Measurement and Estimation of Electrophilic Reactivity for Predictive Toxicology

Equations for estimating binary mixture toxicity: 3-methyl-2-butanone with a series of electrophiles

Mixture toxicity was determined for 32 binary combinations. One chemical was the non-reactive, non-polar narcotic 3-methyl-2-butanone (always chemical A) and the other was a potentially reactive electrophile (chemical B). Bioluminescence inhibition in Allovibrio fischeri was measured at 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX). Concentration-response curves (CRCs) were developed for each chemical and used to develop predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Also, MX CRCs were generated and compared with model predictions using the 45-minute data. Classification of observed mixture toxicity used three specific criteria: 1) predicted IA EC50 vs. CA EC50 values at 45-minutes, 2) consistency of 45-minute MX CRC fit to IA, CA, or otherwise at three effect levels (EC25, EC50 and EC75), and 3) the known/suspected mechanism of toxicity for chemical B. Mixture toxicity was then classified into one of seven groupings. As a result of the predicted IA EC50 being more toxic than the predicted CA EC50, IA represented the greater toxic hazard. For this reason, non-sham MXs having toxicity consistent with CA were classified as being "coincident" with CA rather than mechanistically-consistent with CA. Multiple linear regression analyses were performed to develop equations that can be used to estimate the toxicity of other 3M2B-containing binary mixtures. These equations were developed from the data for both IA and CA, at each exposure duration and effect level. Each equation had a coefficient of determination (r2) above 0.950 and a variance inflation factor <1.2. This approach can potentially reduce the need for mixture testing and is amenable to other model systems and to assays that evaluate toxicity at low effect levels.

Drug Repurposing of ACT001 to Discover Novel Promising Sulfide Prodrugs with Improved Safety and Potent Activity for Neutrophil-Mediated Antifungal Immunotherapy

Neutrophil-mediated immunotherapy is a promising strategy for treating Candida albicans infection due to its potential in dealing with drug-resistant events. Our previous study found that ACT001 exhibited good antifungal immunotherapeutic activity by inhibiting PD-L1 expression in neutrophils, but its strong cytotoxicity and high BBB permeability hindered its antifungal application. To address these deficiencies, a series of novel sulfide derivatives were designed and synthesized based on a slow-release prodrug strategy. Among these derivatives, compound 16 exhibited stronger inhibition of PD-L1 expression, less cytotoxicity to neutrophils, and lower BBB permeability than ACT001. Compound 16 also significantly enhanced neutrophil-mediated antifungal immunity in C. albicans infected mice, with acceptable pharmacokinetic properties and good oral safety. Moreover, pharmacological mechanism studies demonstrated that ACT001 and compound 16 reduced PD-L1 expression in neutrophils by directly targeting STAT3. Briefly, this study provided a novel prototype compound 16 which exhibited great potential in neutrophil-mediated antifungal immunotherapy.

Covalent hits and where to find them

Covalent hits for drug discovery campaigns are neither fantastic beasts nor mythical creatures, they can be routinely identified through electrophile-first screening campaigns using a suite of different techniques. These include biophysical and biochemical methods, cellular approaches, and DNA-encoded libraries. Employing best practice, however, is critical to success. The purpose of this review is to look at state of the art covalent hit identification, how to identify hits from a covalent library and how to select compounds for medicinal chemistry programmes.

Insight into chemically reactive metabolites of aliphatic amine pollutants: A de novo prediction strategy and case study of sertraline

The uncommon metabolic pathways of organic pollutants are easily overlooked, potentially leading to idiosyncratic toxicity. Prediction of their biotransformation associated with the toxic effects is the very purpose that this work focuses, to develop a de novo method to mechanistically predict the reactive toxicity pathways of uncommon metabolites from start aliphatic amine molecules, which employed sertraline triggered by CYP450 enzymes as a model system, as there are growing concerns about the effects on human health posed by antidepressants in the aquatic environment. This de novo prediction strategy combines computational and experimental methods, involving DFT calculations upon sequential growth, in vitro and in vivo assays, dissecting chemically reactive mechanism relevant to toxicity, and rationalizing the fundamental factors. Significantly, desaturation and debenzylation-aromatization as the emerging metabolic pathways of sertraline have been elucidated, with the detection of DNA adducts of oxaziridine metabolite in mice, highlighting the potential reactive toxicity. Molecular orbital analysis supports the reactivity preference for toxicological-relevant C-N desaturation over N-hydroxylation of sertraline, possibly extended to several other aliphatic amines based on the Bell-Evans-Polanyi principle. It was further validated toward some other wide-concerned aliphatic amine pollutants involving atrazine, ε-caprolactam, 6PPD via in silico and in vitro assays, thereby constituting a complete path for de novo prediction from case study to general applications.

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.

The expanding repertoire of covalent warheads for drug discovery

The reactive functionalities of drugs that engage in covalent interactions with the enzyme/receptor residue in either a reversible or an irreversible manner are called 'warheads'. Covalent warheads that were previously neglected because of safety concerns have recently gained center stage as a result of their various advantages over noncovalent drugs, including increased selectivity, increased residence time, and higher potency. With the approval of several covalent inhibitors over the past decade, research in this area has accelerated. Various strategies are being continuously developed to tune the characteristics of warheads to improve their potency and mitigate toxicity. Here, we review research progress in warhead discovery over the past 5 years to provide valuable insights for future drug discovery.

