Special Issue "Advances in Computational Toxicology"

Guest Editor
Prof. Dr. Dale Johnson
¹ Emiliem, Inc., 6027 Christie Avenue, Emeryville, CA 94608, USA
² University of California, Berkeley, Morgan Hall, Berkeley, CA 94720, USA
Website: http://nst.berkeley.edu/faculty/johnson.html
E-Mail: daleejohnson@sbcglobal.net
Phone: +1 (510) 642-7870
Fax: +1 (510) 642-0535

Dear Colleagues,

Computational toxicology is an expanding research area that is becoming a multi-disciplinary fusion of bioinformatics and computational sciences with molecular biology and chemistry. The goal is to create more predictive power in the field of toxicology as it applies to both environmental and therapeutic issues.  The field relies on the application of computer technology and mathematical / computational methods to analyze, model, and predict potential toxicological effects from chemical structures, exposure characteristics, and networks of biological pathways affected by chemicals. The field is progressing rapidly due to increased availability of larger and better curated public databases and open-source predictive tools. Newer technologies for large scale data acquisition and the prediction of biological effects using systems biology methodology are expected to expand the scale and complexity of inquiry to a point where data gaps can be filled with predicted values with a high level of confidence.

Prof. Dr. Dale Johnson
Guest Editor

Submission

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• structural alerts
• analog identification
• categorization
• SAR
• QSAR
• biological pathway perturbations
• systems biology
• bioaccumulation
• persistence
• high-throughput screening
• high-content screening
• toxicogenomics
• metabolomics
• biomonitoring

Type of Paper: Article
Title: Structure-Activity Models for Chemical Inhalation Health Guidance Values
Authors: Catharine J. Collar ¹, Thomas Miller ², Robert M. Garrett ² and Eugene Demchuk ¹
Affiliations: ¹ Agency for Toxic Substances and Disease Registry, Atlanta, GA, USA; E-Mail: eed5@cdc.gov (E.D.)
² Department of Defense, Washington, DC, USA
Abstract: Acute Exposure Guideline Levels (AEGLs) are thoroughly examined data for airborne concentrations of hazardous chemicals. These chemical concentrations are defined as the human threshold limits at five exposure periods ranging from ten minutes to eight hours. Health effects are categorized by the National Academy of Sciences (NAS) as non-disabling (AEGL-1), disabling (AEGL-2), and lethal (AEGL-3). However, AEGLs are finalized for less than sixty compounds, and less than two-hundred are considered interim; far less than the number of potential airborne hazards to the human population. We employed the AEGLs data at the eight hour exposure period, to construct quantitative structure-activity relationship (QSAR) models capable of predicting provisional health guidance values (HGVs) for airborne chemical hazards that have no guidance assigned by the NAS. These models are statistically significant with R2 and Q2 values greater than 0.70 and 0.50, respectively. Since QSAR modeling is driven by uniformity and quantity of underlying data, point-of-departure and endpoint grouping factors can be employed to gain insight into how the model is formulating HGV estimates and what can be done to increase the estimation performance. Hence, we have begun this process by assessing the point-ofdeparture information. The AEGLs datasets were split into subsets of human and rat data and new models were built. With statistical significance comparable or better than parent models, these speciesspecific models were individually analyzed and also employed to validate each other; the compounds of the first model were used as a testing dataset for the second model and vice versa. In the future we plan to: (1) employ additional endpoint grouping factors, (2) model the remaining four time periods, and (3) incorporate other HGVs into the models.

