Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling
Cyanide compounds are extensively used in industries like mining, metallurgy, and chemical synthesis, but their high toxicity presents serious environmental and health risks. This study applies advanced modeling techniques such as Quantitative Structure-Toxicity Relationship (QSTR), Species cyanide-...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Ecotoxicology and Environmental Safety |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325001605 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832573247265177600 |
|---|---|
| author | Kabiruddin Khan Ramin Abdullayev Gopala Krishna Jillella Varun Gopalakrishnan Nair Mahmoud Bousily Supratik Kar Agnieszka Gajewicz-Skretna |
| author_facet | Kabiruddin Khan Ramin Abdullayev Gopala Krishna Jillella Varun Gopalakrishnan Nair Mahmoud Bousily Supratik Kar Agnieszka Gajewicz-Skretna |
| author_sort | Kabiruddin Khan |
| collection | DOAJ |
| description | Cyanide compounds are extensively used in industries like mining, metallurgy, and chemical synthesis, but their high toxicity presents serious environmental and health risks. This study applies advanced modeling techniques such as Quantitative Structure-Toxicity Relationship (QSTR), Species cyanide-Sensitivity Distribution (ScSD), and quantitative Read-Across Structure Toxicity (q-RASTR) to assess cyanide toxicity. A dataset of 25 cyanide salts was analyzed for acute, chronic, and lethal toxicity across species like humans, rats, and fish. Key molecular descriptors, including topological, geometrical, and electronic properties, were computed using ALOGPS 2.1, ChemAxon, and Elemental-Descriptor 1.0. Three machine learning methods MLR, PLS, and kNN were employed to develop predictive models. Further, q-RASTR models were developed to enhance the predictive power by similarity measures concept of the studied cyanides by integrating features from QSTR and ScSD models. These models were validated using external datasets, achieving high accuracy. Key descriptors such as refractivity, water solubility, and lipophilic components significantly influence cyanide toxicity. The combined QSTR, ScSD, and q-RASTR models provide a robust framework for predicting species-specific cyanide-sensitivity, enhancing our understanding of cyanide's molecular toxicity mechanisms. This research aids environmental risk assessment and informs safer regulatory strategies. The results are available for public access at https://nanosens.onrender.com/apps/calTox/index.html#/. |
| format | Article |
| id | doaj-art-c81446da838247059b6413807ec75b4c |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-c81446da838247059b6413807ec75b4c2025-02-02T05:26:38ZengElsevierEcotoxicology and Environmental Safety0147-65132025-02-01291117824Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modelingKabiruddin Khan0Ramin Abdullayev1Gopala Krishna Jillella2Varun Gopalakrishnan Nair3Mahmoud Bousily4Supratik Kar5Agnieszka Gajewicz-Skretna6Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk 80-308, Poland; Corresponding authors.Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk 80-308, PolandDepartment of Pharmaceutical Chemistry, Dr. K. V. Subba Reddy Institute of Pharmacy, Dupadu, Kurnool, Andhra Pradesh 518218, IndiaLaboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk 80-308, PolandLaboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk 80-308, PolandChemometrics and Molecular Modeling Laboratory, Department of Chemistry and Physics, Kean University, 1000 Morris Avenue, Union, NJ 07083, USALaboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk 80-308, Poland; Corresponding authors.Cyanide compounds are extensively used in industries like mining, metallurgy, and chemical synthesis, but their high toxicity presents serious environmental and health risks. This study applies advanced modeling techniques such as Quantitative Structure-Toxicity Relationship (QSTR), Species cyanide-Sensitivity Distribution (ScSD), and quantitative Read-Across Structure Toxicity (q-RASTR) to assess cyanide toxicity. A dataset of 25 cyanide salts was analyzed for acute, chronic, and lethal toxicity across species like humans, rats, and fish. Key molecular descriptors, including topological, geometrical, and electronic properties, were computed using ALOGPS 2.1, ChemAxon, and Elemental-Descriptor 1.0. Three machine learning methods MLR, PLS, and kNN were employed to develop predictive models. Further, q-RASTR models were developed to enhance the predictive power by similarity measures concept of the studied cyanides by integrating features from QSTR and ScSD models. These models were validated using external datasets, achieving high accuracy. Key descriptors such as refractivity, water solubility, and lipophilic components significantly influence cyanide toxicity. The combined QSTR, ScSD, and q-RASTR models provide a robust framework for predicting species-specific cyanide-sensitivity, enhancing our understanding of cyanide's molecular toxicity mechanisms. This research aids environmental risk assessment and informs safer regulatory strategies. The results are available for public access at https://nanosens.onrender.com/apps/calTox/index.html#/.http://www.sciencedirect.com/science/article/pii/S0147651325001605CyanideQ-RASTRQSTRScSDToxicity |
| spellingShingle | Kabiruddin Khan Ramin Abdullayev Gopala Krishna Jillella Varun Gopalakrishnan Nair Mahmoud Bousily Supratik Kar Agnieszka Gajewicz-Skretna Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling Ecotoxicology and Environmental Safety Cyanide Q-RASTR QSTR ScSD Toxicity |
| title | Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling |
| title_full | Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling |
| title_fullStr | Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling |
| title_full_unstemmed | Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling |
| title_short | Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling |
| title_sort | decoding cyanide toxicity integrating quantitative structure toxicity relationships qstr with species sensitivity distributions and q rastr modeling |
| topic | Cyanide Q-RASTR QSTR ScSD Toxicity |
| url | http://www.sciencedirect.com/science/article/pii/S0147651325001605 |
| work_keys_str_mv | AT kabiruddinkhan decodingcyanidetoxicityintegratingquantitativestructuretoxicityrelationshipsqstrwithspeciessensitivitydistributionsandqrastrmodeling AT raminabdullayev decodingcyanidetoxicityintegratingquantitativestructuretoxicityrelationshipsqstrwithspeciessensitivitydistributionsandqrastrmodeling AT gopalakrishnajillella decodingcyanidetoxicityintegratingquantitativestructuretoxicityrelationshipsqstrwithspeciessensitivitydistributionsandqrastrmodeling AT varungopalakrishnannair decodingcyanidetoxicityintegratingquantitativestructuretoxicityrelationshipsqstrwithspeciessensitivitydistributionsandqrastrmodeling AT mahmoudbousily decodingcyanidetoxicityintegratingquantitativestructuretoxicityrelationshipsqstrwithspeciessensitivitydistributionsandqrastrmodeling AT supratikkar decodingcyanidetoxicityintegratingquantitativestructuretoxicityrelationshipsqstrwithspeciessensitivitydistributionsandqrastrmodeling AT agnieszkagajewiczskretna decodingcyanidetoxicityintegratingquantitativestructuretoxicityrelationshipsqstrwithspeciessensitivitydistributionsandqrastrmodeling |