Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment?
The aim of the current study was to develop an in silico model to predict the sensitizing potential of cosmetic ingredients based on their physicochemical characteristics and to compare the predictions with historical animal data and results from “omics”-based in vitro studies. An in silico model wa...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/24/7/6801 |
| _version_ | 1797607765019983872 |
|---|---|
| author | Jadwiga Kalicińska Barbara Wiśniowska Sebastian Polak Radoslaw Spiewak |
| author_facet | Jadwiga Kalicińska Barbara Wiśniowska Sebastian Polak Radoslaw Spiewak |
| author_sort | Jadwiga Kalicińska |
| collection | DOAJ |
| description | The aim of the current study was to develop an in silico model to predict the sensitizing potential of cosmetic ingredients based on their physicochemical characteristics and to compare the predictions with historical animal data and results from “omics”-based in vitro studies. An in silico model was developed with the use of WEKA machine learning software fed with physicochemical and structural descriptors of haptens and trained with data from published epidemiological studies compiled into estimated odds ratio (eOR) and estimated attributable risk (eAR) indices. The outcome classification was compared to the results of animal studies and in vitro tests. Of all the models tested, the best results were obtained for the Naive Bayes classifier trained with 24 physicochemical descriptors and eAR, which yielded an accuracy of 86%, sensitivity of 80%, and specificity of 90%. This model was subsequently used to predict the sensitizing potential of 15 emerging and less-studied haptens, of which 7 were classified as sensitizers: cyclamen aldehyde, N,N-dimethylacrylamide, dimethylthiocarbamyl benzothiazole sulphide, geraniol hydroperoxide, isobornyl acrylate, neral, and prenyl caffeate. The best-performing model (NaiveBayes eAR, 24 parameters), along with an alternative model based on eOR (Random Comittee eOR, 17 parameters), are available for further tests by interested readers. In conclusion, the proposed infotechnomics approach allows for a prediction of the sensitizing potential of cosmetic ingredients (and possibly also other haptens) with accuracy comparable to historical animal tests and in vitro tests used nowadays. In silico models consume little resources, are free of ethical concerns, and can provide results for multiple chemicals almost instantly; therefore, the proposed approach seems useful in the safety assessment of cosmetics. |
| first_indexed | 2024-03-11T05:34:16Z |
| format | Article |
| id | doaj.art-c896301695eb4987b9054aca9e551bae |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T05:34:16Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-c896301695eb4987b9054aca9e551bae2023-11-17T16:56:15ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-04-01247680110.3390/ijms24076801Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment?Jadwiga Kalicińska0Barbara Wiśniowska1Sebastian Polak2Radoslaw Spiewak3Department of Experimental Dermatology and Cosmetology, Jagiellonian University Medical College, ul. Medyczna 9, 30-688 Krakow, PolandDepartment of Social Pharmacy, Jagiellonian University Medical College, ul. Medyczna 9, 30-688 Krakow, PolandDepartment of Social Pharmacy, Jagiellonian University Medical College, ul. Medyczna 9, 30-688 Krakow, PolandDepartment of Experimental Dermatology and Cosmetology, Jagiellonian University Medical College, ul. Medyczna 9, 30-688 Krakow, PolandThe aim of the current study was to develop an in silico model to predict the sensitizing potential of cosmetic ingredients based on their physicochemical characteristics and to compare the predictions with historical animal data and results from “omics”-based in vitro studies. An in silico model was developed with the use of WEKA machine learning software fed with physicochemical and structural descriptors of haptens and trained with data from published epidemiological studies compiled into estimated odds ratio (eOR) and estimated attributable risk (eAR) indices. The outcome classification was compared to the results of animal studies and in vitro tests. Of all the models tested, the best results were obtained for the Naive Bayes classifier trained with 24 physicochemical descriptors and eAR, which yielded an accuracy of 86%, sensitivity of 80%, and specificity of 90%. This model was subsequently used to predict the sensitizing potential of 15 emerging and less-studied haptens, of which 7 were classified as sensitizers: cyclamen aldehyde, N,N-dimethylacrylamide, dimethylthiocarbamyl benzothiazole sulphide, geraniol hydroperoxide, isobornyl acrylate, neral, and prenyl caffeate. The best-performing model (NaiveBayes eAR, 24 parameters), along with an alternative model based on eOR (Random Comittee eOR, 17 parameters), are available for further tests by interested readers. In conclusion, the proposed infotechnomics approach allows for a prediction of the sensitizing potential of cosmetic ingredients (and possibly also other haptens) with accuracy comparable to historical animal tests and in vitro tests used nowadays. In silico models consume little resources, are free of ethical concerns, and can provide results for multiple chemicals almost instantly; therefore, the proposed approach seems useful in the safety assessment of cosmetics.https://www.mdpi.com/1422-0067/24/7/6801cosmetic ingredientssensitizing potentialcontact allergyrisk assessmentinfotechnomicsin silico modelling |
| spellingShingle | Jadwiga Kalicińska Barbara Wiśniowska Sebastian Polak Radoslaw Spiewak Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment? International Journal of Molecular Sciences cosmetic ingredients sensitizing potential contact allergy risk assessment infotechnomics in silico modelling |
| title | Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment? |
| title_full | Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment? |
| title_fullStr | Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment? |
| title_full_unstemmed | Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment? |
| title_short | Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment? |
| title_sort | artificial intelligence that predicts sensitizing potential of cosmetic ingredients with accuracy comparable to animal and in vitro tests how does the infotechnomics compare to other omics in the cosmetics safety assessment |
| topic | cosmetic ingredients sensitizing potential contact allergy risk assessment infotechnomics in silico modelling |
| url | https://www.mdpi.com/1422-0067/24/7/6801 |
| work_keys_str_mv | AT jadwigakalicinska artificialintelligencethatpredictssensitizingpotentialofcosmeticingredientswithaccuracycomparabletoanimalandinvitrotestshowdoestheinfotechnomicscomparetootheromicsinthecosmeticssafetyassessment AT barbarawisniowska artificialintelligencethatpredictssensitizingpotentialofcosmeticingredientswithaccuracycomparabletoanimalandinvitrotestshowdoestheinfotechnomicscomparetootheromicsinthecosmeticssafetyassessment AT sebastianpolak artificialintelligencethatpredictssensitizingpotentialofcosmeticingredientswithaccuracycomparabletoanimalandinvitrotestshowdoestheinfotechnomicscomparetootheromicsinthecosmeticssafetyassessment AT radoslawspiewak artificialintelligencethatpredictssensitizingpotentialofcosmeticingredientswithaccuracycomparabletoanimalandinvitrotestshowdoestheinfotechnomicscomparetootheromicsinthecosmeticssafetyassessment |