Chemical diversity approach for evaluating mechanistic relatedness among toxicological phenomena

Structure-activity relationship analysis of rat mammary carcinogens

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 here the application of the cat-SAR (categorical-SAR) program to two learning sets of rat mammary carcinogens. One set of developed models was based on a comparison of rat mammary carcinogens to rat noncarcinogens (MC-NC), and the second set compared rat mammary carcinogens to rat nonmammary carcinogens (MC-NMC). On the basis of a leave-one-out validation, the best rat MC-NC model achieved a concordance between experimental and predicted values of 84%, a sensitivity of 79%, and a specificity of 89%. Likewise, the best rat MC-MNC model achieved a concordance of 78%, a sensitivity of 82%, and a specificity of 74%. The MC-NMC model was based on a learning set that contained carcinogens in both the active (i.e., mammary carcinogens) and the inactive (i.e., carcinogens to sites other than the mammary gland) categories and was able to distinguish between these different types of carcinogens (i.e., tissue specific), not simply between carcinogens and noncarcinogens. On the basis of a structural comparison between this model and one for Salmonella mutagens, there was, as expected, a significant relationship between the two phenomena since a high proportion of breast carcinogens are Salmonella mutagens. However, when analyzing the specific structural features derived from the MC-NC learning set, a dichotomy was observed between fragments associated with mammary carcinogenesis and mutagenicity and others that were associated with estrogenic activity. Overall, these findings suggest that the MC-NC and MC-NMC models are able to identify structural attributes that may in part address the question of "why do some carcinogens cause breast cancer", which is a different question than "why do some chemicals cause cancer".

SAR and QSAR modeling of endocrine disruptors

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Lack of predictivity of the rat lethality (LD50) test for ecological and human health effects

The relationship between acute toxicity in rats (LD50 values) and indicators of potential health hazards in humans was investigated, based on a chemical population-based paradigm (i.e. the "chemical diversity approach"). These structure-activity relationship-based analyses indicate that high toxicity in rats (i.e. a low LD50 value) is not a good predictor of health effects in humans. In fact, it was found that high acute toxicity to minnows, as well as toxicity to cultured cells, showed significantly greater associations with the potential for health effects than rat LD50 values.

Structure-activity approach to the identification of environmental estrogens: the MCASE approach

A sizable number of environmental contaminants and natural products have been found to possess hormonal activity and have been termed endocrine-disrupting chemicals. Due to the vast number (estimated at about 58,000) of environmental contaminants, their potential to adversely affect the endocrine system, and the paucity of health effects data associated with them, the U.S. Congress was led to mandate testing of these compounds for endocrine-disrupting ability. Here we provide evidence that a computational structure-activity relationship (SAR) approach has the potential to rapidly and cost effectively screen and prioritize these compounds for further testing. Our models were based on data for 122 compounds assayed for estrogenicity in the ESCREEN assay. We produced two models, one for relative proliferative effect (RPE) and one for relative proliferative potency (RPP) for chemicals as compared to the effects and potency of 17beta-estradiol. The RPE and RPP models achieved an 88 and 72% accurate prediction rate, respectively, for compounds not in the learning sets. The good predictive ability of these models and their basis on simple to understand 2-D molecular fragments indicates their potential usefulness in computational screening methods for environmental estrogens.

Environmental odors and health hazards

Using the recently developed and validated 'chemical diversity approach', the potential of chemicals, to be detected by the human olfactory system and to cause adverse health effects, was investigated. The analyses found no significant association between odor perceptibility and potential for inducing health effects.

