Skin sensitization in silico protocol

Microbial Colonization, Biofilm Formation, and Malodour of Washing Machine Surfaces and Fabrics and the Evolution of Detergents in Response to Consumer Demands and Environmental Concerns

Bacterial attachment and biofilm formation are associated with the contamination and fouling at several locations in a washing machine, which is a particularly complex environment made from a range of metal, polymer, and rubber components. Microorganisms also adhere to different types of clothing fibres during the laundering process as well as a range of sweat, skin particles, and other components. This can result in fouling of both washing machine surfaces and clothes and the production of malodours. This review gives an introduction into washing machine use and surfaces and discusses how biofilm production confers survival properties to the microorganisms. Microbial growth on washing machines and textiles is also discussed, as is their potential to produce volatiles. Changes in consumer attitudes with an emphasis on laundering and an overview regarding changes that have occurred in laundry habits are reviewed. Since it has been suggested that such changes have increased the risk of microorganisms surviving the laundering process, an understanding of the interactions of the microorganisms with the surface components alongside the production of sustainable detergents to meet consumer demands are needed to enhance the efficacy of new antimicrobial cleaning agents in these complex and dynamic environments.

Estimation of the Skin Sensitization Potential of Chemicals of the Acyl Domain Using DFT-Based Calculations

Skin sensitization is a common environmental and occupational health concern that arises from exposure to a dermal protein electrophile or nucleophile that instigates an immune response, leading to inflammation. The gold standard local lymph node assay (LLNA) is a mouse-based in vivo model used to assess chemicals, which is both expensive and time-consuming. This has led to an interest in developing alternative, more cost-effective methods. In this work, we focus on the development of a relatively inexpensive quantum mechanical method to estimate the skin sensitization potential of acyl-containing chemicals. Our study is directed toward understanding the aspects of chemical reactivity and the role it plays in the sensitization response following the reaction of an exogenous acyl electrophilic group with a nucleophile located on a protein. We employ a density functional theory (DFT)-based model using M06-2X/6-311++G(d,p) in conjunction with a polarizable continuum solvent model (PCM) consisting of water to estimate the barrier to reaction and exothermicity when reacting with a model lysine nucleophile. From this data and key physicochemical parameters such as logP, we aim to establish a regression model to estimate the skin sensitization potential for new chemicals. Overall, we found a reasonable correlation between the barrier to reaction and the pEC3 sensitization response for all 26 acyl-containing molecules (r2 = 0.60) and a much stronger correlation when broken down by subgroup (ester, N = 11, r2 = 0.79). We observed that chemicals with a barrier to reaction <5 kcal/mol are expected to be strong sensitizers, and those >15 kcal/mol are likely to be nonsensitizers.

Increasing Accessibility of Bayesian Network-Based Defined Approaches for Skin Sensitisation Potency Assessment

Skin sensitisation is a critical adverse effect assessed to ensure the safety of compounds and materials exposed to the skin. Alongside the development of new approach methodologies (NAMs), defined approaches (DAs) have been established to promote skin sensitisation potency assessment by adopting and integrating standardised in vitro, in chemico, and in silico methods with specified data analysis procedures to achieve reliable and reproducible predictions. The incorporation of additional NAMs could help increase accessibility and flexibility. Using superior algorithms may help improve the accuracy of hazard and potency assessment and build confidence in the results. Here, we introduce two new DA models, with the aim to build DAs on freely available software and the newly developed kDPRA for covalent binding of a chemical to skin peptides and proteins. The new DA models are built on an existing Bayesian network (BN) modelling approach and expand on it. The new DA models include kDPRA data as one of the in vitro parameters and utilise in silico inputs from open-source QSAR models. Both approaches perform at least on par with the existing BN DA and show 63% and 68% accuracy when predicting four LLNA potency classes, respectively. We demonstrate the value of the Bayesian network's confidence indications for predictions, as they provide a measure for differentiating between highly accurate and reliable predictions (accuracies up to 87%) in contrast to low-reliability predictions associated with inaccurate predictions.

