Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Oct:116:104688.
doi: 10.1016/j.yrtph.2020.104688. Epub 2020 Jul 1.

Skin sensitization in silico protocol

Candice Johnson  1 , Ernst Ahlberg  2 , Lennart T Anger  3 , Lisa Beilke  4 , Romualdo Benigni  5 , Joel Bercu  6 , Sol Bobst  7 , David Bower  8 , Alessandro Brigo  9 , Sarah Campbell  10 , Mark T D Cronin  11 , Ian Crooks  12 , Kevin P Cross  8 , Tatyana Doktorova  13 , Thomas Exner  13 , David Faulkner  14 , Ian M Fearon  15 , Markus Fehr  16 , Shayne C Gad  17 , Véronique Gervais  18 , Amanda Giddings  19 , Susanne Glowienke  20 , Barry Hardy  13 , Catrin Hasselgren  3 , Jedd Hillegass  21 , Robert Jolly  22 , Eckart Krupp  23 , Liat Lomnitski  24 , Jason Magby  25 , Jordi Mestres  26 , Lawrence Milchak  27 , Scott Miller  8 , Wolfgang Muster  9 , Louise Neilson  28 , Rahul Parakhia  29 , Alexis Parenty  20 , Patricia Parris  30 , Alexandre Paulino  31 , Ana Theresa Paulino  31 , David W Roberts  10 , Harald Schlecker  32 , Reinhard Stidl  33 , Diana Suarez-Rodrigez  34 , David T Szabo  35 , Raymond R Tice  36 , Daniel Urbisch  37 , Anna Vuorinen  16 , Brian Wall  25 , Thibaud Weiler  18 , Angela T White  19 , Jessica Whritenour  38 , Joerg Wichard  32 , David Woolley  39 , Craig Zwickl  40 , Glenn J Myatt  8
Affiliations

Skin sensitization in silico protocol

Candice Johnson et al. Regul Toxicol Pharmacol. 2020 Oct.

