Carcinogenic Potency Database Research Articles

Lack of human tissue-specific correlations for rodent pancreatic and colorectal carcinogens

To better understand the relationships between chemical exposures and human cancer causation, incidence data for human cancer types were identified and pancreatic and colorectal cancers were studied in-depth to assess whether data supporting the causation of pancreatic or colorectal tumors by chemicals in rodents is predictive of causation by the same chemicals of the same tumors in humans. A search of the Carcinogenic Potency Database, the National Toxicology Program (NTP) technical report database, and the published literature identified 38 and 39 chemicals reported to cause pancreatic and colorectal tumors, respectively, in mice or rats. For each of these chemicals, searches were conducted of the International Agency for Research on Cancer monographs, the NTP Report on Carcinogens, and the published literature for evidence of induction of the same tumors in humans. Based on this evaluation, no conclusive evidence was identified to suggest that chemicals reported to cause pancreatic or colorectal tumors in rodents also cause these tumors in humans. These findings suggest that pancreatic tumor data from mouse and rat bioassays are of limited utility with regard to predicting similar tumor induction in humans. For colorectal cancer, a lack of correlation was noted for the vast majority of chemicals.

Chemical structure determines target organ carcinogenesis in rats

SAR models were developed for 12 rat tumour sites using data derived from the Carcinogenic Potency Database. Essentially, the models fall into two categories: Target Site Carcinogen–Non-Carcinogen (TSC-NC) and Target Site Carcinogen–Non-Target Site Carcinogen (TSC-NTSC). The TSC-NC models were composed of active chemicals that were carcinogenic to a specific target site and inactive ones that were whole animal non-carcinogens. On the other hand, the TSC-NTSC models used an inactive category also composed of carcinogens but to any/all other sites but the target site. Leave one out (LOO) validations produced an overall average concordance value for all 12 models of 0.77 for the TSC-NC models and 0.73 for the TSC-NTSC models. Overall, these findings suggest that while the TSC-NC models are able to distinguish between carcinogens and non-carcinogens, the TSC-NTSC models are identifying structural attributes that associate carcinogens to specific tumour sites. Since the TSC-NTSC models are composed of active and inactive compounds that are genotoxic and non-genotoxic carcinogens, the TSC-NTSC models may be capable of deciphering non-genotoxic mechanisms of carcinogenesis. Together, models of this type may also prove useful in anticancer drug development since they essentially contain chemical moieties that target a specific tumour site.

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.

New public QSAR model for carcinogenicity.

BackgroundOne of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.ResultsModels for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.ConclusionCarcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

Applicability of thresholds of toxicological concern in the dietary risk assessment of metabolites, degradation and reaction products of pesticides

Applicability of thresholds of toxicological concern in the dietary risk assessment of metabolites, degradation and reaction products of pesticides

Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals

One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fill the gaps on the toxicological properties of chemicals that affect human health. Carcinogenicity is one of the endpoints under consideration. The information obtained from (quantitative) structure–activity relationship ((Q)SAR) models is accepted as an alternative solution to avoid expensive and time-consuming animal tests. The reported results were obtained within the framework of the European project ‘Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR)’. In this article, we demonstrate intermediate results for counter propagation artificial neural network (CP ANN) models for the prediction category of the carcinogenic potency using two-dimensional (2D) descriptors from different software programs. A total of 805 non-congeneric chemicals were extracted from the Carcinogenic Potency Database (CPDBAS). The resulting models had prediction accuracies for internal (training) and external (test) sets as high as 91–93% and 68–70%, respectively. The sensitivity and specificity of the test set were 69–73 and 63–72% correspondingly. High specificity is critical in models for regulatory use that are aimed at ensuring public safety. Thus, the errors that give rise to false negatives are much more relevant. We discuss how we can increase the number of correctly predicted carcinogens using the correlation between the threshold and the values of the sensitivity and specificity.