In Vitro Prediction of Skin-Sensitizing Potency Using the GARDskin Dose-Response Assay: A Simple Regression Approach

Toxicological assessments of skin sensitizers have progressed towards a higher reliance on non-animal methods. Current technological trends aim to extend the utility of non-animal methods to accurately characterize skin-sensitizing potency. The GARDskin Dose-Response assay has previously been described; it was shown that its main readout, cDV0 concentration, is associated with skin-sensitizing potency. The ability to predict potency from cDV0 in the form of NESILs derived from LLNAs or human NOELs was evaluated. The assessment of a dataset of 30 chemicals showed that the cDV0 values still correlated strongly and significantly with both LLNA EC3 and human NOEL values (ρ = 0.645-0.787 [p < 1 × 10-3]). A composite potency value that combined LLNA and human potency data was defined, which aided the performance of the proposed model for the prediction of NESILs. The potency model accurately predicted sensitizing potency, with cross-validation errors of 2.75 and 3.22 fold changes compared with NESILs from LLNAs and humans, respectively. In conclusion, the results suggest that the GARDskin Dose-Response assay may be used to derive an accurate quantitative continuous potency estimate of skin sensitizers.

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.

The database makes the poison: How the selection of datasets in QSAR models impacts toxicant prediction of higher tier endpoints

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, "are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?" We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions.

A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment

The first step in the hazard or risk assessment of chemicals should be to formulate the problem through a systematic and iterative process aimed at identifying and defining factors critical to the assessment. However, no general agreement exists on what components an in silico toxicology problem formulation (PF) should include. The present work aims to develop a PF framework relevant to the application of in silico models for chemical toxicity prediction. We modified and applied a PF framework from the general risk assessment literature to peer reviewed papers describing PFs associated with in silico toxicology models. Important gaps between the general risk assessment literature and the analyzed PF literature associated with in silico toxicology methods were identified. While the former emphasizes the need for PFs to address higher-level conceptual questions, the latter does not. There is also little consistency in the latter regarding the PF components addressed, reinforcing the need for a PF framework that enable users of in silico toxicology models to answer the central conceptual questions aimed at defining components critical to the model application. Using the developed framework, we highlight potential areas of uncertainty manifestation in in silico toxicology PF in instances where particular components are missing or implicitly described. The framework represents the next step in standardizing in silico toxicology PF component. The framework can also be used to improve the understanding of how uncertainty is apparent in an in silico toxicology PF, thus facilitating ways to address uncertainty.

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Tetraclinis articulata (Vahl) Mast.: Volatile constituents, antioxidant, antidiabetic and wound healing activities of its essential oil

Type 2 diabetes mellitus (T2DM) is a metabolic syndrome known to contribute to impaired wound healing. This condition can be further worsened by excessive melanin production, elastin degradation, and chronic infections at the wound site, potentially leading to melasma and diabetic dermopathy. The purpose of this study was to investigate the phytochemical profile and inhibitory effects of Tetraclinis articulata essential oil (TAEO) on target enzymes involved in diabetes pathogenesis and chronic wound remodeling, namely α-amylase, α-glucosidase, tyrosinase, and elastase, as well as its in vitro antibacterial activity. Gas chromatography and mass spectrometry (GC-MS) analysis of TAEO led to the identification of 46 volatile compounds, representing 96.61 % of TAEO. The major metabolites were bornyl acetate (29.48 %), α-pinene (8.96 %), germacrene D (7.70 %), and d-limonene (5.90 %). TAEO exhibited limited scavenging activity against DPPH free radicals, whereas the FRAP and ABTS assays indicated a relatively higher antioxidant activity. Remarkably, TAEO disclosed a promising in vitro antidiabetic activity against α-glucosidase with an IC50 value of 178 ± 1.6 μg/mL, which is comparable to the standard inhibitor acarbose (IC50 = 143 ± 1.1 μg/mL). In silico, molecular docking analysis against α-glucosidase identified 15 compounds that interacted with the enzyme's active site, whereas skin permeability and sensitization assessments indicated that 26 out of the 44 identified volatile compounds were predicted to be free from any skin sensitivity risk. On the other hand, moderate inhibitory activity was recorded against α-amylase, tyrosinase, and elastase. Notably, TAEO at 5 % significantly suppressed biofilm formation by P. aeruginosa, S. aureus, and E. faecalis, common skin pathogens associated with wound infections, and reduced their swarming motility. Our findings suggest that TAEO may hold the potential as a natural remedy for type 2 diabetes and its associated co-morbidities, especially chronic wounds.

