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Asthma is of concern in occupational toxicology with significant public-health and economic costs. In the absence of benchmark in vivo and in vitro tests, the use of mechanistically sound in silico models is critical to inform hazard and to protect workers from exposure to potentially harmful substances. We recently reported on the computer-aided discovery and REdesign (CADRE) model for respiratory sensitization, which relies on a tiered structure of expert rules, molecular simulations, quantum-mechanics calculations and advanced statistics to accurately identify respiratory sensitizers from first principles. Here, we present an update to this model based on two years of testing in the pharmaceutical space, where we captured the heterogeneity of the underlying experimental evidence in two predictive tiers, thus allowing the practitioner to select an outcome based on their expert assessment of the data reliability and relevance. This user-based tuning of predictive models is critical for end points that lack consensus on what constitutes satisfactory evidence to support a decision in the handling of chemicals for occupational safety.

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

Glycosaminoglycans (GAGs) are negatively charged polysaccharides found on cell surfaces, where they regulate transport pathways of foreign molecules toward the cell. The structural and functional diversity of GAGs is largely attributed to varied sulfation patterns along the polymer chains, which makes understanding their molecular recognition mechanisms crucial. Molecular dynamics (MD) simulations, thanks to their unmatched microscopic resolution, have the potential to be a reference tool for exploring the patterns responsible for biologically relevant interactions. However, the capability of molecular dynamics force fields used in biosimulations to accurately capture sulfation-specific interactions is not well established, partly due to the intrinsic properties of GAGs that pose challenges for most experimental techniques. In this work, we evaluate the performance of molecular dynamics force fields for sulfated GAGs by studying ion pairing of Ca2+to sulfated moieties─N-methylsulfamate and methylsulfate─that resemble N- and O-sulfation found in GAGs, respectively. We tested available nonpolarizable (CHARMM36 and GLYCAM06) and explicitly polarizable (Drude and AMOEBA) force fields, and derived new implicitly polarizable models through charge scaling (prosECCo75 and GLYCAM-ECC75) that are consistent with our developed “charge-scaling” framework. The calcium–sulfamate/sulfate interaction free energy profiles obtained with the tested force fields were compared against reference ab initio molecular dynamics (AIMD) simulations, which serve as a robust alternative to experiments. AIMD simulations indicate that the preferential Ca2+binding mode to sulfated GAG groups is solvent-shared pairing. Only our scaled-charge models agree satisfactorily with the AIMD data, while all other force fields exhibit poorer agreement, sometimes even qualitatively. Surprisingly, even explicitly polarizable force fields display a notable disagreement with the AIMD data, likely attributed to difficulties in their optimization and possible inherent limitations in depicting high-charge-density ion interactions accurately. Finally, the underperforming force fields lead to unrealistic aggregation of sulfated saccharides, which qualitatively disagrees with our understanding of the soft glycocalyx environment. Our results highlight the importance of accurately treating electronic polarization in MD simulations of sulfated GAGs and caution against over-reliance on currently available models without thorough validation and optimization.

•With extended treatment, responses with rilzabrutinib were durable as a monotherapy and with the use of concomitant ITP medication.•Oral rilzabrutinib was well tolerated, with no treatment-related serious events or deaths.

