Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance

Quantum-Mechanics Calculations Elucidate Skin-Sensitizing Pharmaceutical Compounds

Skin sensitization is a critical end point in occupational toxicology that necessitates the use of fast, accurate, and affordable models to aid in establishing handling guidance for worker protection. While many in silico models have been developed, the scarcity of reliable data for active pharmaceutical ingredients (APIs) and their intermediates (together regarded as pharmaceutical compounds) brings into question the reliability of these tools, which are largely constructed using publicly available nonspecialty chemicals. Here, we present the quantum-mechanical (QM) Computer-Aided Discovery and REdesign (CADRE) model, which was developed with the bioactive and structurally complex chemical space in mind by relying on the fundamentals of chemical interactions in key events (versus structural attributes of training-set data). Validated in this study on 345 APIs and intermediates, CADRE achieved 95% accuracy, sensitivity, and specificity and a combined 79% accuracy in assigning potency categories compared to the mouse local lymph node assay data. We show how historical outcomes from CADRE testing in the pharmaceutical space, generated over the past 10 years on ca. 2500 chemicals, can be used to probe the relationships between sensitization mechanisms (or the underlying chemical classes) and the probability of eliciting a sensitization response in mice of a given potency. We believe this information to be of value to both practitioners, who can use it to quickly screen and triage their data sets, as well as to model developers to fine-tune their structure-based tools. Lastly, we leverage our experimentally validated subset of APIs and intermediates to show the importance of dermal permeability on the sensitization potential and potency. We demonstrate that common physicochemical properties used to assess permeation, such as the octanol-water partition coefficient and molecular weight, are poor proxies for the more accurate energy-pair distributions that can be computed from mixed QM and classical simulations using model representations of the stratum corneum.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Insect kinin mimics act as potential control agents for aphids: Structural modifications of Trp4

Insect kinins are endogenous, biologically active peptides with various physiological functions. The use of insect kinins in plant protection is being evaluated by many groups. Some kinins have been chosen as lead compounds for pest control. We previously reported an insect kinin mimic IV-3 that had insecticidal activity. And by introducing a strong electron withdrawing group (-CF₃ ) on the benzene ring (Phe² ), we discovered a compound, L₇ , with better activity than lead IV-3. In this work, taking L₇ as the lead compound, we designed and synthesized 13 compounds to evaluate the influence of position 4 (Trp⁴ ) of insect kinin on insecticidal activity, by replacing the H atom on tryptophan with -CH₃ and -Cl or substituting the indole ring of tryptophan with the benzene, naphthalene, pyridine, imidazole, cyclohexane, and alkyl carboxamides. The aphid bioassay results showed that the compounds M₁ , M₃ , and M₅ were more active than the positive control, pymetrozine. Especially, replacing the side chain by an indole ring with 4-Cl substitution (M₁ , LC₅₀  = 0.0029 mmol/L) increased the aphicidal activity. The structure-activity relationships (SARs) indicated that the side chain benzene ring at this position may be important to the aphicidal activity. In addition, the toxicity prediction by Toxtree, and the toxicity experiments on Apis mellifera suggested that M₁ was no toxicity risk on a non-target organism. It could be used as a selective and bee-friendly insecticide to control aphids.

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space

Asthma is among the most common occupational diseases with considerable public health and economic costs. Chemicals that induce hypersensitivity in the airways can cause respiratory distress and comorbidities with respiratory infections such as COVID. Robust predictive models for this end point are still elusive due to the lack of an experimental benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities. The Computer-Aided Discovery and REdesign (CADRE) platform is a proven strategy for providing robust computational predictions for hazard end points using a tiered hybrid system of expert rules, molecular simulations, and quantum mechanics calculations. The recently developed CADRE model for respiratory sensitization is based on a highly curated data set of structurally diverse chemicals with high-fidelity biological data. The model evaluates absorption kinetics in lung mucosa using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines subsequent reactivity with cell proteins via quantum-mechanics calculations using a multi-tiered regression. The model affords an accuracy above 0.90, with a series of external validations based on literature data in the range of 0.88-0.95. The model is applicable to all low-molecular-weight organics and can inform not only chemical substitution but also chemical redesign to advance development of safer alternatives.

True Grit: A Story of Perseverance Making Two out of Three the First Non-Animal Testing Strategy (Adopted as OECD Guideline No. 497)

In the last two decades, great strides have been made in developing alternative methods to animal testing for regulatory and safety testing. In 2021, a breakthrough in regulatory testing was achieved in that the first test strategies employing non-animal test methods for skin sensitization have been accepted as OECD guideline 497, which falls under the mutual acceptance of data (MAD) by OECD member states. Achieving this goal was a story of hard work and perseverance of the many people involved. This review gives an overview of some of the many aspects and timelines this entailed—just from the perspective of one stakeholder. In the end, the true grit of all involved allowed us to achieve not only a way forward in using test strategies for skin sensitization, but also a new approach to address other complex toxicological effects without the use of animals in the future.