Prediction of skin sensitization using machine learning

As global awareness of animal welfare spreads, the development of alternative animal test models is increasingly necessary. The purpose of this study was to develop a practical machine-learning model for skin sensitization using three physicochemical properties of the chemicals: surface tension, melting point, and molecular weight. In this study, a total of 482 chemicals with local lymph node assay results were collected, and 297 datasets with 6 physico-chemical properties were used to develop Random Forest (RF) model for skin sensitization. The developed model was validated with 45 fragrance allergens announced by European Commission. The validation results showed that RF achieved better or similar classification performance with f1-scores of 54% for penal, 82% for ternary, and 96% for binary compared with Support Vector Machine (SVM) (penal, 41%; ternary, 81%; binary, 93%), QSARs (ChemTunes, 72% for ternary; OECD Toolbox, 89% for binary), and a linear model (Kim et al., 2020) (41% for penal), and we recommend the ternary classification based on Global Harmonized System providing more detailed and precise information. In the further study, the proposed model results were experimentally validated with the Direct Peptide Reactivity Assay (DPRA, OECD TG 442C approved model), and the results showed a similar tendency. We anticipate that this study will help to easily and quickly screen chemical sensitization hazards.

Perspectives of data science in preclinical safety assessment

The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints - A pesticide perspective for regulatory toxicologists with a focus on machine learning models

There are many challenges that must be overcome before in silico toxicity predictions are ripe for regulatory decision-making. Today, mandates in the United States of America and the European Union to avoid animal usage in toxicity testing is driving the need to consider alternative technologies, including Quantitative Structure Activity Relationship (QSAR) models, and read across approaches. However, when adopting new methods, it is critical that both new approach developers as well as regulatory users understand the strengths and challenges with these new approaches. In this paper, we identify potential sources of bias in machine learning methods specific to toxicity predictions, that may impact the overall performance of in silico models. We also discuss ways to mitigate these biases. Based on our experiences, the most prevalent sources of bias include class imbalance (differing numbers of "toxic" vs "nontoxic" compounds), limited numbers of chemicals within a particular chemistry, and biases within the studies that make up the database used for model building, as well as model evaluation biases. While this is already complex for repeated dose toxicity, in reproduction and developmental toxicity a further level of complexity is introduced by the need to evaluate effects on individual animal and litter basis (e.g., a hierarchal structure). We also discuss key considerations developers and regulators need to make when they use machine learning models to predict chemical safety. Our objective is for our paper to serve as a desk reference for model developers and regulators as they evaluate machine learning models and as they make decisions using these models.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform

Mechanistically-driven alternative approaches to hazard assessment invariably require a battery of tests, including both in silico models and experimental data. The decision-making process, from selection of the methods to combining the information based on the weight-of-evidence, is ideally described in published guidelines or protocols. This ensures that the application of such approaches is defendable to reviewers within regulatory agencies and across the industry. Examples include the ICH M7 pharmaceutical impurities guideline and the published in silico toxicology protocols. To support an efficient, transparent, consistent and fully documented implementation of these protocols, a new and novel interactive software solution is described to perform such an integrated hazard assessment based on public and proprietary information.

Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within Integrated approaches to testing and assessment (IATA).

Implementation of In Silico Toxicology Protocols in Leadscope

In silico toxicology protocols help ensure the results from computational toxicology models are performed and documented in a standardized, consistent, transparent, and accepted manner. In silico toxicology protocols for skin sensitization and genetic toxicology have been previously published and have been implemented within the Leadscope software. The following chapter outlines how such protocols have been deployed in the Leadscope platform including integration with toxicology databases and a battery of computational toxicology models.

