We demonstrate how statistical free energy perturbation calculations used in drug discovery can be extended to safer chemical design.

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 highthroughput 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 (S N 2)) 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 S N 2 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 LC 50 values and photomotor response (PMR) no observed effect concentrations (NOECs) in both fathead minnow and zebrafish. This reactivity index, LC 50 values, 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 S N 2 electrophiles. Shared refractory locomotor response patterns among study compounds and two alternative vertebrate models appear informative of electrophilic properties associated with oxidative stress for S N 2 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.

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SignificanceOver the past two decades, green chemistry has empowered sustainability science by advocating for development of chemicals, materials and processes that minimize hazard throughout their lifecycle. There is an urgent need to alleviate the economic and ethical burden of traditional toxicity testing and develop robust and systematic approaches for designing functional yet benign chemicals. While synthetic chemists regularly design for specific industrial or pharmaceutical functions, design for reduced hazard is an emerging field with few practical solutions offered to date. To this end, a new in silico framework is presented that affords accurate and economical redesign of exiting chemicals in commerce with target-specific toxicities. Bridging the fields of computer-aided drug discovery and molecular toxicology, this approach promises to dramatically lower costs associated with new chemical development; reduce time-to-market; and avoid regrettable substitutions of chemicals in products and supply chains. AbstractScreening all existing and new chemicals introduced to commerce annually using computational models is viewed as a promising alternative to the both economically and ethically unviable animal tests. The high throughput screening (HTS) assays and the many quantitative structure-activity relationships (QSARs) developed over the past decade to assess chemical exposure, hazard and risk are a testament to the shift toward cost-effective strategies to comprehensively protect human and environmental health. While existing computational tools can be used to screen chemicals for a host of toxic endpoints, their utility in informing design of safer chemicals is very limited. Being able to rationally design chemicals with minimal biological activity and optimal functional efficacy is the foundational principle of green chemistry; yet, it is also the least-developed field of this discipline. This paper outlines development of a novel computational framework for designing new or redesigning existing chemicals in commerce, while considering both their biological activity and intended function. The presented approach is based on transformed methodology from computational drug discovery, which can be applied to systematically design out target-specific toxicities based on molecular initiating events (MIE) irrespective of chemical class or toxic endpoint. Classical free energy perturbation calculations used in conjunction with Monte Carlo simulations are employed to demonstration this approach for organophosphates, a class of chemicals associated with neurotoxicity, carcinogenicity and endocrine-system disruption. The presented framework promises a strategy for safer chemical design that allows the practitioner to systematically weigh chemical drivers of toxicity, metabolism and functionality in order to determine respective trade-offs and optimal solutions to existing toxic chemicals in commerce. Complementing current predictive toxicology approaches and design guidelines, the presented framework is poised to ad...