Reactivity of Acrylamides Causes Cytotoxicity and Activates Oxidative Stress Response

Acrylamides are widely used industrial chemicals that cause adverse effects in humans or animals, such as carcinogenicity or neurotoxicity. The excess toxicity of these reactive electrophilic chemicals is especially interesting, as it is mostly triggered by covalent reactions with biological nucleophiles, such as DNA bases, proteins, or peptides. The cytotoxicity and activation of oxidative stress response of 10 (meth)acrylamides measured in three reporter gene cell lines occurred at similar concentrations. Most acrylamides exhibited high excess toxicity, while methacrylamides acted as baseline toxicants. The (meth)acrylamides showed no reactivity toward the hard biological nucleophile 2-deoxyguanosine (2DG) within 24 h, and only acrylamides reacted with the soft nucleophile glutathione (GSH). Second-order degradation rate constants (kGSH) were measured for all acrylamides with N,N'-methylenebis(acrylamide) (NMBA) showing the highest kGSH (134.800 M-1 h-1) and N,N-diethylacrylamide (NDA) the lowest kGSH (2.574 M-1 h-1). Liquid chromatography coupled to high-resolution mass spectrometry was used to confirm the GSH conjugates of the acrylamides with a double conjugate formed for NMBA. The differences in reactivity between acrylamides and methacrylamides could be explained by the charge density of the carbon atoms because the electron-donating inductive effect of the methyl group of the methacrylamides lowered their electrophilicity and thus their reactivity. The differences in reactivity within the group of acrylamides could be explained by the energy of the lowest unoccupied molecular orbital and steric hindrance. Cytotoxicity and activation of oxidative stress response were linearly correlated with the second-order reaction rate constants of the acrylamides with GSH. The reaction of the acrylamides with GSH is hence not only a detoxification mechanism but also leads to disturbances of the redox balance, making the cells more vulnerable to reactive oxygen species. The reactivity of acrylamides explained the oxidative stress response and cytotoxicity in the cells, and the lack of reactivity of the methacrylamides led to baseline toxicity.

Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants?

The toxicological profile of any chemical is defined by multiple endpoints and testing procedures, including representative test species from different trophic levels. While computer-aided methods play an increasingly important role in supporting ecotoxicology research and chemical hazard assessment, most of the recently developed machine learning models are directed towards a single, specific endpoint. To overcome this limitation and accelerate the process of identifying potentially hazardous environmental pollutants, we are introducing an effective approach for quantitative, multi-species modeling. The proposed approach is based on canonical correlation analysis that finds a pair(s) of uncorrelated, linear combinations of the original variables that best defines the overall variability within and between multiple biological responses and predictor variables. Its effectiveness was confirmed by the machine learning model for estimating acute toxicity of diverse organic pollutants in aquatic species from three trophic levels: algae (Pseudokirchneriella subcapitata), daphnia (Daphnia magna), and fish (Oryzias latipes). The multi-species model achieved a favorable predictive performance that were in line with predictive models derived for the aquatic organisms individually. The chemical bioavailability and reactivity parameters (n-octanol/water partition coefficient, chemical potential, and molecular size and volume) were important to accurately predict acute ecotoxicity to the three aquatic organisms. To facilitate the use of this approach, an open-source, Python-based script, named qMTM (quantitative Multi-species Toxicity Modeling) has been provided.

Identification of Novel CB2 Ligands through Virtual Screening and In Vitro Evaluation

Cannabinoid receptor type 2 (CB2) is a very promising therapeutic target for a variety of potential indications. However, despite the existence of multiple high affinity CB2 ligands, none have yet been approved as a drug. Therefore, it would be beneficial to explore new chemotypes of CB2 ligands. The recent elucidation of CB2 tertiary structure allows for rational hit identification with structure-based (SB) methods. In this study, we established a virtual screening workflow based on SB techniques augmented with ligand-based ones, including molecular docking, MM-GBSA binding energy calculations, pharmacophore screening, and QSAR. We screened nearly 7 million drug-like, commercially available compounds. We selected 16 molecules for in vitro evaluation and identified two novel, selective CB2 antagonists with K_i values of 65 and 210 nM. Both compounds are structurally diverse from CB2 ligands known to date. The established virtual screening protocol may prove useful for hit identification for CB2 and similar molecular targets. The two novel CB2 ligands provide a desired starting point for future optimization and development of potential drugs.

Cytotoxicity of nitrogenous disinfection byproducts: A combined experimental and computational study

Nitrogenous disinfection byproducts (N-DBPs), such as halocetamides (HAcAms), haloacetonitriles (HANs) and halonitromethanes (HNMs), are emerging DBPs in drinking water. They are more toxic than currently regulated DBPs, attracting more attention to their toxic effects and mechanism. In this study, human embryonic kidney (HEK) 293T cells were employed to explore the cytotoxicity of 29 N-DBPs. The influence of molecular structures and different halogenations on cytotoxicity has been comparatively analyzed. As toxicity is the downstream of chemico-biological interactions, the thiol reactivity of 29 N-DBPs has thus been evaluated by using glutathione (GSH) as a model nucleophile, which is the most prevalent cellular thiol and acts as an antioxidant to protect cells by detoxifying electrophilic compounds. Results show that the cytotoxicity of N-DBPs follows by the order of HAcAms > HANs > HNMs, which is different from their reactivity with GSH (the median of k_GSH ranks as HNMs > HAcAms > HANs). However, a significant correlation (p < 0.001) between log k_GSH and log IC₅₀ (concentration causing 50% inhibition) has been respectively observed for HAcAms and HANs subset and HNMs subset, indicating such chemical reaction is a probable trigger for these DBPs to result in cytotoxicity. Finally, two separate quantitative structure - activity relationship (QSAR) models based on HANs & HAcAms subset and HNMs subset have been developed for estimating IC₅₀ values. The good statistical performance makes the models possible to quickly and accurately predict IC₅₀ values of other N-DBPs, providing basic data for their health risk assessment and greatly reducing in vivo and in vitro experiments.