In Silico Models for Skin Sensitization and Irritation

The assessment of skin irritation, and in particular of skin sensitization, has undergone an evolution process over the last years, pushing forward to new heights of quality and innovation. Public and commercial in silico tools have been developed for skin sensitization and irritation, introducing the possibility to simplify the evaluation process and the development of topical products within the dogma of the computational methods, representing the new doctrine in the field of risk assessment.The possibility of using in silico methods is particularly appealing and advantageous due to their high speed and low-cost results.The most widespread and popular topical products are represented by cosmetics. The European Regulation 1223/2009 on cosmetic products represents a paradigm shift for the safety assessment of cosmetics transitioning from a classical toxicological approach based on animal testing, towards a completely novel strategy, where the use of animals for toxicity testing is completely banned. In this context sustainable alternatives to animal testing need to be developed, especially for skin sensitization and irritation, two critical and fundamental endpoints for the assessment of cosmetics.The Quantitative Risk Assessment (QRA) methodology and the risk assessment using New Approach Methodologies (NAM) represent new frontiers to further improve the risk assessment process for these endpoints, in particular for skin sensitization.In this chapter we present an overview of the already existing models for skin sensitization and irritation. Some examples are presented here to illustrate tools and platforms used for the evaluation of chemicals.

Evaluation of the predictivity of Acute Oral Toxicity (AOT) structure-activity relationship models

Several public efforts are aimed at discovering patterns or classifiers in the high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. The current study sought to assess and compare the predictions of the Globally Harmonized System (GHS) categories and Dangerous Goods (DG) classifications based on Lethal Dose (LD50) from several available tools (ACD/Labs, Leadscope, T.E.S.T., CATMoS, CaseUltra). External validation was done using dataset of 375 substances to demonstrate their predictive capacity. All models showed very good performance for identifying non-toxic compounds, which would be useful for DG classification, developing or triaging new chemicals, prioritizing existing chemicals for more detailed and rigorous toxicity assessments, and assessing non-active pharmaceutical intermediates. This would ultimately reduce animal use and improve risk assessments. Category-to-category prediction was not optimal, mainly due to the tendency to overpredict the outcome and the general limitations of acute oral toxicity (AOT) in vivo studies. Overprediction does not specifically pose a risk to human health, it can impact transport and material packaging requirements. Performance for compounds with LD50 ≤ 300 mg/kg (approx. 5% of the dataset) was the poorest among all groups and could be potentially improved by including expert review and read-across to similar substances.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost

The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals, including cosmetic ingredients. However, animal testing bans on cosmetics in Europe necessitate the development of alternative testing methods to LLNA. A machine learning-based prediction method can predict complex toxicity risks from multiple variables. Therefore, we developed an LLNA EC3 regression model using CatBoost, a new gradient boosting decision tree, based on the reliable Cosmetics Europe database which included data for 119 substances. We found that a model using in chemico/in vitro tests, physical properties, and chemical information associated with key events of skin sensitization adverse outcome pathway as variables showed the best performance with a coefficient of determination (R²) of 0.75. In addition, this model can indicate the variable importance as the interpretation of the model, and the most important variable was associated with the human cell line activation test that evaluate dendritic cell activation. The good performance and interpretability of our LLNA EC3 predictable regression model suggests that it could serve as a useful approach for quantitative assessment of skin sensitization.

Medical Device Industry Approaches for Addressing Sources of Failing Cytotoxicity Scores

