Machine learning-aided design of composite mycotoxin detoxifier material for animal feed
Abstract The development of food and feed additives involves the design of materials with specific properties that enable the desired function while minimizing the adverse effects related with their interference with the concurrent complex biochemistry of the living organisms. Often, the development...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-08410-x |
| _version_ | 1818358122381574144 |
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| author | Giulia Lo Dico Siska Croubels Verónica Carcelén Maciej Haranczyk |
| author_facet | Giulia Lo Dico Siska Croubels Verónica Carcelén Maciej Haranczyk |
| author_sort | Giulia Lo Dico |
| collection | DOAJ |
| description | Abstract The development of food and feed additives involves the design of materials with specific properties that enable the desired function while minimizing the adverse effects related with their interference with the concurrent complex biochemistry of the living organisms. Often, the development process is heavily dependent on costly and time-consuming in vitro and in vivo experiments. Herein, we present an approach to design clay-based composite materials for mycotoxin removal from animal feed. The approach can accommodate various material compositions and different toxin molecules. With application of machine learning trained on in vitro results of mycotoxin adsorption–desorption in the gastrointestinal tract, we have searched the space of possible composite material compositions to identify formulations with high removal capacity and gaining insights into their mode of action. An in vivo toxicokinetic study, based on the detection of biomarkers for mycotoxin-exposure in broilers, validated our findings by observing a significant reduction in systemic exposure to the challenging to be removed mycotoxin, i.e., deoxynivalenol (DON), when the optimal detoxifier is administrated to the animals. A mean reduction of 32% in the area under the plasma concentration–time curve of DON-sulphate was seen in the DON + detoxifier group compared to the DON group (P = 0.010). |
| first_indexed | 2024-12-13T20:23:59Z |
| format | Article |
| id | doaj.art-919876aa259a43149f642016cbcf7b75 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-13T20:23:59Z |
| publishDate | 2022-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-919876aa259a43149f642016cbcf7b752022-12-21T23:32:37ZengNature PortfolioScientific Reports2045-23222022-03-0112111110.1038/s41598-022-08410-xMachine learning-aided design of composite mycotoxin detoxifier material for animal feedGiulia Lo Dico0Siska Croubels1Verónica Carcelén2Maciej Haranczyk3IMDEA Materials InstituteDepartment of Pathobiology, Pharmacology and Zoological Medicine, Faculty of Veterinary Medicine, Ghent UniversityTolsa Group, Carretera de Madrid a Rivas JaramaIMDEA Materials InstituteAbstract The development of food and feed additives involves the design of materials with specific properties that enable the desired function while minimizing the adverse effects related with their interference with the concurrent complex biochemistry of the living organisms. Often, the development process is heavily dependent on costly and time-consuming in vitro and in vivo experiments. Herein, we present an approach to design clay-based composite materials for mycotoxin removal from animal feed. The approach can accommodate various material compositions and different toxin molecules. With application of machine learning trained on in vitro results of mycotoxin adsorption–desorption in the gastrointestinal tract, we have searched the space of possible composite material compositions to identify formulations with high removal capacity and gaining insights into their mode of action. An in vivo toxicokinetic study, based on the detection of biomarkers for mycotoxin-exposure in broilers, validated our findings by observing a significant reduction in systemic exposure to the challenging to be removed mycotoxin, i.e., deoxynivalenol (DON), when the optimal detoxifier is administrated to the animals. A mean reduction of 32% in the area under the plasma concentration–time curve of DON-sulphate was seen in the DON + detoxifier group compared to the DON group (P = 0.010).https://doi.org/10.1038/s41598-022-08410-x |
| spellingShingle | Giulia Lo Dico Siska Croubels Verónica Carcelén Maciej Haranczyk Machine learning-aided design of composite mycotoxin detoxifier material for animal feed Scientific Reports |
| title | Machine learning-aided design of composite mycotoxin detoxifier material for animal feed |
| title_full | Machine learning-aided design of composite mycotoxin detoxifier material for animal feed |
| title_fullStr | Machine learning-aided design of composite mycotoxin detoxifier material for animal feed |
| title_full_unstemmed | Machine learning-aided design of composite mycotoxin detoxifier material for animal feed |
| title_short | Machine learning-aided design of composite mycotoxin detoxifier material for animal feed |
| title_sort | machine learning aided design of composite mycotoxin detoxifier material for animal feed |
| url | https://doi.org/10.1038/s41598-022-08410-x |
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