Integrated decision strategies for skin sensitization hazard

Tools to investigate and avoid drug-hypersensitivity in drug development

INTRODUCTION

Drug hypersensitivity reactions (DHRs) are common adverse effects of pharmaceuticals that clinically resemble allergies, and which are becoming an important burden to healthcare systems. Alongside accurate diagnostic techniques, tools which can predict potential drug-inducing hypersensitivity reactions in the pre-clinical phase are critical. Despite the important adverse reactions linked to immune-mediated hypersensitivity, at present, there are no validated or required in vivo or in vitro methods to screen the sensitizing potential of drugs and their metabolites in the pre-clinical phase. Areas covered: Enhanced prediction in preclinical safety evaluation is extremely important. The purpose of this review is to assess the state of the art of tools available to assess the allergenic potential of drugs and to highlight our current understanding of the molecular mechanisms underlying inappropriate immune activation. Expert opinion: The knowledge that allergenic drugs share common mechanisms of immune cell activation with chemical allergens, and of the definition of the mechanistic pathway to adverse outcomes, can enhance targeting toxicity testing in drug development and hazard assessment of hypersensitivity. Additional efforts and extensive resources are necessary to improve preclinical testing methodologies, including optimization, better design and interpretation of data.

Comparison and validation of an in vitro skin sensitization strategy using a data set of 33 chemical references

Allergic contact dermatitis (ACD) is an adverse health effect that develops following repeated exposure to skin sensitizing chemicals. An animal testing ban has been applied in EU, leading to development of reliably predictive non-animal methods. Several in vitro methods have been developed as alternatives but one single non-animal test method is not been sufficient to fully address since the LLNA test ban. Here, we have selected an ITS (Integrated Testing Strategy) for skin sensitization which focuses on three in vitro methods that covered the first three steps of the AOP (DPRA, SENS-IS or h-CLAT). The aim of this study was to compare these three methods due to the WoE approach based on a 2-out-of-3-assessment. The results of 33 references were compared to in vivo data (especially human). We have shown that tested firstly DPRA and SENS-IS have permitted to conclude on 29 of 33 chemicals, whereas DPRA and h-CLAT on 25, and SENS-IS and h-CLAT on 23. With this sequence, DPRA and SENS-IS and then h-CLAT in case of equivocal results, we conclude more quickly by performing fewer tests. Thereby, we have shown that it is better to follow a preferential sequence than testing chemicals simultaneously with these three methods.

The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability?

New criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs) is suggested. This criterion is calculated with utilization of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The utilization of this criterion improves the predictive potential of QSAR models of dermal permeability coefficient, logKp (cm/h).

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

Many in vitro tests have been developed to screen for potential neurotoxicity. However, only few cell function-based tests have been used for comparative screening, and thus experience is scarce on how to confirm and evaluate screening hits. We addressed these questions for the neural crest cell migration test (cMINC). After an initial screen, a hit follow-up strategy was devised. A library of 75 compounds plus internal controls (NTP80-list), assembled by the National Toxicology Program of the USA (NTP) was used. It contained some known classes of (developmental) neurotoxic compounds. The primary screen yielded 23 confirmed hits, which comprised ten flame retardants, seven pesticides and six drug-like compounds. Comparison of concentration-response curves for migration and viability showed that all hits were specific. The extent to which migration was inhibited was 25-90%, and two organochlorine pesticides (DDT, heptachlor) were most efficient. In the second part of this study, (1) the cMINC assay was repeated under conditions that prevent proliferation; (2) a transwell migration assay was used as a different type of migration assay; (3) cells were traced to assess cell speed. Some toxicants had largely varying effects between assays, but each hit was confirmed in at least one additional test. This comparative study allows an estimate on how confidently the primary hits from a cell function-based screen can be considered as toxicants disturbing a key neurodevelopmental process. Testing of the NTP80-list in more assays will be highly interesting to assemble a test battery and to build prediction models for developmental toxicity.

Systems Toxicology: Real World Applications and Opportunities

Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams ("big data"), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity.

Behaviour of chemical respiratory allergens in novel predictive methods for skin sensitisation

Asthma resulting from sensitisation of the respiratory tract to chemicals is an important occupational health issue, presenting many toxicological challenges. Most importantly there are no recognised predictive methods for respiratory allergens. Nevertheless, it has been found that all known chemical respiratory allergens elicit positive responses in assays for skin sensitising chemicals. Thus, chemicals failing to induce a positive response in skin sensitisation assays such as the local lymph node assay (LLNA) lack not only skin sensitising activity, but also the potential to cause respiratory sensitisation. However, it is unclear whether it will be possible to regard chemicals that are negative in in vitro skin sensitisation tests also as lacking respiratory sensitising activity. To address this, the behaviour of chemical respiratory allergens in the LLNA and in recently validated non-animal tests for skin sensitisation have been examined. Most chemical respiratory allergens are positive in one or more newly validated non-animal test methods, although the situation varies between individual assays. The use of an integrated testing strategy could provide a basis for recognition of most respiratory sensitising chemicals. However, a more complete picture of the performance characteristics of such tests is required before specific recommendations can be made.

