Prediction Of Rodent Carcinogenicity Research Articles

Effects of lowering the proposed top-concentration limit in an in vitro chromosomal aberration test on assay sensitivity and on the reduction of the number of false positives

For the in vitro chromosomal aberration (CA) test, the proposed top-concentration limit will be reduced to ‘10mM or 2mg/mL’ (whichever is lower) in the draft revised OECD (r-OECD) test guideline (TG) 473, down from ‘10mM or 5mg/mL’ in the current OECD TG, which was adopted in 1997 (1997-OECD). It was previously reduced to 1mM or 0.5mg/mL in the International Conference of Harmonization (ICH) S2 (R1) guideline for pharmaceuticals. Reduction of the top-concentration limit is expected to reduce the number of false or misleading positives. However, this reduction may affect the sensitivity or specificity to predict rodent carcinogenicity. Thus, the effect of a reduction in the top-concentration limit on sensitivity and specificity was investigated by use of a dataset on 435 chemicals obtained from the ‘Carcinogenicity and Genotoxicity eXperience’ (CGX) database (267 CA-positives and 168 CA-negatives; 317 carcinogens and 118 non-carcinogens) where three TGs (i.e., 1997-OECD, r-OECD and ICH) were applied. The application of the r-OECD TG did not affect the sensitivity (63.1%) or specificity (59.3%) against carcinogenicity, compared with the 1997-OECD TG (sensitivity 63.1%, specificity 59.3%). However, the application of the ICH TG had certain effects, i.e., a decrease in sensitivity (45.4%) and an increase in specificity (72.9%). A change in the number of CA-positives by the application of each TG was also investigated by use of 124 CA-positives from the Japanese Existing Chemical (JEC) database. The application of r-OECD TG showed a small reduction in CA-positives, but the ICH TG reduced this number by approximately half. More than half of the CA-positives had a molecular weight <200. These results suggest that the r-OECD TG will not affect the sensitivity or specificity for the detection of rodent carcinogens, indicating the usefulness of the guideline. However, nearly no improvement with respect to a reduction in the number of false positives should be expected.

In Vitro Perturbations of Targets in Cancer Hallmark Processes Predict Rodent Chemical Carcinogenesis

Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes to build models for priority setting and further testing. We describe a model for predicting rodent carcinogenicity based on HTS data from 292 chemicals tested in 672 assays mapping to 455 genes. All data come from the EPA ToxCast project. The model was trained on a subset of 232 chemicals with in vivo rodent carcinogenicity data in the Toxicity Reference Database (ToxRefDB). Individual HTS assays strongly associated with rodent cancers in ToxRefDB were linked to genes, pathways, and hallmark processes documented to be involved in tumor biology and cancer progression. Rodent liver cancer endpoints were linked to well-documented pathways such as peroxisome proliferator-activated receptor signaling and TP53 and novel targets such as PDE5A and PLAUR. Cancer hallmark genes associated with rodent thyroid tumors were found to be linked to human thyroid tumors and autoimmune thyroid disease. A model was developed in which these genes/pathways function as hypothetical enhancers or promoters of rat thyroid tumors, acting secondary to the key initiating event of thyroid hormone disruption. A simple scoring function was generated to identify chemicals with significant in vitro evidence that was predictive of in vivo carcinogenicity in different rat tissues and organs. This scoring function was applied to an external test set of 33 compounds with carcinogenicity classifications from the EPA's Office of Pesticide Programs and successfully (p = 0.024) differentiated between chemicals classified as "possible"/"probable"/"likely" carcinogens and those designated as "not likely" or with "evidence of noncarcinogenicity." This model represents a chemical carcinogenicity prioritization tool supporting targeted testing and functional validation of cancer pathways.

Testing computational toxicology models with phytochemicals

Computational toxicology employing quantitative structure-activity relationship (QSAR) modeling is an evidence-based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drug-related substances (e.g. active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programs evaluated at the FDA, are discussed. One software program showed very good performance for predicting non-carcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other rather than based on any one software, the performance for sensitivity was enhanced, However, Chi-square values indicated that the overall predictive performance decreases when using the two computational programs with this particular data set. This study suggests that complementary multiple computational toxicology software need to be carefully selected to improve global QSAR predictions for this complex toxicological endpoint.

Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses

The new European chemicals regulation Registration, Evaluation, Authorization and Restriction of Chemicals entered into force in June 2007 and accelerated the development of quantitative structure-activity relationship (QSAR) models for a variety of endpoints, including carcinogenicity. Here, we would like to present quantitative (continuous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screening of findings of rodent carcinogenicity for 1,481 chemicals accessible via Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected using Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and predicted carcinogenic potency expressed as a tumorgenic dose TD(50) for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good predicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for modeling and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for other toxicological endpoints.

S19: Alternative in vitro methods for carcinogenicity and mutagenicity

The detection of carcinogenic chemicals still remains a major challenge. The only carcinogenicity test accepted by regulatory authorities is the in vivo two-year bioassay. In the area of genotoxicity, although several in vitro assays are currently used, substantial in vivo testing is still required for confirmation of the genotoxic prediction. To face these challenges, the ECVAM key area on carcinogenicity and genotoxicity was established in January 2004 with the initiation of several activities related both to validation and applied research. • Ongoing validation Cell Transformation Assay in BALB/c 3T3 and SHE cells to detect genotoxic and non-genotoxic carcinogens (first international validation study involving Europe, USA and Japan). • Retrospective validation on in vitro Micronucleus Test based on the existing data and the ECVAM criteria for validation. ESAC statement on the validity of the MNT in vitro as an alternative to chromosome aberration test in vitro for genotoxicity testing was issued November 2006 (follow-up validation in vitro/in vivo MNT). • A submission of an in silico tool to predict rodent carcinogenicity is under evaluation. • Current data collection from industry to investigate reduction possibilities, e.g. omission of positive/negative controls in in vivo genotoxicity testing, reduction of the false positive in vitro results that lead to further in vivo testing (follow-up on ECVAM workshop recommendations in collaboration with COLIPA) • Inter-laboratory assessment of genotoxicity tests in target cells. • COMET assay in 3D skin model (in collaboration with COLIPA). • Toxicogenomics-based tests, e.g. exploratory project to search for biomarkers of exposure in BALB/c 3T3. ECVAM is involved also in support to OECD activities (DRP31 and TG development), REACH Implementation Project RIP 3.3.2, the validation of COMET assay coordinated by JaCVAM and the EU Competitive actions: CARCINOGENOMICS and COMICS.

Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity.

BackgroundTo develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem.ObjectivesWe have explored these data in terms of their utility for predicting adverse health effects of the environmental agents.Methods and resultsInitially, the classification k nearest neighbor (kNN) quantitative structure–activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP–HTS studies. We found that compounds classified by HTS as “actives” in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS “inactives” were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors.ConclusionsOur studies suggest that combining NTP–HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology.

A radial-distribution-function approach for predicting rodent carcinogenicity

Carcinogenic activity has been investigated using the Radial-Distribution-Function (RDF) approach. A discriminant model was developed to predict the carcinogenic and non-carcinogenic activity on a data set of 188 compounds. The percentage of overall classification was 76.4% for the carcinogenic chemicals and 72.5% for the non-carcinogenic chemicals. The predictive power of the model was validated by two tests: a cross-validation by the resubstitution technique and a test set (compounds not used in the development of the model) with 79.3 and 72.5% good classification, respectively. The RDF descriptors were compared with eight other methodologies; Constitutional, Molecular walks counts, Galvez topological charge indices, 2D autocorrelations, Randić molecular profiles, Geometrical, 3D-MORSE, and WHIM, demonstrating that the RDF descriptors are better to the rest of the approaches used.

Computer-aided rodent carcinogenicity prediction

The potential of the computer program PASS (Prediction Activity Spectra for Substances) to predict rodent carcinogenicity for chemical compounds was studied. PASS predicts carcinogenicity of chemical compounds on the basis of their structural formula and of structure–activity relationship analysis of known carcinogens and non-carcinogens. The data on structures and experimental results of 2-year carcinogenicity assays for 412 chemicals from the NTP (National Toxicological Program) and 1190 chemicals from the CPDB (Carcinogenic Potency Database) were used in our study. The predictions take into consideration information about species and sex of animals. For evaluation of the predictive accuracy we used two procedures: leave-one-out cross-validation (LOO CV) and leave-20%-out cross-validation. In the last case we randomly divided the studied data set 20 times into two subsets. The data from the first subset, containing 80% of the compounds, were added to the PASS training set (which includes about 46,000 compounds with about 1500 biological activity types collected during the last 20 years to predict biological activity spectra), the second subset with 20% of the compounds was used as an evaluation set. The mean accuracy of prediction calculated by LOO CV is about 73% for NTP compounds in the ‘equivocal’ category of carcinogenic activity and 80% for NTP compounds in the ‘evidence’ category of carcinogenicity. The mean accuracy of prediction for the CPDB database is 89.9% calculated by LOO CV and 63.4% calculated by leave-20%-out cross-validation. Influence of incorporation of species and sex data on the accuracy of carcinogenicity prediction was also investigated. It was shown that the accuracy was increased only for data on male animals.

