Kirk Arvidson

Understanding Genetic Toxicity Through Data Mining: The Process of Building Knowledge by Integrating Multiple Genetic Toxicity Databases

ABSTRACT Genetic toxicity data from various sources were integrated into a rigorously designed database using the ToxML schema. The public database sources include the U.S. Food and Drug Administration (FDA) submission data from approved new drug applications, food contact notifications, generally recognized as safe food ingredients, and chemicals from the NTP and CCRIS databases. The data from public sources were then combined with data from private industry according to ToxML criteria. The resulting “integrated” database, enriched in pharmaceuticals, was used for data mining analysis. Structural features describing the database were used to differentiate the chemical spaces of drugs/candidates, food ingredients, and industrial chemicals. In general, structures for drugs/candidates and food ingredients are associated with lower frequencies of mutagenicity and clastogenicity, whereas industrial chemicals as a group contain a much higher proportion of positives. Structural features were selected to analyze endpoint outcomes of the genetic toxicity studies. Although most of the well-known genotoxic carcinogenic alerts were identified, some discrepancies from the classic Ashby-Tennant alerts were observed. Using these influential features as the independent variables, the results of four types of genotoxicity studies were correlated. High Pearson correlations were found between the results of Salmonella mutagenicity and mouse lymphoma assay testing as well as those from in vitro chromosome aberration studies. This paper demonstrates the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of tests rather than relying on a single toxicity test of a given chemical. This paper presents data mining/profiling methods applied in a weight-of-evidence approach to assess potential for genetic toxicity, and to guide the development of intelligent testing strategies.

New Publicly Available Chemical Query Language, CSRML, To Support Chemotype Representations for Application to Data Mining and Modeling

Chemotypes are a new approach for representing molecules, chemical substructures and patterns, reaction rules, and reactions. Chemotypes are capable of integrating types of information beyond what is possible using current representation methods (e.g., SMARTS patterns) or reaction transformations (e.g., SMIRKS, reaction SMILES). Chemotypes are expressed in the XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be encoded not only with connectivity and topology but also with properties of atoms, bonds, electronic systems, or molecules. CSRML has been developed in parallel with a public set of chemotypes, i.e., the ToxPrint chemotypes, which are designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space, as well as to represent chemical patterns and properties especially relevant to various toxicity concerns. A software application, ChemoTyper has also been developed and made publicly available in order to enable chemotype searching and fingerprinting against a target structure set. The public ChemoTyper houses the ToxPrint chemotype CSRML dictionary, as well as reference implementation so that the query specifications may be adopted by other chemical structure knowledge systems. The full specifications of the XML-based CSRML standard used to express chemotypes are publicly available to facilitate and encourage the exchange of structural knowledge.

A structural feature-based computational approach for toxicology predictions

Importance of the field: Evaluation of pharmaceutical-related toxicities using quantitative structure–activity relationship (QSAR) software as decision support tools is becoming practical and is of keen interest to scientists in both product safety and discovery. QSARs can be used to predict preclinical and clinical endpoints, drug metabolism, pharmacokinetics and mechanisms responsible for toxicity. These in silico tools are of interest in supporting regulatory review processes, and priority setting in research and product development. Areas covered in this review: A critical assessment of the current capabilities of a new technology, the Leadscope Model Applier, is presented. Possible strengths and limitations of this technology with emphasis on the chemoinformatics method are described, and supporting literature citations date back to 1983. What the reader will gain: Insight will be gained into the Leadscope Model Applier technology for structural feature-based QSAR models and its potential capability for chemical inference if the training sets are transparently open. Currently, however, there is a lack of transparency due to the protection of the proprietary training set. Take home message: Further research and development is needed in the creation of more stringently validated models with greater transparency and better balance between sensitivity and specificity.

In Silico Toxicological Screening of Natural Products

ABSTRACT This study closely examines six well-known naturally occurring dietary chemicals (estragole, pulegone, aristolochic acid I, lipoic acid, 1-octacosanol, and epicatechin) with known human exposure, chemical metabolism, and mechanism of action (MOA) using in silico screening methods. The goal of this study was to take into consideration the available information on these chemicals in terms of MOA and experimentally determined toxicological data, and compare them to the in silico predictive modeling results produced from a series of computational toxicology software. After these analyses, a consensus modeling prediction was formulated in light of the weight of evidence for each natural product. We believe this approach of examining the experimentally determined mechanistic data for a given chemical and comparing it to in silico generated predictions and data mining is a valid means to evaluating the utility of the computational software, either alone or in combination with each other. We find that consensus predictions appear to be more accurate than the use of only one or two software programs and our in silico results are in very good agreement with the experimental toxicity data for the natural products screened in this study.