Title: Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of CYP3A4 and CYP2D6 Enzymes
Authors: Brooks McPhail ¹, Yunfeng Tie ¹, Huixiao Hong ², Bruce A. Pearce ², Laura Schnackenberg ², Luis G. Valerio, Jr. ³, Eugene Demchuk ¹, James C. Fuscoe ², Weida Tong ², Dan Buzatu ², Jon G. Wilkes ², Bruce Fowler ¹ and Richard D. Beger ²
Affiliations: ¹ Division of Toxicology, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta GA 30333, USA; E-Mail: edemchuk@cdc.gov
² Division of Systems Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA
³ Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, 20993 USA
Abstract: An interagency collaboration was established to model adverse interactions with cytochrome P450 (CYP P450) enzymes.  We used spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches to categorize inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 enzymes.  The models were trained using 602 reference compounds, and an additional 100 chemicals were used to evaluate model performance in an external validation test.  SDAR is a novel modeling approach that relies on discriminant analysis applied to binned 1D ¹³C and ¹⁵N nuclear magnetic resonance (NMR) spectral descriptors.  For the first time, the present work introduces SDAR modeling in combined 1D ¹³C and 1D ¹⁵N NMR domains.  It was found that increasing the binning size of 1D ¹³C NMR and ¹⁵N NMR spectra caused an increase in the leave-10%-out cross-validation (CV) performance both in terms of accuracy and sensitivity.  The results of SDAR modeling were verified using SAR. For SAR modeling a decision forest approach utilizing from 6 to 17 Mold² descriptors was used.  Average accuracies of SDAR and SAR models in 100 leave-10%-out CV tests were 60% and 61% for CYP3A4; and 62% and 70% for CYP2D6, respectively.  The accuracies of SDAR and SAR models in the external validation test were 73% and 86% for CYP3A4; and 76% and 90% for CYP2D6, respectively.  Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data.  Based on unique structural descriptors, the new SDAR modeling method complements the existing SAR and QSAR techniques providing an independent estimator that can increase confidence in structure-activity assessment.

Type of Paper: Review
Title: Aggregating Data for Computational Toxicology Applications: The EPA ACToR System
Authors: Richard S. Judson ¹, Matthew Martin ¹, Peter Egeghy ², Sumit Gangwal ¹, Ann M. Richard ¹, Martija Wolf ³, Tommy Cathey ³, Thomas Transue ³, Doris Smith ¹, James Vail ¹, Alicia Frame ¹, Shad Mosher ¹and Elaine A. Cohen Hubal ¹
Affiliations: ¹ U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, NC; E-Mails: judson.richard@epa.gov (R.S.J); martin.matt@epa.gov (M.M.); gangwal.sumit@epa.gov (S.G.); richard.ann@epa.gov (A.M.R.); hubal.elaine@epa.gov (E.A.C.H);  smith.doris@epa.gov (D.S.); vail.james@epa.gov (J.V.); frame.alicia@epa.gov (A.F.)
² U.S. EPA, National Exposure Research Laboratory, Research Triangle Park, NC; E-Mail: egeghy.peter@epa.gov (P.E.)
³ Lockheed Martin, Research Triangle Park, NC; E-Mails: wolf.marti@epa.gov (M.W.), Cathey.tommy@epa.gov (T.C.); transue.tom@epa.gov (T.T.)
Abstract: Computational toxicology is an emerging field that combines data from high-throughput methods, chemical structure analyses and other biological domains (e.g. genes, proteins, cells, tissues) to understand and predict the underlying mechanistic causes of chemical toxicity. One of the major approaches used is to build models based on large collections of data, and this drives the need to compile data sets into computable formats. The EPA has developed a large data resource called ACToR (Aggregated Computational Toxicology Resource) to support these data-intensive efforts. ACToR comprised four main repositories: core ACToR (chemical identifiers and structure, summary data on hazard, exposure, use, and other domains), ToxRefDB (Toxicity Reference Database, a compilation of detailed in vivo toxicity data form guideline studies), ExpoCastDB (detailed exposure data from large surveys of human exposure), and ToxCastDB (data from high-throughput screening programs, including links to underlying biological information such as genes and pathways). The EPA DSSTox program provides expert-reviewed chemical structures and other information for these and other high-interest public inventories. Overall, the ACToR system contains information on about 500,000 chemicals from over 1,000 different sources. The entire system is built using open source tools and is freely available to download. This review describes the organization of the data repository and provides selected examples of uses to which it has been put.
Disclaimer: The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.
Keywords: computational toxicology; database; hazard; exposure; high-throughput screening; ACToR; ToxCastDB; ExpoCastDB; ToxRefDB; DSSTox

