bibx-1382bs and Chemical-and-Drug-Induced-Liver-Injury

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

The pyrimido-pyrimidine BIBX 1382 BS inhibits the intracellular tyrosine kinase domain of the epidermal growth factor receptor (EGFR), thus specifically reverting the aberrant enzymatic activity from overexpressed and constitutively activated EGFR. A phase I and pharmacokinetic study of this new specific molecule was carried out. After initially performing an accelerated titration design from the first toxicities onwards, a modified Fibonacci scheme was used to escalate the daily oral dose. The following dosages and cycles (defined as treatment during 28 days) were applied: 25 mg: 6; 50 mg: 3; 100 mg: 6; 200 mg: 7; 150 mg: 3. Over a 10 months accrual phase, 11 patients (pts) (7 females, 4 males) with a median age of 63 years (range 50-73 years), World Health Organization Performance Status (WHO PS) 0:5 pts, 1:6 pts and miscellaneous solid tumours were entered. The median number of cycles applied per pt was 2 (range 1-7). Reversible, dose-dependent increase of liver enzymes (maximal Common Toxicity Criteria (CTC) grades: gamma-glutamyl transferase (GGT): 4, aspartate aminotransferase (GOT): 3, alanine aminotransferase (GPT): 3, alkaline phosphatase (AP): 3, bilirubin: 3) occurred. Oral medication yielded plasma levels far below those expected to be efficacious. In conclusion, target plasma levels could not be reached via the oral route at a reasonable dosage. Meanwhile, a preclinically unknown metabolite was identified from the urine of one patient. Subsequently, this metabolite was found to be abundant in patient plasma. The metabolite was demonstrated to be pharmacologically inactive. Due to a dose-limiting increase of liver enzymes, low bioavailability of BIBX 1382 BS and the detection of a pharmacologically inactive metabolite, this trial was discontinued.

Topics: Administration, Oral; Adult; Aged; Antineoplastic Agents; Chemical and Drug Induced Liver Injury; Dose-Response Relationship, Drug; Enzyme Inhibitors; ErbB Receptors; Female; Follow-Up Studies; Hematologic Diseases; Humans; Male; Middle Aged; Neoplasms; Organic Chemicals; Treatment Outcome

Drug-induced liver injury (DILI) is a leading cause of drugs failing during clinical trials and being withdrawn from the market.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