SkinSensDB: a curated database for skin sensitization assays

Skin sensitization is an important toxicological endpoint for chemical hazard determination and safety assessment. Prediction of chemical skin sensitizer had traditionally relied on data from rodent models. The development of the adverse outcome pathway (AOP) and associated alternative in vitro assays have reshaped the assessment of skin sensitizers. The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance. Current computational models to predict skin sensitization mainly based on in vivo assays without incorporating alternative in vitro assays. However, there are few freely available databases integrating both the in vivo and the in vitro skin sensitization assays for development of AOP-based skin sensitization prediction models. To facilitate the development of AOP-based prediction models, a skin sensitization database named SkinSensDB has been constructed by curating data from published AOP-related assays. In addition to providing datasets for developing computational models, SkinSensDB is equipped with browsing and search tools which enable the assessment of new compounds for their skin sensitization potentials based on data from structurally similar compounds. SkinSensDB is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.

It takes a village: Stakeholder participation is essential to transforming science

Efforts toward replacing the use of animals in toxicology testing have begun to make significant headway in the last several years, due to co-operative and pragmatic efforts on the part of many stakeholders, and the public pressure that non-governmental advocacy organisations represent. Science-focused advocacy organisations have a unique role to play in these efforts, as they often have flexibility to adapt quickly to keep a project going and forge connections among different kinds of stakeholders to help encourage buy-in. This year, meaningful progress has been made, especially in regulatory laws and policies, which will lead to the replacement of animals in toxicology testing. In order to keep this momentum, we need to measure progress -- but this requires improved transparency and regular reporting of animal use. In addition, we should consider how strategies that have successfully reduced and replaced animal use in toxicology can be applied to basic biomedical research practices.

Prediction of skin sensitization potency using machine learning approaches

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd.

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.

State of the art in non-animal approaches for skin sensitization testing: from individual test methods towards testing strategies

The hazard assessment of skin sensitizers relies mainly on animal testing, but much progress is made in the development, validation and regulatory acceptance and implementation of non-animal predictive approaches. In this review, we provide an update on the available computational tools and animal-free test methods for the prediction of skin sensitization hazard. These individual test methods address mostly one mechanistic step of the process of skin sensitization induction. The adverse outcome pathway (AOP) for skin sensitization describes the key events (KEs) that lead to skin sensitization. In our review, we have clustered the available test methods according to the KE they inform: the molecular initiating event (MIE/KE1)-protein binding, KE2-keratinocyte activation, KE3-dendritic cell activation and KE4-T cell activation and proliferation. In recent years, most progress has been made in the development and validation of in vitro assays that address KE2 and KE3. No standardized in vitro assays for T cell activation are available; thus, KE4 cannot be measured in vitro. Three non-animal test methods, addressing either the MIE, KE2 or KE3, are accepted as OECD test guidelines, and this has accelerated the development of integrated or defined approaches for testing and assessment (e.g. testing strategies). The majority of these approaches are mechanism-based, since they combine results from multiple test methods and/or computational tools that address different KEs of the AOP to estimate skin sensitization potential and sometimes potency. Other approaches are based on statistical tools. Until now, eleven different testing strategies have been published, the majority using the same individual information sources. Our review shows that some of the defined approaches to testing and assessment are able to accurately predict skin sensitization hazard, sometimes even more accurate than the currently used animal test. A few defined approaches are developed to provide an estimate of the potency sub-category of a skin sensitizer as well, but these approaches need further independent evaluation with a new dataset of chemicals. To conclude, this update shows that the field of non-animal approaches for skin sensitization has evolved greatly in recent years and that it is possible to predict skin sensitization hazard without animal testing.

What Happens after Activation of Ascaridole? Reactive Compounds and Their Implications for Skin Sensitization

To replace animal testing and improve the prediction of skin sensitization, significant attention has been directed to the use of alternative methods. The direct peptide reactivity assay (DPRA), the regulatory agencies' approved alternative in chemico method, has been applied for understanding the sensitization capacity of activated ascaridole. Ascaridole, the oxidative metabolite of α-terpinene, is considered to be one of the components responsible for the contact allergy associated with essential oils derived from Chenopodium and Melaleuca species. The recently developed high-throughput screening based on the dansyl cysteamine (HTS-DCYA) method was applied to understand the reported enhanced reactivity of activated ascaridole and possibly to identify the resulting elusive radical or other reactive species. For the first time, a substituted cyclohexenone was identified as a potential electrophilic intermediate resulting in higher depletion of nucleophilic DCYA, along with several nonreactive byproducts of ascaridole via a radical degradation mechanism. Formation of electrophilic species via radical degradation is one of the possible pathways should be considered for the peptide reactivity of in aged tea tree oil or oils rich in terpinenes along with commonly believed reactants, allylic-epoxides and allylic-peroxides.

Multivariate models for prediction of human skin sensitization hazard

One of the Interagency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens™ assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63-79%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.