{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:56:49Z","timestamp":1792796209414,"version":"4.5.0"},"update-to":[{"DOI":"10.12688\/f1000research.172567.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000}}],"reference-count":73,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"crossref","award":["U24ES033155"],"award-info":[{"award-number":["U24ES033155"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>Background Chemicals can perturb gene functions to affect chronic human diseases, and a significant amount of biological knowledge involved in environmental health is available in public databases. Combining information across resources can assist in the discovery of novel testable hypotheses related to how chemical exposures influence human diseases, such as autism. Methods The Comparative Toxicogenomics Database (CTD) is a public resource that provides curated content for chemicals, genes, phenotypes, diseases, and exposures. The AOP-Wiki is a repository of adverse outcome pathways (AOPs) that provide defined biological frameworks describing disease processes. Here, we intersect CTD toxicogenomic content with the AOP-Wiki to identify environmental chemicals that could potentially modulate key steps in autism. Results We identify numerous chemical stressors that intersect with the individual events of the autism AOP, including bisphenol compounds, per\/polyfluoroalkyl substances, pesticides, metals, and air pollutants, suggesting a wide range of environmental factors that could synergize to potentially affect autism. By integrating additional CTD curated content for three autism-associated chemicals (bisphenol A, particulate matter, and valproic acid), we discover other mechanisms, including specific genes (e.g., SLC1A1, GSTP1, CNTNAP2) and phenotypes (e.g., lipid metabolism, inflammatory response, social behavior) that can be used to help refine or expand this AOP or create an entirely new pathway for autism. Furthermore, related diseases are identified to build interconnected networks, mechanistically linking autism to fatty liver disease, intellectual disability, and cancer. Conclusions We demonstrate the value of integrating content from different resources to address environmental health questions related to autism etiology and co-morbidities. Importantly, our methodology is easily adapted for any AOP in the AOP-Wiki to identify potential environmental influences on the disease process and help support or refine AOPs. This analysis underscores the importance of standardizing public databases to make them efficiently interoperable for enhanced shared utility across the numerous bioknowledge digital landscapes.<\/ns3:p>","DOI":"10.12688\/f1000research.172567.2","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T09:09:08Z","timestamp":1777280948000},"page":"1266","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health"],"prefix":"10.12688","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5741-7128","authenticated-orcid":false,"given":"Allan Peter","family":"Davis","sequence":"first","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas C.","family":"Wiegers","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3182-9493","authenticated-orcid":false,"given":"Daniela","family":"Sciaky","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5308-1535","authenticated-orcid":false,"given":"Fern","family":"Barkalow","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brent","family":"Wyatt","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jolene","family":"Wiegers","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roy","family":"McMorran","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sakib","family":"Abrar","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolyn J.","family":"Mattingly","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences Raleigh North Carolina 27695 North Carolina Sate University"},{"name":"Center for Human Health and the Environment Raleigh North Carolina 27695 North Carolina State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"62-65","DOI":"10.1038\/sj.tpj.6500225","article-title":"Chemical databases for environmental health and clinical research.","volume":"186","author":"C Mattingly","year":"2009","journal-title":"Toxicol. 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Issue with selected AOP-Status \u201copen for adoption\u201d. <\/b> \n<b>AUTHORS\u2019 RESPONSE<\/b>: The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to help inform, refine, expand, and hopefully advance the status of underdeveloped AOPs.&nbsp; When we initially submitted our manuscript (17 November 2025), the AOP-Wiki contained 532 AOPs, of which only 7% were designated as endorsed by WPHA\/WNT, with the remaining 93% of the AOP-Wiki content described as empty (74%), under development (16%), under review (3%), and none with ESCA approval (0%).&nbsp; In our minds, these statistics are indicative of a critical need for new methodologies to advance the status of these languishing AOPs as a way to improve the overall utility and resourcefulness of the AOP-Wiki.