Molecular fragments associated with non-genotoxic carcinogens, as detected using a software program based on graph theory: their usefulness to predict carcinogenicity

Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

A multiple in silico program approach for the prediction of mutagenicity from chemical structure

We have conducted an evaluation of three of the most widely used commercial toxicity prediction programs, Toxicity Prediction by Komputer Assisted Technology (TOPKAT), Deductive Estimation of Risk from Existing Knowledge (DEREK) for Windows (DfW) and CASETOX. The three programs were evaluated for their ability to predict Ames test mutagenicity using 520 proprietary drug candidate (Test set 1) and 94 commercial (Test set 2) compounds. The study demonstrates that these three commercially available programs are useful, with limitations in their ability to predict mutagenicity over a wide range of chemical space, i.e. global predictivity. Individually, each of the programs performed at an acceptable level for overall accuracy, i.e. the ability to predict the correct outcome. However, analysis of the predictions indicates that the overall accuracy figure is heavily weighted by the ability of the programs to correctly predict non-mutagens, whereas none of the programs individually performed well in the prediction of novel mutagenic structures, i.e. Ames positive compounds. The performance of these programs' in predicting Ames positive mutagens appeared to be independent of the chemical utility of the compound, i.e. industrial, agricultural or pharmaceutical. The combination of program predictions provided some improvement in overall accuracy, sensitivity and specificity.

Fragment generation and support vector machines for inducing SARs

We present a new approach to the induction of SARs based on the generation of structural fragments and support vector machines (SVMs). It is tailored for bio-chemical databases, where the examples are two-dimensional descriptions of chemical compounds. The fragment generator finds all fragments (i.e. linearly connected atoms) that satisfy user-specified constraints regarding their frequency and generality. In this paper, we are querying for fragments within a minimum and a maximum frequency in the dataset. After fragment generation, we propose to apply SVMs to the problem of inducing SARs from these fragments. We conjecture that the SVMs are particularly useful in this context, as they can deal with a large number of features. Experiments in the domains of carcinogenicity and mutagenicity prediction show that the minimum and the maximum frequency queries for fragments can be answered within a reasonable time, and that the predictive accuracy obtained using these fragments is satisfactory. However, further experiments will have to confirm that this is a viable approach to inducing SARs.

Knowledge discovery and data mining in toxicology

Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.

Methods for predicting carcinogenic hazards: new opportunities coming from recent developments in molecular oncology and SAR studies

Without epidemiological evidence, and prior to either short-term tests of genotoxicity or long-term tests of carcinogenicity in rodents, an initial level of information about the carcinogenic hazard of a chemical that perhaps has been designed on paper, but never synthesized, can be provided by structure-activity relationship (SAR) studies. Herein, we have reviewed the interesting strategies developed by human experts and/or computerized approaches for the identification of structural alerts that can denote the possible presence of a carcinogenic hazard in a novel molecule. At a higher level of information, immediately below epidemiological evidence, we have discussed carcinogenicity experiments performed in new types of genetically engineered small rodents. If a dominant oncogene is already mutated, or if an allele of a recessive oncogene is inactivated, we have a model animal with (n-1) stages in the process of carcinogenesis. Both genotoxic and receptor-mediated carcinogens can induce cancers in 20-40% of the time required for classical murine strains. We have described the first interesting results obtained using these new artificial animal models for carcinogenicity studies. We have also briefly discussed other types of engineered mice (lac operon transgenic mice) that are especially suitable for detecting mutagenic effects in a broad spectrum of organs and tissues and that can help to establish mechanistic correlations between mutations and cancer frequencies in specific target organs. Finally, we have reviewed two complementary methods that, while obviously also feasible in rodents, are especially suitable for biomonitoring studies. We have illustrated some of the advantages and drawbacks related to the detection of DNA adducts in target and surrogate tissues using the 32P-DNA postlabeling technique, and we have discussed the possibility of biomonitoring mutations in different human target organs using a molecular technique that combines the activity of restriction enzymes with polymerase chain reaction (RFLP/PCR). Prediction of carcinogenic hazard and biomonitoring are very wide-ranging areas of investigation. We have therefore selected five different subfields for which we felt that interesting innovations have been introduced in the last few years. We have made no attempt to systematically cover the entire area: such an endeavor would have produced a book instead of a review article.

