Improving confidence in (Q)SAR predictions under Canada’s Chemicals Management Plan – a chemical space approach$

Abstract

One of the key challenges of Canada’s Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model’s ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model’s predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.     

SAR—The U.S. Regulatory Perspective

Abstract

Structure-Activity Relationships (SAR) have been used for over a decade by the U.S. EPA's Office of Pollution Prevention and Toxics (OPPT) in their new chemicals program. The development and use of SAR resulted from the need to make rapid risk-based decisions on thousands of new chemicals per year while seldom receiving data on chemical properties, potential exposures, or hazards to humans or organisms in the environment. Qualitative SAR and quantitative SAR methods (QSAR) have been used to fill some of these data gaps by estimating the potential properties and hazards of such chemicals. SAR has been used to assess chemical hazards, identify testing needs, and set priorities. Validation of these SAR assessment tools is an ongoing process.     

Quantitative Structure–Toxicity Relationships Using Tops-Mode. 1. Nitrobenzene Toxicity to Tetrahymena Pyriformis

Abstract

The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach (Estrada, E. SAR QSAR Environ. Res. 2000, 11, 55–73) has been introduced to the study of toxicological properties. The toxicity of 42 nitrobenzenes was studied with this approach obtaining a good quantitative structure–toxicity model. For the first time we compare the use of eight different weights in the diagonal entries of the bond matrix for selecting the best TOPS-MODE model. TOPS-MODE was used to derive the contribution of different fragments to the toxicity of studied compounds. These contributions were applied to calculate toxicity substituent constants for the groups present in the nitrobenzenes studied.     

Chemical diversity approach for evaluating mechanistic relatedness among toxicological phenomena

The CASE/MULTICASE structure-activity relationship (SAR) system was used to assess a new procedure to investigate the mechanistic relatedness of various toxicological endpoints. The method consisted of predicting the activity of 10,000 randomly selected chemicals using validated and characterized SAR models from a variety of biological and toxicological endpoints. The prevalence of chemicals predicted to possess the ability to induce two or more toxicological effects simultaneously should provide a measure of the mechanistic relatedness of these phenomena. Eight toxicological endpoints were predicted and the results were compared to predictions based on an eye irritation SAR model. Allergic contact dermatitis demonstrated a 29.6% greater than expected overlap between expected and observed results (p < 0.001). Similar results were seen for respiratory hypersensitivity (33.1%), sensory irritation (28.9%), cell toxicity (25.9%), and Ah receptor binding (19.8%). A lesser degree of overlap was seen between eye irritation and Salmonella mutagenicity (11.5%) and the inhibition of gap junction intercellular communication (6.7%). Moreover, a negative overlap, suggesting possibly an antagonistic phenomena, was observed between eye irritation and alpha 2 mu-induced nephropathy. These results indicate that this method can provide a useful tool to investigate mechanistic relatedness between diverse toxicological endpoints.     

Development,Characterization and Application of Predictive-Toxicology Models

Abstract

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined.

Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity).

Methods have been developed to combine SAR submodels and thereby improve predictive performance.

Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.     

Decision tree SAR models for developmental toxicity based on an FDA/TERIS database

Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the accuracy of prediction. Also, the model ensemble approach reduced the variability of prediction measures compared to the single model approach. Further research with data derived from animal species- and endpoint-specific components of an extended and refined FDA/TERIS database has the potential to derive SAR models that would be useful in the developmental risk assessment of the thousands of untested chemicals.     

Sar modeling of unbalanced data sets

Abstract

The increased acceptance of SAR approaches to hazard identification has led us to investigate methods to improve the predictive performance of SAR models. In the present study we demonstrate that although on theoretical grounds the ratio of active to inactive chemicals in the learning set should be unity, SAR models can ?tolerate‘ an unbalanced range in ratios from 3 : 1 (i.e., 75% actives) to 1 : 2 (i.e., 33% actives) and still perform adequately. On the other hand SAR models derived from learning sets with ratios in excess of 4 : 1 (80% actives), even when corrected for the initial ratio do not perform satisfactorily.     

Computational Toxicology and the Generation of Mechanistic Hypotheses: γ-Butyrolactone

Abstract

In this study, we use SAR approaches in an attempt to elucidate the action of γ-butyrolactone (GBL), an illicit drug and a dietary supplement, that can cause coma and deaths in humans while exhibiting low systemic toxicity towards rodents.

The lack of systemic toxicity of GBL and of its metabolite(s) was also predicted by validated SAR models. In fact using diverse SAR models, the only significant SAR prediction was that GBL had the potential for inhibiting human cytochrome P4502D6 (CYP2D6). However, inhibition of that isozyme is not necessarily associated with toxicity. It is suggested that GBL users also abuse other substances. When GBL inhibits CYP2D6 this may prevent the CYP2D6-mediated detoxification of other toxicants simultaneously consumed by the GBL user.     