&nbsp; The AOP used in our demonstration (i.e., AOP:522) is a good representative candidate, because, as this reviewer noted, it is \u201copen for adoption\u201d, currently poorly populated in the AOP-Wiki itself, and has equivocal limiting supporting evidence from the literature (although enough to warrant the construction of and deposition in the AOP-Wiki).&nbsp; As Mari-Bauset et al. (2018) [rather than the mistakenly cited Mandy and Lai, 2016] conclude in their article about EDC and ASD: \u201cincomplete understanding of biological mechanisms precludes the establishment of a causal relationship\u201d, supporting the primary need to better understand the biological mechanistic steps that can link these disrupting chemicals to ASD, such as the development of a more robust AOP.&nbsp; In our manuscript, we demonstrate how researchers can (1) leverage CTD to identify environmental chemicals that could modulate autism etiology via this AOP and (2) then use those identified chemicals to point researchers in mechanistically-supported directions to further develop and refine AOP:522 to a better position for testability and advancement.&nbsp; To help clarify our intention, we have improved the text.&nbsp; In response to similar comments from Reviewer 2, we have edited the last paragraph of the \u201cIntroduction\u201d to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP.  \n<b>2. Manuscript would benefit from an analysis workflow<\/b> \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented.&nbsp; This file is now available as an additional extended data set via the updated figshare link.  \n<b>3. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility<\/b> \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; The crux of this work is to demonstrate how users can leverage CTD chemical content and readily integrate it with AOPs to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with AOP:522 and (2) then use these chemicals to discover new, potential mechanistic steps to further develop and refine AOP:522 to a better position for testability and advancement.&nbsp; A step-by-step instruction guide is now provided (see new Supplemental Figures at figshare) for all the manual queries and analyses, enabling both validation and reproducibility.&nbsp; However, it is important to understand that CTD is updated each month with new curated content from the recently published literature; this is one of the strengths of CTD. Thus, query results can change on a monthly basis (as currently explained in our \u201cMethods\u201d section).&nbsp; Importantly, results from newer queries do not invalidate findings, but rather provide additional data from the more recent literature.&nbsp; In Figure 2, we grouped the 76 prioritized chemicals simply as a display convenience (e.g., clustering together similar chemicals, such as metals, phthalates, pesticides, etc.).&nbsp; The clustering itself does not impart any additional knowledge to the figure (compared to not clustering the chemicals).&nbsp; We performed that task, as described in the manuscript, using web searches or shared term parentage in the CTD Chemical vocabulary hierarchy.&nbsp; For example, \u201cdibutyl phthalate\u201d, \u201cdiethylhexyl phthalate\u201d, and \u201cmonobutyl phthalate\u201d, all share \u201cPhthalic Acids\u201d as a common parent in the CTD chemical hierarchy, and thus can be grouped as such.&nbsp; Figure 2 is just as valid and reproducible if the chemicals were not clustered, and instead listed alphabetically.  \n<b>4. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100<\/b> \n<b>AUTHORS\u2019 RESPONSE<\/b>: In our manuscript, we did not base estrogenic effects only on the two estrogen genes (ESR1 and ESR2).&nbsp; Rather, we mapped this MIE:112 to four CTD terms (Table 1): the two estrogen receptor genes ESR1 and ESR2 plus, importantly, two additional estrogen-signaling related phenotypes (which include all of their GO-phenotype descendants as well) to capture chemicals that still affect the estrogen receptor signaling pathway but without necessarily affecting the specific estrogen receptors.&nbsp; It is important to understand that at CTD a chemical-phenotype interaction does not (necessarily) have to involve a gene, but is simply a curated annotation describing how a chemical causes a biological outcome reported in the literature; e.g., \u201cchemical X results in decreased intracellular estrogen receptor signaling pathway in MCF-7 cells\u201d is a chemical-phenotype interaction reported by an author without any discussion or knowledge of the genes involved.&nbsp; This specifically enables CTD to capture literature-based chemical-induced outcomes without having to know a priori what genes are involved.&nbsp; We would direct the reviewer to our detailed description of this CTD curation module to better understand what we mean as chemical-phenotype interactions (Davis et al., 2018). This CTD curation paradigm allows CTD to capture important data for chemical-induced events directly, without having to involve any genes (either known or unknown by the author).