Endocannabinoid system and psychiatry: in search of a neurobiological basis for detrimental and potential therapeutic effects.

Public concern on mental health has noticeably increased given the high prevalence of neuropsychiatric disorders. Cognition and emotionality are the most affected functions in neuropsychiatric disorders, i.e., anxiety disorders, depression, and schizophrenia. In this review, most relevant literature on the role of the endocannabinoid (eCB) system in neuropsychiatric disorders will be presented. Evidence from clinical and animal studies is provided for the participation of CB1 and CB2 receptors (CB1R and CB2R) in the above mentioned neuropsychiatric disorders. CBRs are crucial in some of the emotional and cognitive impairments reported, although more research is required to understand the specific role of the eCB system in neuropsychiatric disorders. Cannabidiol (CBD), the main non-psychotropic component of the Cannabis sativa plant, has shown therapeutic potential in several neuropsychiatric disorders. Although further studies are needed, recent studies indicate that CBD therapeutic effects may partially depend on facilitation of eCB-mediated neurotransmission. Last but not least, this review includes recent findings on the role of the eCB system in eating disorders. A deregulation of the eCB system has been proposed to be in the bases of several neuropsychiatric disorders, including eating disorders. Cannabis consumption has been related to the appearance of psychotic symptoms and schizophrenia. In contrast, the pharmacological manipulation of this eCB system has been proposed as a potential strategy for the treatment of anxiety disorders, depression, and anorexia nervosa. In conclusion, the eCB system plays a critical role in psychiatry; however, detrimental consequences of manipulating this endogenous system cannot be underestimated over the potential and promising perspectives of its therapeutic manipulation.

Use of Hy's law and a new composite algorithm to predict acute liver failure in patients with drug-induced liver injury.

BACKGROUND & AIMS Hy's Law, which states that hepatocellular drug-induced liver injury (DILI) with jaundice indicates a serious reaction, is used widely to determine risk for acute liver failure (ALF). We aimed to optimize the definition of Hy's Law and to develop a model for predicting ALF in patients with DILI. METHODS We collected data from 771 patients with DILI (805 episodes) from the Spanish DILI registry, from April 1994 through August 2012. We analyzed data collected at DILI recognition and at the time of peak levels of alanine aminotransferase (ALT) and total bilirubin (TBL). RESULTS Of the 771 patients with DILI, 32 developed ALF. Hepatocellular injury, female sex, high levels of TBL, and a high ratio of aspartate aminotransferase (AST):ALT were independent risk factors for ALF. We compared 3 ways to use Hy's Law to predict which patients would develop ALF; all included TBL greater than 2-fold the upper limit of normal (×ULN) and either ALT level greater than 3 × ULN, a ratio (R) value (ALT × ULN/alkaline phosphatase × ULN) of 5 or greater, or a new ratio (nR) value (ALT or AST, whichever produced the highest ×ULN/ alkaline phosphatase × ULN value) of 5 or greater. At recognition of DILI, the R- and nR-based models identified patients who developed ALF with 67% and 63% specificity, respectively, whereas use of only ALT level identified them with 44% specificity. However, the level of ALT and the nR model each identified patients who developed ALF with 90% sensitivity, whereas the R criteria identified them with 83% sensitivity. An equal number of patients who did and did not develop ALF had alkaline phosphatase levels greater than 2 × ULN. An algorithm based on AST level greater than 17.3 × ULN, TBL greater than 6.6 × ULN, and AST:ALT greater than 1.5 identified patients who developed ALF with 82% specificity and 80% sensitivity. CONCLUSIONS When applied at DILI recognition, the nR criteria for Hy's Law provides the best balance of sensitivity and specificity whereas our new composite algorithm provides additional specificity in predicting the ultimate development of ALF.