Potential risk of impurities in pesticides: Elucidating structurally related impurities using ZODIAC

Widespread ecological consequences of using neonicotinoids

Neonicotinoids are neuro-active insecticides chemically similar to nicotine, and have been the most widely used insecticides for nearly two decades^​1​. By stimulating nicotinic acetylcholine receptors, they disrupt the nervous system of insects, resulting in the death or harm of a wide range of both intended and unintended insect species^​2​. They are water-solulable and thus absorbed by plants, making them pervasive throughout the entire plant, including leaves, flowers, nectar, and pollen^​2​. The widespread use of neonicotinoids is posing a chronic threat to pollinating insects and has been linked to a range of adverse ecological consequences^​3​. Recognizing these risks, several countries imposed restrictions on neonicotinoid use. 

Thiacloprid: a threat to health and environment

Thiacloprid belongs to the first generation of neonicotinoid insecticides. It is particularly effective in controlling a diverse range of sucking and chewing insects, such as aphids, moth scale insects, codling moths, and weevils. Notably, thiacloprid has consistently appeared as one of the most frequently detected insecticides in environmental samples^​4​, raising concerns due to its persistent and extensive contamination, which has prompted deep apprehension about exposure to humans^​5​. Chronic exposure to thiacloprid is associated with the potential for serious neonicotinoid toxicity, including the development of conditions like Parkinson’s and Alzheimer’s disease^​6​, damage to human fertility^​7​, and the risk of causing liver cancer^​8​. In response to these concerns, the European Commission took action by banning THI from the European market.

Identifying pesticide impurities using ZODIAC

Impurities in insecticides represent a critical aspect of safety and environmental considerations. These impurities can potentially pose more severe risks than the main active compound. Variations in manufacturing processes and storage conditions can lead to significant differences in the impurity profiles. Identifying and elucidating structurally related impurities in these complex chemical mixtures is challenging^​9​, usually combining several analytical methods. Researchers at the National Institute of Metrology, China, suggest an impurity profiling protocol^​10​ for thiacloprid using our software SIRIUS, including ZODIAC and CSI:FingerID. ZODIAC^​11​ is our enhanced molecular formula identification method, which is part of our SIRIUS software. SIRIUS ranks molecular formulas for each compound individually using fragmentation trees and isotope pattern analysis. ZODIAC improves these identifications by taking similarities of compounds in the dataset into account.

Thiacloprid impurities are mainly transformations of the two main moieties

Thiacloprid impurities mainly originated from precursors, by-products, or degradation products, which often exhibited a similar chemical structure. The chemical structure of thiacloprid is a composition of two moieties: 6-chloro-3-pyridinyl (CP) and 2-cyaniminothiazolidine (CIT). Fragments resulting from cleavage and decomposition of the CIT moiety were found to be widely present in the impurities helping significantly with their structural elucidation. The researchers elucidated 18 impurities and grouped them into five groups according to their structural difference: (1) precursor substitution, (2) CP moiety substitution,  (3) CIT moiety substitution, (4) both CP and CIT substitution and (5) substitution within the CH2 bridge between the two moieties. 

Beyond searching among known structures

Out of the identified impurities, 14 were reported for the first time and lacked a CAS (Chemical Abstracts Service) number. Due to that, the authors claim that “CSI:FingerID […] didn’t provide much useful information for structure elucidation.”^​10​ While this observation holds when conducting searches within public chemical databases, it’s important to note that CSI:FingerID is not restricted to any specific database. It can also be effectively employed to explore a database comprising potential structures, which are, for instance, generated by tools like BioTransformer. This approach proves especially valuable for identifiying unknown transformation products of pesticides or drugs, offering a more suitable search space for structural elucidation.