Chemical Carcinogenesis of the Gastrointestinal Tract in Rodents: An Overview with Emphasis on NTP Carcinogenesis Bioassays

Cancers of the stomach and large intestine (LI) are the second and fourth leading causes of human cancer mortality. A review of the National Toxicology Program (NTP) database and the Carcinogenic Potency Database (CPDB) reveals that chemically induced neoplasms of the gastrointestinal tract (GIT) are relatively common. Within the GIT, epithelial tumors of the forestomach in mice and rats and LI of the rat are most common. Generally, there is a high species concordance for forestomach with at least 26 chemicals inducing tumors in both species. Glandular stomach tumors are rare, and the few reported are usually neuroendocrine tumors (carcinoids) originating from the enterochromaffin-like (ECL) cells. Of 290 carcinogenic agents identified by the NTP, 19 (7%) caused intestinal neoplasia, 14 in the rat and 5 in the mouse. Neoplasms occurred in both males and females, exclusively in the small intestine (SI) of the mouse and in the LI or both SI and LI in the rat. Enteric carcinogens (NTP) frequently induced neoplasms at other alimentary sites (oral cavity, esophagus, and stomach). In conclusion, the most common induced GIT tumors are squamous neoplasms of the forestomach, glandular neoplasms of the stomach are rare, and rats appear more prone to developing LI (colorectal) cancer compared to mice.

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.

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

A dataset comprising 55 chemicals with hepatocarcinogenic potency indices was collected from the Carcinogenic Potency Database with the aim of developing QSAR models enabling prediction of the above unwanted property for New Chemical Entities. The dataset was rationally split into training and test sets by means of a sphere-exclusion type algorithm. Among the many algorithms explored to search regression models, only a Support Vector Machine (SVM) method led to a QSAR model, which was proved to pass rigorous validation criteria, in accordance with the OECD guidelines. The proposed model is capable to explain the hepatocarcinogenic toxicity and could be exploited for predicting this property for chemicals at the early stage of their development, so optimizing resources and reducing animal testing.

CPDB: Carcinogenic Potency Database

ABSTRACT The Carcinogenic Potency Database reports analyses of animal cancer tests on 1,547 chemicals. These tests are used in support of cancer risk assessments for humans. Results are searchable and are made available via the National Library of Medicine's (NLM) TOXNET system. This column will provide background information on the database, as well as present search basics.

Quantitative Structure−Carcinogenicity Relationship for Detecting Structural Alerts in Nitroso Compounds: Species, Rat; Sex, Female; Route of Administration, Gavage

Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.

Analysis of 2,3,7,8-Tetrachlorodibenzo-p-dioxin-induced Proteome Changes in 5L Rat Hepatoma Cells Reveals Novel Targets of Dioxin Action Including the Mitochondrial Apoptosis Regulator VDAC2

As part of a comprehensive survey of the impact of the environmental pollutant and hepatocarcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the proteome of hepatic cells, we have performed a high resolution two-dimensional gel electrophoresis study on the rat hepatoma cell line 5L. 78 protein species corresponding to 73 different proteins were identified as up- or down-regulated following exposure of the cells to 1 nm TCDD for 8 h. There was an overlap of only nine proteins with those detected as altered by TCDD in our recent study using the non-gel-based isotope-coded protein label method (Sarioglu, H., Brandner, S., Jacobsen, C., Meindl, T., Schmidt, A., Kellermann, J., Lottspeich, F., and Andrae, U. (2006) Quantitative analysis of 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced proteome alterations in 5L rat hepatoma cells using isotope-coded protein labels. Proteomics 6, 2407-2421) indicating a strong complementarity of the two approaches. For the majority of the altered proteins, an effect of TCDD on their abundance or posttranslational modifications had not been known before. Several observations suggest that a sizable fraction of the proteins with altered abundance was induced as an adaptive response to TCDD-induced oxidative stress that was demonstrated using the fluorescent probe dihydrorhodamine 123. A prominent group of these proteins comprised various enzymes for which there is evidence that their expression is regulated via the Keap1/Nrf2/antioxidant response element pathway. Other proteins included several involved in the maintenance of mitochondrial energy production and the regulation of the mitochondrial apoptotic pathway. A particularly intriguing finding was the up-regulation of the mitochondrial outer membrane pore protein, voltage-dependent anion channel-selective protein 2 (VDAC2), which was dependent on the presence of a functional aryl hydrocarbon receptor. The regulatability of VDAC2 protein abundance has not been described previously. In view of the recently discovered central role of VDAC2 as an inhibitor of the activation of the proapoptotic protein BAK and the mitochondrial apoptotic pathway, the present data point to a hitherto unrecognized mechanism by which TCDD may affect cellular homeostasis and survival.