&nbsp; Thus, chemicals that affect estrogen receptor signaling by \u201cmechanisms which are not understood\u201d (Marino, 2006) can still be captured in CTD because we curate the direct chemical-phenotype relationship itself, obviating the need to invoke any unknown gene\/mechanism.&nbsp; If a user chooses to broaden their interpretation of \u201cestrogen receptor antagonism\u201d, they can select a more expansive phenotype in the ontology by using a parent term, such as \u201csteroid hormone mediated pathway\u201d (GO:0043401), which now expands the query range and at this writing returns 231 unique chemicals instead of the original 180 chemicals shown in Table 1 for the more specific phenotype.&nbsp; Curating phenotype data as an ontology (instead of as presumptive \u201cgene set) is an advantage, and enables users to broaden or narrow their searches by navigating the vocabulary to the level they prefer.&nbsp; If deemed necessary, users can always map this particular AOP step to additional genes beyond ESR1 and ESR2 if they prefer: this will only bring back additional chemical data for analysis.&nbsp; Finally, a user could try to leverage Natural Language Processing (NLP)-based mapping tools that attempt to link KEs first to \u201cgene lists\u201d and then find CTD chemicals that interact with those gene sets.&nbsp; NLP methods, however, are notoriously complex and difficult to follow and validate for the non-technical user, and, unfortunately, the results may go out of date quickly unless the NLP tool is updated on a consistent schedule; in fact, spot checks by CTD found some strange and equivocal mappings (discussed below) performed by such NLP methods.&nbsp; The main point here, however, is that individual users have the ability to leverage CTD to their level of specificity, comfort, and skill level to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with this AOP and (2) then use these chemicals to discover new, potential mechanistic steps to further develop, expand, and improve the AOP to a better position for testability and advancement.&nbsp; Different users might decide to use different terms to retrieve data, and that\u2019s perfectly fine.&nbsp; The user ultimately decides how large of a data-net he\/she wants to cast and then can decide which molecular mechanisms retrieved from those results make the most sense to include in a testable updated\/new AOP.&nbsp; CTD provides the chemical data-set and evidence to fill in the mechanistic knowledge gaps connecting exposure to an adverse outcome.&nbsp; To better reflect this, we edited all occurrences in the \u201cResults and Discussion\u201d of \u201cThis KE mapped to\u2026\u201d to the better phrase of \u201cWe mapped this KE to\u2026\u201d to emphasize that users are in control of the process here.  \n<b>5. Supplemental training materials are suggested to facilitate reproducibility of the methods presented.<\/b> \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented.&nbsp; This file is now available as an additional extended data set via the updated figshare link.  \n<b>6. Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype).<\/b> \n<b>AUTHORS\u2019 RESPONSE<\/b>: We wholeheartedly agree and promote the idea of the AOP research community coming together to diligently standardize their mappings, identification, and definitions.&nbsp; We attempted to use many of the third party tools currently promoted by the AOP-Wiki in our original mapping and analysis, but unfortunately, we began to realize that many of these resources are unreliable, as they were either outdated, non-intuitive as to how to use, no longer available as a web application, or simply returned generic links back to the AOP-Wiki without providing any new context:  AOP-DB (as defined by Mortensen, Senn, 2021; Pittman, 2018) is listed as a third-party tool on the AOP-Wiki, but is no longer accessible from its stated URL, and the EPA site appears to have been last updated May 2021 and also is inaccessible from its stated URL.  Wiki Kaptis has a copyright stamp of 2022, and the information menu states that it only contains data extracted from the AOP-Wiki up to 2023.&nbsp; Our searches using official AOP-Wiki terms such as: AOP:522, autism (AO:2209), estrogen receptor antagonism (MIE:112), ERK1\/2 inhibition (KE:2207), and aberrant synaptic formation and plasticity (KE:2208) yielded no results.  The AOP-KB is a tool provided by the OECD, but again, appears to be limited in functionality.&nbsp; A user can enter any term (e.g., \u201cestrogen receptor\u201d) and retrieve term matches to AOPs and KEs such as \u201cAndrogen receptor activation leading to prostate cancer\u201d, but this result simply links back to the AOP-Wiki, with no explanation as to why this AOP would be returned as a result for \u201cestrogen receptor\u201d.&nbsp; It is not obvious how the AOP-KB determined that \u201cestrogen receptor\u201d is involved in this AOP.