Regulatory acceptance of in silico approaches for the safety assessment of cosmetic-related substances

The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints

The continuous quest for the quick, accurate, and efficient methods for filling the gaps in the toxicity data of commercial chemicals is the need of the hour. Thus, it has become essential to develop simple and improved modeling strategies that aim to generate more accurate predictions. Recently, quantitative Read-Across Structure-Activity Relationship (q-RASAR) modeling has been reported to enhance the external predictivity of QSAR models. However, the cross-validation metrics of some q-RASAR models show compromised values compared to those of the corresponding QSAR models. We report here an improved q-RASAR workflow coupled with the Arithmetic Residuals in K-groups Analysis (ARKA) framework. This improved workflow (ARKA-RASAR) considers two important aspects: the contribution of different QSAR descriptors to different experimental response ranges, and the identification of similarity among close congeners based on both the selected QSAR descriptors and the contribution of different QSAR descriptors to different experimental response ranges. A simple, free, and user-friendly Java-based tool, Multiclass ARKA-v1.0, has been developed to compute the multiclass ARKA descriptors. In this study, five different toxicity datasets previously used for the development of QSAR and q-RASAR models were considered. We developed hybrid ARKA models that consist of a combination of QSAR descriptors and ARKA descriptors. These hybrid feature spaces were used to compute RASAR descriptors and develop ARKA-RASAR models. We used the same modeling strategies used to develop the previously reported QSAR and q-RASAR models for a fair comparison. Additionally, these modeling algorithms are straightforward, reproducible, and transferable. A multi-criteria decision-making statistical approach, the Sum of Ranking Differences (SRD), indicated that the ARKA-RASAR models are the best-performing models, considering training, test, and cross-validation statistics. The least significant difference procedure ensured that the SRD values were significantly different for most models, presenting an unbiased workflow. True external validation using a set of pesticide metabolites and predicting their early-stage acute fish toxicity using relevant ARKA-RASAR models was also carried out and yielded encouraging results. The promising results and the ease of computation of ARKA and RASAR descriptors using our tools suggest that the ARKA-RASAR modeling framework may be a potential choice for developing highly robust and predictive models for filling the gaps in environmental toxicity data.

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

Chaotic neural network algorithm with competitive learning integrated with partial Least Square models for the prediction of the toxicity of fragrances in sanitizers and disinfectants

This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)-PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities. We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact.

The dark side of beauty: an in-depth analysis of the health hazards and toxicological impact of synthetic cosmetics and personal care products

Over the past three decades, the popularity of cosmetic and personal care products has skyrocketed, largely driven by social media influence and the propagation of unrealistic beauty standards, especially among younger demographics. These products, promising enhanced appearance and self-esteem, have become integral to contemporary society. However, users of synthetic, chemical-based cosmetics are exposed to significantly higher risks than those opting for natural alternatives. The use of synthetic products has been associated with a variety of chronic diseases, including cancer, respiratory conditions, neurological disorders, and endocrine disruption. This review explores the toxicological impact of beauty and personal care products on human health, highlighting the dangers posed by various chemicals, the rise of natural ingredients, the intricate effects of chemical mixtures, the advent of nanotechnology in cosmetics, and the urgent need for robust regulatory measures to ensure safety. The paper emphasizes the necessity for thorough safety assessments, ethical ingredient sourcing, consumer education, and collaboration between governments, regulatory bodies, manufacturers, and consumers. As we delve into the latest discoveries and emerging trends in beauty product regulation and safety, it is clear that the protection of public health and well-being is a critical concern in this ever-evolving field.

Evaluation of the Safety of Cosmetic Ingredients and Their Skin Compatibility through In Silico and In Vivo Assessments of a Newly Developed Eye Serum

The term "risk assessment" is often substituted with "safety assessment", to demonstrate the safe properties of cosmetic ingredients and formulations. With respect to the actual legislative framework, the proper use of in silico evaluation could offer a representative non-animal substitute for the toxicity evaluation of cosmetic ingredients. The in silico assessment needs to be integrated with other lines of proof (in vitro and/or in vivo data) in the form of a complex methodology in order to demonstrate the safety evaluation of cosmetic ingredients/products. The present study aimed to develop and characterize a new cosmetic formulation, designed for the skin care of the periorbital area. Quality control comprising stability, physicochemical, and microbiological evaluation was performed. Another objective of this study was to present a screening model for the safety evaluation of the cosmetic formulation by identifying individual ingredients, and to confirm the skin compatibility based on in vivo evaluation. The results demonstrated the in silico and in vivo safety profile of the cosmetic ingredients used in the present formulation. In silico evaluation, using a novel, specific software applicable for the risk evaluation of ingredients and formulations, showed that the incorporated ingredients were non-mutagenic and non-sensitizing, and considering the margin of safety (MoS), the cosmetic raw materials could be considered safe. Skin compatibility was confirmed by the patch test performed under dermatological control, evidencing the "non-irritating" potential of the developed cosmetic formulation.

