Quantitative structure-activity relationship of the mutagenicity of substituted N-nitroso-N-benzylmethylamines: possible implications for carcinogenicity

A core in vitro genotoxicity battery comprising the Ames test plus the in vitro micronucleus test is sufficient to detect rodent carcinogens and in vivo genotoxins

In vitro genotoxicity testing needs to include tests in both bacterial and mammalian cells, and be able to detect gene mutations, chromosomal damage and aneuploidy. This may be achieved by a combination of the Ames test (detects gene mutations) and the in vitro micronucleus test (MNvit), since the latter detects both chromosomal aberrations and aneuploidy. In this paper we therefore present an analysis of an existing database of rodent carcinogens and a new database of in vivo genotoxins in terms of the in vitro genotoxicity tests needed to detect their in vivo activity. Published in vitro data from at least one test system (most were from the Ames test) were available for 557 carcinogens and 405 in vivo genotoxins. Because there are fewer publications on the MNvit than for other mammalian cell tests, and because the concordance between the MNvit and the in vitro chromosomal aberration (CAvit) test is so high for clastogenic activity, positive results in the CAvit test were taken as indicative of a positive result in the MNvit where there were no, or only inadequate data for the latter. Also, because Hprt and Tk loci both detect gene-mutation activity, a positive Hprt test was taken as indicative of a mouse-lymphoma Tk assay (MLA)-positive, where there were no data for the latter. Almost all of the 962 rodent carcinogens and in vivo genotoxins were detected by an in vitro battery comprising Ames+MNvit. An additional 11 carcinogens and six in vivo genotoxins would apparently be detected by the MLA, but many of these had not been tested in the MNvit or CAvit tests. Only four chemicals emerge as potentially being more readily detected in MLA than in Ames+MNvit--benzyl acetate, toluene, morphine and thiabendazole--and none of these are convincing cases to argue for the inclusion of the MLA in addition to Ames+MNvit. Thus, there is no convincing evidence that any genotoxic rodent carcinogens or in vivo genotoxins would remain undetected in an in vitro test battery consisting of Ames+MNvit.

Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

Structure-Based Optimization of Azole Antifungal Agents by CoMFA, CoMSIA, and Molecular Docking

In a continuing effort to develop highly potent azole antifungal agents, the three-dimensional quantitative structure-activity relationship methods, CoMFA and CoMSIA, were applied using a set of novel azole antifungal compounds. The binding mode of the compounds at the active site of lanosterol 14alpha-demethylase was further explored using the flexible docking method. Various hydrophobic, van der Waals, pi-pi stacking, and hydrogen bonding interactions were observed between the azoles and the enzyme. Based on results from the molecular modeling, a receptor-based pharmacophore model was established to guide the rational optimization of the azole antifungal agents. Thus, a total of 57 novel azoles were designed and synthesized by a three-step optimization process. In vitro antifungal assay revealed that the antifungal activities of these novel azoles were greatly improved, which confirmed the reliability of the model from molecular modeling.