Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity
Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/11/1/124 |
| _version_ | 1797415143616806912 |
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| author | Tung X. Trinh Jongwoon Kim |
| author_facet | Tung X. Trinh Jongwoon Kim |
| author_sort | Tung X. Trinh |
| collection | DOAJ |
| description | Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models. |
| first_indexed | 2024-03-09T05:43:36Z |
| format | Article |
| id | doaj.art-dc7926a3de7846728d24c0810e712e3c |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-09T05:43:36Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-dc7926a3de7846728d24c0810e712e3c2023-12-03T12:23:01ZengMDPI AGNanomaterials2079-49912021-01-0111112410.3390/nano11010124Status Quo in Data Availability and Predictive Models of Nano-Mixture ToxicityTung X. Trinh0Jongwoon Kim1Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, KoreaChemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, KoreaCo-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.https://www.mdpi.com/2079-4991/11/1/124nano-mixturetoxicitydata curationpredictive models |
| spellingShingle | Tung X. Trinh Jongwoon Kim Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity Nanomaterials nano-mixture toxicity data curation predictive models |
| title | Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity |
| title_full | Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity |
| title_fullStr | Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity |
| title_full_unstemmed | Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity |
| title_short | Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity |
| title_sort | status quo in data availability and predictive models of nano mixture toxicity |
| topic | nano-mixture toxicity data curation predictive models |
| url | https://www.mdpi.com/2079-4991/11/1/124 |
| work_keys_str_mv | AT tungxtrinh statusquoindataavailabilityandpredictivemodelsofnanomixturetoxicity AT jongwoonkim statusquoindataavailabilityandpredictivemodelsofnanomixturetoxicity |