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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.

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Phospholipidosis (PLD) is characterized by the excessive intracellular accumulation of phospholipids. It is well established that a large number of cationic amphiphilic drugs have the potential to induce PLD. In the present study, we describe two facile in vitro methods to determine the PLD-inducing potential of a molecule. The first approach is based on a recent study by (Sawada et al., 2005, Toxicol. Sci. 83, 282-292) in which 17 genes were identified as potential biomarkers of PLD in HepG2 cells. To confirm the utility of this gene panel, we treated HepG2 cells with PLD-positive and -negative compounds and then analyzed gene expression using real-time PCR. Our initial analysis, which used a single dose of each drug, correctly identified five of eight positive compounds and four of four negative compounds. We then increased the doses of the three false negatives (amiodarone, tamoxifen, and loratadine) and found that the changes in gene expression became large enough to correctly identify them as PLD-inducing drugs. Our results suggest that a range of concentrations should be used to increase the accuracy of prediction in this assay. Our second approach utilized a fluorescently labeled phospholipid (LipidTox) which was added to the media of growing HepG2 cells along with compounds positive and negative for PLD. Phospholipid accumulation was determined using confocal microscopy and, more quantitatively, using a 96-well plate assay and a fluorescent plate reader. Using an expanded set of compounds, we show that this assay correctly identified 100% of PLD-positive and -negative compounds. Dose-dependent increases in intracellular fluorescent phospholipid accumulation were observed. We found that this assay was less time consuming, more sensitive, and higher throughput than gene expression analysis. To our knowledge, this study represents the first validation of the use of LipidTox in identifying drugs that can induce PLD.

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.

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.

A symposium at the 40th anniversary of the Environmental Mutagen Society, held from October 24–28, 2009 in St. Louis, MO, surveyed the current status and future directions of genetic toxicology. This article summarizes the presentations and provides a perspective on the future. An abbreviated history is presented, highlighting the current standard battery of genotoxicity assays and persistent challenges. Application of computational toxicology to safety testing within a regulatory setting is discussed as a means for reducing the need for animal testing and human clinical trials, and current approaches and applications of in silico genotoxicity screening approaches across the pharmaceutical industry were surveyed and are reported here. The expanded use of toxicogenomics to illuminate mechanisms and bridge genotoxicity and carcinogenicity, and new public efforts to use high-throughput screening technologies to address lack of toxicity evaluation for the backlog of thousands of industrial chemicals in the environment are detailed. The Tox21 project involves coordinated efforts of four U.S. Government regulatory/research entities to use new and innovative assays to characterize key steps in toxicity pathways, including genotoxic and nongenotoxic mechanisms for carcinogenesis. Progress to date, highlighting preliminary test results from the National Toxicology Program is summarized. Finally, an overview is presented of ToxCast™, a related research program of the U.S. Environmental Protection Agency, using a broad array of high throughput and high content technologies for toxicity profiling of environmental chemicals, and computational toxicology modeling. Progress and challenges, including the pressing need to incorporate metabolic activation capability, are summarized.

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.