Type of Paper: Review
Title: Computational Analysis of Axonal Transport: a Novel Assessment of Neurotoxicity, Neuronal Development and Neuronal Functions
Authors: Yoshio Goshima et al.
Affiliation: Department of Molecular Pharmacology and Neurobiology, Yokohama City University Graduate School of Medicine, Yokohama, Japan; E-Mail: goshima@med.yokohama-cu.ac.jp
Abstract: Axonal transport plays a crucial role in neuronal morphogenesis, survival, and function. Despite its importance, however, the molecular mechanisms of axonal transport remain mostly unknown because a simple and quantitative assay system for axonal transport has been lacking. In order to better characterize the molecular mechanisms involved in axonal transport, we formulate a novel computer-assisted monitoring system of axonal transport. Potential uses of this system and implications for future studies will be discussed.

Title: Chemical Exposure and Breast Cancer: Computational Approaches to Identify Compounds of Concern
Authors: Elena Chan ¹, Lellean JeBaily ², Rosa Chan ^1,3, Ruthann Rudel ⁴, Shanaz Dairkee ⁵, Sarah Janssen ⁶, Megan Schwarzman ¹, Lauren Zeise ⁷ and Dale Johnson ¹
Affiliatinon: ¹ University of California, Berkeley, Berkeley, CA 92093, USA; E-Mail: daleejohnson@sbcglobal.net
² Genego/Thomsen Reuters, St Joseph, MI 48202, USA;
³ Level Playing Field Institute, San Francisco, CA 92093, USA;
⁴ Silent Spring Institute, Newton, MA 02114, USA;
⁵ California Pacific Medical Center, San Francisco, CA 92093, USA;
⁶ Natural Resources Defense Council, San Francisco, CA 92093, USA;
⁷ Office of Environmental Health Hazard Assessment, Cal EPA, Oakland, CA 92093, USA
Abstract: This research was conducted to evaluate in silico methods for identifying and prioritizing compounds of concern that may increase the risk of human breast cancer. Candidates identified would reflect possible mechanistic causality. Hence they could enter a pipeline for subsequent rigorous testing through an experimental methods-based algorithm proposed by the California Breast Cancer and Chemicals Policy Project. Criteria for selection were NHANES biomonitoring data, presence in breast tissue/milk, hormonal activity, compound reactivity, modulation of critical metabolic enzymes or transporters in breast tissue, and interaction with known human breast cancer targets. The primary datasets included 216 compounds associated with mammary tumors in mice and rats (Rudel et al., 2007) and 315 compounds with a specific subset of 30 causing mammary gland tumors (only) in female rats (Cunningham et al., 2008). A systematic process for filling information gaps was established using Lazar, CAESAR, OECD Toolbox, Toxtree, Pubmed, ToxNet, among others; and MetaDrug from the GeneGo/Thomsen Reuters’ MetaCore/MetaDrug applications. Specific molecular targets and activating pathways were identified for each compound using MetaDrug and the literature and the potential for each compound to activate key metabolic enzymes expressed in breast tissue from pertinent published information and QSAR modeling. A majority of compounds testing positive for mutagenicity and carcinogenicity contained structural alerts associated with such activity. Moreover, 11 compounds predicted to modulate human breast cancer related targets/ pathways, and 25 were predicted to activate key metabolic enzymes known to play an important role in carcinogen metabolism. This approach provides valuable assistance in the development of a roadmap to guide chemical screening for agents likely to be involved in breast cancer development and progression. In addition it provides a template for linking chemical exposure to other relevant human diseases.