Metabolism of paracetamol and phenacetin in relation to debrisoquine oxidation phenotype

A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds

Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the development of tools for identifying hazardous chemicals and the increase of mechanistic understanding of their toxicity is a major goal for scientific research. We constructed a new knowledge-based expert system accounting the effect of different substituents for the prediction of mutagenicity (Ames test) of aromatic amines, a class of compounds of major concern because of their widespread application in industry. The herein presented model implements a series of user-defined structural rules extracted from a database of 616 primary aromatic amines, with their Ames test outcomes, aimed at identifying mutagenic and non-mutagenic chemicals. The chemical rationale behind such rules is discussed. Besides assessing the model's ability to correctly classify aromatic amines, its predictivity was further evaluated on a second database of 354 azo dyes, another class of chemicals of major concern, whose toxicity has been predicted on the basis of the toxicity of aromatic amines potentially generated from the metabolic reduction of the azo bond. Good performance in classification on both the amine (MCC, Matthews Correlation Coefficient=0.743) and the azo dye (MCC=0.584) datasets confirmed the predictive power of the model, and its suitability for use on a wide range of chemicals. Finally, the model was compared with a series of well-known mutagenicity predicting software. The good performance of our model compared with other mutagenicity models, especially in predicting azo dyes, confirmed the usefulness of this expert system as a reliable support to in vitro mutagenicity assays for screening and prioritization purposes. The model has been fully implemented as a KNIME workflow and is freely available for downstream users.

Evaluation of Adverse Drug Properties with Cryopreserved Human Hepatocytes and the Integrated Discrete Multiple Organ Co-culture (IdMOC(TM)) System

Human hepatocytes, with complete hepatic metabolizing enzymes, transporters and cofactors, represent the gold standard for in vitro evaluation of drug metabolism, drug-drug interactions, and hepatotoxicity. Successful cryopreservation of human hepatocytes enables this experimental system to be used routinely. The use of human hepatocytes to evaluate two major adverse drug properties: drug-drug interactions and hepatotoxicity, are summarized in this review. The application of human hepatocytes in metabolism-based drug-drug interaction includes metabolite profiling, pathway identification, P450 inhibition, P450 induction, and uptake and efflux transporter inhibition. The application of human hepatocytes in toxicity evaluation includes in vitro hepatotoxicity and metabolism-based drug toxicity determination. A novel system, the Integrated Discrete Multiple Organ Co-culture (IdMOC) which allows the evaluation of nonhepatic toxicity in the presence of hepatic metabolism, is described.

Adverse drug reactions and debrisoquine/sparteine (P450IID6) polymorphism in patients with fibromyalgia

OBJECTIVE

To assess the frequency of adverse drug reaction in patients with fibromyalgia in relation to medications prescribed for this condition. To evaluate the potential role of the P450IID6 phenotype in the pathogenesis of these adverse drug reactions.

METHODS

Thirty-five patients with fibromyalgia were assessed using a structured questionnaire with demographic and clinical data and perceived adverse drug reactions. A sample of 60 patients with rheumatoid arthritis and 62 patients with localized back pain served as controls. The P450IID6 phenotype was determined for each of the fibromyalgia patients.

RESULTS

Overall, 141 patients had used NSAID and 79 (56%) of them reported adverse effects. Antidepressant drugs were used by 68 patients and 35 (51%) patients had adverse effects. Muscle relaxant drugs were used by 48 patients and 15 (31%) of them reported side effects. Analgesics were used by 122 patients and 22 (18%) had experienced adverse effects. Statistical differences in the frequency of adverse effects were found with antidepressant drugs in the fibromyalgia group, compared with rheumatoid arthritis (p=0.01) and back pain (p=0.02). Four of the 35 patients (11.4%) had a metabolic ratio (M.R.) greater than 0.30 (log M.R.= -0.52) indicative of the poor metabolizers (PM) phenotype. M.R. varied from 0.005 (log M.R. = -2.30) to 4.99 (log M.R. = 0.70).

CONCLUSIONS

The problem of adverse drug reactions in fibromyalgia patients does not appear to correlate with the PM phenotype of the P450IID6 oxidative enzyme. It also is unlikely that altered xenobiotic detoxification attributable to this PM phenotype would have a significant role in the development of fibromyalgia.