Gradient boosting approaches can outperform logistic regression for risk prediction in cutaneous allergy

BACKGROUND

Contact allergy is a major clinical and public health challenge. It is important to identify individuals who are at risk and perform patch testing to identify relevant allergens. Predicting clinical risk on the basis of input parameters is common in clinical medicine and traditionally has been achieved with linear models.

OBJECTIVES

We hypothesized that the risk of a clinically relevant positive patch test could be predicted according to clinical and demographic parameters.

METHODS

We compared the predictive accuracy of logistic regression with more sophisticated machine learning approaches such as gradient boosting, in the prediction of patch testing results.

RESULTS

We found that both logistic regression and more sophisticated machine learning approaches were able to predict the risk of positive patch tests. For certain predictions, including the overall risk of a clinically relevant positive patch test, gradient boosting approaches can outperform logistic regression.

CONCLUSIONS

These findings suggest that complex nonlinear interactions between input variables are relevant in risk prediction. While a risk prediction model cannot replace the judgment of an experienced clinician, quantifying the risk of a clinically relevant positive patch test result has the potential to assist in decision making and to inform discussions with patients.

In silico Prediction of Skin Sensitization: Quo vadis?

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

Human-Derived In Vitro Models Used for Skin Toxicity Testing Under REACh

In regulatory toxicology, in vivo studies are still prevailing, and human-derived in vitro models are mostly used in testing for local toxicity to the skin and the eyes. A single in vitro model may be limited to address one or few molecular or cellular events leading to adverse outcomes. Hence, in many instances their regulatory use involves the combination of several in vitro models to assess the hazard potential of test substance. A so-called defined approach combines different testing methods and a 'data interpretation procedure' to obtain a comprehensive overall assessment which is used for the regulatory hazard classification of the test substance.Validation is a prerequisite of regulatory acceptance of new testing methods: This chapter provides an overview of the method development from an experimental method to a test guideline via application of GIVIMP (good in vitro method practice), standardization, validation to the regulatory adoption as an OECD test guidelines. Quandaries associated with the validation towards reference data from in vivo animal studies with limited accuracy and limited human relevance are discussed, as well as uncertainty and limitations arising from restricted applicability and technical and biological variance of the in vitro methods.This chapter provides an overview of human-derived in vitro models currently adopted as OECD test guidelines: From the first skin corrosion tests utilizing reconstructed human epidermis models (RhE), to models to test for skin irritation, phototoxicity, eye irritation, and skin sensitization. The latter is using a battery of different methods and defined approaches which are still under discussion for their regulatory adoption. They will be a vanguard of future applications of human-derived models in regulatory toxicology. RhEs for testing of genotoxicity and of dermal penetration and absorption, have been developed, underwent validation studies and may soon be adopted for regulatory use; these are included in this chapter.

Going All In: A Strategic Investment in In Silico Toxicology

As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (versus regulatory needs) is called for to achieve this goal. Using economic analogy in the title of this work, we argue that a prudent investment is to go all-in to support in silico model development, rather than gamble our future by keeping the status quo of a "balanced portfolio" of testing approaches. We discuss two paths to future in silico toxicology-one based on big-data statistics ("broadsword"), and the other based on direct modeling of molecular interactions ("scalpel")-and offer rationale that the latter approach is more transparent, is better aligned with our quest for fundamental knowledge, and has a greater potential to succeed if we are willing to transform our toxicity-testing paradigm.

Prediction of the skin sensitising potential and potency of compounds via mechanism-based binary and ternary classification models

Skin sensitisation, one of the most frequent forms of human immune toxicity, is authenticated to be a significant endpoint in the field of drug discovery and cosmetics. Due to the drawbacks of traditional animal testing methods, in silico methods have advanced to study skin sensitisation. In this study, mechanism-based binary and ternary classification models were constructed with a comprehensive data set. 1007 compounds were collected to develop five series of local and global models based on mechanisms. In each series, compounds were classified into five groups according to EC3 values, and applied as training sets, test sets and external validation sets. For each of the five series, 81 binary classification models and 81 ternary classification models were acquired via 9 molecular fingerprints and 9 machine learning methods using a novel KNIME workflow. Meanwhile, the applicability domains for the best 10 models were figured out to certify the rationality of prediction effect. In addition, 8 toxic substructures probably causing skin sensitisation were identified to speculate whether a compound is a skin sensitiser. The mechanism-based prediction models and the toxic substructures can be applied to predict the skin sensitising potential and potency of compounds.