Biocidal products: Opportunities in risk assessment, management, and communication

In the coronavirus disease 2019 era, biocidal products are increasingly used for controlling harmful organisms, including microorganisms. However, assuring safety against adverse health effects is a critical issue from a public health standpoint. This study aimed to provide an overview of key aspects of risk assessment, management, and communication that ensure the safety of biocidal active ingredients and products. The inherent characteristics of biocidal products make them effective against pests and pathogens; however, they also possess potential toxicities. Therefore, public awareness regarding both the beneficial and potential adverse effects of biocidal products needs to be increased. Biocidal active ingredients and products are regulated under specific laws: the Federal Insecticide, Fungicide, and Rodenticide Act for the United States; the European Union (EU) Biocidal Products Regulation for the EU; and the Consumer Chemical Products and Biocide Safety Management Act for the Republic of Korea. Risk management also needs to consider the evidence of enhanced sensitivity to toxicities in individuals with chronic diseases, given the increased prevalence of these conditions in the population. This is particularly important for post-marketing safety assessments of biocidal products. Risk communication conveys information, including potential risks and risk-reduction measures, aimed at managing or controlling health or environmental risks. Taken together, the collaborative effort of stakeholders in risk assessment, management, and communication strategies is critical to ensuring the safety of biocidal products sold in the market as these strategies are constantly evolving.

Development and Evaluation of a Novel Anti-Ageing Cream Based on Hyaluronic Acid and Other Innovative Cosmetic Actives

The importance of incorporating hyaluronic acid (HA) as a cosmetic ingredient in skin care formulations emerged lately because the amount of HA naturally found in the epidermis decreases with age, and when applied to the skin through cosmetic products, it confers hydration and reduces the appearance of wrinkles. Currently, the diversity of cosmetic products for mature skin and the use of various and innovative active ingredients supporting their anti-ageing effect represent ample proof that the cosmetic industry is currently relying on these actives. The main objective of this study was the development of an anti-ageing formulation, incorporating HA and different other active ingredients. The developed formulation contains a novel complex of natural waxes, with an essential role in the restoration of the skin's hydro-lipid barrier, in combination with innovative active ingredients-like low-molecular hyaluronic acid (LMW-HA), sodium hyaluronate (NaHA), ectoin, gold, and an anti-ageing botanical complex-contributing to optimal skin hydration specifically designed to reduce the visible signs of ageing. An important objective was represented by the skin compatibility and topography assessment after 28 days (D28) of regular application of the developed cream. Stability testing, physicochemical characteristics, and microbiological control, including efficacy testing of the used preservative (challenge test) were performed for the cosmetic formulation. In silico approaches were applied to demonstrate the safety of cosmetic-related substances and the risk assessment of the cosmetic formulation. Safety and instrumental evaluation were performed to demonstrate the skin tolerance-the compatibility and the efficacy, respectively-of the developed anti-ageing cream. As result, quality control of the developed cosmetic formulation evidenced an appropriate cosmetic preparation with desirable aspect and adequate physicochemical characteristics. The concentrations of restricted ingredients like preservatives and UV filters were in accordance with those recommended by the Regulation (EC) No. 1223/2009 and so were considered to be safe. Additionally, according to the margin of safety (MoS) calculation, cosmetic ingredients incorporated in the developed formulation could be considered safe. The developed formulation was very well tolerated, and wrinkle depth and length in the periorbital area were significantly reduced after 28-day cosmetic treatment. Subjects' assessment questionnaires revealed self-perceived benefits referring to the cosmetic qualities and efficacy of the anti-ageing cream. This study confirmed the skin tolerance and efficacy of the new complex anti-ageing cream incorporating HA, microencapsulated sodium hyaluronate, ectoin, and a botanical extract. The formulated cosmetic product could serve as a daily care for mature skin to alleviate the effects of skin ageing.

