A meta-analysis platform methodology for determining the comparative effectiveness of antihepatitis C virus regimens

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Cost-Outcome Description of PEG-IFN-α2b+RBV for Hepatitis C: Results Based on the Interferon Database

Economic evaluation of drugs is used in decision-making on medical care and public policy. Recently, real-world data (RWD) have been used in the analysis. In this study, we discuss the risk and benefits of using RWD for economic evaluation. We conducted a cost-outcome description with RWD from a nationwide registry providing information on hepatitis treatment in Japan and estimated the utility of the analysis. We evaluated the cost-outcome description of peginterferon plus ribavirin (PEG-IFN-α2b+RBV) treatment in hepatitis C virus (HCV)-infected patients. Simulations were based on a Markov model. The cohorts were set using data from the registry and we assumed a societal perspective for the calculation of costs. The dose and drug cost were chosen based on the Japanese Guidelines for the Management of Hepatitis C Virus Infection or package inserts. Model details and parameters were as described in previous studies. The simulations were performed for a period of 10 years with no discount rate. We estimated 2.5 million JPY per Quality Adjusted Life Year (QALY) in 48-week PEG-IFN-α2b+RBV treatment for a period of 10 years. The results of this study are in agreement with previous HCV treatment economic evaluation studies in Japan. We analyzed the statistics of the HCV-infected patients at each disease stage using the data in our registry and calculated the costs. The results of this study more closely reflect a real-world clinical situation compared to the widely used randomized clinical trial method, which estimates clinical trial results and scenarios.