&nbsp; Similarly, a search with \u201cautism\u201d returns no results, suggesting the AOP-KB is also not current and out-of-date with the AOP-Wiki.  AOP-WIKI EXPLORER (as defined by Kumar et al. 2024) is no longer accessible from its stated URL.  We next attempted to use a tool that employs NLP to map curated genes to KEs (Saarimaki et al. (2023), but the resource also seems to be out of date, and spot-checks performed by us gave questionable results: e.g., MIE:112 (\u201cestrogen receptor antagonism\u201d) was mapped to 59 genes, but surprisingly did not include ESR2, the second critical estrogen receptor in humans; similarly, KE:195 (\u201cNMDARs inhibition\u201d) contained only seven GRIN genes, missing 16 other GRIN genes that we included in our CTD analysis.&nbsp; The other KEs we checked (KE:2207, KE:2208, KE:2209) do not even exist in the file, presumably because of its outdatedness.  Based upon these unsatisfying and inconsistent approaches, in this manuscript, we decided to map the six AOP terms to CTD genes and phenotypes manually ourselves, as a typical CTD user would find it necessary to do.&nbsp; Going forward, it would be more advantageous if the AOP-Wiki recommended tools that are more easily and readily accessible, stable, intuitive to use, and designed for data currency, otherwise they risk becoming out of date with diminishing value as time goes on.  \n<b>7. Please reference the literature and relevant contributions in this area.<\/b> \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; Thank you.&nbsp; To the \u201cLimitations and Strengths\u201d section, we have now added citations for: Ives et al. (2017): Creating a Structured AOP Knowledgebase via Ontology-Based Annotations; Pittman et al. (2018): AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks; Mortensen et al. (2021): The 2021 update of the EPA\u2019s adverse outcome pathway database; and Kumar et al. (2024): AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models.","URL":"https:\/\/f1000research.com\/articles\/14-1266\/v2#referee-comment-15955","order":1,"name":"referee-comment-15955","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.190304.r462004, Jason Obrien, Environment and Climate Change Canada, Ottawa, 26 Mar 2026, version 1, 3 approved with reservations","URL":"https:\/\/f1000research.com\/articles\/14-1266\/v1#referee-response-462004","order":2,"name":"referee-response-462004","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Allan Davis<\/b>; \n<i>Posted: 14 Apr 2026<\/i>; \n<b>1. Comment regarding Methods section: <\/b>There are a lot of \u201cresults\u201d presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; We have edited the \u201cMethods\u201d section to make it more generic and instead moved the specific \u201cresults\u201d to the more appropriate \u201cResults\u201d section of the manuscript.  \n<b>2. Comment regarding selected AOP<\/b>: The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is \u201cempty\u201d and \u201copen for adoption\u201d) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; In response to similar comments from Reviewer 1, we have edited the last paragraph of the \u201cIntroduction\u201d to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP.  \n<b>3. Comment regarding KE:386 mapping<\/b>: The CTD terms selected for this KE do not seem very well matched. The KE is called \u201cdecreased neuronal network function\u201d, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are \u201cnervous system development\u201d and \u201cnervous system process\u201d, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; As the reviewer notes, we believe a limitation in this study is the mapping of KE terms (often weakly defined or susceptible to interpretation) from the AOP-Wiki to their best corresponding matches in CTD.&nbsp; We discuss this in the \u201cResults\u201d section for KE:195 (which is sometimes described as \u201cNMDAR inhibition\u201d and at other times as \u201cdecreased NMDAR expression\u201d in the AOP-Wiki). For AOP:522, KE:2208 (\u201caberrant, synaptic formation and plasticity\u201d) is immediately upstream to KE:386 (\u201cdecrease of neuronal network function\u201d).&nbsp; However, KE:2208 is poorly defined in the AOP-Wiki (with only a key event title and no other context) while the downstream KE:386 is almost overly defined with extensive descriptions in different sections of the webpage (ranging from the measurement of synaptic activity to neuron and brain development).&nbsp; This makes it challenging to interpret the best mapping for these two supposedly distinct KEs.&nbsp; To further highlight this issue, we have added two new passages to the \u201cResults\u201d: \u201cMapping terms across databases, however, is not always straightforward and can be subject to interpretation, and individual users can exercise flexibility to arrive at different translations, especially with respect to the level of granularity.