Framework for In Silico Toxicity Screening of Novel Odorants
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe manuscript is clear. However, more discussion should be done. In particular, the study by Shin et al., 2019 already published a model to predict NOAEC. Discuss the comparison between this model and the new one presented in the manuscript, in terms of purposes, statistics, pros, cons, and differences.
Line 57. Authors claim: “Both models are validated and accepted”. The reference given is from the author of the models. Accaptance by definition regards one or more different institute. Validation, preferably, should be documented by independent assessment. Thus, this claim must be substantiated by proper references. In this case authors use two models, thus references must cover both.
Author Response
Comment 1: The manuscript is clear. However, more discussion should be done. In particular, the study by Shin et al., 2019 already published a model to predict NOAEC. Discuss the comparison between this model and the new one presented in the manuscript, in terms of purposes, statistics, pros, cons, and differences.
Response 1: Thank you for the suggestion. Text added to the discussion lines 234-241.
Comment 2: Line 57. Authors claim: “Both models are validated and accepted”. The reference given is from the author of the models. Accaptance by definition regards one or more different institute. Validation, preferably, should be documented by independent assessment. Thus, this claim must be substantiated by proper references. In this case authors use two models, thus references must cover both.
Response 2: Additional citations are provided for the validation of the in silico decision trees used in the assessment, lines 56-75.
Reviewer 2 Report
Comments and Suggestions for Authors
Major remarks:
- The introduction contains the conclusion of the study, not aim of the study. Also, it should be expand on current state of the research in field of in silico assessment toxicity of odorants.
- From the title and introduction it is not clear the type of odorants (used in cosmetics or something else).
- The authors did not conduct an appropriate statistical analysis to confirm the predictive ability of the model; therefore, the paper lacks scientific significance.
Minor remarks:
- In the text, reference numbers should be placed in square brackets [ ], and placed before the punctuation. References must be numbered in order of appearance in the text and cited according to the ACS style guide.
- line 47: correct „demosntrates „
Author Response
Major remarks:
Comment 1: The introduction contains the conclusion of the study, not aim of the study. Also, it should be expand on current state of the research in field of in silico assessment toxicity of odorants.
Response 1: Thank you for the suggestion. We added the aim to the introduction and additional background information on the in silico decision trees used in the approach, lines 56-75.
Comment 2: From the title and introduction it is not clear the type of odorants (used in cosmetics or something else).
Response 2: Additional information on the odorants was added to the introduction, lines 39-42.
Comment 3: The authors did not conduct an appropriate statistical analysis to confirm the predictive ability of the model; therefore, the paper lacks scientific significance.
Response 3: We clarified that a safety test (not a validation) of the model was conducted. It is not clear what statistical analysis would be appropriate for such a test except for reporting the descriptive statistics (i.e., proportion of chemicals with safety margin below 1). See Section 2.2, line 164. Section 3.1 line 184.
Minor remarks:
Comment 4: In the text, reference numbers should be placed in square brackets [ ], and placed before the punctuation. References must be numbered in order of appearance in the text and cited according to the ACS style guide.
Response 4: The Toxics editor provided a style template for this. References are now properly formatted.
Comment 5: line 47: correct „demosntrates „
Response 5: Corrected.
Reviewer 3 Report
Comments and Suggestions for AuthorsThe authors have formulated a framework aimed at estimating the toxicology-based maximum solution concentrations for novel odorants through in silico structure-based predictions. This serves as compelling evidence that the proposed in silico methodology facilitates the determination of toxicology-based maximum solution concentrations for chemicals, leveraging open-source models and software.
Overall, the research findings of this study hold practical value and are worthy of publication in this journal.
Before submission for publication, the authors need to refine the manuscript in line with the following comments:
- The authors should provide a more detailed account of data sources. For instance, they should list the referenced data and literature information one by one in the supplementary materials.
- The authors need to further clarify which parameters are used to evaluate the robustness and predictive capability of the model. The formulas for calculating these parameters should also be presented.
- The authors should offer a comprehensive introduction to the modeling algorithms employed. Given the widespread application of machine learning methods in QSAR model construction, it is advisable for the authors to consider incorporating multiple modeling algorithms.
- The applicability domain of the model developed by the authors should be characterized.
- The authors should elucidate the mechanistic underpinnings of the developed model by integrating descriptor analysis.
Author Response
The authors have formulated a framework aimed at estimating the toxicology-based maximum solution concentrations for novel odorants through in silico structure-based predictions. This serves as compelling evidence that the proposed in silico methodology facilitates the determination of toxicology-based maximum solution concentrations for chemicals, leveraging open-source models and software.
Overall, the research findings of this study hold practical value and are worthy of publication in this journal.
Before submission for publication, the authors need to refine the manuscript in line with the following comments:
Comment 1: The authors should provide a more detailed account of data sources. For instance, they should list the referenced data and literature information one by one in the supplementary materials.
Response 1: Thank you for this suggestion. Additional information on the decision trees used in the approach is provided in the introduction, lines 56-75. Additionally, we have provided an Excel file to the editor that contains the source data for the graphs in the paper.
Comment 2: The authors need to further clarify which parameters are used to evaluate the robustness and predictive capability of the model. The formulas for calculating these parameters should also be presented.
Response 2: We clarified that a safety test (not a validation) of the model was conducted. It is not clear what statistical analysis would be appropriate for such a test except for reporting the descriptive statistics (i.e., proportion of chemicals with safety margin below 1). See Section 2.2, line 164. Section 3.1 line 184.
Comment 3: The authors should offer a comprehensive introduction to the modeling algorithms employed. Given the widespread application of machine learning methods in QSAR model construction, it is advisable for the authors to consider incorporating multiple modeling algorithms.
Response 3: We appreciate the suggestion but respectfully believe this is beyond the scope of the manuscript and aim to provide a transparent.
Comment 4: The applicability domain of the model developed by the authors should be characterized.
Response 4: The background information on the decision trees provides a description of the applicability domain. Please refer to lines 56-75.
Comment 5: The authors should elucidate the mechanistic underpinnings of the developed model by integrating descriptor analysis.
Response 5: Please see our response to Comment 2.
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper is ready for publishing.
Reviewer 3 Report
Comments and Suggestions for AuthorsThe authors have revised the manuscript according to the comments of the reviewer.