The results of five coded compounds: genistein, metaproterenol, rotenone, p-anisidine and resorcinol tested in the pH 6.7 Syrian hamster embryo cell morphological transformation assay

The pH 6.7 Syrian hamster embryo (SHE) cell morphological transformation assay is a short-term in vitro test that has been used to predict rodent carcinogenicity. Previous reports have indicated that the SHE assay has an overall concordance of approximately 80% with the 2 year rodent bioassay. We selected five compounds, genistein, metaproterenol, rotenone, p-anisidine and resorcinol, that had extensive genotoxicity and carcinogenicity data and tested them in the standard 7 day exposure SHE assay. Somewhat surprisingly, the SHE assay misclassified the actual rodent carcinogenicity of four out of the five test compounds. It is difficult to explain these findings as the actual mechanisms of SHE cell morphological transformation are currently unknown. However, it is obvious that in these studies there was no simple correlation between in vitro genotoxicity, morphological transformation in SHE cells and rodent carcinogenicity. Clearly, further research is required to accurately assess the role of the SHE assay in the carcinogenic risk assessment of new chemical entities.

Enhanced prediction of potential rodent carcinogenicity by utilizing comet assay and apoptotic assay in combination

The comet assay has been recently validated as a sensitive and specific test system for the quantification of DNA damage. The objectives of this study are to investigate the utility of comet assay for detecting mutagens with 11 substances that demonstrated positive results in at least one test among four standard short-term genotoxicity tests, and to evaluate its ability to predict rodent carcinogenicity. Out of 11 test substances, positive comet results were obtained for colchicine, hydroxyurea and actinomycin D. No effect on DNA migration, determined as the tail moment, was found with theophylline or 2,4-dinitrophenol. Bisphenol A, vinblastine, paclitaxel and p-anisidine appeared cytotoxic clastogens because these induced tail moment at concentrations showing 60% or less cell survival. In addition, among three test substances showing the bimodal distribution of DNA damage, which is a characteristic of apoptosis, true apoptosis result was obtained for camptothecin and dexamethasone with the Annexin V affinity assay. With this limited data-set, an investigation into the predictive value of these short-term genotoxicity tests for determining the carcinogenicity showed that comet assay has relatively high sensitivity and superior specificity to other four short-term genotoxicity assay. Therefore, our data suggest that comet assay, especially in combination with apoptotic assay, would be a good predictive test to minimize false-positives in evaluation of the potential rodent carcinogenicity.

Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices

MDL QSAR (formerly SciVision QSAR IS) software is one of the several software systems under evaluation by the Informatics and Computational Safety Analysis Staff (ICSAS) of the FDA Center for Drug Evaluation and Research for regulatory and scientific decision support applications. MDL QSAR software contains an integrated set of tools for similarity searching, compound clustering, and modeling molecular structure related parameters that includes 240 electrotopological E-state, connectivity, and other descriptors. These molecular descriptors can be statistically correlated with toxicological or biological endpoints. The goal of this research was to evaluate the feasibility of using MDL QSAR software to develop structure–activity relationship (SAR) models that can be used to predict the carcinogenic potential of pharmaceuticals and organic chemicals. A validation study of 108 compounds that include 86 pharmaceuticals and 22 chemicals that were not present in a control rodent carcinogenicity data set of 1275 compounds demonstrated that MDL QSAR models had excellent coverage (93%) and good sensitivity (72%) and specificity (72%) for rodent carcinogenicity. The software correctly predicted 72% of non-carcinogenic compounds and compounds with carcinogenic findings. E-state descriptors contributed to more than half of the SAR models used to predict carcinogenic activity. W e believe that electrotopological E-state descriptors and QSAR IS (MDL QSAR) software are promising new in silico approaches for modeling and predicting rodent carcinogenicity and may have application for other toxicological endpoints.

