Drug-induced autoimmune liver disease: A diagnostic dilemma of an increasingly reported disease.

The aetiology of autoimmune hepatitis (AIH) is uncertain but the disease can be triggered in susceptible patients by external factors such as viruses or drugs. AIH usually develops in individuals with a genetic background mainly consisting of some risk alleles of the major histocompatibility complex (HLA). Many drugs have been linked to AIH phenotypes, which sometimes persist after drug discontinuation, suggesting that they awaken latent autoimmunity. At least three clinical scenarios have been proposed that refers to drug- induced autoimmune liver disease (DIAILD): AIH with drug-induced liver injury (DILI); drug induced-AIH (DI-AIH); and immune mediated DILI (IM-DILI). In addition, there are instances showing mixed features of DI-AIH and IM-DILI, as well as DILI cases with positive autoantibodies. Histologically distinguishing DILI from AIH remains a challenge. Even more challenging is the differentiation of AIH from DI-AIH mainly relying in histological features; however, a detailed standardised histologic evaluation of large cohorts of AIH and DI-AIH patients would probably render more subtle features that could be of help in the differential diagnosis between both entities. Growing information on the relationship of drugs and AIH is being available, being drugs like statins and biologic agents more frequently involved in cases of DIAILD. In addition, there is some evidence on the fact that patients diagnosed with DIAILD may have had a previous episode of hepatotoxicity. Further collaborative studies in DIAILD will strengthen the knowledge and understanding of this intriguing and complex disorder which might represent different phenotypes across the spectrum of disease.

Drug assessment by a Pharmacy and Therapeutics committee: from drug selection criteria to use in clinical practice.

BACKGROUND In Spain, hospital medicines are assessed and selected by local Pharmacy and Therapeutics committees (PTCs). Of all the drugs assessed, cancer drugs are particularly important because of their budgetary impact and the sometimes arguable added value with respect to existing alternatives. This study analyzed the PTC drug selection process and the main objective was to evaluate the degree of compliance of prescriptions for oncology drugs with their criteria for use. METHODS This was a retrospective observational study (May 2007 to April 2010) of PTC-assessed drugs. The variables measured to describe the committee's activity were number of drugs assessed per year and number of drugs included in any of these settings: without restrictions, with criteria for use, and not included in formulary. These drugs were also analyzed by therapeutic group. To assess the degree of compliance of prescriptions, a score was calculated to determine whether prescriptions for bevacizumab, cetuximab, trastuzumab, and bortezomib were issued in accordance with PTC drug use criteria. RESULTS The PTC received requests for inclusion of 40 drugs, of which 32 were included in the hospital formulary (80.0%). Criteria for use were established for 28 (87.5%) of the drugs included. In total, 293 patients were treated with the four cancer drugs in eight different therapeutic indications. The average prescription compliance scores were as follows: bevacizumab, 83% for metastatic colorectal cancer, 100% for metastatic breast cancer, and 82.3% for non-small-cell lung cancer; cetuximab, 62.0% for colorectal cancer and 50% for head and neck cancer; trastuzumab, 95.1% for early breast cancer and 82.4% for metastatic breast cancer; and bortezomib, 63.7% for multiple myeloma. CONCLUSION The degree of compliance with criteria for use of cancer drugs was reasonably high. PTC functions need to be changed so that they can carry out more innovative tasks, such as monitoring conditions for drug use.

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.