Refine and Strengthen SAR-Based Read-Across by Considering Bioactivation and Modes of Action

Structure–activity relationship (SAR)-based read-across is an important and effective method to establish the safety of a data-poor target chemical (structure of interest (SOI)) using hazard data from structurally similar source chemicals (analogues). Many methods use quantitative similarity scores to evaluate the structural similarity for searching and selecting analogues as well as for evaluating analogue suitability. However, studies suggest that read-across based purely on structural similarity cannot accurately predict the toxicity of an SOI. As mechanistic data become available, we gain a greater understanding of the mode of action (MOA), the relationship between structures and metabolism/bioactivation pathways, and the existence of “activity cliffs” in chemical chain length, which can improve the analogue rating process. For this purpose, the current work identifies a series of classes of chemicals where a small change at a key position can result in a significant change in metabolism and bioactivation pathways and may eventually result in significant changes in chemical toxicity that have a big impact on the suitability of analogues for read-across. Additionally, a series of SAR-based read-across case studies are presented, which cover a variety of chemical classes that commonly link to different toxic endpoints. The case study results indicate that SAR-based read-across can be refined and strengthened by considering MOAs or proposed reactive metabolite formation pathways, which can improve the overall accuracy, consistency, transparency, and confidence in evaluating analogue suitability

Refine and Strengthen SAR-Based Read-Across by Considering Bioactivation and Modes of Action

Structure–activity relationship (SAR)-based read-across is an important and effective method to establish the safety of a data-poor target chemical (structure of interest (SOI)) using hazard data from structurally similar source chemicals (analogues). Many methods use quantitative similarity scores to evaluate the structural similarity for searching and selecting analogues as well as for evaluating analogue suitability. However, studies suggest that read-across based purely on structural similarity cannot accurately predict the toxicity of an SOI. As mechanistic data become available, we gain a greater understanding of the mode of action (MOA), the relationship between structures and metabolism/bioactivation pathways, and the existence of “activity cliffs” in chemical chain length, which can improve the analogue rating process. For this purpose, the current work identifies a series of classes of chemicals where a small change at a key position can result in a significant change in metabolism and bioactivation pathways and may eventually result in significant changes in chemical toxicity that have a big impact on the suitability of analogues for read-across. Additionally, a series of SAR-based read-across case studies are presented, which cover a variety of chemical classes that commonly link to different toxic endpoints. The case study results indicate that SAR-based read-across can be refined and strengthened by considering MOAs or proposed reactive metabolite formation pathways, which can improve the overall accuracy, consistency, transparency, and confidence in evaluating analogue suitability

Framework for Identifying Chemicals with Structural Features Associated with the Potential to Act as Developmental or Reproductive Toxicants

Developmental and reproductive toxicity (DART) end points are important hazard end points that need to be addressed in the risk assessment of chemicals to determine whether or not they are the critical effects in the overall risk assessment. These hazard end points are difficult to predict using current in silico tools because of the diversity of mechanisms of action that elicit DART effects and the potential for narrow windows of vulnerability. DART end points have been projected to consume the majority of animals used for compliance with REACH; thus, additional nonanimal predictive tools are urgently needed. This article presents an empirically based decision tree for determining whether or not a chemical has receptor-binding properties and structural features that are consistent with chemical structures known to have toxicity for DART end points. The decision tree is based on a detailed review of 716 chemicals (664 positive, 16 negative, and 36 with insufficient data) that have DART end-point data and are grouped into defined receptor binding and chemical domains. When tested against a group of chemicals not included in the training set, the decision tree is shown to identify a high percentage of chemicals with known DART effects. It is proposed that this decision tree could be used both as a component of a screening system to identify chemicals of potential concern and as a component of weight-of-evidence decisions based on structure–activity relationships (SAR) to fill data gaps without generating additional test data. In addition, the chemical groupings generated could be used as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and ultimately in the development of refined SAR principles for DART that incorporate mode of action (adverse outcome pathways)

Framework for Identifying Chemicals with Structural Features Associated with the Potential to Act as Developmental or Reproductive Toxicants

Developmental and reproductive toxicity (DART) end points are important hazard end points that need to be addressed in the risk assessment of chemicals to determine whether or not they are the critical effects in the overall risk assessment. These hazard end points are difficult to predict using current in silico tools because of the diversity of mechanisms of action that elicit DART effects and the potential for narrow windows of vulnerability. DART end points have been projected to consume the majority of animals used for compliance with REACH; thus, additional nonanimal predictive tools are urgently needed. This article presents an empirically based decision tree for determining whether or not a chemical has receptor-binding properties and structural features that are consistent with chemical structures known to have toxicity for DART end points. The decision tree is based on a detailed review of 716 chemicals (664 positive, 16 negative, and 36 with insufficient data) that have DART end-point data and are grouped into defined receptor binding and chemical domains. When tested against a group of chemicals not included in the training set, the decision tree is shown to identify a high percentage of chemicals with known DART effects. It is proposed that this decision tree could be used both as a component of a screening system to identify chemicals of potential concern and as a component of weight-of-evidence decisions based on structure–activity relationships (SAR) to fill data gaps without generating additional test data. In addition, the chemical groupings generated could be used as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and ultimately in the development of refined SAR principles for DART that incorporate mode of action (adverse outcome pathways)

F1-F4 semen quality comparison.

<p>* compared with control group, P<0.05.</p

Control group and DEHP experimental group cryptochidism group compared to control group of biological process down regulation of pathway analysis.

<p>Control group and DEHP experimental group cryptochidism group compared to control group of biological process down regulation of pathway analysis.</p

Quantitative real-time PCR Primers for DNA methytransferases and β-actin.

<p>Quantitative real-time PCR Primers for DNA methytransferases and β-actin.</p

Control group and DEHP experimental group offspring (F1-F4) male rat’s DNMT3a expression.

<p>High expression of dnmt1 in the F1 and F2 generation, F3 significantly reduced and F4 generation basic without expression. A:Control;B:F1;C:F2;D:F3 E:F4 (×200)</p

Three kinds of DNA methyltransferase mRNA expression.

<p>* compared with control group, P<0.05</p><p>Three kinds of DNA methyltransferase mRNA expression.</p

Control group and DEHP experimental group offspring (F1-F4) male rat’s testicular histology expression.

<p>A:F1;B:F2;C:F3;D:F4 E: Control (200×).</p