In vitro determination of carcinogenicity of sixty‐four compounds using a bovine papillomavirus DNA‐carrying C3H/10T½ cell line

A new in vitro test for predicting rodent carcinogenicity is evaluated against a testing database of 64 chemicals including both genotoxic and nongenotoxic carcinogens and carcinogens that normally require addition of an S-9 microsomal fraction for detection in the bacterial mutagenicity assay. The assay uses focus formation in a stable, bovine papillomavirus type 1 (BPV-1) DNA carrying C3H/10T½ mouse embryo fibroblast cell line (T1) that does not require transfection, infection with virus, isolation of primary cells from animals, or addition of a microsomal fraction. Of a total database of 64 compounds, 92% of the carcinogens, promoters, or noncarcinogens were correctly predicted. Based on previously reported results, the test of bacterial mutagenicity would have correctly predicted 58% of carcinogens, promoters or noncarcinogens and the Syrian hamster embryo test would have correctly predicted 87% of carcinogens, promoters, or noncarcinogens of this database. Of carcinogens that normally require addition of an S-9 fraction, T1 cells correctly predicted rodent carcinogenicity of polyaromatic hydrocarbons, aflatoxins, azo-compounds, nitrosamines, and hydrazine without the addition of an S-9 fraction. Of nongenotoxic carcinogens, T1 cells correctly predicted diethylstilbestroel, diethylhexylphthalate, acetamides, alkyl halides, ethyl carbamate, and phorbol ester tumour promoters. Environ. Mol. Mutagen. 35:300–311, 2000 © 2000 Wiley-Liss, Inc.

In vitro determination of carcinogenicity of sixty-four compounds using a bovine papillomavirus DNA-carrying C3H/10T� cell line

A new in vitro test for predicting rodent carcinogenicity is evaluated against a testing database of 64 chemicals including both genotoxic and nongenotoxic carcinogens and carcinogens that normally require addition of an S-9 microsomal fraction for detection in the bacterial mutagenicity assay. The assay uses focus formation in a stable, bovine papillomavirus type 1 (BPV-1) DNA carrying C3H/10T(1/2) mouse embryo fibroblast cell line (T1) that does not require transfection, infection with virus, isolation of primary cells from animals, or addition of a microsomal fraction. Of a total database of 64 compounds, 92% of the carcinogens, promoters, or noncarcinogens were correctly predicted. Based on previously reported results, the test of bacterial mutagenicity would have correctly predicted 58% of carcinogens, promoters or noncarcinogens and the Syrian hamster embryo test would have correctly predicted 87% of carcinogens, promoters, or noncarcinogens of this database. Of carcinogens that normally require addition of an S-9 fraction, T1 cells correctly predicted rodent carcinogenicity of polyaromatic hydrocarbons, aflatoxins, azo-compounds, nitrosamines, and hydrazine without the addition of an S-9 fraction. Of nongenotoxic carcinogens, T1 cells correctly predicted diethylstilbestroel, diethylhexylphthalate, acetamides, alkyl halides, ethyl carbamate, and phorbol ester tumour promoters.

Predicting rodent carcinogenicity using potency measures of the in vitro sister chromatid exchange and chromosome aberration assays

In vitro sister chromatid exchange (SCE) and chromosome aberration (ABS) tests have been extensively used to identify potential rodent carcinogens. A number of measures of potency were developed to describe in vitro SCE and ABS test results: the dose needed to induce a unit increase over the control; the lowest effective dose; the slope of the ordinary linear regression; the maximum observed slope; and the maximum fold increase over background. The ability of these potency measures to predict the qualitative and quantitative carcinogenicity of chemicals was compared to the predictivity of the qualitative in vitro responses. The results of the analyses showed that the quantitative measures of the SCE or ABS responses only minimally increased the predictivity of carcinogenesis when compared to the predictivity using the qualitative responses.

Predicting rodent carcinogenicity using potency measures of the in vitro sister chromatid exchange and chromosome aberration assays

Predicting rodent carcinogenicity using potency measures of the in vitro sister chromatid exchange and chromosome aberration assays

Prediction of rodent carcinogenicity utilizing a battery of in vitro and in vivo genotoxicity tests

Prediction of rodent carcinogenicity utilizing a battery of in vitro and in vivo genotoxicity tests

Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity

In this paper, we report on a multi-year collaboration among computer scientists, toxicologists, chemists, and a statistician, in which the RL induction program was used to assist toxicologists in analyzing relationships among various features of chemical compounds and their carcinogenicity in rodents. Our investigation demonstrated the utility of knowledge-based rule induction in the problem of predicting rodent carcinogenicity and the place of rule induction in the overall process of discovery. Flexibility of the program in accepting different definitions of background knowledge and preferences was considered essential in this exploratory effort. This investigation has made significant contributions not only to predicting carcinogenicity and non-carcinogenicity in rodents, but to understanding how to extend a rule induction program into an exploratory data analysis tool.

