{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T03:25:18Z","timestamp":1787714718520,"version":"build-2784847793"},"reference-count":14,"publisher":"Compuscript, Ltd.","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The assessment of a compound\u2019s potential DNA reactivity (mutagenicity) is a critical component of its safety evaluation. The biological activity of a compound is generally understood to be closely associated with its chemical structure. Because computer-aided quantitative structure-activity relationship (QSAR) models can provide high-throughput and rapid evaluation of compounds, the development of robust computational prediction models has substantial practical value. Herein, we developed the Decrypt Substance Toxicity (DSTOX) Mutagen Profiler software through a multi-strategy fusion approach. By integrating statistical rule-based models, expert knowledge rule-based models, analogue search, carcinogenicity data search, and International Council for Harmonisation (ICH) classification determination, the software generates mutagenicity assessment classification reports adhering to ICH M7 guidelines. Internal validation of the DSTOX statistical model indicated an accuracy of 92.0%. When combined with expert knowledge rule-based models and analogue cross-referencing, the DSTOX model exhibited robust mutagenicity prediction performance. External validation achieved an accuracy of 84.0% and a sensitivity (recall) of 82.8% for mutagenic compounds. The model\u2019s performance metrics were generally comparable to those of current internationally recognized mainstream prediction tools.<\/jats:p>","DOI":"10.15212\/amm-2026-0033","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T03:15:07Z","timestamp":1787714107000},"source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of DSTOX: an integrated QSAR platform adhering to ICH M7 guidelines for mutagenicity assessment of drug impurities"],"prefix":"10.15212","volume":"5","author":[{"given":"Guo","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyao","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zejian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinqi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5858","published-online":{"date-parts":[[2026]]},"reference":[{"key":"r1","volume-title":"M7(R3): Assessment and Control of DNA Reactive (mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk","author":"International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH)","year":"2023"},{"key":"r2","article-title":"US FDA Experience in the Regulatory Application of (Q)SAR","volume-title":"Society of Toxicology \u2013 Computational Toxicology Specialty Section Webinar","author":"NL Kruhlak","year":"2020"},{"key":"r3","volume-title":"The E-book on QSAR and REACH: Theory, Guidance and Applications","author":"E Benfenati","year":"2016"},{"key":"r4","volume-title":"Test No. 471: Bacterial Reverse Mutation Test. In OECD Guidelines for the Testing of Chemicals","author":"Organisation for Economic Co-operation and Development (OECD)","year":"2020"},{"key":"r5","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1186\/1752-153X-4-S1-S2","article-title":"An Open Source Multistep Model to Predict Mutagenicity from Statistical Analysis and Relevant Structural Alerts","volume":"4","author":"T Ferrari","year":"2010","journal-title":"Chemistry Central Journal"},{"key":"r6","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1021\/ci900161g","article-title":"Benchmark Data Set for In Silico Prediction of Ames Mutagenicity","volume":"49","author":"K Hansen","year":"2009","journal-title":"Journal of Chemical Information and Modeling"},{"key":"r7","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1021\/cr100222q","article-title":"Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology","volume":"111","author":"R Benigni","year":"2011","journal-title":"Chemical Reviews"},{"key":"r8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1093\/mutage\/ges059","article-title":"In Vitro Cell Transformation Assays for an Integrated, Alternative Assessment of Carcinogenicity: A Data-based Analysis","volume":"28","author":"R Benigni","year":"2013","journal-title":"Mutagenesis"},{"key":"r9","first-page":"2106","article-title":"Structure Alerts of Genotoxic Impurities","volume":"23","author":"L Ma","year":"2014","journal-title":"Chinese Journal of New Drugs"},{"key":"r10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/mutage\/gey031","article-title":"Improvement of Quantitative Structure\u2013Activity Relationship (QSAR) Tools for Predicting Ames Mutagenicity: Outcomes of the Ames\/QSAR International Challenge Project","volume":"34","author":"M Honma","year":"2019","journal-title":"Mutagenesis"},{"key":"r11","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1186\/s12943-024-02161-1","article-title":"Nanoparticles and Bone Microenvironment: A Comprehensive Review for Malignant Bone Tumor Diagnosis and Treatment","volume":"23","author":"Y Guan","year":"2024","journal-title":"Molecular Cancer"},{"key":"r12","article-title":"Targeting Tumor and Bone Microenvironment: Novel Therapeutic Opportunities for Castration-resistant Prostate Cancer Patients with Bone Metastasis","volume":"1879","author":"S Li","year":"2024","journal-title":"Biochimica et Biophysica Acta (BBA). Reviews on Cancer"},{"key":"r13","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1080\/1062936X.2023.2284902","article-title":"Evaluation of QSAR Models for Predicting Mutagenicity: Outcome of the Second Ames\/QSAR International Challenge Project","volume":"34","author":"A Furuhama","year":"2023","journal-title":"SAR and QSAR in Environmental Research"},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrestox.6c00082","article-title":"AmesNet: A Deep Learning Model Enhancing Generalization in Ames Mutagenicity Prediction","author":"TJ Umansky","year":"2026","journal-title":"Chemical Research in Toxicology"}],"container-title":["Acta Materia Medica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/scienceopen.com\/document_file\/e342fd97-9eaf-4e62-84ba-c54cd8abfdcd\/ScienceOpen\/amm20260033.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/scienceopen.com\/hosted-document?doi=10.15212\/AMM-2026-0033","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T03:15:17Z","timestamp":1787714117000},"score":1,"resource":{"primary":{"URL":"https:\/\/scienceopen.com\/hosted-document?doi=10.15212\/AMM-2026-0033"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":14,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.15212\/amm-2026-0033","relation":{},"ISSN":["2737-7946"],"issn-type":[{"value":"2737-7946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}