Exploration of structural requirements for azole chemicals towards human aromatase CYP19A1 activity: Classification modeling, structure-activity relationships and read-across study

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

A KNIME Workflow to Assist the Analogue Identification for Read-Across, Applied to Aromatase Activity

The reduction and replacement of in vivo tests have become crucial in terms of resources and animal benefits. The read-across approach reduces the number of substances to be tested, exploiting existing experimental data to predict the properties of untested substances. Currently, several tools have been developed to perform read-across, but other approaches, such as computational workflows, can offer a more flexible and less prescriptive approach. In this paper, we are introducing a workflow to support analogue identification for read-across. The implementation of the workflow was performed using a database of azole chemicals with in vitro toxicity data for human aromatase enzymes. The workflow identified analogues based on three similarities: structural similarity (StrS), metabolic similarity (MtS), and mechanistic similarity (McS). Our results showed how multiple similarity metrics can be combined within a read-across assessment. The use of the similarity based on metabolism and toxicological mechanism improved the predictions in particular for sensitivity. Beyond the results predicting a large population of substances, practical examples illustrate the advantages of the proposed approach.

Current trends in read-across applications for chemical risk assessments and chemical registrations in the Republic of Korea

Read-across, an alternative approach for hazard assessment, has been widely adopted when in vivo data are unavailable for chemicals of interest. Read-across is enabled via in silico tools such as quantitative structure activity relationship (QSAR) modeling. In this study, the current status of structure activity relationship (SAR)-based read-across applications in the Republic of Korea (ROK) was examined considering both chemical risk assessments and chemical registrations from different sectors, including regulatory agencies, industry, and academia. From the regulatory perspective, the Ministry of Environment (MOE) established the Act on Registration and Evaluation of Chemicals (AREC) in 2019 to enable registrants to submit alternative data such as information from read-across instead of in vivo data to support hazard assessment and determine chemical-specific risks. Further, the Ministry of Food and Drug Safety (MFDS) began to consider read-across approaches for establishing acceptable intake (AI) limits of impurities occurring during pharmaceutical manufacturing processes under the ICH M7 guideline. Although read-across has its advantages, this approach also has limitations including (1) lack of standardized criteria for regulatory acceptance, (2) inconsistencies in the robustness of scientific evidence, and (3) deficiencies in the objective reliability of read-across data. The application and acceptance rate of read-across may vary among regulatory agencies. Therefore, sufficient data need to be prepared to verify the hypothesis that structural similarities might lead to similarities in properties of substances (between source and target chemicals) prior to adopting a read-across approach. In some cases, additional tests may be required during the registration process to clarify long-term effects on human health or the environment for certain substances that are data deficient. To improve the quality of read-across data for regulatory acceptance, cooperative efforts from regulatory agencies, academia, and industry are needed to minimize limitations of read-across applications.