A review on potential approach for in silico toxicity analysis of respirable fraction of ambient particulate matter

Epidemiological and toxicological studies have shown the adverse effect of ambient particulate matter (PM) on respiratory and cardiovascular systems inside the human body. Various cellular and acellular assays in literature use indicators like ROS generation, cell inflammation, mutagenicity, etc., to assess PM toxicity and associated health effects. The presence of toxic compounds in respirable PM needs detailed studies for proper understanding of absorption, distribution, metabolism, and excretion mechanisms inside the body as it is difficult to accurately imitate or simulate these mechanisms in lab or animal models. The leaching kinetics of the lung fluid, PM composition, retention time, body temperature, etc., are hard to mimic in an artificial experimental setup. Moreover, the PM size fraction also plays an important role. For example, the ultrafine particles may directly enter systemic circulations while coarser PM10 may be trapped and deposited in the tracheo-bronchial region. Hence, interpretation of these results in toxicity models should be done judiciously. Computational models predicting PM toxicity are rare in the literature. The variable composition of PM and lack of proper understanding for their synergistic role inside the body are prime reasons behind it. This review explores different possibilities of in silico modeling and suggests possible approaches for the risk assessment of PM particles. The toxicity testing approach for engineered nanomaterials, drugs, food industries, etc., have also been investigated for application in computing PM toxicity.

In Silico Analyses of a Promising Drug Candidate for the Treatment of Amyotrophic Lateral Sclerosis Targeting Superoxide Dismutase I Protein

Amyotrophic lateral sclerosis (ALS) is the most prevalent motor neuron disorder in adults, which is associated with a highly disabling condition. To date, ALS remains incurable, and the only drugs approved by the FDA for its treatment confer a limited survival benefit. Recently, SOD1 binding ligand 1 (SBL-1) was shown to inhibit in vitro the oxidation of a critical residue for SOD1 aggregation, which is a central event in ALS-related neurodegeneration. In this work, we investigated the interactions between SOD1 wild-type and its most frequent variants, i.e., A4V (NP_000445.1:p.Ala5Val) and D90A (NP_000445.1:p.Asp91Val), with SBL-1 using molecular dynamics (MD) simulations. The pharmacokinetics and toxicological profile of SBL-1 were also characterized in silico. The MD results suggest that the complex SOD1-SBL-1 remains relatively stable and interacts within a close distance during the simulations. This analysis also suggests that the mechanism of action proposed by SBL-1 and its binding affinity to SOD1 may be preserved upon mutations A4V and D90A. The pharmacokinetics and toxicological assessments suggest that SBL-1 has drug-likeness characteristics with low toxicity. Our findings, therefore, suggested that SBL-1 may be a promising strategy to treat ALS based on an unprecedented mechanism, including for patients with these frequent mutations.

A State-of-the-Art Review on the Alternatives to Animal Testing for the Safety Assessment of Cosmetics

Almost a decade after the stipulated deadline in the 7th amendment to the EU Cosmetics Directive, which bans the marketing of animal-tested cosmetics in the EU from 2013, animal experimentation for cosmetic-related purposes remains a topic of animated debate. Cosmetic industry continues to be scrutinised for the practice, despite its leading role in funding and adopting innovation in this field. This paper aims to provide a state-of-the-art review of the field on alternative testing methods, also known as New Approach Methodologies (NAMs), with the focus on assessing the safety of cosmetic ingredients and products. It starts with innovation drivers and global regulatory responses, followed by an extensive, endpoint-specific overview of accepted/prospective NAMs. The overview covers main developments in acute toxicity, skin corrosion/irritation, serious eye damage/irritation, skin sensitisation, repeated dose toxicity, reproductive toxicity/endocrine disruption, mutagenicity/genotoxicity, carcinogenicity, photo-induced toxicity, and toxicokinetics. Specific attention was paid to the emerging in silico methodology. This paper also provides a brief overview of the studies on public perception of animal testing in cosmetics. It concludes with a view that educating consumers and inviting them to take part in advocacy could be an effective tool to achieve policy changes, regulatory acceptance, and investment in innovation.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Appraisal of Bioactive Compounds of Betel Fruit as Antimalarial Agents by Targeting Plasmepsin 1 and 2: A Computational Approach