Detection of PCB77 by Antibody-Coated Competitive Fluorescent Quantitative Immuno-PCR Using Molecular Beacon

Abstract

A rapid and quantitative technique is urgently needed in detecting toxicological and carcinogenic polychlorinated biphenyls (PCBs) in the environment. In this study, a high sensitive real time fluorescent quantitative immuno-PCR (FQ-IPCR) approach using molecular beacon (MB) was developed to detect PCB77, which was classified as a human carcinogen. MB-based FQ-IPCR was then performed on serial dilutions of known PCB77 concentrations equivalent to 10-fold dilutions of 10–10⁵ fg/mL. A correlation coefficient of 0.997 was identified, and a linear relationship between 10 fg/mL and 100 pg/mL, with y = 0.543x + 21.779, was obtained. Three soil samples were determined by MB-based FQ-IPCR to proof the validity of the method, and the results of which were further confirmed by conventional enzyme-linked immuno sorbent assay (ELISA). Based on sensitivity and reproduction, the MB-based real time FQ-IPCR technique will become a promising monitoring tool for environmental endocrine disruptors.     

Integration of chemical data using XML

Internet resources for the toxicological and pharmacological communities offer considerable information, however, the data is generally not amenable to machine-driven reuse. Adoption of XML standards by these (and related) communities will revolutionize the process of sharing information, promoting collaboration and discovery.     

Pressurized planar electrochromatography as a supporting tool for qualitative toxicological chemical analysis with thin-layer chromatography and UV–Vis spectrometry

ABSTRACT

Pressurized planar electrochromatography (PPEC) was applied to support qualitative toxicological chemical analysis performed with thin-layer chromatography (TLC) and UV–Vis spectrometry. Based on retention/migration distance data of substances obtained in TLC and PPEC systems and database of their wavelength maxima of remission UV–Vis spectra as well, a combined fit factor was calculated for substance identification. The involvement of PPEC, TLC, and UV–Vis spectral data together in calculation of the combined fit factor lead to its lower values for substances, which were not identical with reference, in comparison with those when the combined fit factor was calculated using TLC and spectral data only. The results evidence that involvement of PPEC data in qualitative toxicological chemical analysis performed with TLC and UV–Vis spectrometry enhances reliability of it.     

Enantioselective HPLC separation of bioactive C5-chiral 2-pyrazolines on lux amylose-2 and lux cellulose-2: Comparative and mechanistic approaches

Stereoselective analytical HPLC separations have been developed for a series of biologically active chiral 2-pyrazolines (1-22) to be used in monitoring their resolution reactions or to custom semipreparative HPLC separations prior to biological assessment of both enantiomers. Polysaccharide-based chiral stationary phases (CSPs), namely, Lux amylose-2 and cellulose-2, have been used. Both normal (n-hexane/ethanol) and polar organic (ethanol, methanol, acetonitrile, or mixtures thereof) elution modes were very beneficial for the achievement of baseline separations. The impact of various chemical moieties embedded in the structures of 2-pyrazolines 1-22 and the adopted stationary phases on chiral recognition has been investigated. A case of reversed order of elution following alterations in either stationary phase or elution mode has been observed. Our findings recommend that normal elution mode can be used for optimizing semipreparative HPLC methods whereas polar organic mobile phases (such as acetonitrile and ethanol) are more suited to stereoselective reactions monitoring, routine quality control work, or for pharmacological and toxicological assays. These results settle the implementation of polysaccharide-based CSPs using different elution modes and declare the practicality of such CSPs in stereoselective HPLC.     

Green chemistry & chemical stewardship certificate program: a novel,interdisciplinary approach to green chemistry and environmental health education

ABSTRACT

The Green Chemistry & Chemical Stewardship Certificate Program was designed using the Community of Inquiry (COI) model as a framework for developing curriculum that engages students across the entire program to meet interdisciplinary, professional development program outcomes. The COI framework allows faculty and course developers to develop courses that consider cognitive, social, and teaching presence as equal components of successful learning experiences. In this program, students focus on systems thinking around green chemistry, business, environmental health, chemical alternative assessment tools, and social and environmental justice. They complete a capstone project that identifies a particular environmental or human health issue associated with a chemical and suggest suitable substitutions that are less harmful but equally effective. This paper describes the program’s curriculum, partnerships, delivery modalities, and student feedback as a framework developing professional development opportunities that offer a rich interdisciplinary experience for learners.     

The Application of Structure-Activity Relationships (SARs) in the Aquatic Toxicity Evaluation of Discrete Organic Chemicals

Abstract

The Office of Pollution Prevention and Toxics (OPPT), United States Environmental Protection Agency (USEPA) routinely uses structure-activity relationships (SAR) for the aquatic hazard assessment of new chemicals submitted under Section 5 of the Toxic Substances Control Act (TSCA). With 15 years of experience and the general acceptance of toxicity predictions based on SARs, OPPT has expanded the use and application of the methodology to include existing chemicals used in printing, dry cleaning, and paint stripping. SAR analysis has also been used in the hazard evaluation of the U.S. and EU/OECD high production volume (HPV) chemicals. This paper describes the assumptions, limitations, and methodology for the use of SARs to evaluate large sets of discrete organic chemicals.