vitamin-k-1 and Chemical-and-Drug-Induced-Liver-Injury

Topics: Chemical and Drug Induced Liver Injury; Databases, Factual; Drug Labeling; Humans; Pharmaceutical Preparations; Risk

Drug-induced hepatotoxicity is potentially lethal. Liver transplant patients receive a large number of medications and adverse drug reactions, and drug-drug interactions must be closely monitored.. We report a case of a 29-year-old liver transplant patient who suffered liver injury most likely induced by drug interaction between capecitabine and warfarin. Vitamin K1 caused skin rash possibly because of the distribution and metabolism characteristic of the drug in this patient.. Close monitoring and prompt discontinuation of the drugs with high volume of distribution and metabolized through the liver are necessary to avoid drug-drug interaction in liver transplant patients.

Topics: Adult; Anticoagulants; Antimetabolites, Antineoplastic; Capecitabine; Chemical and Drug Induced Liver Injury; Deoxycytidine; Drug Eruptions; Drug Interactions; Drug Monitoring; Fluorouracil; Follow-Up Studies; Humans; Liver Transplantation; Male; Tissue Distribution; Vitamin K 1; Warfarin

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60-70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the "Rule of Three" was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity.

Topics: Animals; Anti-Infective Agents; Anti-Inflammatory Agents; Chemical and Drug Induced Liver Injury; Databases, Factual; Drug-Related Side Effects and Adverse Reactions; Humans; Liver; Models, Biological; Predictive Value of Tests

Topics: Anemia; Anemia, Aplastic; Anemia, Hemolytic; Anticoagulants; Blood Coagulation; Chemical and Drug Induced Liver Injury; Cholecystitis; Coronary Disease; Dietary Fats; Factor VIII; Globulins; Hemophilia A; Hepatitis; Hypertension; Intracranial Embolism; Intracranial Embolism and Thrombosis; Jaundice; Jaundice, Chronic Idiopathic; Jaundice, Obstructive; Leukemia; Liver Cirrhosis; Liver Neoplasms; Myocardial Infarction; Purpura; Rabbits; Research; Toxicology; Vitamin K 1