Effects of amino acids on the formation and distribution of glycerol core aldehydes during deep frying

Glyceryl core aldehyde (GCAs) are hazard factors produced during the frying process using oils and fats, and GCAs control and mitigation research is very important. This study investigated the effects of adding amino acids (methionine, glycine, and histidine) at 2.5, 5, and 10 mM on the formation and distribution of four GCAs during frying. High oleic sunflower oil (HOSO) was selected as frying oil for French fries. After 12 h of frying, the content of GCAs in the tert-butylhydroquinone-treated group (0.02 wt%, 1.1 mM) decreased by 29 % compared with the control group. The addition of methionine, glycine, and histidine decreased the total GCAs by 51 %, 28 %, and 27 %, respectively. The total GCAs content was best inhibited by methionine, while glycine and histidine were not significantly different from TBHQ. Methionine addition significantly reduced GCAs (9-oxo), GCAs (10-oxo-8), and GCAs (11-oxo-9) by 39 %, 78 %, and 80 %, respectively, while histidine was the most potent inhibitor of GCAs (8-oxo), which decreased by 40 %. Methionine also proved effective in slowing degradation of frying oil quality. These results provide a new direction for decreasing GCAs in frying systems.

An End Point-Specific Framework for Read-Across Analog Selection for Human Health Effects

Integrating computational chemistry and toxicology can improve the read-across analog approach to fill data gaps in chemical safety assessment. In read-across, structure-related parameters are compared between a target chemical with insufficient test data and one or more materials with sufficient data. Recent advances have focused on enhancing the grouping or clustering of chemicals to facilitate toxicity prediction via read-across. Analog selection ascertains relevant features, such as physical-chemical properties, toxicokinetic-related properties (bioavailability, metabolism, and degradation pathways), and toxicodynamic properties of chemicals with an emphasis on mechanisms or modes of action. However, each human health end point (genotoxicity, skin sensitization, phototoxicity, repeated dose toxicity, reproductive toxicity, and local respiratory toxicity) provides a different critical context for analog selection. Here six end point-specific, rule-based schemes are described. Each scheme creates an end point-specific workflow for filling the target material data gap by read-across. These schemes are intended to create a transparent rationale that supports the selected read-across analog(s) for the specific end point under study. This framework can systematically drive the selection of read-across analogs for each end point, thereby accelerating the safety assessment process.

Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces

We have used molecular dynamics (MD) simulations with hybrid quantum mechanics/molecular mechanics (QM/MM) potentials to investigate the reaction mechanism for covalent inhibition of cathepsin K and assess the reversibility of inhibition. The computed free energy profiles suggest that a nucleophilic attack by the catalytic cysteine on the inhibitor warhead and proton transfer from the catalytic histidine occur in a concerted manner. The results indicate that the reaction is more strongly exergonic for the alkyne-based inhibitors, which bind irreversibly to cathepsin K, than for the nitrile-based inhibitor odanacatib, which binds reversibly. Gas-phase energies were also calculated for the addition of methanethiol to structural prototypes for a number of warheads of interest in cysteine protease inhibitor design in order to assess electrophilicity. The approaches presented in this study are particularly applicable to assessment of novel warheads, and computed transition state geometries can be incorporated into molecular models for covalent docking.

Alterations to the broad-spectrum formin inhibitor SMIFH2 modulate potency but not specificity

SMIFH2 is a small molecule inhibitor of the formin family of cytoskeletal regulators that was originally identified in a screen for suppression of actin polymerization induced by the mouse formin Diaphanous 1 (mDia1). Despite widespread use of this compound, it is unknown whether SMIFH2 inhibits all human formins. Additionally, the nature of protein/inhibitor interactions remains elusive. We assayed SMIFH2 against human formins representing six of the seven mammalian classes and found inhibitory activity against all formins tested. We synthesized a panel of SMIFH2 derivatives and found that, while many alterations disrupt SMIFH2 activity, substitution of an electron-donating methoxy group in place of the bromine along with halogenation of the furan ring increases potency by approximately five-fold. Similar to SMIFH2, the active derivatives are also pan-inhibitors for the formins tested. This result suggests that while potency can be improved, the goal of distinguishing between highly conserved FH2 domains may not be achievable using the SMIFH2 scaffold.

