167 related articles for article (PubMed ID: 36232156)
1. SkinSensPred as a Promising in Silico Tool for Integrated Testing Strategy on Skin Sensitization.
Wang SS; Wang CC; Tung CW
Int J Environ Res Public Health; 2022 Oct; 19(19):. PubMed ID: 36232156
[TBL] [Abstract][Full Text] [Related]
2. Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance.
Teubner W; Mehling A; Schuster PX; Guth K; Worth A; Burton J; van Ravenzwaay B; Landsiedel R
Regul Toxicol Pharmacol; 2013 Dec; 67(3):468-85. PubMed ID: 24090701
[TBL] [Abstract][Full Text] [Related]
3. A development of a graph-based ensemble machine learning model for skin sensitization hazard and potency assessment.
Jeon B; Lim MH; Choi TH; Kang BC; Kim S
J Appl Toxicol; 2022 Nov; 42(11):1832-1842. PubMed ID: 35792566
[TBL] [Abstract][Full Text] [Related]
4. Reconstructed human epidermis-based testing strategy of skin sensitization potential and potency classification using epidermal sensitization assay and in silico data.
Mizumachi H; Suzuki S; Sakuma M; Natsui M; Imai N; Miyazawa M
J Appl Toxicol; 2024 Mar; 44(3):415-427. PubMed ID: 37846211
[TBL] [Abstract][Full Text] [Related]
5. Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides.
Wang CC; Wang SS; Liao CL; Tsai WR; Tung CW
J Pestic Sci; 2022 Nov; 47(4):184-189. PubMed ID: 36514692
[TBL] [Abstract][Full Text] [Related]
6. Integrated decision strategies for skin sensitization hazard.
Strickland J; Zang Q; Kleinstreuer N; Paris M; Lehmann DM; Choksi N; Matheson J; Jacobs A; Lowit A; Allen D; Casey W
J Appl Toxicol; 2016 Sep; 36(9):1150-62. PubMed ID: 26851134
[TBL] [Abstract][Full Text] [Related]
7. Assessment of a defined approach based on a stacking prediction model to identify skin sensitization hazard.
Tourneix F; Alépée N; Detroyer A; Eilstein J; Martinozzi Teissier S; Nardelli L; Noçairi H; Pauloin T; Piroird C; Del Bufalo A
Toxicol In Vitro; 2019 Oct; 60():134-143. PubMed ID: 31100378
[TBL] [Abstract][Full Text] [Related]
8. Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships.
Luechtefeld T; Maertens A; McKim JM; Hartung T; Kleensang A; Sá-Rocha V
J Appl Toxicol; 2015 Nov; 35(11):1361-1371. PubMed ID: 26046447
[TBL] [Abstract][Full Text] [Related]
9. Replacing the refinement for skin sensitization testing: Considerations to the implementation of adverse outcome pathway (AOP)-based defined approaches (DA) in OECD guidelines.
Kolle SN; Landsiedel R; Natsch A
Regul Toxicol Pharmacol; 2020 Aug; 115():104713. PubMed ID: 32562760
[TBL] [Abstract][Full Text] [Related]
10. Non-animal methods to predict skin sensitization (II): an assessment of defined approaches
Kleinstreuer NC; Hoffmann S; Alépée N; Allen D; Ashikaga T; Casey W; Clouet E; Cluzel M; Desprez B; Gellatly N; Göbel C; Kern PS; Klaric M; Kühnl J; Martinozzi-Teissier S; Mewes K; Miyazawa M; Strickland J; van Vliet E; Zang Q; Petersohn D
Crit Rev Toxicol; 2018 May; 48(5):359-374. PubMed ID: 29474122
[TBL] [Abstract][Full Text] [Related]
11. Improved defined approaches for predicting skin sensitization hazard and potency in humans.
Li H; Bai J; Zhong G; Lin H; He C; Dai R; Du H; Huang L
ALTEX; 2019; 36(3):363-372. PubMed ID: 30685780
[TBL] [Abstract][Full Text] [Related]
12. A triangular approach for the validation of new approach methods for skin sensitization.
Natsch A; Landsiedel R; Kolle SN
ALTEX; 2021; 38(4):669-677. PubMed ID: 34247252
[TBL] [Abstract][Full Text] [Related]
13. Prediction of skin sensitization potency using machine learning approaches.
Zang Q; Paris M; Lehmann DM; Bell S; Kleinstreuer N; Allen D; Matheson J; Jacobs A; Casey W; Strickland J
J Appl Toxicol; 2017 Jul; 37(7):792-805. PubMed ID: 28074598
[TBL] [Abstract][Full Text] [Related]
14. Filling the concept with data: integrating data from different in vitro and in silico assays on skin sensitizers to explore the battery approach for animal-free skin sensitization testing.
Natsch A; Emter R; Ellis G
Toxicol Sci; 2009 Jan; 107(1):106-21. PubMed ID: 18832184
[TBL] [Abstract][Full Text] [Related]
15. Evaluation of the global performance of eight in silico skin sensitization models using human data.
Golden E; Macmillan DS; Dameron G; Kern P; Hartung T; Maertens A
ALTEX; 2021; 38(1):33-48. PubMed ID: 32388570
[TBL] [Abstract][Full Text] [Related]
16. Multivariate models for prediction of human skin sensitization hazard.
Strickland J; Zang Q; Paris M; Lehmann DM; Allen D; Choksi N; Matheson J; Jacobs A; Casey W; Kleinstreuer N
J Appl Toxicol; 2017 Mar; 37(3):347-360. PubMed ID: 27480324
[TBL] [Abstract][Full Text] [Related]
17. Development of an in silico evaluation system that quantitatively predicts skin sensitization using OECD Guideline No. 497 ITSv2 defined approach for skin sensitization classification.
Asai T; Umeshita K; Sakurai M; Sakane S
Food Chem Toxicol; 2024 Mar; 185():114444. PubMed ID: 38253282
[TBL] [Abstract][Full Text] [Related]
18. Integrated skin sensitization assessment based on OECD methods (II): Hazard and potency by combining kinetic peptide reactivity and the "2 out of 3" Defined Approach.
Natsch A; Gerberick GF
ALTEX; 2022; 39(4):647-655. PubMed ID: 35404468
[TBL] [Abstract][Full Text] [Related]
19. A review of substances found positive in 1 of 3 in vitro tests for skin sensitization.
Kolle SN; Natsch A; Gerberick GF; Landsiedel R
Regul Toxicol Pharmacol; 2019 Aug; 106():352-368. PubMed ID: 31112722
[TBL] [Abstract][Full Text] [Related]
20. Computational approaches for skin sensitization prediction.
Wilm A; Kühnl J; Kirchmair J
Crit Rev Toxicol; 2018 Oct; 48(9):738-760. PubMed ID: 30488745
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