&nbsp; In the AOP-Wiki, KE terms are defined in multiple sections, including the \u201cevent title\u201d (a mandatory descriptive title for the KE), the \u201cevent component\u201d (an optional ontology term, if known or applicable), and an \u201cevent description\u201d (an optional descriptive passage about the biological state being measured and its role in biology).&nbsp; We attempted to balance these descriptions for each unique KE to make the best mappings to CTD.\u201d and then later: \u201cWhile KE:386 uses a broad event title (\u201cdecreased neuronal network function\u201d), its event component is more nuanced (\u201cdecreased synaptic signaling\u201d), which conceptually overlaps with the upstream key event KE:2208 (see above).&nbsp; However, the event description for KE:386 details the neuronal network processes in the developing and mature brain.&nbsp; To limit duplicative mapping used for KE:2208 (\u201csynaptic signaling\u201d GO:0099536), and, more importantly, to cover the essential features of neuron and brain development, we decided to map this KE more broadly to two CTD phenotypes: \u201cnervous system development\u201d (GO:0007399) and \u201cnervous system process\u201d (GO:0050877) which includes \u201cneurogenesis\u201d (GO:0022008), \u201cbrain development\u201d (GO:0007420), and \u201ctransmission of nerve impulse\u201d (GO:0019226), among many other neuronal functions and processes\u201d.  \n<b>4. Comment 1 regarding support for AOP<\/b>: The authors state that their results \u201cprovide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522\u201d. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; We have now added text to elaborate this point in the \u201cResults\u201d: \u201cLinking literature-based chemical-gene-phenotype events (curated from mechanistic studies) into biologically plausible pathways can support KERs as well as provide testable hypotheses for additional experiments to quantify empirical evidence connecting these intermediate events in response to the same chemical stressor and via shared mechanistic genes (25).\u201d  \n<b>5. Comment 2 regarding support for AOP<\/b>: The authors propose that the identified relationships can be used to \u201cexpand AOP development and interconnectivity\u201d. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; Using CTD tetramers to discover new potential intermediate mechanistic phenotypes\/KEs and construct new AOP series creates a highly interconnected map of linked phenotypes\/KEs that have numerous edges via shared chemical-gene, chemical-phenotype, and gene-phenotype relationships (see Figure 4); this offers curated literature-based support for KERs as well as new avenues for developing additional experiments to further validate the KERs.&nbsp; Also, results from CTD Tetramers Query are ranked by an \u201cEvidence Strength Score\u201d, calculated as the product score of the number of references used in each of the four lines of supporting evidence needed to construct a CGPD-tetramer.&nbsp; Thus, computational tetramers with a higher number of source articles across the five supporting lines of evidence categories will have a higher calculated \u201cEvidence Strength Score\u201d and be ranked at the top of the output results, providing users with an option to filter tetramers with a higher amount of underlying literature support; this is also diagrammed as step 4 in the newly added Supplementary Figure S3.&nbsp; We have added to the description of our new model AOP constructed from shared tetramer data, describing how these identified tetramers provide \u201coverlapping genes shared between the phenotypes as mechanistic links connecting the key events and provide support for KER evidence\u201d.&nbsp; This is also currently described in the Figure 4 legend: \u201cGenes shared between any two phenotypes (boxes with listed gene symbols connected by dotted arcs) provide additional mechanistic links further supporting the numerous KERs between the events\u201d.  \n<b>6. Comment regarding hypothetical AOP<\/b>: How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks, per Reviewer 3) and manually ordered them at four levels of biological organization.&nbsp; We have added expanded text for clarification in the \u201cResults\u201d. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process.  \n<b>7. Minor Typo<\/b>: Page 7, paragraph 1: Prioritized chemicals were groupED into categories \n<b>AUTHORS\u2019 RESPONSE<\/b>:&nbsp; We have fixed this typo.","URL":"https:\/\/f1000research.com\/articles\/14-1266\/v2#referee-comment-15954","order":3,"name":"referee-comment-15954","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.198672.r479505, Brian N Chorley, US Environmental Protection Agency, Research Triangle Park, North Carolina, 02 Jun 2026, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/14-1266\/v2#referee-response-479505","order":4,"name":"referee-response-479505","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was supported by the National Institute of Environmental Health Sciences [U24 ES033155].  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":5,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}