Impact of solvent dry down, vehicle pH and slowly reversible keratin binding on skin penetration of cosmetic relevant compounds: I. Liquids

To measure progress and evaluate performance of the newest UB/UC/P&G skin penetration model we simulated an 18-compound subset of finite dose in vitro human skin permeation data taken from a solvent-deposition study of cosmetic-relevant compounds (Hewitt et al., J. Appl. Toxicol. 2019, 1-13). The recent model extension involved slowly reversible binding of solutes to stratum corneum keratins. The selected subset was compounds that are liquid at skin temperature. This set was chosen to distinguish between slow binding and slow dissolution effects that impact solid phase compounds. To adequately simulate the physical experiments there was a need to adjust the evaporation mass transfer coefficient to better represent the diffusion cell system employed in the study. After this adjustment the model successfully predicted both dermal delivery and skin surface distribution of 12 of the 18 compounds. Exceptions involved compounds that were cysteine-reactive, highly water-soluble or highly ionized in the dose solution. Slow binding to keratin, as presently parameterized, was shown to significantly modify the stratum corneum kinetics and diffusion lag times, but not the ultimate disposition, of the more lipophilic compounds in the dataset. Recommendations for further improvement of both modeling methods and experimental design are offered.

Kinetics and Reaction Mechanism of Biothiols Involved in SNAr Reactions: An Experimental Study

Few kinetic parameters, or reaction rates, are known up to date in detail about 1-chloro and 1-fluoro-2,4-dinitrobenzene (ClDNB and FDNB, respectively) with a series of biothiols in aqueous media. These biological nucleophiles with thiol groups have been widely used as a reference in nucleophile reactivity assays due to their prevalence and cellular abundance. The main aim of this study was to elucidate the reaction mechanism based on Brönsted-type plots and reactivity patterns of the electrophile/nucleophile pairs. A complete kinetic study was performed in terms of the comparison of Brönsted-type slope parameters (β_nuc) for the reactions and was used for assigning the mechanism and the rate-determining step associated with the reaction route. A mass spectrometry analysis demonstrated that the nucleophilic center of the biothiols is the -SH group and there is only one kinetic product. The kinetic study suggests that the reaction mechanism might be the borderline between concerted and stepwise pathways. An amine–enol equilibrium for the most reactive nucleophiles appears to be the main determining factor controlling the nucleophilic attack in the nucleophilic aromatic substitution reactions investigated, highlighting the anionic form for these nucleophiles. This amine–enol equilibrium involves a hydrogen bond which stabilizes the intermediate species in the reaction pathway. Thus, intramolecular bonds are formed and enhance the nucleophilic strength through the contribution of the solvent surrounding the electrophile/nucleophile pairs. Finally, we highlight the importance of the formation of electrophile/nucleophile adducts that could modify structures and/or functions of biological systems with potential toxic effects. Therefore, it is essential to know all these kinetic and reactivity patterns and their incidence on other studies.

High-Throughput Assessment of the Abiotic Stability of Test Chemicals in In Vitro Bioassays

Abiotic stability of chemicals is not routinely tested prior to performing in vitro bioassays, although abiotic degradation can reduce the concentration of test chemicals leading to the formation of active or inactive transformation products, which may lead to misinterpretation of bioassay results. A high-throughput workflow was developed to measure the abiotic stability of 22 test chemicals in protein-rich aqueous media under typical bioassay conditions at 37 °C for 48 h. These test chemicals were degradable in the environment according to a literature review. The chemicals were extracted from the exposure media at different time points using a novel 96-pin solid-phase microextraction. The conditions were varied to differentiate between various reaction mechanisms. For most hydrolyzable chemicals, pH-dependent degradation in phosphate-buffered saline indicated that acid-catalyzed hydrolysis was less important than reactions with hydroxide ions. Reactions with proteins were mainly responsible for the depletion of the test chemicals in the media, which was simulated by bovine serum albumin (BSA) and glutathione (GSH). 1,2-Benzisothiazol-3(2H)-one, 2-methyl-4-isothiazolinone, and l-sulforaphane reacted almost instantaneously with GSH but not with BSA, indicating that GSH is a good proxy for reactivity with electrophilic amino acids but may overestimate the actual reaction with three-dimensional proteins. Chemicals such as hydroquinones or polyunsaturated chemicals are prone to autoxidation, but this reaction is difficult to differentiate from hydrolysis and could not be simulated by the oxidant N-bromosuccinimide. Photodegradation played a minor role because cells are exposed in incubators in the dark and simulations with high light intensities did not yield realistic degradation. Stability predictions from various in silico prediction models for environmental conditions can give initial indications of the stability but were not always consistent with the experimental stability in bioassays. As the presented workflow can be performed in high throughput under realistic bioassay conditions, it can be used to provide an experimental database for developing bioassay-specific stability prediction models.

Effect of Atmospheric Aging on Soot Particle Toxicity in Lung Cell Models at the Air–Liquid Interface: Differential Toxicological Impacts of Biogenic and Anthropogenic Secondary Organic Aerosols (SOAs)

Background:

Secondary organic aerosols (SOAs) formed from anthropogenic or biogenic gaseous precursors in the atmosphere substantially contribute to the ambient fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] burden, which has been associated with adverse human health effects. However, there is only limited evidence on their differential toxicological impact.

Objectives:

We aimed to discriminate toxicological effects of aerosols generated by atmospheric aging on combustion soot particles (SPs) of gaseous biogenic (β-pinene) or anthropogenic (naphthalene) precursors in two different lung cell models exposed at the air–liquid interface (ALI).

Methods:

Mono- or cocultures of lung epithelial cells (A549) and endothelial cells (EA.hy926) were exposed at the ALI for 4 h to different aerosol concentrations of a photochemically aged mixture of primary combustion SP and β-pinene (SOAβPIN-SP) or naphthalene (SOANAP-SP). The internally mixed soot/SOA particles were comprehensively characterized in terms of their physical and chemical properties. We conducted toxicity tests to determine cytotoxicity, intracellular oxidative stress, primary and secondary genotoxicity, as well as inflammatory and angiogenic effects.

