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Lhasa Limited's Defined Approach

Lhasa has created a free, online application implementing its Defined Approach for skin sensitisation

Lhasa’s Defined Approach

General Approach

Validation of Lhasa’s Defined Approach

References

Lhasa’s Defined Approach

Lhasa Limited has created a web application which implements its defined approach (DA) for skin sensitisation, as published in Regulatory Toxicology and Pharmacology1.

This DA combines in silico predictions from Derek Nexus with results from OECD-validated in chemico/in vitro assays (DPRA, KeratinoSens™, LuSens, h-CLAT, U-SENS™)2–4 to provide a hazard and potency prediction without the use of in vivo animal tests.

The DA is not yet live; however, we invite you to register your interest by clicking the button below. We will then email you with updates as appropriate. 

General Approach

The DA takes the chemical of interest and applies exclusion criteria based on chemical and molecular properties (Derek prediction, metabolism, logP, lysine reactivity) to prioritise the in silico/in chemico/in vitro information sources.

The web application is thus able to direct users to the most appropriate assays based on their known limitations1–4. The prioritised information sources are then combined in a 2 out of 3 approach to provide a hazard prediction of skin sensitiser or non-sensitiser.

If the chemical is predicted to be a sensitiser, a subsequent potency prediction model is run based on the existing EC3 prediction model in Derek5. The model uses a k-nearest neighbours algorithm and can be thought of as an automated read-across prediction. 

The potency predictions are based on mouse (local lymph node assay) and human in vivo data for the most structurally similar and mechanistically relevant chemicals. 

For chemicals predicted to be sensitisers, the model provides the user with a potency category ranging from weak to extreme, and an associated GHS sub-classification of either 1A or 1B6. Chemicals which are predicted to be non-sensitisers are not classified according to GHS criteria6.

Validation of Lhasa’s Defined Approach 

The DA has been evaluated using 210 chemicals and correctly identified the hazard (sensitiser/non-sensitiser) for 85% and 86% (Figure 1) and the GHS classification for 73% and 76% (Figure 2) of the dataset when compared to LLNA and human data, respectively1.

Figure 1: The Performance of the DA in Predicting the Hazard of a Dataset of 210 Chemicals 

Figure 2: The Performance of the DA in Predicting the GHS Classification of a Dataset of 210 Chemicals 
 

References

  1. Macmillan, D. S. & Chilton, M. L. A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria. Regul. Toxicol. Pharmacol. 101, 35–47 (2019).
  2. OECD. Test No. 442C: In Chemico Skin Sensitisation: Direct Peptide Reactivity Assay (DPRA). (OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris., 2015).
  3. OECD. Key Event Based Test Guideline 442D: In Vitro Skin Sensitisation Assays Addressing The Key Event On Keratinocyte Activation On The Adverse Outcome Pathway For Skin Sensitisation. (OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris., 2018).
  4. OECD. Key Event Based Test Guideline 442E: In Vitro Skin Sensitisation Assays Addressing The Key Event On Activation Of Dendritic Cells On The Adverse Outcome Pathway For Skin Sensitisation. (OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris., 2018).
  5. Canipa, S. J. et al. A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces. J. Appl. Toxicol. 37, 985–995 (2017).
  6. United Nations. GLOBALLY HARMONIZED SYSTEM OF CLASSIFICATION AND LABELLING OF CHEMICALS (GHS) - Seventh revised edition. (2017).

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