Since their advent over 50 years ago, aromatic polyamides have remained desirable for their thermal stability, mechanical strength, and overall versatility. Chain‐growth condensation (CGC) polymerization has made possible the synthesis of well‐defined polyamides with narrow molecular weight distributions for applications ranging from block copolymers to polymer brushes. However, the solubility of aromatic polyamides has always been a major obstacle to overcome due to aromatic π‐π stacking and intramolecular hydrogen bonding. In this article, we introduce an aryl ether functionality into the polyamide backbone to increase backbone flexibility and overall solubility. Through dropwise addition of the aryl ether functionalized monomer with a lithium disilazide base and a phenyl 4‐(dimethylcarbamoyl)benzoate initiator, we report the synthesis of a series of substituted poly(amide‐ether)s through CGC polymerization with low polydispersities (<1.1) and well‐defined molecular weights. Computational methods revealed that the polymerization mechanism is unlike other similar amino ester monomers bearing naphthalene and biphenyl aromatic units in that monomer self‐deactivation is weakened by the ether linkage. The self‐condensation of the amide‐ether monomer was nevertheless suppressed through reaction optimization, challenging the traditional requirement of monomer self‐deactivation to achieve a well‐defined CGC polymerization. The side‐chain on poly(amide‐ether) backbone was also modified from an octyl group to a 4‐(octyloxy)benzyl protecting group for postpolymerization removal with trifluoroacetic acid to yield the unsubstituted aromatic poly(amide‐ether), which showed a significant improvement in solubility from traditional unsubstituted aromatic polyamides, such as poly(p‐benzamide).

AbstractIntegral experiments covering neutron leakage from geometrically simple assemblies with a 252Cf source inside are very valuable tools usable in the validation of transport cross-section data since geometric uncertainties play a much smaller role in simple geometric assemblies than in complex assemblies as for example reactor pressure vessel geometry. Since 252Cf spontaneous fission is a standard neutron source, the uncertainties connected with the source neutron spectrum can be even neglected. The paper refers to validation efforts of neutron leakage from an ~50 × 50 × 50-cm stainless steel block in the Research Center Rez. Both the neutron leakage flux at a distance of 1 m from the center of the cubical assembly using stilbene spectrometry and activation rates at different positions of the assembly were evaluated. In addition to experiments, main sources of uncertainty were identified and evaluated. The results of the stilbene measurements are consistent with the activation measurement results.

N-Nitroso contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all these compounds are created equal, and some are relatively benign chemicals. Understanding the structure–activity relationships (SARs) that drive hazards in one molecule versus another is key to both protecting human health and alleviating costly and sometimes inaccurate animal testing. Here, we report on an extension of the CADRE (computer-aided discovery and REdesign) platform, which is used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict the carcinogenic potency of N-nitroso compounds. The model distinguishes compounds in three potency categories with 77% accuracy in external testing, which surpasses the reproducibility of rodent cancer bioassays and constraints imposed by limited (high-quality) data. The robustness of predictions for more complex pharmaceuticals is maximized by capturing key SARs using quantum mechanics, that is, by hinging the model on the underlying chemistry versus chemicals in the training set. To this end, the present approach can be leveraged in a quantitative hazard assessment and to offer qualitative guidance using electronic structure comparisons between well-studied analogues and unknown contaminants.

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

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

Birch reduction is a time-proven way to hydrogenate aromatic hydrocarbons (such as benzene), which relies on the reducing power of electrons released from alkali metals into liquid ammonia. We have succeeded to characterize the key intermediates of the Birch reduction process─the solvated electron and dielectron and the benzene radical anion─using cyclic voltammetry and photoelectron spectroscopy, aided by electronic structure calculations. In this way, we not only quantify the electron binding energies of these species, which are decisive for the mechanism of the reaction, but also use Birch reduction as a case study to directly connect the two seemingly unrelated experimental techniques.

