Androgen receptor binding affinity of pesticide "active" formulation ingredients. QSAR evaluation by COREPA method

Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.

Methods for building QSARs

Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models are increasingly used in toxicology, ecotoxicology, and pharmacology for predicting the activity of the molecules from their physicochemical properties and/or their structural characteristics. However, the design of such models has many traps for unwary practitioners. Consequently, the purpose of this chapter is to give a practical guide for the computation of SAR and QSAR models, point out problems that may be encountered, and suggest ways of solving them. Attempts are also made to see how these models can be validated and interpreted.

Androgen receptor binding affinity: a QSAR evaluation

The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used to describe the structural requirements for eliciting rat androgen receptor (AR) binding affinity, accounting for molecular flexibility. Chemical affinity for AR binding was related to the distances between nucleophilic sites and structural features describing electronic and hydrophobic interactions between the receptor and ligands. Categorical models were derived for each binding affinity range in terms of specific distances, local (maximal donor delocalizability associated with the oxygen atom of the A ring), global nucleophilicity (partial positive surface areas and energy of the highest occupied molecular orbital) and hydrophobicity (log Kow) of the molecules. An integral screening tool for predicting binding affinity to AR was constructed as a battery of models, each associated with different activity bins. The quality of the screening battery of models was assessed using a high value (0.9) of the Pearson contingency coefficient. The predictability of the model was assessed by testing the model performance on external validation sets. A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain. Some of the selected chemicals were tested for AR transcriptional activation. The experimental results confirmed the theoretical predictions.

Decision trees versus support vector machine for classification of androgen receptor ligands

With the current concern of limiting experimental assays, increased interest now focuses on in silico models able to predict toxicity of chemicals. Endocrine disruptors cover a large number of environmental and industrial chemicals which may affect the functions of natural hormones in humans and wildlife. Structure-activity models are now increasingly used for predicting the endocrine disruption potential of chemicals. In this study, a large set of about 200 chemicals covering a broad range of structural classes was considered in order to categorize their relative binding affinity (RBA) to the androgen receptor. Classification of chemicals into four activity groups, with respect to their log RBA value, was carried out in a cascade of recursive partitioning trees, with descriptors calculated from CODESSA software and encoding topological, geometrical and quantum chemical properties. The hydrophobicity parameter (log P), Balaban index, and descriptors relying on charge distribution (maximum partial charge, nucleophilic index on oxygen atoms, charged surface area, etc.) appear to play a major role in the chemical partitioning. Separation of strongly active compounds was rather straightforward. Similarly, about 90% of the inactive compounds were identified. More intricate was the separation of active compounds into subsets of moderate and weak binders, the task requiring a more complex tree. A comparison was made with support vector machine yielding similar results.

Framework for use of toxicity screening tools in context-based decision-making

One of the principal applications of toxicology data is to inform risk assessments and support risk management decisions that are protective of human health. Ideally, a risk assessor would have available all of the relevant information on (a) the toxicity profile of the agent of interest; (b) its interactions with living systems; and (c) the known or projected exposure scenarios: to whom, how much, by which route(s), and how often. In practice, however, complete information is seldom available. Nonetheless, decisions still must be made. Screening-level assays and tools can provide support for many aspects of the risk assessment process, as long as the limitations of the tools are understood and to the extent that the added uncertainty the tools introduce into the process can be characterized and managed. Use of these tools for decision-making may be an end in itself for risk assessment and decision-making or a preliminary step to more extensive data collection and evaluation before assessments are undertaken or completed and risk management decisions made. This paper describes a framework for the application of screening tools for human health decision-making, although with some modest modification, it could be made applicable to environmental settings as well. The framework consists of problem formulation, development of a screening strategy based on an assessment of critical data needs, and a data analysis phase that employs weight-of-evidence criteria and uncertainty analyses, and leads to context-based decisions. Criteria for determining the appropriate screening tool(s) have been identified. The choice and use of the tool(s) will depend on the question and the level of uncertainty that may be appropriate for the context in which the decision is being made. The framework is iterative, in that users may refine the question(s) as they proceed. Several case studies illustrate how the framework may be used effectively to address specific questions for any endpoint of toxicity.

SAR and QSAR modeling of endocrine disruptors

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

POPs: a QSAR system for developing categories for persistent, bioaccumulative and toxic chemicals and their metabolites

This paper presents the framework of a QSAR-based decision support system which provides a rapid screening of potential hazards, classification of chemicals with respect to risk management thresholds, and estimation of missing data for the early stages of risk assessment. At the simplest level, the framework is designed to rank hundreds of chemicals according to their profile of persistence, bioaccumulation potential and toxicity often called the persistent organic pollutant (POP) profile or the PBT (persistent bioaccumulative toxicant) profile. The only input data are the chemical structure. The POPs framework enables decision makers to introduce the risk management thresholds used in the classification of chemicals under various authorities. Finally, the POPs framework advances hazard identification by integrating a metabolic simulator that generates metabolic map for each parent chemical. Both the parent chemicals and plausible metabolites are systematically evaluated for metabolic activation and POPs profile.

Uncertainties for endocrine disrupters: our view on progress

The hypothesis that hormonally active compounds in the environment--endocrine disrupters--are having a significant impact on human and ecological health has captured the public's attention like no other toxicity concern since the publication of Rachel Carson's Silent Spring 1962. In the early 1990s, Theo Colborn and others began to synthesize information about the potential impacts of endocrine-mediated toxicity in the scientific literature (Colborn and Clement, 1992) and the popular press (Colborn et al., 1997). Recognizing the possibility of an emerging health threat, the U.S. Environmental Protection Agency (EPA) convened two international workshops in 1995 (Ankley et al., 1997; Kavlock et al., 1996) that identified research needs relative to future risk assessments for endocrine-disrupting chemicals (EDCs). These workshops identified effects on reproductive, neurological, and immunological function, as well as carcinogenesis as the major endpoints of concern and made a number of recommendations for research. Subsequently, the EPA developed a research strategy to begin addressing the recommendations (EPA, 1998a), and the federal government as a whole, working through the White House's Committee on the Environment and Natural Resources, increased funding levels and coordinated research programs to fill the major data gaps (Reiter et al., 1998). In parallel with these research efforts that were attempting to define the scope and nature of the endocrine disruptor hypothesis, the U.S. Congress added provisions to the Food Quality Protection Act (FQPA) and the Safe Drinking Water Act of 1996 to require the testing of food-use pesticides and drinking water contaminants, respectively, for estrogenicity and other hormonal activity. These bills were enacted into law, giving the EPA the mandate to implement them. The EPA, with the help of an external advisory committee, the Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC), determined that other hormonal activity should include androgens and compounds that affect thyroid function, and expanded the mandate to include all chemicals under EPA's jurisdiction, potentially including the 70,000 chemicals regulated under the Toxic Substances Control Act (Endocrine Disruptor Screening and Testing Advisory Committee [EDSTAC], 1998). EDSTAC recommended an extensive process of prioritization, screening, and testing of chemicals for endocrine-disrupting activity, including a screening battery that involves a combination of at least eight in vitro and in vivo assays spanning a number of taxa (EDSTAC, 1998). What started out as a hypothesis has become one of the biggest testing programs conceived in the history of toxicology and the only one that has ever been based on mechanism of action as its premise. As we pass the 10th anniversary of the emergence of the endocrine disruptor hypothesis, it is useful to look back on the progress that has been made in answering the nine questions posed as data gaps in the EPA's research strategy (EPA, 1998a)--not only to see what we have learned, but also to examine whether the questions are still appropriate for the goal, what gaps remain, and what directions should be emphasized in the future.