In many countries, the fruit of betel (Piper betle Linn) is traditionally used as medicine for treating malaria. It is a fatal disease, and existing medications are rapidly losing potency, necessitating the development of innovative pharmaceutics. The current study attempted to determine the compounds in the n-hexane fraction of betel fruit extract and investigate the potential inhibition of bioactive compounds against aspartic protease plasmepsin 1 (PDB ID: 3QS1) and plasmepsin 2 (PDB ID: 1LEE) of Plasmodium falciparum using a computational approach. The ethanol extract was fractionated into n-hexane and further analyzed using gas chromatography-mass spectrometry (GC-MS) to obtain information regarding the compounds contained in betel fruit. Each compound's potential antimalarial activity was evaluated using AutoDock Vina and compared to artemisinin, an antimalarial drug. Molecular dynamics simulations (MDSs) were performed to evaluate the stability of the interaction between the ligand and receptors. Results detected 20 probable compounds in the n-hexane extract of betel fruit based on GC-MS analysis. The docking study revealed that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- has the highest binding affinity for plasmepsin 1 and plasmepsin 2. The compound exhibits a similar interaction with artemisinin at the active site of the receptors. The compound does not violate Lipinski's rules of five. It belongs to class 5 toxicity with an LD50 of 3000 mg/kg. MDS results showed stable interactions between the compound and the receptors. Our study concluded that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- from betel fruit has the potential to be further investigated as a potential inhibitor of the aspartic protease plasmepsin 1 and plasmepsin 2 of Plasmodium falciparum.

In Silico and In Vitro Evaluation of the Antimicrobial Potential of Bacillus cereus Isolated from Apis dorsata Gut against Neisseria gonorrhoeae

Antimicrobial resistance is a major public health and development concern on a global scale. The increasing resistance of the pathogenic bacteria Neisseria gonorrhoeae to antibiotics necessitates efforts to identify potential alternative antibiotics from nature, including insects, which are already recognized as a source of natural antibiotics by the scientific community. This study aimed to determine the potential of components of gut-associated bacteria isolated from Apis dorsata, an Asian giant honeybee, as an antibacterial against N. gonorrhoeae by in vitro and in silico methods as an initial process in the stage of new drug discovery. The identified gut-associated bacteria of A. dorsata included Acinetobacter indicus and Bacillus cereus with 100% identity to referenced bacteria from GenBank. Cell-free culture supernatants (CFCS) of B. cereus had a very strong antibacterial activity against N. gonorrhoeae in an in vitro antibacterial testing. Meanwhile, molecular docking revealed that antimicrobial lipopeptides from B. cereus (surfactin, fengycin, and iturin A) had a comparable value of binding-free energy (BFE) with the target protein receptor for N. gonorrhoeae, namely penicillin-binding protein (PBP) 1 and PBP2 when compared with the ceftriaxone, cefixime, and doxycycline. The molecular dynamics simulation (MDS) study revealed that the surfactin remains stable at the active site of PBP2 despite the alteration of the H-bond and hydrophobic interactions. According to this finding, surfactin has the greatest antibacterial potential against PBP2 of N. gonorrhoeae.

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.

Beyond dermal exposure: The respiratory tract as a target organ in hazard assessments of cosmetic ingredients

Dermal contact is the main route of exposure for most cosmetics; however, inhalation exposure could be significant for some formulations (e.g., aerosols, powders). Current cosmetic regulations do not require specific tests addressing respiratory irritation and sensitisation, and despite the prohibition of animal testing for cosmetics, no alternative methods have been validated to assess these endpoints to date. Inhalation hazard is mainly determined based on existing human and animal evidence, read-across, and extrapolation of data from different target organs or tissues, such as the skin. However, because of mechanistic differences, effects on the skin cannot predict effects on the respiratory tract, which indicates a substantial need for the development of new approach methodologies addressing respiratory endpoints for inhalable chemicals in general. Cosmetics might present a particularly significant need for risk assessments of inhalation exposure to provide a more accurate toxicological evaluation and ensure consumer safety. This review describes the differences in the mechanisms of irritation and sensitisation between the skin and the respiratory tract, the progress that has already been made, and what still needs to be done to fill the gap in the inhalation risk assessment of cosmetic ingredients.

NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features

Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data and learn quantitative feature-toxicity relationships to yield predictive models. In this work, we adopted a principled approach to this problem by formulating a novel feature space based on intrinsic and extrinsic physicochemical properties, including periodic table properties but exclusive of in vitro characteristics such as cell line, cell type, and assay method. An optimal hypothesis space was developed by applying variance inflation analysis to the correlation structure of the features. Consequent to a stratified train-test split, the training dataset was balanced for the toxic outcomes and a mapping was then achieved from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models, namely, logistic regression, random forest, support vector machines, and neural networks. Evaluation on an unseen test set yielded >96% balanced accuracy for the random forest, and neural network with one-hidden-layer models. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and an embedded applicability check. Interpretability investigations of the models identified the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the best of the above models. NanoTox is the first open-source nanotoxicology pipeline, freely available under the GNU General Public License (https://github.com/NanoTox).

