Distribution of toxicity values across different species and modes of action of pesticides from PESTIMEP and PPDB databases

Abstract

The continuous use of compounds contained in commodities such as processed food, medicines, and pesticides, demands safety measures, in particular, for those in direct contact with humans and the environment. Safety measures have evolved and regulations are now in place around the globe. In the case of pesticides, attempts have been made to use toxicological data to inform of potentially harmful compounds either across species, on different routes of exposure, or entirely new chemicals. The generation of models, based on statistical and molecular modeling studies, allows for such predictions. However, the use of these models is framed by the available data, the experimental errors, the complexity of the measurement, and the available computational algorithms, among other factors. In this work, we present the methodologies used for extrapolation across different species and routes of administration and show the appropriateness of developing predictive models of pesticides based on their type and mode of action. The analyses include comparisons based on structural characteristics and physicochemical properties. Whenever possible, the scope and limitations of the methodologies are discussed. We expect that this work will serve as a useful introductory guide of the tools employed in the toxicity assessment of agrochemical compounds.

Fig. 1. Representative core structures of pesticides. (A) Organochlorides (aldrin, chlorothalonil, and heptachlor epoxide). (B) Organophosphorates (mevinphos, TEPP, and phorate). (C) Carbamates (aldicarb, bendiocarb, and propoxur). (D) Pyrethroids (permethrin). (E) Sulfonylureas (bensulide and perfluidone).

Fig. 2. Chemical space representation of pesticides and approved drugs based on physicochemical properties relevant in drug development and pesticides. Chemical space using druglike properties: (A) FDA and pesticides, (B) FDA and (C) pesticides, and their corresponding loading plots (D), (E) and (F). Chemical space using pesticide-like properties: (G) FDA and pesticides, (H) FDA and (I) pesticides, and their corresponding loading plots (J), (K) and (L). Physicochemical properties: molecular weight (weight), octanol/water partition coefficient (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), number of rotable bonds (RotB), polarizability (Pol), hydrophobicity (Hydroph), solubility (log S), acidity (Ac), basicity (Bas), reactivity (Rx), number of chlorine atoms (Cl), number of phosphorus atoms (P), oxygen atoms (nO), hydrogen atoms (nH), atoms in aromatic system (Arom) and atoms different from carbon (Non_C). The loading coefficients are summarized in ESI (Tables S3–S6†). The first three PC accounted for 60–93% of variance.

Fig. 3. Distributions of toxicity values by MOA for (A) insecticides, (B) herbicides, and (C) fungicides.

Fig. 4. Chemical space representation of pesticides based on physicochemical properties relevant in pesticides. (A) FDA approved drugs, and pesticides classified by MOA and pesticides contained in T.E.S.T (B) insecticides, (C) fungicides, and (D) herbicides. Physicochemical properties: molecular weight (weight), octanol/water partition coefficient (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), number of rotable bonds (RotB), polarizability (Pol), hydrophobicity (Hydroph), solubility (log S), acidity (Ac), basicity (Bas), reactivity (Rx), number of chlorine atoms (Cl), number of phosphorus atoms (P), oxygen atoms (nO), hydrogen atoms (nH), aromatic atoms (Arom) and atoms different from carbon (Non_C). The first three PC accounted for 60–93% of variance.