Replacement of animal testing by integrated approaches to testing and assessment (IATA): a call for in vivitrosi

Alternative methods to animal use in toxicology are evolving with new advanced tools and multilevel approaches, to answer from one side to 3Rs requirements, and on the other side offering relevant and valid tests for drugs and chemicals, considering also their combination in test strategies, for a proper risk assessment.

While stand-alone methods, have demonstrated to be applicable for some specific toxicological predictions with some limitations, the new strategy for the application of New Approach Methods (NAM), to solve complex toxicological endpoints is addressed by Integrated Approaches for Testing and Assessment (IATA), aka Integrated Testing Strategies (ITS) or Defined Approaches for Testing and Assessment (DA). The central challenge of evidence integration is shared with the needs of risk assessment and systematic reviews of an evidence-based Toxicology. Increasingly, machine learning (aka Artificial Intelligence, AI) lends itself to integrate diverse evidence streams.

In this article, we give an overview of the state of the art of alternative methods and IATA in toxicology for regulatory use for various hazards, outlining future orientation and perspectives. We call on leveraging the synergies of integrated approaches and evidence integration from in vivo, in vitro and in silico as true in vivitrosi.

Evaluating Skin Sensitization Via Soft and Hard Multivariate Modeling

Allergic contact dermatitis is the most frequent manifestation of immunotoxicity in humans with a prevalence rate of 15% to 20% over general population. Skin sensitization is a complex end point that was for a long time being evaluated using animal testing. Great efforts have been made to completely substitute the use of animals and replace them by integrating data from in vitro and in chemico assays with in silico calculated parameters. However, it remains undefined how to make the best use of the cumulative data in such a way that information gain is maximized and accomplished with the fewest number of tests possible. In this work, 3 skin sensitization prediction models were considered: one to discriminate sensitizers from non-sensitizers, considering a 2-level scale; one according to the GHS, considering a 3-level scale; and the other to categorize potency in a 6-level scale, according to available human data. We used a data set of known human skin allergens for which in vitro, in chemico, and in silico descriptors where available to build classifiers based on soft and hard multivariate modeling. Model building, optimization, and refinement resulted in 100% accuracy in distinguishing between sensitizers and non-sensitizers. The same model was able to perform the characterization, in 3 and 6 levels, respectively, with 98.8 and 97.5% accuracy. Combining data from in vitro and in chemico tests with in silico descriptors is relatively simple to implement and some predictors are fitting the adverse outcome pathway for skin sensitization.

A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria

A decision tree-based defined approach (DA) has been designed using exclusion criteria based on applicability domain knowledge of in chemico/in vitro information sources covering key events 1-3 in the skin sensitisation adverse outcome pathway and an in silico tool predicting the adverse outcome (Derek Nexus). The hypothesis is that using exclusion criteria to de-prioritise less applicable assays and/or in silico outcomes produces a rational, transparent, and reliable DA for the prediction of skin sensitisation potential. Five exclusion criteria have been established: Derek Nexus reasoning level, Derek Nexus negative prediction, metabolism, lipophilicity, and lysine-reactivity. These are used to prioritise the most suitable information sources for a given chemical and results from which are used in a '2 out of 3' approach to provide a prediction of hazard. A potency category (and corresponding GHS classification) is then assigned using a k-Nearest Neighbours model containing human and LLNA data. The DA correctly identified the hazard (sensitiser/non-sensitiser) for 85% and 86% of a dataset with reference LLNA and human data. The correct potency category was identified for 59% and 68% of chemicals, and the GHS classification accurately predicted for 73% and 76% with reference LLNA and human data, respectively.

Isothiazolinone derivatives and allergic contact dermatitis: a review and update

Allergic contact dermatitis (ACD) from isothiazolinones has frequently been described in the literature. Following an epidemic of sensitization to methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) in the 1980s, and more recently to MI, the Scientific Committee on Consumer Safety of the European Commission banned their use in leave-on products, while restricting that in rinse-off cosmetics. Despite a decreasing prevalence of ACD from MCI/MI and MI, cases caused by occupational exposure and non-cosmetic isothiazolinone sources are on the rise. Moreover, sensitization to newer and lesser known isothiazolinones has been reported. This paper reviews the epidemiology of contact allergy to different isothiazolinones, clinical presentation of isothiazolinone-induced ACD, most relevant sensitization sources and potential cross-reactions between isothiazolinone derivatives. It also provides an update on recent legislative measures.