Making reliable negative predictions of human skin sensitisation using an in silico fragmentation approach

Chilton, Martyn L.; Macmillan, Donna S.; Steger‐Hartmann, Thomas; Hillegass, Jedd M.; Bellion, Phillip; Vuorinen, Anna; Etter, Sylvain; Smith, Benjamin; White, Angela; Sterchele, Paul; Smedt, Ann De; Glogovac, Milica; Glowienke, Susanne; O’Brien, D.; Parakhia, R.

doi:10.1016/j.yrtph.2018.03.015

Cited by 24 publications

(19 citation statements)

References 17 publications

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“…They were particularly high for metal and metal salts, as well as for substituted phenols and their precursors. In a recent evaluation (Chilton et al 2018), Derek Nexus obtained a sensitivity of 54% and a specificity of 77% when used to discriminate between 302 skin sensitizers and 683 non-sensitizers measured with different animal testing systems. Derek Nexus can be combined with Meteor Nexus to also assess the skin sensitization potential of likely metabolites.…”

Section: Hybrid In Silico Modelsmentioning

confidence: 99%

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Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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Drugs, cosmetics, preservatives, fragrances, pesticides, metals, and other chemicals can cause skin sensitization. The ability to predict the skin sensitization potential and potency of substances is therefore of enormous importance to a host of different industries, to customers' and workers' safety. Animal experiments have been the preferred testing method for most risk assessment and regulatory purposes but considerable efforts to replace them with non-animal models and in silico models are ongoing. This review provides a comprehensive overview of the computational approaches and models that have been developed for skin sensitization prediction over the last 10 years. The scope and limitations of rule-based approaches, read-across, linear and nonlinear (quantitative) structure-activity relationship ((Q)SAR) modeling, hybrid or combined approaches, and models integrating computational methods with experimental results are discussed followed by examples of relevant models. Emphasis is placed on models that are accessible to the scientific community, and on model validation. A dedicated section reports on comparative performance assessments of various approaches and models. The review also provides a concise overview of relevant data sources on skin sensitization.

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Section: Hybrid In Silico Modelsmentioning

confidence: 99%

“…These strategies include the use of machine learning algorithms on encrypted data , as well as the allocation of data by so-called honest brokers. The latter has already resulted, for example, in the contribution of data from nine Lhasa member organizations to the evaluation of Derek Nexus (Chilton et al 2018).…”

Section: Outlook and Conclusionmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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“…Although this has been discussed as a promising strategy to increase the applicability of models, it has also prompted controversial discussions regarding the quality and relevance of the data [15,16]. The second strategy is to develop focused models based on small, focused data sets of high-quality [17,18,19,20,21]. The third strategy is to pursue a middle way that aims for a favorable balance between quantity and quality of the data.…”

Section: Introductionmentioning

confidence: 99%

Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability

Wilm

Stork

Bauer

et al. 2019

IJMS

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The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is the local lymph node assay (LLNA). The goal of this work was to develop in silico models for the prediction of the skin sensitization potential of small molecules that go beyond the state of the art, with larger LLNA data sets and, most importantly, a robust and intuitive definition of the applicability domain, paired with additional indicators of the reliability of predictions. We explored a large variety of molecular descriptors and fingerprints in combination with random forest and support vector machine classifiers. The most suitable models were tested on holdout data, on which they yielded competitive performance (Matthews correlation coefficients up to 0.52; accuracies up to 0.76; areas under the receiver operating characteristic curves up to 0.83). The most favorable models are available via a public web service that, in addition to predictions, provides assessments of the applicability domain and indicators of the reliability of the individual predictions.

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“…(Q)SAR models developed so far for nephrotoxicity, specifically, are summarised in Table 3 with details on the exact endpoint, number and type of molecules the model is based on, the method used, results, as well as strengths and weaknesses of the approach below. More detailed information on these QSAR models are provided in Appendix C [87,99,252,[262][263][264][265][266][267][268][269][270][271][272][273][274][275][276] of the supplementary information. It is also noted that QSAR models have been developed to predict renal clearance, which were examined in more detail elsewhere [56].…”

Section: (Q)sar Modelsmentioning

confidence: 99%

“…Derek Nexus is a knowledge-based expert system that uses SAs to provide in silico prediction of toxicity [264]. C. 4 Myshkin et al [265] Myshkin et al [265] described the construction and validation of QSAR models based on a database of organ-level toxicity and the identification of toxicophores.…”

Section: C3 Derek Nexusmentioning

confidence: 99%

A critical review of adverse effects to the kidney: mechanisms, data sources, andin silicotools to assist prediction

Pletz

Enoch

Jais

et al. 2018

Expert Opinion on Drug Metabolism & Toxicology

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Introduction: The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilise multi-scale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled. Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo and human data that may assist in the development of in silico models which in turn may shed light on the interrelationships between nephrotoxicity mechanisms.

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Making reliable negative predictions of human skin sensitisation using an in silico fragmentation approach

Cited by 24 publications

References 17 publications

Computational approaches for skin sensitization prediction

Computational approaches for skin sensitization prediction

Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability

A critical review of adverse effects to the kidney: mechanisms, data sources, andin silicotools to assist prediction

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