Ronald D. Snyder

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTStereospecific method to (E) and (Z) terminal fluoroolefins and its application to the synthesis of 2'-deoxy-2'-fluoromethylenenucleosides as potential inhibitors of ribonucleoside diphosphate reductaseJames R. McCarthy, Donald P. Matthews, David M. Stemerick, Edward W. Huber, Philippe Bey, Bruce J. Lippert, Ronald D. Snyder, and Prasad S. SunkaraCite this: J. Am. Chem. Soc. 1991, 113, 19, 7439–7440Publication Date (Print):September 1, 1991Publication History Published online1 May 2002Published inissue 1 September 1991https://doi.org/10.1021/ja00019a061RIGHTS & PERMISSIONSArticle Views650Altmetric-Citations175LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (302 KB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information Get e-Alerts

A range of genomics technologies are increasingly becoming integrated with existing scientific disciplines to broaden and strengthen existing capabilities and open new avenues of research in drug discovery and development. Examples of these new research fields are proteomics, pharmacogenomics, metabolomics and toxicogenomics. Here we review the application of toxicogenomics to improve the evaluation of drug safety, mechanism of action and toxicity in the drug discovery and development process.

Bioflavonoids are naturally occurring polyphenols with intriguing and varied therapeutic and chemoprotective activities generally ascribed to their antioxidant properties. However, many flavonoids have also been shown to be genotoxic in a variety of prokaryotic, eukaryotic, and in vivo systems. The mechanistic basis for this genotoxicity has not been fully elucidated, although structure-activity relationship studies have identified requisite flavonoid structural features. We utilized Chinese hamster V79 cells to evaluate the relationships between DNA intercalation ability, topoisomerase II interactions, reactive oxygen species (ROS) generation, and clastogenicity in a series of 14 bioflavonoids. Five of the flavonoids examined, luteolin, quercetin, genistein, apigenin, and acacetin, were strongly clastogenic. This clastogenicity was shown to require DNA intercalation (with the exception of genistein) and was substantially reduced by catalytic inhibitors of DNA topoisomerase II. The transition metals Cu(II) and Mn(II) formed chelates with and/or modified the structure and biological activity of some flavonoids but no consistent relationship could be demonstrated between metal reactivity and clastogenicity. There was no clear association between generation of ROS and clastogenicity. The data presented herein are consistent with a model in which the genotoxicity of most flavonoids arises via DNA intercalation and topo II poisoning, likely mediated through metabolism to flavonoid quinones. Interestingly, other flavonoids such as myricetin, daidzein, baicalein, fisetin, and galangin were catalytic topo II inhibitors, rather than poisons. These studies further validate the use of cell-based approaches for detecting drug/topo II interactions and raise interesting questions relating to biological and chemical mechanisms of flavonoids.

Computational models are currently being used by regulatory agencies and within the pharmaceutical industry to predict the mutagenic potential of new chemical entities. These models rely heavily, although not exclusively, on bacterial mutagenicity data of nonpharmaceutical-type molecules as the primary knowledge base. To what extent, if any, this has limited the ability of these programs to predict genotoxicity of pharmaceuticals is not clear. In order to address this question, a panel of 394 marketed pharmaceuticals with Ames Salmonella reversion assay and other genetic toxicology findings was extracted from the 2000-2002 Physicians' Desk Reference and evaluated using MCASE, TOPKAT, and DEREK, the three most commonly used computational databases. These evaluations indicate a generally poor sensitivity of all systems for predicting Ames positivity (43.4-51.9% sensitivity) and even poorer sensitivity in prediction of other genotoxicities (e.g., in vitro cytogenetics positive; 21.3-31.9%). As might be expected, all three programs were more highly predictive for molecules containing carcinogenicity structural alerts (i.e., the so-called Ashby alerts; 61% +/- 14% sensitivity) than for those without such alerts (12% +/- 6% sensitivity). Taking all genotoxicity assay findings into consideration, there were 84 instances in which positive genotoxicity results could not be explained in terms of structural alerts, suggesting the possibility of alternative mechanisms of genotoxicity not relating to covalent drug-DNA interaction. These observations suggest that the current computational systems when applied in a traditional global sense do not provide sufficient predictivity of bacterial mutagenicity (and are even less accurate at predicting genotoxicity in tests other than the Salmonella reversion assay) to be of significant value in routine drug safety applications. This relative inability of all three programs to predict the genotoxicity of drugs not carrying obvious DNA-reactive moieties is discussed with respect to the nature of the drugs whose positive responses were not predicted and to expectations of improving the predictivity of these programs. Limitations are primarily a consequence of incomplete understanding of the fundamental genotoxic mechanisms of nonstructurally alerting drugs rather than inherent deficiencies in the computational programs. Irrespective of their predictive power, however, these programs are valuable repositories of structure-activity relationship mutagenicity data that can be useful in directing chemical synthesis in early drug discovery.