pafuramidine and Chemical-and-Drug-Induced-Liver-Injury

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

DB289 is the first oral drug shown in clinical trials to have efficacy in treating African trypanosomiasis (African sleeping sickness). Mild liver toxicity was noted but was not treatment limiting. However, development of DB289 was terminated when several treated subjects developed severe kidney injury, a liability not predicted from preclinical testing. We tested the hypothesis that the kidney safety liability of DB289 would be detected in a mouse diversity panel (MDP) comprised of 34 genetically diverse inbred mouse strains. MDP mice received 10 days of oral treatment with DB289 or vehicle and classical renal biomarkers blood urea nitrogen (BUN) and serum creatinine (sCr), as well as urine biomarkers of kidney injury were measured. While BUN and sCr remained within reference ranges, marked elevations were observed for kidney injury molecule-1 (KIM-1) in the urine of sensitive mouse strains. KIM-1 elevations were not always coincident with elevations in alanine aminotransferase (ALT), suggesting that renal injury was not linked to hepatic injury. Genome-wide association analyses of KIM-1 elevations indicated that genes participating in cholesterol and lipid biosynthesis and transport, oxidative stress, and cytokine release may play a role in DB289 renal injury. Taken together, the data resulting from this study highlight the utility of using an MDP to predict clinically relevant toxicities, to identify relevant toxicity biomarkers that may translate into the clinic, and to identify potential mechanisms underlying toxicities. In addition, the sensitive mouse strains identified in this study may be useful in screening next-in-class compounds for renal injury.

Topics: Administration, Oral; Animals; Benzamidines; Biomarkers; Blood Urea Nitrogen; Chemical and Drug Induced Liver Injury; Creatinine; Female; Genetic Markers; Genetic Predisposition to Disease; Genome-Wide Association Study; Hepatitis A Virus Cellular Receptor 1; Kidney; Kidney Diseases; Male; Membrane Proteins; Mice; Risk Assessment; Species Specificity; Toxicity Tests; Trypanocidal Agents

Drug-induced liver injury (DILI) is a leading cause of drugs failing during clinical trials and being withdrawn from the market. Comparative analysis of drugs based on their DILI potential is an effective approach to discover key DILI mechanisms and risk factors. However, assessing the DILI potential of a drug is a challenge with no existing consensus methods. We proposed a systematic classification scheme using FDA-approved drug labeling to assess the DILI potential of drugs, which yielded a benchmark dataset with 287 drugs representing a wide range of therapeutic categories and daily dosage amounts. The method is transparent and reproducible with a potential to serve as a common practice to study the DILI of marketed drugs for supporting drug discovery and biomarker development.

Topics: Animals; Benchmarking; Biomarkers, Pharmacological; Chemical and Drug Induced Liver Injury; Drug Design; Drug Labeling; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmaceutical Preparations; Reproducibility of Results; United States; United States Food and Drug Administration

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