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.

Regulatory use of computational toxicology tools and databases at the United States Food and Drug Administration's Office of Food Additive Safety.

Over 10 years ago, the Office of Food Additive Safety (OFAS) in the FDAs Center for Food Safety and Applied Nutrition implemented the formal use of structure–activity relationship analysis and quantitative structure–activity relationship (QSAR) analysis in the premarket review of food-contact substances. More recently, OFAS has implemented the use of multiple QSAR software packages and has begun investigating the use of metabolism data and metabolism predictive models in our QSAR evaluations of food-contact substances. In this article, we provide an overview of the programs used in OFAS as well as a perspective on how to apply multiple QSAR tools in the review process of a new food-contact substance.

FDA toxicity databases and real-time data entry

Structure-searchable electronic databases are valuable new tools that are assisting the FDA in its mission to promptly and efficiently review incoming submissions for regulatory approval of new food additives and food contact substances. The Center for Food Safety and Applied Nutritions Office of Food Additive Safety (CFSAN/OFAS), in collaboration with Leadscope, Inc., is consolidating genetic toxicity data submitted in food additive petitions from the 1960s to the present day. The Center for Drug Evaluation and Research, Office of Pharmaceutical Sciences Informatics and Computational Safety Analysis Staff (CDER/OPS/ICSAS) is separately gathering similar information from their submissions. Presently, these data are distributed in various locations such as paper files, microfiche, and non-standardized toxicology memoranda. The organization of the data into a consistent, searchable format will reduce paperwork, expedite the toxicology review process, and provide valuable information to industry that is currently available only to the FDA. Furthermore, by combining chemical structures with genetic toxicity information, biologically active moieties can be identified and used to develop quantitative structure-activity relationship (QSAR) modeling and testing guidelines. Additionally, chemicals devoid of toxicity data can be compared to known structures, allowing for improved safety review through the identification and analysis of structural analogs. Four database frameworks have been created: bacterial mutagenesis, in vitro chromosome aberration, in vitro mammalian mutagenesis, and in vivo micronucleus. Controlled vocabularies for these databases have been established. The four separate genetic toxicity databases are compiled into a single, structurally-searchable database for easy accessibility of the toxicity information. Beyond the genetic toxicity databases described here, additional databases for subchronic, chronic, and teratogenicity studies have been prepared.

Thresholds of Toxicological Concern for cosmetics-related substances: New database, thresholds, and enrichment of chemical space

A new dataset of cosmetics-related chemicals for the Threshold of Toxicological Concern (TTC) approach has been compiled, comprising 552 chemicals with 219, 40, and 293 chemicals in Cramer Classes I, II, and III, respectively. Data were integrated and curated to create a database of No-/Lowest-Observed-Adverse-Effect Level (NOAEL/LOAEL) values, from which the final COSMOS TTC dataset was developed. Criteria for study inclusion and NOAEL decisions were defined, and rigorous quality control was performed for study details and assignment of Cramer classes. From the final COSMOS TTC dataset, human exposure thresholds of 42 and 7.9 μg/kg-bw/day were derived for Cramer Classes I and III, respectively. The size of Cramer Class II was insufficient for derivation of a TTC value. The COSMOS TTC dataset was then federated with the dataset of Munro and colleagues, previously published in 1996, after updating the latter using the quality control processes for this project. This federated dataset expands the chemical space and provides more robust thresholds. The 966 substances in the federated database comprise 245, 49 and 672 chemicals in Cramer Classes I, II and III, respectively. The corresponding TTC values of 46, 6.2 and 2.3 μg/kg-bw/day are broadly similar to those of the original Munro dataset.

CHAPTER 22 – Carcinogenesis: mechanisms and models*

Animals have always been exposed to thousands of chemical substances in their daily lives. This exposure may come from the food they eat, the water they drink, the air they breathe, etc. A high level of exposure to many of these chemicals may cause cancer in humans and animals. Cancer is a term that is commonly used to indicate a group of diseases characterized by uncontrolled cell proliferation and usually the spread of these abnormal cells. The three main classes of agents (carcinogens) causing cancers are chemicals, radiation, and viruses. In this chapter, chemical carcinogenesis is emphasized, and viral and radiation carcinogenesis is discussed briefly. Cancer has become an increasingly prominent disease in recent times, but incidence of cancer is documented through writings thousands of years ago. Recent advances in molecular genetics have provided researchers with additional tools to study the mechanisms and the molecular biology of cancer. The knowledge gained from such studies form the foundation of ones understanding of the process of carcinogenesis.