Synergy between systemic toxicity and genotoxicity: relevance to human cancer risk

The health risk manager and policy analyst must frequently make recommendations based upon incomplete toxicity data. This is a situation which is encountered in the evaluation of human carcinogenic risks as animal cancer bioassay results are often not available. In this study, in order to assess the relevance of other possible indicators of carcinogenic risks, we used the "chemical diversity approach" to estimate the magnitude of the human carcinogenic risk based upon Salmonella mutagenicity and systemic toxicity data of the "universe of chemicals" to which humans have the potential to be exposed. Analyses of the properties of 10,000 agents representative of the "universe of chemicals" suggest that chemicals that have genotoxic potentials as well as exhibiting greater systemic toxicity are more likely to be carcinogens than non-genotoxicants or agents that exhibit lesser toxicity. Since "genotoxic" carcinogenicity is a hallmark of recognized human carcinogens, these findings are relevant to human cancer risk assessment.

A substructure-based SAR model for odor perception in humans relevant to health risk assessments

The ability of human to perceive odors is a very complex phenomenon involving the selective binding of molecules to approximately 1000 olfactory receptors. Accordingly, the derivation of a substructure-based SAR model can be expected to be problematic. Yet, based upon published data on odor thresholds of volatile organic chemicals, we were able to derive such an SAR model. An examination of the structural determinants and related modulators indicates that lipophilicity is a major contributor to olfactory perception. The availability of a substructure-based SAR model permits an examination of the relationship between the presence in the environment of odorous chemicals and public health risks.

A data mining approach for the elucidation of the action of putative etiological agents: application to the non-genotoxic carcinogenicity of genistein

A procedure designated "the virtual similarity index" (VSI) is described to determine the probability that two or more toxicants are related mechanistically. The approach is structure-activity relationship (SAR) based and generates the virtual toxicological profiles of the chemicals under investigation. It also determines the similarities between them. That commonality is compared to the frequency with which it is found among a population of 10,000 chemicals representing the "universe of chemicals". The similarities between the candidate chemicals and chemicals known to act by other recognized mechanisms are also determined. If the similarities between the candidate chemicals are significantly greater than for the non-related ones, the chemicals are assumed to act by a common mechanism. In that context, the putative non-genotoxic mechanism responsible for the carcinogenicity of genistein (GEN) and its relationship to the action of diethylstilbestrol is examined.

The emerging importance of predictive ADME simulation in drug discovery

Absorption, distribution, metabolism and excretion (ADME) studies, are widely used in drug discovery to optimize the balance of properties necessary to convert leads into good medicines. However, throughput using traditional methods is now too low to support recent developments in combinatorial and library chemistry, which have generated many more molecules of interest. To the more enlightened practitioners of ADME science, this situation is generating both the problem and the solution: an opportunity is now forming, with the use of higher throughput ADME screens and computational models, to access this wide chemical diversity and to dissect out the rules that dictate a pharmacokinetic or metabolic profile. In the future we could see ADME properties designed-in from the first principles in drug design.

Prevalence of mutagens in the environment: experimental data versus simulations

The recent availability of experimental data on the mutagenicity in Salmonella of a subset of high production volume chemicals allowed a comparison with the estimate derived from computer-based simulations. The prevalence of mutagens in the subsets was not significantly different: 20% versus 19.5% based upon experimental and simulation data, respectively. This provides support for the use of the rapid and low-cost simulation approach.

Allergic contact dermatitis and its relationship to carcinogenicity

The relationship between allergic contact dermatitis (ACD) and carcinogenicity was investigated using a recently developed and validated simulation approach. The analyses indicated that while there are electrophilic and non-electrophilic components to ACD, these were not identical to those operating in carcinogenicity. Accordingly, with respect to carcinogenicity prediction, the results of ACD do not improve the results based upon mutagenicity testing alone, the latter being a surrogate for potential electrophilicity.

