Integrated testing and assessment approaches for skin sensitization: a commentary

Abstract

A Bayesian integrated testing strategy (ITS) approach, aiming to assess skin sensitization potency, has been presented, in which data from various types of in vitro assays are integrated and assessed in combination for their ability to predict in vivo skin sensitization data. Here we discuss this approach and compare it to our quantitative mechanistic modeling (QMM) approach based on physical organic chemistry. The main findings of the Bayesian study are consistent with our chemistry-based approach and our previously published assessment of the key determinants of sensitization potency, in particular the relatively high predictive value found for chemical reactivity data and the relatively low predictive value for bioavailability parameters. As it stands at present the Bayesian approach does not utilize the full range of predictive capability that is already available, and aims only to assign potency categories rather than numerical potency values per se. In contrast, for many chemicals the QMM approach can already provide numerical potency predictions. However, the Bayesian approach may have potential for those chemicals where a chemistry modeling approach cannot provide a complete answer (e.g. pro-electrophiles whose in cutaneo activation cannot currently be modeled confidently). Nonetheless, our main message is of the importance of leveraging chemistry insights and read-across approaches to the fullest extent possible.

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

“…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.…”

“…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].…”

“…While the above mentioned approaches heavily rely on biological tests to explain and predict sensitization potency there is another view-that skin sensitization can be predicted by in depth understanding of chemistry alone [17,24]. A chemistry based approach combines the knowledge of reactivity domains, reactivity measurements, reactivity molecular descriptors and kinetics of reaction rates to predict sensitization potency.…”

“…A chemistry based approach combines the knowledge of reactivity domains, reactivity measurements, reactivity molecular descriptors and kinetics of reaction rates to predict sensitization potency. Albeit as equally viable as published-to-date results indicate [17,24], this school of thought has been receiving less attention. The chemistry based approach overcomes perceived dynamic range limitation of biological assays by using the kinetic rate as one of the key variables.…”

“…Among ITSs for skin sensitization performance evaluations based on an external dataset were published only for three approaches: Jaworska et al [24,25,28], Luechtefeld et al [27] and Strickland et al [31]. In addition to good statistical performance which is necessary, the ability to explain an input data conflict is an additional critical criterion to the successful use of ITS.…”