Results:

We observed considerable toxicity-related outcomes in cells treated with either SOA type. Greater adverse effects were measured for SOANAP-SP compared with SOAβPIN-SP in both cell models, whereas the nano-sized soot cores alone showed only minor effects. At the functional level, we found that SOANAP-SP augmented the secretion of malondialdehyde and interleukin-8 and may have induced the activation of endothelial cells in the coculture system. This activation was confirmed by comet assay, suggesting secondary genotoxicity and greater angiogenic potential. Chemical characterization of PM revealed distinct qualitative differences in the composition of the two secondary aerosol types.

Discussion:

In this study using A549 and EA.hy926 cells exposed at ALI, SOA compounds had greater toxicity than primary SPs. Photochemical aging of naphthalene was associated with the formation of more oxidized, more aromatic SOAs with a higher oxidative potential and toxicity compared with β-pinene. Thus, we conclude that the influence of atmospheric chemistry on the chemical PM composition plays a crucial role for the adverse health outcome of emissions. https://doi.org/10.1289/EHP9413

IN VIVO NEUROPHYSIOLOGICAL ASSESSMENT OF IN SILICO PREDICTIONS OF NEUROTOXICITY: CITRONELLAL, 3,4-DICHLORO-1-BUTENE, AND BENZYL BROMOACETATE

Neurotoxicants may be widespread in the environment and can produce serious health impacts in the human population. Screening programs that use in vitro methods have generated data for thousands of chemicals. However, these methods often do not evaluate repeated or prolonged exposures, which are required for many neurotoxic outcomes. Additionally, the data produced by such screening methods may not include mechanisms which play critical biological roles necessary for in vivo neurotoxicity. The Hard and Soft Acids and Bases (HSAB) in silico model focuses on chemical structure and electrophilic properties which are important to the formation of protein adducts. A group of structurally diverse chemicals have been evaluated with an in silico screening approach incorporating HSAB parameters. However, the predictions from the expanded chemical space have not been evaluated using in vivo methods. Three chemicals predicted to be cumulative toxicants were selected for in vivo neurotoxicological testing. Adult male Long-Evans rats were treated orally with citronellal (CIT), 3,4-dichloro-1-butene (DCB), or benzyl bromoacetate (BBA) for 8 weeks. Behavioral observations were recorded weekly to assess motor function. Peripheral neurophysiological measurements were derived from nerve excitability (NE) tests which involved compound muscle action potentials (CMAPs) in the tail and foot, and mixed nerve action potentials (MNAPs) in the tail. Compound nerve action potentials (CNAPs) and nerve conduction velocity (NCV) in the tail were also quantified. Peripheral inputs into the central nervous system were examined using somatosensory evoked potentials recorded from the cortex (SEP_CTX) and cerebellum (SEP_CEREB). CIT or BBA did not result in significant alterations to peripheral nerve or somatosensory function. DCB reduced grip-strength and altered peripheral nerve function. The MNAPs required less current to reach 50% amplitude and had a lower calculated rheobase, suggesting increased excitability. Increased CNAP amplitudes and greater NCV were also observed. Novel changes were found in the SEP_CTX with an abnormal peak forming in the early portion of the waveforms of treated rats, and decreased latencies and increased amplitudes were observed in SEP_CEREB recordings. These data contribute to testing an expanded chemical space from an in silico HSAB model for predicting cumulative neurotoxicity and may assist with prioritizing chemicals to protect human health.

Fatty acid nitroalkene reversal of established lung fibrosis

Tissue fibrosis occurs in response to dysregulated metabolism, pro-inflammatory signaling and tissue repair reactions. For example, lungs exposed to environmental toxins, cancer therapies, chronic inflammation and other stimuli manifest a phenotypic shift to activated myofibroblasts and progressive and often irreversible lung tissue scarring. There are no therapies that stop or reverse fibrosis. The 2 FDA-approved anti-fibrotic drugs at best only slow the progression of fibrosis in humans. The present study was designed to test whether a small molecule electrophilic nitroalkene, nitro-oleic acid (NO₂-OA), could reverse established pulmonary fibrosis induced by the intratracheal administration of bleomycin in C57BL/6 mice. After 14 d of bleomycin-induced fibrosis development in vivo, lungs were removed, sectioned and precision-cut lung slices (PCLS) from control and bleomycin-treated mice were cultured ex vivo for 4 d with either vehicle or NO₂-OA (5 μM). Biochemical and morphological analyses showed that over a 4 d time frame, NO₂-OA significantly inhibited pro-inflammatory mediator and growth factor expression and reversed key indices of fibrosis (hydroxyproline, collagen 1A1 and 3A1, fibronectin-1). Quantitative image analysis of PCLS immunohistology reinforced these observations, revealing that NO₂-OA suppressed additional hallmarks of the fibrotic response, including alveolar epithelial cell loss, myofibroblast differentiation and proliferation, collagen and α-smooth muscle actin expression. NO₂-OA also accelerated collagen degradation by resident macrophages. These effects occurred in the absence of the recognized NO₂-OA modulation of circulating and migrating immune cell activation. Thus, small molecule nitroalkenes may be useful agents for reversing pathogenic fibrosis of lung and other organs.