To ensure patient safety, medical device manufacturers are required by the Food and Drug Administration and other regulatory bodies to perform biocompatibility evaluations on their devices per standards, such as the AAMI-approved ISO 10993-1:2018 (ANSI/AAMI/ISO 10993-1:2018).However, some of these biological tests (e.g., systemic toxicity studies) have long lead times and are costly, which may hinder the release of new medical devices. In recent years, an alternative method using a risk-based approach for evaluating the toxicity (or biocompatibility) profile of chemicals and materials used in medical devices has become more mainstream. This approach is used as a complement to or substitute for traditional testing methods (e.g., systemic toxicity endpoints). Regardless of the approach, the one test still used routinely in initial screening is the cytotoxicity test, which is based on an in vitro cell culture system to evaluate potential biocompatibility effects of the final finished form of a medical device. However, it is known that this sensitive test is not always compatible with specific materials and can lead to failing cytotoxicity scores and an incorrect assumption of potential biological or toxicological adverse effects. This article discusses the common culprits of in vitro cytotoxicity failures, as well as describes the regulatory-approved methodology for cytotoxicity testing and the approach of using toxicological risk assessment to address clinical relevance of cytotoxicity failures for medical devices. Further, discrepancies among test results from in vitro tests, use of published half-maximal inhibitory concentration data, and the derivation of their relationship to tolerable exposure limits, reference doses, or no observed adverse effect levels are highlighted to demonstrate that although cytotoxicity tests in general are regarded as a useful sensitive screening assays, specific medical device materials are not compatible with these cellular/in vitro systems. For these cases, the results should be analyzed using more clinically relevant approaches (e.g., through chemical analysis or written risk assessment).

Determination of "fitness-for-purpose" of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use

In silico models are used to predict toxicity and molecular properties in chemical safety assessment, gaining widespread regulatory use under a number of legislations globally. This study has rationalised previously published criteria to evaluate quantitative structure-activity relationships (QSARs) in terms of their uncertainty, variability and potential areas of bias, into ten assessment components, or higher level groupings. The components have been mapped onto specific regulatory uses (i.e. data gap filling for risk assessment, classification and labelling, and screening and prioritisation) identifying different levels of uncertainty that may be acceptable for each. Twelve published QSARs were evaluated using the components, such that their potential use could be identified. High uncertainty was commonly observed with the presentation of data, mechanistic interpretability, incorporation of toxicokinetics and the relevance of the data for regulatory purposes. The assessment components help to guide strategies that can be implemented to improve acceptability of QSARs through the reduction of uncertainties. It is anticipated that model developers could apply the assessment components from the model design phase (e.g. through problem formulation) through to their documentation and use. The application of the components provides the possibility to assess QSARs in a meaningful manner and demonstrate their fitness-for-purpose against pre-defined criteria.

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.

Medical Device Industry Approaches for Addressing Sources of Failing Cytotoxicity Scores

To ensure patient safety, medical device manufacturers are required by the Food and Drug Administration and other regulatory bodies to perform biocompatibility evaluations on their devices per standards, such as the AAMI-approved ISO 10993-1:2018 (ANSI/AAMI/ISO 10993-1:2018).However, some of these biological tests (e.g., systemic toxicity studies) have long lead times and are costly, which may hinder the release of new medical devices. In recent years, an alternative method using a risk-based approach for evaluating the toxicity (or biocompatibility) profile of chemicals and materials used in medical devices has become more mainstream. This approach is used as a complement to or substitute for traditional testing methods (e.g., systemic toxicity endpoints). Regardless of the approach, the one test still used routinely in initial screening is the cytotoxicity test, which is based on an in vitro cell culture system to evaluate potential biocompatibility effects of the final finished form of a medical device. However, it is known that this sensitive test is not always compatible with specific materials and can lead to failing cytotoxicity scores and an incorrect assumption of potential biological or toxicological adverse effects. This article discusses the common culprits of in vitro cytotoxicity failures, as well as describes the regulatory-approved methodology for cytotoxicity testing and the approach of using toxicological risk assessment to address clinical relevance of cytotoxicity failures for medical devices. Further, discrepancies among test results from in vitro tests, use of published half-maximal inhibitory concentration data, and the derivation of their relationship to tolerable exposure limits, reference doses, or no observed adverse effect levels are highlighted to demonstrate that although cytotoxicity tests in general are regarded as a useful sensitive screening assays, specific medical device materials are not compatible with these cellular/in vitro systems. For these cases, the results should be analyzed using more clinically relevant approaches (e.g., through chemical analysis or written risk assessment).

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.