Further evaluation of COMPACT, the molecular orbital approach for the prospective safety evaluation of chemicals

The molecular dimensions and electronic structures of the first group of 100 US NCI/NTP miscellaneous chemicals, evaluated for potential carcinogenicity by computer-optimized molecular parametric analysis for chemical toxicity (COMPACT) have been re-determined. Using improved criteria for cytochrome P450 (CYP) substrate specificity, re-defined for CYP1 as having a COMPACT radius (ΔE−9.5) 2+(a/d 2−7.8) 2 of <6.5, and for CYP2E as having a collision diameter of 6.5Å or less and Δ E<15.5, the likely substrates of CYP1 and CYP2E, which are regarded as potential carcinogens, have been identified. In addition, log P values have been taken into account; those chemicals with log P<0 are non-lipophilic substrates unlikely to reach the activating cytochrome enzymes, and have been regarded as non-carcinogens. The second group of 100 US NCI/NTP chemicals have also now been categorized by COMPACT into CYP1 and CYP2E substrates, and their potential carcinogenicities evaluated. Of the 203 chemicals in the 2 groups, those positive in the rodent two-species life-span carcinogenicity study (rodent assay) were 53%, those positive in the Ames test (mutagenicity) were 48%, and those positive in the COMPACT programme (carcinogenicity, mutagenicity, cytotoxicity) were 54%. Concordance between the COMPACT prediction of carcinogenicity/cytotoxicity and rodent two species life-span carcinogenicity data for the 203 chemicals is 69%, and correlation of COMPACT with Ames test data is 61%. The sensitivity of COMPACT for predicting rodent carcinogenicity is 72%, whereas the sensitivity of the Ames test for predicting carcinogenicity for the 203 chemicals was only 57%. The degree (severity) of rodent carcinogenicity also showed correlation with the COMPACT predictive evaluations of the chemicals.

P XIX.1 - P XIX.1 Prediction of rodent carcinogenicity by using a battery of in vitro and in vivo mutation tests

P XIX.1 - P XIX.1 Prediction of rodent carcinogenicity by using a battery of in vitro and in vivo mutation tests

Predicting rodent carcinogenicity from mutagenic potency measured in the Ames Salmonella assay.

Many in vitro tests have been developed to identify chemicals that can damage cellular DNA or cause mutations, and secondarily to identify potential carcinogens. The test receiving by far the most use and attention has been the Salmonella (SAL) mutagenesis test developed by Ames and colleagues [(1973): Proc Natl Acad Sci USA 70:2281-2285; (1975): Mutat Res 31:347-364], because of its initial promise of high qualitative (YES/NO) predictivity for cancer in rodents and, by extension, in humans. In addition to the initial reports of high qualitative predictivity, there was also an early report by Meselson and Russell [in Hiatt HH et al (1977): "Origins of Human Cancer, Book C: Human Risk Assessment," pp 1473-1481] of a quantitative relationship between mutagenic potency measured in SAL and carcinogenic potency measured in rodents, for a small number of chemicals. However, other reports using larger numbers of chemicals have found only very weak correlations. The primary purpose of this study was to determine whether mutagenic potency, as measured in a number of different ways, could be used to improve predictivity of carcinogenicity, either qualitatively or quantitatively. To this end, eight measures of SAL mutagenic potency were used. This study firmly establishes that the predictive relationship between mutagenic potency in SAL and rodent carcinogenicity is, at best, weak. When predicting qualitative carcinogenicity, only qualitative mutagenicity is useful; none of the quantitative measures of potency considered improves the carcinogenicity prediction. In fact, when qualitative mutagenicity is forced out of the model, the quantitative measures are still not predictive of carcinogenicity. When predicting quantitative carcinogenicity, several possible methods were considered for summarizing potency over all experiments; however, in all cases, the relationship between mutagenic potency predictors and quantitative carcinogenicity is very weak.