Structure-to-process design framework for developing safer pesticides

Design, Synthesis, and Biological Evaluation of Pyridazinone-Containing Derivatives As Novel Protoporphyrinogen IX Oxidase Inhibitor

Protoporphyrinogen IX oxidase (PPO, E.C. 1.3.3.4) plays a pivotal role in chlorophyll biosynthesis in plants, making it a prime target for herbicide development. In this study, we conducted an investigation aimed at discovering PPO-inhibiting herbicides. Through this endeavor, we successfully identified a series of novel compounds based on the pyridazinone scaffold. Following structural optimization and biological assessment, compound 10ae, known as ethyl 3-((6-fluoro-5-(6-oxo-4-(trifluoromethyl)pyridazin-1(6H)-yl)benzo[d]thiazol-2-yl)thio)propanoate, emerged as a standout performer. It exhibited robust activity against Nicotiana tabacum PPO (NtPPO) with an inhibition constant (Ki) value of 0.0338 μM. Concurrently, we employed molecular simulations to obtain further insight into the binding mechanism with NtPPO. Additionally, another compound, namely, ethyl 2-((6-fluoro-5-(5-methyl-6-oxo-4-(trifluoromethyl)pyridazin-1(6H)-yl)benzo[d]thiazol-2-yl)thio)propanoate (10bh), demonstrated broad-spectrum and highly effective herbicidal properties against all six tested weeds (Leaf mustard, Chickweed, Chenopodium serotinum, Alopecurus aequalis, Poa annua, and Polypogon fugax) at the dosage of 150 g a.i./ha through postemergence application in a greenhouse. This work identified a novel lead compound (10bh) that showed good activity in vitro and excellent herbicidal activity in vivo and had promising prospects as a new PPO-inhibiting herbicide lead.

Stability Study and Handling Recommendations for Multiresidue Pesticide Mixes under Diverse Storage Conditions for LC-MS/MS and GC-MS/MS

BACKGROUND

In response to the growing global need for pesticide residue testing, laboratories must develop versatile analytical methods and workflows to produce scientifically sound results. One of the many challenges faced by food chemists is acquiring suitable pesticide certified reference materials (CRMs) to calibrate analytical equipment, monitor method performance, and confirm the identity and concentration of hundreds of pesticide residues in food samples. CRM producers invest considerable resources to ensure the stability of their products.

OBJECTIVE

To present proper CRM handling and storage practices as guidance to ensure stability based on the results of several multiresidue pesticide stability studies.

METHODS

The open ampoule and combined multiresidue mix studies were conducted under controlled conditions. New ampoules containing multiresidue pesticide CRM mixtures were opened and compared to previously opened ampoules at multiple intervals while stored under freezing and refrigerated temperatures. Both LC- and GC-amenable pesticides (>200 residues) were combined and stored under typical laboratory conditions. Studies were performed with and without celery matrix.

RESULTS

The open ampoule study showed high levels of stability for all mixtures. All GC residues remained stable over the duration of the experiment. A week after opening LC multiresidue pesticide mixtures showed minor degradation. After combination of the multiresidue pesticide mixtures, degradation occurred rapidly for both the GC and LC mixtures.

CONCLUSION

Multiresidue pesticide mixtures are stable as ampullated until they are opened. Once the contents of a kit were opened and combined, decreasing stability was observed over time. This was true for both the LC and GC kits. Working mixtures of CRMs for instrument calibration should be made daily.

HIGHLIGHTS

This article shows a novel approach for measuring stability of CRM mixes. In-depth analysis of multiresidue pesticide mixtures and the stability that can be expected before and after mixing under typical storage conditions is described.

Synthesis and Biological Activity Evaluation of Benzoxazinone-Pyrimidinedione Hybrids as Potent Protoporphyrinogen IX Oxidase Inhibitor

Protoporphyrinogen IX oxidase (PPO/Protox, E.C. 1.3.3.4) is recognized as one of the most important targets for herbicide discovery. In this study, we report our ongoing research efforts toward the discovery of novel PPO inhibitors. Specifically, we identified a highly potent new compound series containing a pyrimidinedione moiety and bearing a versatile building block-benzoxazinone scaffold. Systematic bioassays resulted in the discovery of compound 7af, ethyl 4-(7-fluoro-6-(3-methyl-2,6-dioxo-4-(trifluoromethyl)-3,6-dihydropyrimidin-1(2H)-yl)-3-oxo-2,3-dihydro-4H-benzo[b][1,4]oxazin-4-yl)butanoate, which exhibited broad-spectrum and excellent herbicidal activity at the dosage of 37.5 g a.i./ha through postemergence application. The inhibition constant (Ki) value of 7af to Nicotiana tabacum PPO (NtPPO) was 14 nM, while to human PPO (hPPO), it was 44.8 μM, indicating a selective factor of 3200, making it the most selective PPO inhibitor to date. Moreover, molecular simulations further demonstrated the selectivity and the binding mechanism of 7af to NtPPO and hPPO. This study not only identifies a candidate that showed excellent in vivo bioactivity and high safety toward humans but also provides a paradigm for discovering PPO inhibitors with improved performance through molecular simulation and structure-guided optimization.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

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 silico toxicology can be further advanced by integrating QM with artificial intelligence.

Mimicking reductive dehalogenases for efficient electrocatalytic water dechlorination

Electrochemical technology is a robust approach to removing toxic and persistent chlorinated organic pollutants from water; however, it remains a challenge to design electrocatalysts with high activity and selectivity as elaborately as natural reductive dehalogenases. Here we report the design of high-performance electrocatalysts toward water dechlorination by mimicking the binding pocket configuration and catalytic center of reductive dehalogenases. Specifically, our designed electrocatalyst is an assembled heterostructure by sandwiching a molecular catalyst into the interlayers of two-dimensional graphene oxide. The electrocatalyst exhibits excellent dechlorination performance, which enhances reduction of intermediate dichloroacetic acid by 7.8 folds against that without sandwich configuration and can selectively generate monochloro-groups from trichloro-groups. Molecular simulations suggest that the sandwiched inner space plays an essential role in tuning solvation shell, altering protonation state and facilitating carbon-chlorine bond cleavage. This work demonstrates the concept of mimicking natural reductive dehalogenases toward the sustainable treatment of organohalogen-contaminated water and wastewater.

Toxicity Tests for Chemical Pesticide Registration: Requirement Differences among the United States, the European Union, Japan, and China?

Pesticide registration is a scientific, legal, and administrative process that checks if a pesticide is safe and effective for its intended use before it can be used. The toxicity test is a key part of pesticide registration, which includes human health and ecological effect testing. Different countries adopt their own toxicity test criteria for pesticide registration guidelines. However, these differences, which may help accelerate the progress of pesticide registration and reduce the number of animals used, are yet to be explored and compared. Herein, we outlined the details and compared the differences between the toxicity tests in the United States, the European Union, Japan, and China. Some differences lie in the types and waiver policy, while others are in new approach methodologies (NAMs). On the basis of these differences, there is great potential for the optimization of NAMs during the toxicity tests. It is expected that this perspective can contribute to developing and adopting NAMs.

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space

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 silico tools 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.