Peptide couplers (also known as amide bond-forming reagents or coupling reagents) are broadly used in organic chemical syntheses, especially in the pharmaceutical industry. Yet, occupational health hazards associated with this chemical class are largely unexplored, which is disconcerting given the intrinsic reactivity of these compounds. Several case studies involving occupational exposures reported adverse respiratory and dermal health effects, providing initial evidence of chemical sensitization. To address the paucity of toxicological data, a pharmaceutical cross-industry task force was formed to evaluate and assess the potential of these compounds to cause eye and dermal irritation as well as corrosivity and dermal sensitization. The goal of our work was to inform health and safety professionals as well as pharmaceutical and organic chemists of the occupational health hazards associated with this chemical class. To that end, 25 of the most commonly used peptide couplers and five hydrolysis products were selected for in vivo, in vitro, and in silicotesting. Our findings confirmed that dermal sensitization is a concern for this chemical class with 21/25 peptide couplers testing positive for dermal sensitization and 15 of these being strong/extreme sensitizers. We also found that dermal corrosion and irritation (8/25) as well as eye irritation (9/25) were health hazards associated with peptide couplers and their hydrolysis products (4/5 were dermal irritants or corrosive and 4/5 were eye irritants). Resulting outcomes were synthesized to inform decision making in peptide coupler selection and enable data-driven hazard communication to workers. The latter includes harmonized hazard classifications, appropriate handling recommendations, and accurate safety data sheets, which support the industrial hygiene hierarchy of control strategies and risk assessment. Our study demonstrates the merits of an integrated, in vivo-in silicoanalysis, applied here to the skin sensitization endpoint using the Computer-Aided Discovery and REdesign (CADRE) and Derek Nexus programs. We show that experimental data can improve predictive models by filling existing data gaps while, concurrently, providing computational insights into key initiating events and elucidating the chemical structural features contributing to adverse health effects. This interactive, interdisciplinary approach is consistent with Green Chemistry principles that seek to improve the selection and design of less hazardous reagents in industrial processes and applications.

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

We report valence band photoelectron spectroscopy measurements of gas-phase and liquid-phase benzene as well as those of benzene dissolved in liquid ammonia, complemented by electronic structure calculations. The origins of the sizable gas-to-liquid-phase shifts in electron binding energies deduced from the benzene valence band spectral features are quantitatively characterized in terms of the Born–Haber solvation model. This model also allows to rationalize the observation of almost identical shifts in liquid ammonia and benzene despite the fact that the former solvent is polar while the latter is not. For neutral solutes like benzene, it is the electronic polarization response determined by the high frequency dielectric constant of the solvent, which is practically the same in the two liquids, that primarily determines the observed gas-to-liquid shifts.

Development of high-performing pesticides with tunable degradation properties is vital to increasing the safety and effectiveness of tomorrow’s analogs. Chromophoric dissolved organic matter in the excited triple state (3CDOM*) is known to play a key role in the removal of pesticides via indirect photodegradation. However, the potential of these transformations to guide the design of safer chemicals has not yet been fully realized. Here, we report a two-tier computational framework developed to probe and predict both kinetics and thermodynamics of 3CDOM*-pesticide interactions. In the first tier, robust in silicomodels were constructed by fitting free energies obtained from density functional theory (DFT) calculations to cell potentials and second-order rate constants for the 3CDOM*-pesticide electron transfer. In the second tier, Gibbs free energies and corresponding free energy barriers, determined in solution using the Marcus theory, were applied to develop a quick yet accurate screening approach based on the frontier molecular orbital (FMO) Theory. Being highly mechanistic and spanning ca. 1500 unique 3CDOM*-pesticide interactions, our approach is both robust and broadly applicable. To that end, the outcomes of our computational models were integrated into an easy-to-use decision framework that can guide structure-based design of less persistent pesticide analogs.