Nongenotoxic carcinogens: development of detection methods based on mechanisms: a European project

While the accumulation of genetic changes in a somatic cell is considered essential for the genesis of a cancer, it has become clear that not all carcinogens are genotoxic, suggesting that some carcinogens indirectly participate in the generation of genetic changes during carcinogenesis. A European project funded by the European Community was thus conceived to study mechanisms of nongenotoxic aspects of carcinogenesis. Two main strategical approaches were adapted: (i) to study whether and how Syrian hamster embryo (SHE), Syrian hamster dermal (SHD) and BALB/c 3T3 cell transformation systems simulate in vivo carcinogenesis, and to examine whether they can detect nongenotoxic carcinogens; (ii) to study, refine and validate mechanisms-based end-points for detection of nongenotoxic carcinogens. For mechanisms-based research, the proposed end-points included gap junctional intercellular communication (GJIC) inhibition, altered expression of critical genes, immortalization and aberrant cell proliferation. We also selected model compounds commonly usable for various endpoints. Our major results can be summarized as follows: (1) SHE and BALB/c 3T3 transformation systems reflect both genotoxic and nongenotoxic carcinogenic events; they detect not only genotoxic but also many although not all, nongenotoxic carcinogens. This is further supported by the fact that both genotoxic and nongenotoxic carcinogens were able to immortalize SHD cells. (2) Many nongenotoxic carcinogens, although not all, inhibit GJIC in vitro as well as in vivo. Mechanistic studies suggest an important role of blocked GJIC in carcinogenesis and that different mechanisms are involved in inhibition of the communication by different agents used. However, inhibition of GJIC is not a prerequisite for the enhancement (or induction) of transformation of SHE or BALB/c 3T3 cells. (3) Among compounds examined, there was a good correlation between induction of micronuclei and cell transformation in SHE cells while no such correlation was found between the induction of cell transformation and ornithine decarboxylase activity. (4) Two transgenic mouse mutation assays (lacI and lacZ) were established and validated. The genotoxin dimethylnitrosamine was shown to be mutagenic to the liver in both assays. Ortho-anisidine, a bladder-specific carcinogen that was inactive in standard rodent genetic toxicity assays was uniquely mutagenic to the bladder of the transgenic mice. The peroxisome proliferator methyl clofenipate was established as nonmutagenic to the liver of both transgenic mice. That eliminated DNA damage as a cause of the liver tumours produced by this chemical and weakened the idea that induced cell division leads to mutation induction. (5) With an in vitro DNA replication model, it was found that DNA damage induced by genotoxic agents can be responsible for inhibition of DNA replication, while certain nongenotoxic agents such as phorbol esters increase DNA replication. (6) An attempt to use structure-activity relationship for subfamilies of nongenotoxic carcinogens, e.g., receptor-mediated carcinogens, has been initiated with some promising results. Our results support the idea that there are multiple nongenotoxic mechanisms in carcinogenesis, and that working hypothesis-oriented approaches are encouraged rather than simple screening of chemicals in developing test systems for the detection of nongenotoxic carcinogens.

A computerized connectivity approach for analyzing the structural basis of mutagenicity in Salmonella and its relationship with rodent carcinogenicity

We have applied a new software program, based on graph theory and developed by our group, to predict mutagenicity in Salmonella. The software analyzes, as information in input, the structural formula and the biological activities of a relatively large database of chemicals to generate any possible molecular fragment with size ranging from two to ten nonhydrogen atoms, and detects (as predictors of biological activity) those fragments statistically associated with the biological property investigated. Our previous work used the program to predict carcinogenicity in small rodents. In the current work we applied a modified version of the program, which bases its predictions solely on the most important fragment present in a given molecule, considering as practically negligible the effects of additional less important fragments. For Salmonella mutagenicity we used a database of 551 compounds, and the program achieved a level of predictivity (73.9%) comparable to that obtained by other authors using the Computer Automated Structure Evaluation (CASE) program. We evaluated the relative contributions of biophores and biophobes to overall predictivity: biophores tended to be more important than biophobes, and chemicals containing both biophores and biophobes were more difficult to predict. Many of the molecular fragments identified by the program as being strongly associated with mutagenic activity were similar to the structural alerts identified by the human experts Ashby and Tennant. Our results tend to confirm that structural alerts useful to predict Salmonella mutagenicity are generally not very strong predictors of rodent carcinogenicity. Although the predictivity level achieved for oncogenic activity improved when the program was directly trained with carcinogenicity data, carcinogenicity as a biological endpoint was still more difficult to predict than Salmonella mutagenicity.