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Small molecule electrophiles, pleiotropic anti-inflammatory and anti-fibrotic drugs.

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NO₂-OA inhibits activated myofibroblasts, induces dedifferentiation to fibroblasts.

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NO₂-OA activates extracellular matrix degradation by macrophages.

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NO₂-OA promotes proliferation of alveolar type 1 and 2 epithelial cells.

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NO₂-OA reverses established lung fibrosis in murine lung slices.

Characterization of an aromatic trifluoromethyl ketone as a new warhead for covalently reversible kinase inhibitor design

An aromatic trifluoromethyl ketone moiety was characterized as a new warhead for covalently reversible kinase inhibitor design to target the non-catalytic cysteine residue. Potent and selective covalently reversible inhibitors of FGFR4 kinase were successfully designed and synthesized by utilizing this new warhead. The binding mode of a representative inhibitor was fully characterized by using multiple technologies including MALDI-TOF mass spectrometry, dialysis assay and X-ray crystallographic studies etc. This functional group was also successfully applied to discovery of a new JAK3 inhibitor, suggesting its potential application in designing other kinase inhibitors.

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.

Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods

There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets; more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed. In this context, this study verifies the utility of similarity-based machine learning methods in predicting the acute aquatic toxicity of diverse organic chemicals on Daphnia magna and Oryzias latipes. Two similarity-based methods were tested that employ a limited training dataset, most similar to a given fitting point, instead of using the entire dataset that encompasses a wide range of chemicals. The kernel-weighted local polynomial approach had a number of advantages over the distance-weighted k-nearest neighbor (k-NN) algorithm. The results highlight the importance of lipophilicity, electrophilic reactivity, molecular polarizability, and size in determining acute toxicity. The rigorous model validation ensures that this approach is an important tool for estimating toxicity in new or untested chemicals.

Discrimination of active and inactive substances in cytotoxicity based on Tox21 10K compound library: Structure alert and mode of action

In vitro cytotoxicity assay is an ideal alternative method for the in vivo toxicity in the risk assessment of pollutants in environment. However, modes of action (MOAs) of cytotoxicity have not been investigated for a wide range of compounds. In this paper, binomial and recursive partitioning analysis were carried out between the cytotoxicity and molecular descriptors for 8981 compounds. The results showed that cytotoxicity is strongly related to the chemical hydrophobicity and excess molar refraction, indicating the bio-uptake and chemical-receptor interaction through π and n electron pair play important roles in the cytotoxicity. The decision tree derived from recursive partitioning analysis revealed that the studied compounds could be divided into 25 groups and their structural characteristics could be used as structure alert to identify active and inactive compounds in cytotoxicity. The descriptors used in the decision tree revealed that chemical ionization and bioavailability could affect the cytotoxicity for ionizable and highly hydrophobic compounds. Comparison of MOAs based on Verhaar's classification scheme showed that many inert or less inert compounds were inactive substance, and many reactive or specifically-acting compounds were active substances in the cytotoxicity. In vitro toxicity assay instead of in vivo toxicity assay can be used in the environmental hazard and risk assessment of organic pollutants. The descriptors used in the binomial equation and decision tree reveal that chemical hydrophobicity, ionization and solubility play very important roles for identification of active and inactive compounds. The results obtained in this paper are valuable for understanding the modes of action in cytotoxicity and in vivo-in vitro toxicity relationship.

The maximum carbonyl ratio (MCR) as a new index for the structural classification of secondary organic aerosol components

RATIONALE

Organic aerosols (OA) account for a large fraction of atmospheric fine particulate matter and thus are affecting climate and public health. Elucidation of the chemical composition of OA is the key for addressing the role of ambient fine particles at the atmosphere-biosphere interface and mass spectrometry is the main method to achieve this goal.

METHODS

High-resolution mass spectrometry (HRMS) is on its way to becoming one of the most prominent analytical techniques, also for the analysis of atmospheric aerosols. The combination of high mass resolution and accurate mass determination allows the elemental compositions of numerous compounds to be easily elucidated. Here a new parameter for the improved classification of OA is introduced - the maximum carbonyl ratio (MCR) - which is directly derived from the molecular composition and is particularly suitable for the identification and characterization of secondary organic aerosols (SOA).

RESULTS

The concept is exemplified by the analysis of ambient OA samples from two measurement sites (Hyytiälä, Finland; Beijing, China) and of laboratory-generated SOA based on ultrahigh-performance liquid chromatography (UHPLC) coupled to Orbitrap MS. To interpret the results, MCR-Van Krevelen (VK) diagrams are generated for the different OA samples and the individual compounds are categorized into specific areas in the diagrams. The results show that the MCR index is a valuable parameter for representing atmospheric SOA components in composition and structure-dependent visualization tools such as VK diagrams.

CONCLUSIONS

The MCR index is suggested as a tool for a better characterization of the sources and the processing of atmospheric OA components based on HRMS data. Since the MCR contains information on the concentration of highly electrophilic organic compounds in particulate matter (PM) as well as on the concentration of organic (hydro)peroxides, the MCR could be a promising metric for identifying health-related particulate matter parameters by HRMS.

