral resistance remains a big concern. The design of peptidomimetic inhibitors (PIs) able to fit and fill the conserved substrate envelope region within the active site helped avoid contact with the vulnerable sites of the most common resistance-associated substitutions Arg155, Ala156, and Asp168. Herein, we give an overview of HCV NS3 PIs discovered during the past decade, and we deeply discuss the rationale behind the structural optimization efforts essential to achieve pangenotypic activity.

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.