Estimation of molecular similarity based on 4D-QSAR analysis: formalism and validation

The 4D-QSAR paradigm has been used to develop a formalism to estimate molecular similarity measures as a function of conformation, alignment, and atom type. It is possible to estimate the molecular similarity of pairs of molecules based upon the entire ensemble of conformational states each molecule can adopt at a given temperature, normally room temperature. Molecular similarity can be measured in terms of the types of atoms composing each molecule leading to multiple measures of molecular similarity. Multiple measures of molecular similarity can also arise from using different alignment rules to perform relative molecular similarity, RMS, analysis. An alignment independent method of determining molecular similarity measures, referred to as absolute molecular similarity, AMS, analysis has been developed. Various sets and libraries of compounds, including the amino acids, are analyzed using 4D-QSAR molecular similarity analysis to demonstrate the features of the formalism. Exploration of molecular similarity as a function of chirality, identification of common embedded 3D pharmacophores in compounds, and elucidation of 3D-isosteric compounds from structurally diverse libraries are carried out in the application studies.

Predicting human safety: screening and computational approaches

Current preclinical safety evaluation programs use a combination of computational methods, mechanistic in vitro screening and - primarily - in vivo experimentation to predict human toxicity. The rapid transition of pharmaceutical R&D into electronic R&D (e-R&D) makes it imperative that predictive safety testing also develops into an information-rich, knowledge-based process in the near future. Accordingly, enhanced databases and computational tools are expected to change the way the pharmaceutical industry assesses drug toxicity during discovery and early development. Expert use of prediction tools should lead to lower failure rates in drug development and decrease the cost and time involved in successful drug approval.

A battery of cell toxicity assays as predictors of eye irritation: a feasibility study

The newly developed "chemical diversity approach" was used to determine whether or not it is likely that a panel of in vitro cell toxicity assays capable of predicting in vivo eye irritation could be assembled. The analyses, based upon available and validated structure-activity relationship models of toxicity in cultured human HeLa cells and murine Balb/c 3T3 cells, indicate that a battery of cytotoxicity tests could provide a viable alternative to the animal-based procedure.

Structural Basis of the Toxicity of Chemicals in Cultured Human HeLa Cells

The newly developed "chemical diversity approach" was used to determine whether or not it is likely that a panel of in vitro cell toxicity assays capable of predicting in vivo eye irritation could be assembled. The analyses, based upon available and validated structure-activity relationship models of toxicity in cultured human HeLa cells and murine Balb/c 3T3 cells, indicate that a battery of cytotoxicity tests could provide a viable alternative to the animal-based procedure.

The high production volume chemical challenge program: the relevance of the in vivo micronucleus assay

The in vivo rodent bone marrow micronucleus assay (Mnt) has assumed a pivotal role in screening strategies for the identification of substances potentially carcinogenic to humans. The analysis of the results of the current international 5-year effort to provide toxicological data for high production volume chemicals will play a crucial role in developing future strategies for identifying health hazards. As part of that program, consideration is being given to accepting either in vitro genotoxicity data or results of the Mnt. The present analyses indicate that for hazard identification purposes that, in fact, in vitro genotoxicity test results, such as those derived from the Salmonella mutagenicity assay, may be an acceptable alternative.

The High Production Volume Chemical Challenge Program : The Rodent LD50 and its Possible Replacement

The High Production Volume Chemical Challenge Program provides an opportunity to re-examine the usefulness and informational value of tests currently used to obtain preliminary hazard identification data. With a view to assessing the mechanistic information provided by the rodent LD50 test and to ascertain the possibility of replacing it with other "more acceptable" assays, we used a recently developed approach to determine the relationship of the LD50 assay to other toxicological protocols. Our analyses indicate that, of the assays examined, the LD50 was significantly related to toxicity in cultured cells and to binding at the Ah receptor.

SOS chromotest and mutagenicity in Salmonella: evidence for mechanistic differences

An examination of the relationship of the experimental results obtained with chemicals tested in the SOS chromotest and for mutagenicity in Salmonella indicates that the two assays respond to different genotoxic stimuli. Furthermore, the relationship between results obtained in these assays and in rodents carcinogenicity bioassays suggests that the short-term assays respond to a different spectrum of carcinogens. The same conclusions were reached based upon an analysis of the structural features associated with these three phenomena. With respect to using these short-term assays to predict carcinogens, the present results suggest that they are not equivalent, but complement one another.