Assessment and characterization of tomato lipophilic electrophiles and their potential contribution to nutraceutical properties via SKN-1/Nrf2 signaling activation

Phytochemical electrophiles are drawing significant attention due to their properties to modulate signaling pathways related to cellular homeostasis. The aim of this study was to develop new tools to examine the electrophilic activity in food and predict their beneficial effects on health. We developed a spectrophotometric assay based on the nitrobenzenethiol (NBT) reactivity, as a thiol-reactive nucleophile, to screen electrophiles in tomato fruits. The method is robust, simple, inexpensive, and could be applied to other types of food. We quantified the electrophile activity in a tomato collection and associated this activity with the pigment composition. Thus, we identified lycopene, β- and γ-carotenes, 16 by-products of carotenoid oxidation and 18 unknown compounds as NBT-reactive by HPLC-MS/MS. The potential benefits of NBT-reactive compounds on health were evaluated in the in vivo model of C. elegans where they activated the SKN-1/Nrf2 pathway, evidencing the ability of electrophilic compounds to induce a biological response.

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.

Benchmarking the mechanisms of frequent hitters: limitation of PAINS alerts

In 2010, the pan-assay interference compounds (PAINS) rule was proposed to identify false-positive compounds, especially frequent hitters (FHs), in biological screening campaigns, and has rapidly become an essential component in drug design. However, the specific mechanisms remain unknown, and the result validation and follow-up processing schemes are still unclear. In this review, a large benchmark collection of >600,000 compounds sourced from databases and the literature, including six common false-positive mechanisms, was used to evaluate the detection ability of PAINS. In addition, 400 million purchasable molecules from the ZINC database were also applied to PAINS screening. The results indicate that the PAINS rule is not suitable for the screening of all types of false-positive results and needs more improvement.

Reactivity-directed analysis - a novel approach for the identification of toxic organic electrophiles in drinking water

Drinking water consumption results in exposure to complex mixtures of organic chemicals, including natural and anthropogenic chemicals and compounds formed during drinking water treatment such as disinfection by-products. The complexity of drinking water contaminant mixtures has hindered efforts to assess associated health impacts. Existing approaches focus primarily on individual chemicals and/or the evaluation of mixtures, without providing information about the chemicals causing the toxic effect. Thus, there is a need for the development of novel strategies to evaluate chemical mixtures and provide insights into the species responsible for the observed toxic effects. This critical review introduces the application of a novel approach called Reactivity-Directed Analysis (RDA) to assess and identify organic electrophiles, the largest group of known environmental toxicants. In contrast to existing in vivo and in vitro approaches, RDA utilizes in chemico methodologies that investigate the reaction of organic electrophiles with nucleophilic biomolecules, including proteins and DNA. This review summarizes the existing knowledge about the presence of electrophiles in drinking water, with a particular focus on their formation in oxidative treatment systems with ozone, advanced oxidation processes, and UV light, as well as disinfectants such as chlorine, chloramines and chlorine dioxide. This summary is followed by an overview of existing RDA approaches and their application for the assessment of aqueous environmental matrices, with an emphasis on drinking water. RDA can be applied beyond drinking water, however, to evaluate source waters and wastewater for human and environmental health risks. Finally, future research demands for the detection and identification of electrophiles in drinking water via RDA are outlined.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

10 years into the resurgence of covalent drugs

In the first decade of targeted covalent inhibition, scientists have successfully reversed the previous trend that had impeded the use of covalent inhibition in drug development. Successes in the clinic, mainly in the field of kinase inhibitors, are existing proof that safe covalent inhibitors can be designed and employed to develop effective treatments. The case of KRAS_G12C covalent inhibitors entering clinical trials in 2019 has been among the hottest topics discussed in drug discovery, raising expectations for the future of the field. In this perspective, an overview of the milestones hit with targeted covalent inhibitors, as well as the promise and the needs of current research, are presented. While recent results have confirmed the potential that was foreseen, many questions remain unexplored in this branch of precision medicine.

Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors

Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.

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.

Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

The Key Characteristics of Carcinogens: Relationship to the Hallmarks of Cancer, Relevant Biomarkers, and Assays to Measure Them

The key characteristics (KC) of human carcinogens provide a uniform approach to evaluating mechanistic evidence in cancer hazard identification. Refinements to the approach were requested by organizations and individuals applying the KCs. We assembled an expert committee with knowledge of carcinogenesis and experience in applying the KCs in cancer hazard identification. We leveraged this expertise and examined the literature to more clearly describe each KC, identify current and emerging assays and in vivo biomarkers that can be used to measure them, and make recommendations for future assay development. We found that the KCs are clearly distinct from the Hallmarks of Cancer, that interrelationships among the KCs can be leveraged to strengthen the KC approach (and an understanding of environmental carcinogenesis), and that the KC approach is applicable to the systematic evaluation of a broad range of potential cancer hazards in vivo and in vitro We identified gaps in coverage of the KCs by current assays. Future efforts should expand the breadth, specificity, and sensitivity of validated assays and biomarkers that can measure the 10 KCs. Refinement of the KC approach will enhance and accelerate carcinogen identification, a first step in cancer prevention.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC₅₀) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity

In the past decade, the pharmaceutical industry has paid closer attention to covalent drugs. Differently from standard noncovalent drugs, these compounds can exhibit peculiar properties, such as higher potency or longer duration of target inhibition with a potentially lower dosage. These properties are mainly driven by the reactive functional group present in the compound, the so-called warhead that forms a covalent bond with a specific nucleophilic amino-acid on the target. In this work, we report the possibility to combine ab initio activation energies with machine-learning to estimate covalent compound intrinsic reactivity. The idea behind this approach is to have a precise estimation of the transition state barriers, and thus of the compound reactivity, but with the speed of a machine-learning algorithm. We call this method "BIreactive". Here, we demonstrate this approach on acrylamides and 2-chloroacetamides, two warhead classes that possess different reaction mechanisms. In combination with our recently implemented truncation algorithm, we also demonstrate the possibility to use BIreactive not only for fragments but also for lead-like molecules. The generic nature of this approach allows also the extension to several other warheads. The combination of these factors makes BIreactive a valuable tool for the covalent drug discovery process in a pharmaceutical context.

Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps

A valuable approach to chemical safety assessment is the use of read-across chemicals to provide safety data to support the assessment of structurally similar chemicals. An inventory of over 6000 discrete organic chemicals used as fragrance materials in consumer products has been clustered into chemical class-based groups for efficient search of read-across sources. We developed a robust, tiered system for chemical classification based on (1) organic functional group, (2) structural similarity and reactivity features of the hydrocarbon skeletons, (3) predicted or experimentally verified Phase I and Phase II metabolism, and (4) expert pruning to consider these variables in the context of specific toxicity end points. The systematic combination of these data yielded clusters, which may be visualized as a top-down hierarchical clustering tree. In this tree, chemical classes are formed at the highest level according to organic functional groups. Each subsequent subcluster stemming from classes in this hierarchy of the cluster is a chemical cluster defined by common organic functional groups and close similarity in the hydrocarbon skeleton. By examining the available experimental data for a toxicological endpoint within each cluster, users can better identify potential read-across chemicals to support safety assessments.

Application of the hard and soft, acids and bases (HSAB) theory as a method to predict cumulative neurotoxicity

Xenobiotic electrophiles can form covalent adducts that may impair protein function, damage DNA, and may lead a range of adverse effects. Cumulative neurotoxicity is one adverse effect that has been linked to covalent protein binding as a Molecular Initiating Event (MIE). This paper describes a mechanistic in silico chemical screening approach for neurotoxicity based on Hard and Soft Acids and Bases (HSAB) theory. We evaluated the applicability of HSAB-based electrophilicity screening protocol for neurotoxicity on 19 positive and 19 negative reference chemicals. These reference chemicals were identified from the literature, using available information on mechanisms of neurotoxicity whenever possible. In silico screening was based on structural alerts for protein binding motifs and electrophilicity index in the range of known neurotoxicants. The approach demonstrated both a high positive prediction rate (82-90 %) and specificity (90 %). The overall sensitivity was relatively lower (47 %). However, when predicting the toxicity of chemicals known or suspected of acting via non-specific adduct formation mechanism, the HSAB approach identified 7/8 (sensitivity 88 %) of positive control chemicals correctly. Consequently, the HSAB-based screening is a promising approach of identifying possible neurotoxins with adduct formation molecular initiating events. While the approach must be expanded over time to capture a wider range of MIEs involved in neurotoxicity, the mechanistic nature of the screen allows users to flag chemicals for possible adduct formation MIEs. Thus, the HSAB based toxicity screening is a promising strategy for toxicity assessment and chemical prioritization in neurotoxicology and other health endpoints that involve adduct formation.

Comparative reactivity analysis of small-molecule thiol surrogates

Targeted covalent inhibitors represent an increasingly popular approach to modulate challenging drug targets. Since covalent and non-covalent interactions are both contributing to the affinity of these compounds, evaluation of their reactivity is a key-step to find feasible warheads. There are well-established HPLC- and NMR-based kinetic assays to tackle this task, however, they use a variety of cysteine-surrogates including cysteamine, cysteine or acetyl-cysteine and GSH. The diverse nature of the thiol sources often makes the results incomparable that prevents compiling a comprehensive knowledge base for the design of covalent inhibitors. To evaluate kinetic measurements from different sources we performed a comparative analysis of the different thiol surrogates against a designed set of electrophilic fragments equipped with a range of warheads. Our study included seven different thiol models and 13 warheads resulting in a reactivity matrix analysed thoroughly. We found that the reactivity profile might be significantly different for various thiol models. Comparing the different warheads, we concluded that - in addition to its human relevance - glutathione (GSH) provided the best estimate of reactivity with highest number of true positives identified.

Advances in covalent kinase inhibitors

This comprehensive review details recent advances, challenges and innovations in covalent kinase inhibition within a 10 year period (2007–2018).

Nucleophilicity of Glutathione: A Link to Michael Acceptor Reactivities

Deprotonated glutathione is among the most potent biological nucleophiles and plays an important physiological role in cellular detoxification by forming covalent conjugates with Michael acceptors. The electrophilicity E of various Michael acceptors was characterized recently according to the Patz-Mayr relation lg k2 =sN (N+E). We now determined the nucleophilic reactivity (N, sN ) of glutathione (GSH) in aqueous solution at 20 °C to connect published GSH reactivities (kGSH ) with Mayr's electrophilicity scale (E). In this way, electrophilicities E of more than 70 Michael acceptors could be estimated, which can now be used to systematically predict novel reactions with the multitude of nucleophiles whose nucleophilicity parameters N/sN are known.

Comparing and contrasting the coverage of publicly available structural alerts for protein binding

The molecular initiating event for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides a starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTS patterns and their associated chemical-biological reaction domains have been compared. The goals are 1) to better understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-tissue based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each 'consensus' reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.