Both chronic and acute (binge) alcohol drinking are important health and economic concerns worldwide and prominent risk factors for the development of alcoholic liver disease (ALD). There are no FDA-approved medications to prevent or to treat any stage of ALD. Therefore, discovery of novel therapeutic strategies remains a critical need for patients with ALD. Relevant experimental animal models that simulate human drinking patterns and mimic the spectrum and severity of alcohol-induced liver pathology in humans are critical to our ability to identify new mechanisms and therapeutic targets. There are several animal models currently in use, including the most widely utilized chronic ad libitum ethanol (EtOH) feeding (Lieber–DeCarli liquid diet model), chronic intragastric EtOH administration (Tsukamoto–French model), and chronic-plus-binge EtOH challenge (Bin Gao—National Institute on Alcohol Abuse and Alcoholism (NIAAA) model). This review provides an overview of recent advances in rodent models of binge EtOH administration which help to recapitulate different features and etiologies of progressive ALD. These models include EtOH binge alone, and EtOH binge coupled with chronic EtOH intake, a high fat diet, or endotoxin challenge. We analyze the strengths, limitations, and translational relevance of these models, as well as summarize the liver injury outcomes and mechanistic insights. We further discuss the application(s) of binge EtOH models in examining alcohol-induced multi-organ pathology, sex- and age-related differences, as well as circadian rhythm disruption.

Pharmacovigilance is the science of collection, detection, and assessment of adverse events associated with pharmaceutical products for the ongoing monitoring and understanding of those products’ safety profiles. Part of this process, signal management, encompasses the activities of signal detection, signal validation/confirmation, signal evaluation, and ultimately, final assessment as to whether a safety signal constitutes a new causal adverse drug reaction. Artificial intelligence is a group of technologies including machine learning and natural language processing that are revolutionizing multiple industries through intelligent automation. Here, we present a critical evaluation of studies leveraging artificial intelligence in signal management to characterize the benefits and limitations of the technology, the level of transparency, and our perspective on best practices for the future. To this end, PubMed and Embase were searched cumulatively for terms pertaining to signal management and artificial intelligence, machine learning, or natural language processing. Information pertaining to the artificial intelligence model used, hyperparameter settings, training/testing data, performance, feature analysis, and more was extracted from included articles. Common signal detection methods included k-means, random forest, and gradient boosting machine. Machine learning algorithms generally outperformed traditional frequentist or Bayesian measures of disproportionality per various metrics, showing the potential utility of advanced machine learning technologies in signal detection. In signal validation and evaluation, natural language processing was typically applied. Overall, methodological transparency was mixed and only some studies leveraged “gold standard” publicly available positive and negative control datasets. Overall, innovation in pharmacovigilance signal management is being driven by machine learning and natural language processing models, particularly in signal detection, in part because of high-performing bagging methods such as random forest and gradient boosting machine. These technologies may be well poised to accelerate progress in this field when used transparently and ethically. Future research is needed to assess the applicability of these techniques across various therapeutic areas and drug classes in the broader pharmaceutical industry.

Emerging evidence suggests that soluble epoxide hydrolase (sEH) inhibition is a valuable therapeutic strategy for the treatment of numerous diseases, including those of the liver. sEH rapidly degrades cytochrome P450-produced epoxygenated lipids (epoxy-fatty acids), which are synthesized from omega-3 and omega-6 polyunsaturated fatty acids, that generally exert beneficial effects on several cellular processes. sEH hydrolysis of epoxy-fatty acids produces dihydroxy-fatty acids which are typically less biologically active than their parent epoxide. Efforts to develop sEH inhibitors have made available numerous compounds that show therapeutic efficacy and a wide margin of safety in a variety of different diseases, including non-alcoholic fatty liver disease, liver fibrosis, portal hypertension, and others. This review summarizes research efforts which characterize the applications, underlying effects, and molecular mechanisms of sEH inhibitors in these liver diseases and identifies gaps in knowledge for future research.