The transcription factor Nrf2a induces a cellular antioxidant response and provides protection against chemical-induced oxidative stress, as well as playing a critical role in development and disease. Zebrafish are a powerful model to study the role of Nrf2a in these processes but have been limited by reliance on transient gene knockdown techniques or mutants with only partial functional alteration. We developed several lines of zebrafish carrying different null (loss of function, LOF) or hyperactive (gain of function, GOF) mutations to facilitate our understanding of the Nrf2a pathway in protecting against oxidative stress. The mutants confirmed Nrf2a dependence for induction of the antioxidant genes gclc, gstp, prdx1, and gpx1aand identified a role for Nrf2a in the baseline expression of these genes, as well as for sod1. Specifically, the 4-fold induction of gstpby tert-butyl hydroperoxide (tBHP) in wild type fish was abolished in LOF mutants. In addition, baseline gstpexpression in GOF mutants increased by 12.6-fold and in LOF mutants was 0.8-fold relative to wild type. Nrf2a LOF mutants showed increased sensitivity to the acute toxicity of cumene hydroperoxide (CHP) and tBHP throughout the first 4 days of development. Conversely, GOF mutants were less sensitive to CHP toxicity during the first 4 days of development and were protected against the toxicity of both hydroperoxides after 4 dpf. Neither gain nor loss of Nrf2a modulated the toxicity of R-(−)-carvone (CAR), despite the ability of this compound to potently induce Nrf2a-dependent antioxidant genes. Similar to other species, GOF zebrafish mutants exhibited significant growth and survival defects. In summary, these new genetic tools can be used to facilitate the identification of downstream gene targets of Nrf2a, better define the role of Nrf2a in the toxicity of environmental chemicals, and further the study of diseases involving altered Nrf2a function.

Sustainable molecular design of less hazardous chemicals promises to reduce risks to public health and the environment. Computational chemistry modeling coupled with alternative toxicology models (e.g., larval fish) present unique high-throughput opportunities to understand structural characteristics eliciting adverse outcomes. Numerous environmental contaminants with reactive properties can elicit oxidative stress, an important toxicological response associated with diverse adverse outcomes (i.e., cancer, diabetes, neurodegenerative disorders, etc.). We examined a common chemical mechanism (bimolecular nucleophilic substitution (SN2)) associated with oxidative stress using property-based computational modeling coupled with acute (mortality) and sublethal (glutathione, photomotor behavior) responses in the zebrafish (Danio rerio) and the fathead minnow (Pimephales promelas) models to identify whether relationships exist among biological responses and molecular attributes of industrial chemicals. Following standardized methods, embryonic zebrafish and larval fathead minnows were exposed separately to eight different SN2 compounds for 96 h. Acute and sublethal responses were compared to computationally derived in silico chemical descriptors. Specifically, frontier molecular orbital energies were significantly related to acute LC50values and photomotor response (PMR) no observed effect concentrations (NOECs) in both fathead minnow and zebrafish. This reactivity index, LC50values, and PMR NOECs were also significantly related to whole body glutathione (GSH) levels, suggesting that acute and chronic toxicity results from protein adduct formation for SN2 electrophiles. Shared refractory locomotor response patterns among study compounds and two alternative vertebrate models appear informative of electrophilic properties associated with oxidative stress for SN2 chemicals. Electrophilic parameters derived from frontier molecular orbitals were predictive of experimental in vivo acute and sublethal toxicity. These observations provide important implications for identifying and designing less hazardous industrial chemicals with reduced potential to elicit oxidative stress through bimolecular nucleophilic substitution.

New atom-economical alternatives to Wittig chemistry are needed to construct olefins from carbonyl compounds, but none have been developed to-date. Here we report an atom-economical olefination of carbonyls via aldol-decarbonylative coupling of aldehydes using robust and recyclable supported Pd catalysts, producing only CO and H2O as waste. The reaction affords homocoupling of aliphatic aldehydes, as well as heterocoupling of aliphatic and aromatic ones. Computations provide insight into the selectivity and thermodynamics of the reaction. The tandem aldol-decarbonylation reaction opens the door to exploration of new carbonyl reactivity to construct olefins.