Skin toxicology and 3Rs-Current challenges for public health protection

Driven by the fast paced development of complex test systems in vitro, mass spectrometry and omics, we finally have the tools to unravel the molecular events that underlie toxicological adversity. Yet, timely regulatory adaptation of these new tools continues to pose major challenges even for organs readily accessible such as skin. The reasons for this encompass a need for conservatism as well as the need of tests to serve an existing regulatory framework rather than to produce scientific knowledge. It is important to be aware of this in order to align regulatory skin toxicity with the 3R principles more readily. While most chemical safety testing is still based on animal data, regulatory frameworks have seen a strong push towards non-animal approaches. The endpoints corrosion, irritation, sensitisation, absorption and phototoxicity, for example, can now be covered in vitro with the corresponding test guidelines (TGs) being made available by the OECD. However, in vitro approaches tend to be more reductionist. Hence, a combination of several tests is usually preferable to achieve satisfying predictivity. Moreover, the test systems and their combined use need to be standardised and are therefore subject not only to validation but also to the ongoing development of so-called integrated approaches to testing and assessment (IATAs). Concomitantly, skin models are being refined to deliver the complexity required for increased applicability and predictivity. Given the importance of regulatory applicability for 3R-derived approaches to have a long-lasting impact, this review examines the state of regulatory implementation and perspectives, respectively.

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

Evaluation of in silico tools to predict the skin sensitization potential of chemicals

Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE Ultra) or rule based (OECD Toolbox, Toxtree, Derek Nexus). In practice, several of these in silico tools are used in gap filling and read-across, but here their use was limited to make predictions based on presence/absence of structural features associated to sensitization. The top 400 ranking substances of the ATSDR 2011 Priority List of Hazardous Substances were selected as a starting point. Experimental information was identified for 160 chemically diverse substances (82 positive and 78 negative). The prediction for skin sensitization potential was compared with the experimental data. Rule-based tools perform slightly better, with accuracies ranging from 0.6 (OECD Toolbox) to 0.78 (Derek Nexus), compared with statistical tools that had accuracies ranging from 0.48 (Vega) to 0.73 (CASE Ultra - LLNA weak model). Combining models increased the performance, with positive and negative predictive values up to 80% and 84%, respectively. However, the number of substances that were predicted positive or negative for skin sensitization in both models was low. Adding more substances to the dataset will increase the confidence in the conclusions reached. The insights obtained in this evaluation are incorporated in a web database www.asopus.weebly.com that provides a potential end user context for the scope and performance of different in silico tools with respect to a common dataset of curated skin sensitization data.

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.

Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules

Introduction

Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage.

Results

The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably).

Conclusions

Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.

Analysis of publically available skin sensitization data from REACH registrations 2008-2014

The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization.

A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found.

A chemical similarity map was produced using PubChem’s 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel’s module recognition algorithm revealing wide module-dependent variation.

Approximately 31% of mapped chemicals are Michael’s acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value.

A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity.

This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction.

Setting Occupational Exposure Limits for Chemical Allergens--Understanding the Challenges

Chemical allergens represent a significant health burden in the workplace. Exposures to such chemicals can cause the onset of a diverse group of adverse health effects triggered by immune-mediated responses. Common responses associated with workplace exposures to low molecular weight (LMW) chemical allergens range from allergic contact dermatitis to life-threatening cases of asthma. Establishing occupational exposure limits (OELs) for chemical allergens presents numerous difficulties for occupational hygiene professionals. Few OELs have been developed for LMW allergens because of the unique biological mechanisms that govern the immune-mediated responses. The purpose of this article is to explore the primary challenges confronting the establishment of OELs for LMW allergens. Specific topics include: (1) understanding the biology of LMW chemical allergies as it applies to setting OELs; (2) selecting the appropriate immune-mediated response (i.e., sensitization versus elicitation); (3) characterizing the dose (concentration)-response relationship of immune-mediated responses; (4) determining the impact of temporal exposure patterns (i.e., cumulative versus acute exposures); and (5) understanding the role of individual susceptibility and exposure route. Additional information is presented on the importance of using alternative exposure recommendations and risk management practices, including medical surveillance, to aid in protecting workers from exposures to LMW allergens when OELs cannot be established.

Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties

Background

Advanced structure-activity relationship (SAR) modeling can be used as an alternative tool for identification of skin sensitizers and in improvement of the medical diagnosis and more effective practical measures to reduce the causative chemical exposures. It can also circumvent ethical concern of using animals in toxicological tests, and reduce time and cost. Compounds with aniline or phenol moieties represent two large classes of frequently skin sensitizing chemicals but exhibiting very variable, and difficult to predict, potency. The mechanisms of action are not well-understood.