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.

Structure–activity relationship analysis tools: Validation and applicability in predicting carcinogens

OncoLogic ® and MultiCASE (MCASE) are two structure–activity relationship (SAR) analysis software programs available to screen compounds for potential carcinogenicity. Ashby–Tennant structural alerts [Ashby, J., Tennant, R.W., 1991. Definitive relationships among chemical structure, carcinogenicity, and mutagenicity for 301 chemicals tested by the US NTP. Mutat. Res. 257, 229–306] and genetic toxicity testing may also be used to assess/predict this endpoint. Six-hundred and fifty compounds tested for carcinogenicity whose results were tabulated in the Carcinogenic Potency Database (CPDB) were used to validate and compare the predictivity of OncoLogic ® (Version 4.1), MCASE (Version 3.1), Ashby–Tennant structural alerts, and genetic toxicity testing, individually and in combination. The sensitivity of the methods for predicting carcinogens and the specificity for predicting non-carcinogens was examined. Potent carcinogens, defined as those with TD 50 values of less than 6.25 mg/kg bw/d, were then examined separately. It is concluded that SAR analysis programs and structural alerts perform well for compounds with low human exposure levels and have the potential to supplement the results of routinely requested genetic toxicity tests in a weight-of-evidence approach in predicting carcinogenicity, although each method of analysis has limitations regarding applicability.

Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds

Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, theoretical approaches such as the one proposed here – Quantitative Structure–Activity Relationship (QSAR) – are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the Topological Substructural Molecular Design (TOPS-MODE) approach, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises nitrosoureas (14 chemicals), N-nitrosamines (18 chemicals) C-nitroso-compounds (1 chemical), nitrosourethane (1 chemical) and nitrosoguanidine (1 chemical), which have been bioassayed in male rat using gavage as the route of administration. Here we are especially concerned in gathering the role of both parameters on the carcinogenic activity of this family of compounds. First, the regression model was derived, upon removal of one identified nitrosamine outlier, and was able to account for more than 84% of the variance in the experimental activity. Second, the TOPS-MODE approach afforded the bond contributions – expressed as fragment contributions to the carcinogenic activity – that can be interpreted and provide tools for better understanding the mechanisms of carcinogenesis. Finally, and most importantly, we demonstrate the potentialities of this approach towards the recognition of structural alerts for carcinogenicity predictions.

SMIREP: Predicting Chemical Activity from SMILES

Most approaches to structure-activity-relationship (SAR) prediction proceed in two steps. In the first step, a typically large set of fingerprints, or fragments of interest, is constructed (either by hand or by some recent data mining techniques). In the second step, machine learning techniques are applied to obtain a predictive model. The result is often not only a highly accurate but also hard to interpret model. In this paper, we demonstrate the capabilities of a novel SAR algorithm, SMIREP, which tightly integrates the fragment and model generation steps and which yields simple models in the form of a small set of IF-THEN rules. These rules contain SMILES fragments, which are easy to understand to the computational chemist. SMIREP combines ideas from the well-known IREP rule learner with a novel fragmentation algorithm for SMILES strings. SMIREP has been evaluated on three problems: the prediction of binding activities for the estrogen receptor (Environmental Protection Agency's (EPA's) Distributed Structure-Searchable Toxicity (DSSTox) National Center for Toxicological Research estrogen receptor (NCTRER) Database), the prediction of mutagenicity using the carcinogenic potency database (CPDB), and the prediction of biodegradability on a subset of the Environmental Fate Database (EFDB). In these applications, SMIREP has the advantage of producing easily interpretable rules while having predictive accuracies that are comparable to those of alternative state-of-the-art techniques.

Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity

lazar is a new tool for the prediction of toxic properties of chemical structures. It derives predictions for query structures from a database with experimentally determined toxicity data. lazar generates predictions by searching the database for compounds that are similar with respect to a given toxic activity and calculating the prediction from their activities. Apart form the prediction, lazar provides the rationales (structural features and similar compounds) for the prediction and a reliable condence index that indicates, if a query structure falls within the applicability domain of the training database.Leave-one-out (LOO) crossvalidation experiments were carried out for 10 carcinogenicity endpoints ({female/male} {hamster/mouse/rat} carcinogenicity and aggregate endpoints {hamster/mouse/rat} carcinogenicity and rodent carcinogenicity) and Salmonella mutagenicity from the Carcinogenic Potency Database (CPDB). An external validation of Salmonella mutagenicity predictions was performed with a dataset of 3895 structures. Leave-one-out and external validation experiments indicate that Salmonella mutagenicity can be predicted with 85% accuracy for compounds within the applicability domain of the CPDB. The LOO accuracy of lazar predictions of rodent carcinogenicity is 86%, the accuracies for other carcinogenicity endpoints vary between 78 and 95% for structures within the applicability domain.

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.

Supplement to the Carcinogenic Potency Database (CPDB): Results of Animal Bioassays Published in the General Literature through 1997 and by the National Toxicology Program in 1997–1998

The Carcinogenic Potency Database (CPDB) is a systematic and unifying resource that standardizes the results of chronic, long-term animal cancer tests which have been conducted since the 1950s. The analyses include sufficient information on each experiment to permit research into many areas of carcinogenesis. Both qualitative and quantitative information is reported on positive and negative experiments that meet a set of inclusion criteria. A measure of carcinogenic potency, TD(50) (daily dose rate in mg/kg body weight/day to induce tumors in half of test animals that would have remained tumor-free at zero dose), is estimated for each tissue-tumor combination reported. This article is the ninth publication of a chronological plot of the CPDB; it presents results on 560 experiments of 188 chemicals in mice, rats, and hamsters from 185 publications in the general literature updated through 1997, and from 15 Reports of the National Toxicology Program in 1997-1998. The test agents cover a wide variety of uses and chemical classes. The CPDB Web Site (http://potency.berkeley.edu/) presents the combined database of all published plots in a variety of formats as well as summary tables by chemical and by target organ, supplemental materials on dosing and survival, a detailed guide to using the plot formats, and documentation of methods and publications. The overall CPDB, including the results in this article, presents easily accessible results of 6153 experiments on 1485 chemicals from 1426 papers and 429 NCI/NTP (National Cancer Institute/National Toxicology program) Technical Reports. A tab-separated format of the full CPDB for reading the data into spreadsheets or database applications is available on the Web Site.

BUILDING AN ORGAN-SPECIFIC CARCINOGENIC DATABASE FOR SAR ANALYSES

FDA reviewers need a means to rapidly predict organ-specific carcinogenicity to aid in evaluating new chemicals submitted for approval. This research addressed the building of a database to use in developing a predictive model for such an application based on structure–activity relationships (SAR). The Internet availability of the Carcinogenic Potency Database (CPDB) provided a solid foundation on which to base such a model. The addition of molecular structures to the CPDB provided the extra ingredient necessary for SAR analyses. However, the CPDB had to be compressed from a multirecord to a single record per chemical database; multiple records representing each gender, species, route of administration, and organ-specific toxicity had to be summarized into a single record for each study. Multiple studies on a single chemical had to be further reduced based on a hierarchical scheme. Structural cleanup involved removal of all chemicals that would impede the accurate generation of SAR type descriptors from commercial software programs; that is, inorganic chemicals, mixtures, and organometallics were removed. Counterions such as Na, K, sulfates, hydrates, and salts were also removed for structural consistency. Structural modification sometimes resulted in duplicate records that also had to be reduced to a single record based on the hierarchical scheme. The modified database containing 999 chemicals was evaluated for liver-specific carcinogenicity using a variety of analysis techniques. These preliminary analyses all yielded approximately the same results with an overall predictability of about 63%, which was comprised of a sensitivity of about 30% and a specificity of about 77%.