Abstract

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

Keywords: (Q)SAR; Computational toxicology; Computational toxicology protocols; Defined approach; Expert alerts; Expert review; Extractables and leachables; In silico; In silico toxicology; Integrated approaches to testing and assessment (IATA); Skin sensitization.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1.
Figure 1.
A generic hazard assessment framework that shows the relationship between the key components of the protocol
Figure 2.
Figure 2.
The hazard assessment framework describing the in silico components relevant for skin sensitization. In silico models could be developed for any effect or mechanism within grey boxes.
Figure 3.
Figure 3.
Adverse Outcome Pathway (AOP) for skin sensitization. MIE- molecular initiating event, KE (1–4) - Key Events 1–4.
Figure 4.
Figure 4.
The hazard assessment framework annotated with sections that discuss the assessment and confidence score of each endpoint.
Figure 5.
Figure 5.
Decision tree showing how an overall assessment and confidence score could be derived for the covalent interaction of skin proteins. The confidence scores are based on RS1 experimental data: assuming relevant data and high reliability, and, in practice, confidence scores may need to be adjusted based on reliability scores, SM Table 8. *If a pro-reactivity domain is assigned and the metabolic site (determined using structural alerts for skin metabolism) coincides with the pro-reactivity domain center then the reversal in assessment occurs. If the metabolic site and the reactivity domain center do not align then the assessment is inconclusive. §§The inconclusive result is applicable in situations where structural alerts could be used to determine if a structure is expected to undergo metabolism but not identify the metabolites. In this case, since the reactivity of the metabolite cannot be confirmed, a conclusion cannot be made on the assessment. If the reactivity of the metabolites could be predicted then the final assessment depends on the metabolite reactivity.
Figures 6A.
Figures 6A.
Decision trees showing how an overall assessment and confidence score could be derived for the ‘events in keratinocytes’. The confidence scores here are based on RS1 experimental data: assuming relevant data and high reliability, and, in practice, confidence scores may need to be adjusted based on reliability scores.
Figures 6B.
Figures 6B.
Decision trees showing how an overall assessment and confidence score could be derived for the ‘events in keratinocytes’. The confidence scores here are based on RS1 experimental data: assuming relevant data and high reliability, and, in practice, confidence scores may need to be adjusted based on reliability scores.
Figure 7.
Figure 7.
Decision tree showing how an overall assessment and confidence score could be derived for the ‘events in dendritic cells’ based on the h-CLAT assay. The confidence scores here are based on RS1 experimental data: assuming relevant data and high reliability, and, in practice, confidence scores may need to be adjusted based on reliability scores.
Figure 8.
Figure 8.
Decision tree showing how an overall assessment and confidence score could be derived for the ‘events in dendritic cells’ based on the U-SENS™ and IL-8 Luc assay data. The confidence scores here are based on RS1 experimental data: assuming relevant data and high reliability, and, in practice, confidence scores may need to be adjusted based on reliability scores, SM Table 10.
Figure 9.
Figure 9.
Decision tree showing how an overall assessment and confidence score could be derived for the “Events in rodent lymphocytes” based on the LLNA. The confidence scores here are based on RS1/2 experimental data (except in the case of *): assuming relevant data and high reliability, and, in practice, confidence scores may need to be adjusted based on reliability scores. *Concentrations tested in the LLNA are either non-irritating or mildly irritating. The low confidence score reflects the non-specific increase in lymphocyte proliferation that could occur with irritants.
Figure 10.
Figure 10.
Decision tree showing how an overall assessment and confidence score could be derived for the ‘skin sensitization in rodents’ endpoint based on guinea pig tests. The confidence scores here are based on RS1 experimental data (except in the case of *): assuming relevant data and high reliability, and, in practice, confidence scores may need to be adjusted based on reliability scores. *GPMT/BT challenge concentrations are non-irritating; however, deviations from OECD 406 may reduce the relevance of the study and decrease the confidence in the endpoint.
Figure 11a.
Figure 11a.
Derivation of the ‘skin sensitization in vitro’ endpoint using the “AOP 2 out of 3” approach (Case 1a)
Figure 11b.
Figure 11b.
Derivation of the ‘Skin Sensitization in Rodents’ endpoint
Figure 11c.
Figure 11c.
Derivation of the ‘skin Sensitization in Humans’ endpoint from the weight of evidence presented from the ‘Skin Sensitization skin in vitro’ and ‘Skin Sensitization in Rodents’ endpoints. DPT data is also used to support the overall assessment.
Figure 12a.
Figure 12a.
Derivation of the ‘skin sensitization in vitro’ endpoint using the “AOP 2 out of 3” approach (Case 2)
Figure 12b.
Figure 12b.
Derivation of the ‘Skin Sensitization in Humans’ using the “AOP 2 out of 3” approach (Case 2)
See this image and copyright information in PMC

References

    1. Anderson Stacey E., Siegel Paul D., and Meade BJ. 2011. “The LLNA: A Brief Review of Recent Advances and Limitations.” Journal of Allergy 2011:424203. - PMC - PubMed
    1. Api Anne Marie, Parakhia Rahul, OʼBrien Devin, and Basketter David A.. 2017. “Fragrances Categorized According to Relative Human Skin Sensitization Potency.” Dermatitis : Contact, Atopic, Occupational, Drug 28(5):299–307. - PMC - PubMed
    1. Aptula Aynur O. and Roberts David W.. 2006. “Mechanistic Applicability Domains for Nonanimal-Based Prediction of Toxicological End Points: General Principles and Application to Reactive Toxicity.” Chemical Research in Toxicology 19(8):1097–1105. - PubMed
    1. Ball Nicholas, Cagen Stuart, Carrillo Juan-Carlos, Certa Hans, Eigler Dorothea, Emter Roger, Faulhammer Frank, Garcia Christine, Graham Cynthia, Haux Carl, Kolle Susanne N., Kreiling Reinhard, Natsch Andreas, and Mehling Annette. 2011. “Evaluating the Sensitization Potential of Surfactants: Integrating Data from the Local Lymph Node Assay, Guinea Pig Maximization Test, and in Vitro Methods in a Weight-of-Evidence Approach.” Regulatory Toxicology and Pharmacology 60(3):389–400. - PubMed
    1. Basketter DA, Gerberick GF, and Kimber I. 2001. “Skin Sensitisation, Vehicle Effects and the Local Lymph Node Assay.” Food and Chemical Toxicology 39(6):621–27. - PubMed