Integrated approaches to testing and assessment as a tool for the hazard assessment and risk characterization of cosmetic preservatives

The safety assessment of cosmetic products is based on the safety of the ingredients, which requires information on chemical structures, toxicological profiles, and exposure data. Approximately 6% of the population is sensitized to cosmetic ingredients, especially preservatives and fragrances. In this context, the aim of this study was to perform a hazard assessment and risk characterization of benzalkonium chloride (BAC), benzyl alcohol (BA), caprylyl glycol (CG), ethylhexylglycerin (EG), chlorphenesin (CP), dehydroacetic acid (DHA), sodium dehydroacetate (SDH), iodopropynyl butylcarbamate (IPBC), methylchloroisothiazolinone and methylisothiazolinone (MCI/MIT), methylisothiazolinone (MIT), phenoxyethanol (PE), potassium sorbate (PS), and sodium benzoate (SB). Considering the integrated approaches to testing and assessment (IATA) and weight of evidence (WoE) as a decision tree, based on published safety reports. The hazard assessment was composed of a toxicological matrix correlating the toxicity level, defined as low (L), moderate (M), or high (H) and local or systemic exposure, considering the endpoints of skin sensitization, skin irritation, eye irritation, phototoxicity, acute oral toxicity, carcinogenicity, mutagenicity/genotoxicity, and endocrine activity. In a risk assessment approach, most preservatives had a margin of safety (MoS) above 100, except for DHA, SDH, and EG, considering the worst-case scenario (100% dermal absorption). However, isolated data do not set up a safety assessment. It is necessary to carry out a rational risk characterization considering hazard and exposure assessment to estimate the level of risk of an adverse health outcome, based on the concentration in a product, frequency of use, type of product, route of exposure, body surface location, and target population.

Refining in vitro and in silico neurotoxicity approaches by accounting for interspecies and interindividual differences in toxicodynamics

INTRODUCTION

The process of chemical risk assessment traditionally relies on animal experiments and associated default uncertainty factors to account for interspecies and interindividual differences. To work toward a more precise and personalized risk assessment, these uncertainty factors should be refined and replaced by chemical-specific adjustment factors (CSAFs).

AREAS COVERED

This concise review discusses alternative (in vitro/in silico) approaches that can be used to assess interspecies and interindividual differences in toxicodynamics, ranging from targeted to more integrated approaches. Although data are available on interspecies differences, the increasing use of human-induced pluripotent stem cell (hiPSC)-derived neurons may provide opportunities to also assess interindividual variability in neurotoxicity. More integrated approaches, like adverse outcome pathways (AOPs) can provide a more quantitative understanding of the toxicodynamics of a chemical.

EXPERT OPINION

To improve chemical risk assessment, refinement of uncertainty factors is crucial. In vitro and in silico models can facilitate the development of CSAFs, but still these models cannot always capture the complexity of the in vivo situation, thereby potentially hampering regulatory acceptance. The combined use of more integrated approaches, like AOPs and physiologically based kinetic models, can aid in structuring data and increasing suitability of alternative approaches for regulatory purposes.

Reactivity prediction in aza-Michael additions without transition state calculations: the Ames test for mutagenicity

Animal testing remains a contentious ethical issue in predictive toxicology. Thus, a fast, versatile, low-cost quantum chemical model is presented for predicting the risk of Ames mutagenicity in a series of 1,4 Michael acceptor type compounds. This framework eliminates the need for transition state calculations, and uses an intermediate structure to probe the reactivity of aza-Michael acceptors. This model can be used in a variety of settings e.g., the design of targeted covalent inhibitors and polyketide biosyntheses.

Density Functional Theory in the Prediction of Mutagenicity: A Perspective

As a field, computational toxicology is concerned with using in silico models to predict and understand the origins of toxicity. It is fast, relatively inexpensive, and avoids the ethical conundrum of using animals in scientific experimentation. In this perspective, we discuss the importance of computational models in toxicology, with a specific focus on the different model types that can be used in predictive toxicological approaches toward mutagenicity (SARs and QSARs). We then focus on how quantum chemical methods, such as density functional theory (DFT), have previously been used in the prediction of mutagenicity. It is then discussed how DFT allows for the development of new chemical descriptors that focus on capturing the steric and energetic effects that influence toxicological reactions. We hope to demonstrate the role that DFT plays in understanding the fundamental, intrinsic chemistry of toxicological reactions in predictive toxicology.

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.