Here we report a vertically integrated in vitro– in silicostudy that aims to elucidate the molecular initiating events involved in the induction of oxidative stress (OS) by seven diverse chemicals (cumene hydroperoxide, t-butyl hydroperoxide, hydroquinone, t-butyl hydroquinone, bisphenol A, Dinoseb, and perfluorooctanoic acid). To that end, we probe the relationship between chemical properties, cell viability, glutathione (GSH) depletion, and antioxidant gene expression. Concentration-dependent effects on cell viability were assessed by MTT assay in two Hepa-1 derived mouse liver cell lines: a control plasmid vector transfected cell line (Hepa-V), and a cell line with increased glutamate-cysteine ligase (GCL) activity and GSH content (CR17). Changes to intracellular GSH content and mRNA expression levels for the Nrf2-driven antioxidant genes Gclc, Gclm, heme oxygenase-1 (Hmox1), and NADPH quinone oxidoreductase-1 (Nqo1) were monitored after sublethal exposure to the chemicals. In silicomodels of covalent and redox reactivity were used to rationalize differences in activity of quinones and peroxides. Our findings show CR17 cells were generally more resistant to chemical toxicity and showed markedly attenuated induction of OS biomarkers; however, differences in viability effects between the two cell lines were not the same for all chemicals. The results highlight the vital role of GSH in protecting against oxidative stress-inducing chemicals as well as the importance of probing molecular initiating events in order to identify chemicals with lower potential to cause oxidative stress.

Insulating materials can in principle be made metallic by applying pressure. In the case of pure water, this is estimated1to require a pressure of 48 megabar, which is beyond current experimental capabilities and may only exist in the interior of large planets or stars2–4. Indeed, recent estimates and experiments indicate that water at pressures accessible in the laboratory will at best be superionic with high protonic conductivity5, but not metallic with conductive electrons1. Here we show that a metallic water solution can be prepared by massive doping with electrons upon reacting water with alkali metals. Although analogous metallic solutions of liquid ammonia with high concentrations of solvated electrons have long been known and characterized6–9, the explosive interaction between alkali metals and water10,11has so far only permitted the preparation of aqueous solutions with low, submetallic electron concentrations12–14. We found that the explosive behaviour of the water–alkali metal reaction can be suppressed by adsorbing water vapour at a low pressure of about 10−4millibar onto liquid sodium–potassium alloy drops ejected into a vacuum chamber. This set-up leads to the formation of a transient gold-coloured layer of a metallic water solution covering the metal alloy drops. The metallic character of this layer, doped with around 5 × 1021electrons per cubic centimetre, is confirmed using optical reflection and synchrotron X-ray photoelectron spectroscopies.

Quality improvement is increasingly being used within healthcare as an operating model to empower and enable teams of staff and service users at the point of care to find solutions to complex quality and safety issues. Adopting quality improvement methods in healthcare poses several challenges, and many healthcare providers have faced barriers in embedding a culture that nurtures and supports a systematic approach to problem-solving at the point of care. This article proposes a simple framework with three components to help healthcare systems avoid the common barriers to introducing quality improvement interventions. First, simplify the language and accentuate the similarities between methods. Second, support those applying quality improvement with skilled, accessible improvement expertise and applying evidence-based adult education theories. Third, design quality improvement interventions so that they strengthen a shared purpose by allowing teams to decide what to prioritise and involving patients and family members as equal partners in quality improvement work.

Whether conducting a risk, hazard, or alternatives assessment, one invariably struggles with the task of reconciling multiple available values of toxicological thresholds into a single outcome. When combining multiple pieces of evidence from many different sources, it is important to consider the role of data uncertainty. Uncertainty is inherent to all scientific data. However, in toxicological assessments, controversies and uncertainties are typically understated; they lack methodological transparency; or they poorly integrate qualitative and quantitative sources of information. Similarly, in model development, data curation is rarely performed with sufficient rigor, particularly when applying big data statistics. To overcome the hurdles of a decision process that must reconcile divergent data, we developed an uncertainty scoring tool that can be trained to reproduce specific decision-making paradigms and ensure consistency in the practitioner’s judgment across complex scenarios. While designed to aid with ecotoxicological assessments and predictive model development, the tool’s applicability extends to any decision-making process that calls for synthesis of incongruent data. Here, we highlight the development process, as well as demonstrate the method’s utility in several prototypical ecotoxicological case studies.