No abstract

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.

No abstract

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.

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.