Methods

A group of mechanistically hard-to-be-classified aniline and phenol chemicals were collected. An in silico model was established by statistical analysis of quantum descriptors for the determination of the relationship between their chemical structures and skin sensitization potential. The sensitization mechanisms were investigated based on the features of the established model. Then the model was utilized to analyze a subset of FDA approved drugs containing aniline and/or phenol groups for prediction of their skin sensitization potential.

Results and discussion

A linear discriminant model using the energy of the highest occupied molecular orbital (ϵ_HOMO) as the descriptor yielded high prediction accuracy. The contribution of ϵ_HOMO as a major determinant may suggest that autoxidation or free radical binding could be involved. The model was further applied to predict allergic potential of a subset of FDA approved drugs containing aniline and/or phenol moiety. The predictions imply that similar mechanisms (autoxidation or free radical binding) may also play a role in the skin sensitization caused by these drugs.

Conclusions

An accurate and simple quantum mechanistic model has been developed to predict the skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol chemicals. The model could be useful for the skin sensitization potential predictions of a subset of FDA approved drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/2050-6511-15-76) contains supplementary material, which is available to authorized users.

Assessment of the skin sensitising potency of the lower alkyl methacrylate esters

There is continued interest in, and imperatives for, the classification of contact allergens according to their relative skin sensitising potency. However, achieving that end can prove problematic, not least when there is an apparent lack of concordance between experimental assessments of potency and the prevalence allergic contact dermatitis as judged by clinical experience. For the purpose of exploring this issue, and illustrating the important considerations that are required to reach sound judgements about potency categorisation, the lower alkyl methacrylate esters (LAM) have been employed here as a case study. Although the sensitising potential of methyl methacrylate (MMA) has been reviewed previously, there is available new information that is relevant for assessment of skin sensitising potency. Moreover, for the purposes of this article, analyses have been extended to include also other LAM for which relevant data are available: ethyl methacrylate (EMA), n-butyl methacrylate (nBMA), isobutyl methacrylate (iBMA), and 2-ethylhexyl methacrylate (EHMA). In addressing the skin sensitising activity of these chemicals and in drawing conclusions regarding relative potency, a number of sources of information has been considered, including estimates of potency derived from local lymph node assay (LLNA) data, the results of guinea pig assays, and data derived from in silico methods and from recently developed in vitro approaches. Moreover, clinical experience of skin sensitisation of humans by LAM has also been evaluated. The conclusion drawn is that MMA and other LAM are contact allergens, but that none of these chemicals has any more than weak skin sensitising potency. We have also explored here the possible bases for this modest sensitising activity. Finally, the nature of exposure to LAM has been reviewed briefly and on the basis of that information, together with an understanding of skin sensitising potency, a risk assessment has been prepared.

Towards AOP application--implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization

Since the OECD published the Adverse Outcome Pathway (AOP) for skin sensitization, many efforts have focused on how to integrate and interpret nonstandard information generated for key events in a manner that can be practically useful for decision making. These types of frameworks are known as Integrated Approaches to Testing and Assessment (IATA). Here we have outlined an IATA for skin sensitization which focuses on existing information including non testing approaches such as QSAR and read-across. The IATA was implemented into a pipeline tool using OASIS technology to provide a means of systematically collating and compiling relevant information which could be used in an assessment of skin sensitization potential. A test set of 100 substances with available skin sensitization information was profiled using the pipeline IATA. In silico and in chemico profiling information alone was able to correctly predict skin sensitization potential, with a preliminary accuracy of 73.85%. Information from other relevant endpoints (e.g., Ames mutagenicity) was found to improve the accuracy (to 87.6%) when coupled with a reaction chemistry mechanistic understanding. This pipeline platform could be useful in the assessment of skin sensitization potential and marks a step change in how non testing approaches can be practically applied.

Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals

The currently available animal-free methods for the detection of skin sensitizing potential of chemicals seem promising. However, no single method is able to comprehensively represent the complexity of the processes involved in skin sensitization. To ensure a mechanistic basis and cover the complexity, multiple methods should be integrated into a testing strategy, in accordance with the adverse outcome pathway that describes all key events in skin sensitization. Although current majority voting testing strategies have proven effective, the performance of individual methods is not taken into account. To that end, we designed a tiered strategy based on complementary characteristics of the included methods, and compared it to a majority voting approach. This tiered testing strategy was able to correctly identify all 41 chemicals tested. In terms of total number of experiments required, the tiered testing strategy requires less experiments compared to the majority voting approach. On the other hand, this tiered strategy is more complex due the number of different alternative methods required, and predicted costs are similar for both strategies. Both the tiered and majority voting strategies provide a mechanistic basis for skin sensitization testing, but the strategy most suitable for regulatory decision-making remains to be determined.