&lt;i&gt;In silico&lt;/i&gt; risk assessment for skin sensitization using artificial neural network analysis

Tsujita-Inoue, Kyoko; Atobe, Tomomi; Hirota, Morihiko; Ashikaga, Takao; Kouzuki, Hirokazu

doi:10.2131/jts.40.193

Cited by 19 publications

(9 citation statements)

References 36 publications

(24 reference statements)

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“…Pragmatic approaches, such as the 2-of-3 concept (Bauch et al, 2012) are delivered promising results. Machine learning systems, such as Bayesian models, artificial neural networks and support vector machines, turned out to be valuable tools to establish prediction models for hazard (and partially potency) information for specific compounds (Hirota et al, 2013; Jaworska et al, 2015; Tsujita-Inoue et al, 2015). Furthermore, those systems are able to refine and improve predictive parameters easily by virtue of extended training sets.…”

Section: Industries Test Standards Unmet Needs and Potential For mentioning

confidence: 99%

Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing

Marx¹,

Andersson

Bahinski

et al. 2016

ALTEX

191

229

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Summary The recent advent of microphysiological systems – microfluidic biomimetic devices that aspire to emulate the biology of human tissues, organs and circulation in vitro – is envisaged to enable a global paradigm shift in drug development. An extraordinary US governmental initiative and various dedicated research programs in Europe and Asia have led recently to the first cutting-edge achievements of human single-organ and multi-organ engineering based on microphysiological systems. The expectation is that test systems established on this basis would model various disease stages, and predict toxicity, immunogenicity, ADME profiles and treatment efficacy prior to clinical testing. Consequently, this technology could significantly affect the way drug substances are developed in the future. Furthermore, microphysiological system-based assays may revolutionize our current global programs of prioritization of hazard characterization for any new substances to be used, for example, in agriculture, food, ecosystems or cosmetics, thus, replacing laboratory animal models used currently. Thirty-five experts from academia, industry and regulatory bodies present here the results of an intensive workshop (held in June 2015, Berlin, Germany). They review the status quo of microphysiological systems available today against industry needs, and assess the broad variety of approaches with fit-for-purpose potential in the drug development cycle. Feasible technical solutions to reach the next levels of human biology in vitro are proposed. Furthermore, key organ-on-a-chip case studies, as well as various national and international programs are highlighted. Finally, a roadmap into the future is outlined, to allow for more predictive and regulatory-accepted substance testing on a global scale.

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Section: Industries Test Standards Unmet Needs and Potential For mentioning

confidence: 99%

Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing

Marx¹,

Andersson

Bahinski

et al. 2016

ALTEX

191

229

View full text Add to dashboard Cite

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“…ITS potency assessment approaches developed to date include 3-way and 4-way LLNA EC3 deterministic classification [19][20][21], pEC3 (molar equivalent of EC3) prediction [22][23][24], 4-way probabilistic EC3 classification [25][26][27] and 4-way probabilistic pEC3 classification with a possibility to estimate any percentile of pEC3 distribution [28]. All other ITS approaches mentioned in this mini-review are for hazard estimation only.…”

Section: Its Approaches State Of the Artmentioning

confidence: 99%

“…Approaches based on machine learning algorithms are very popular. Among them are linear regression regression-based methods [23,24,30] and nonlinear methods like neural networks [19,20], support vector machines [31] and random-forest models [27].…”

Section: Its Approaches State Of the Artmentioning

confidence: 99%

Integrated Testing Strategies for Skin Sensitization Hazard and Potency Assessment—State of the Art and Challenges

Jaworska

2016

Cosmetics

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Abstract:The paper provides an overview of existing Integrated Testing Strategies (ITS) for assessing hazard and potency of skin sensitization. The ITS research is active, diverse and constantly evolving as new assays are being developed and new mechanistic insights are discovered. Despite the need to assess potency, the majority of the ITS approaches developed to date assess hazard only. Reasons for this situation are analyzed and include, for example, the dynamic range of existing alternative assays versus the range of in vivo responses, but also sporadic use of kinetic information and molar units. Depending on the application, regulatory or product development, standardized and nonstandard ITS approaches will be developed. Challenges to practical applications, with focus on regulatory are discussed.

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“…Applications of machine learning methods to the construction of DAs are Bayesian networks (BN) (Jaworska et al, 2011;Jaworska et al, 2013), Artificial Neural Networks (ANN) (Hirota et al, 2013;Tsujita-Inoue et al, 2014;Hirota et al, 2015;Tsujita-Inoue et al, 2015), Naïve…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…The overall conclusion is also based on a majority vote from test results from sequential steps in the strategy. The Artificial Neural Network (ANN) concept Hirota et al 2013;Tsujita-Inoue et al 2015) or the Bayesian networks Jaworska and Hoffmann, 2010;Jaworska et al, 2013;Jaworska et al, 2015) offer probabilistic approaches to predict skin sensitisation potential or potency using different physicochemical properties and information from nonanimal testing methods. The IDS DA (Matheson, 2015;Strickland et al, 2016) uses different machine learning approaches, i.e.…”

Section: Balancing Information Gains and Costsmentioning

confidence: 99%

An economic approach to non-animal toxicity testing for skin sensitisation

Leontaridou¹

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<i>In silico</i> risk assessment for skin sensitization using artificial neural network analysis

Abstract: In silico risk assessment for skin sensitization using arti ia neura net ork ana sis oko su ita noue omomi to e ori iko irota akao s ikaga an irokazu ouzuki

Cited by 19 publications

References 36 publications

Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing

Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing

Integrated Testing Strategies for Skin Sensitization Hazard and Potency Assessment—State of the Art and Challenges

An economic approach to non-animal toxicity testing for skin sensitisation

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