Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.07.21.739763

An Insectified Caco-2 Cell-Based assay to monitor pesticide Transport Processes and Pharmacokinetics

Abstract

Intestinal insect cells are integral to pharmacokinetic research, unfortunately they do not serve as cell models for more advanced studies comparable to transport studies typically conducted in mammalian models. To overcome this limitation, in this study, we developed a Caco-2 cell line that has been genetically modified to mimic insect-like characteristics, providing a robust insectified screening platform to monitor transport processes of xenobiotics. This platform consists of Wild Type (Pgpwild type) cells, an Pgp Knockout (PgpKO) line to eliminate endogenous background interference, and species-specific P-gp rescue lines. These rescue lines allow for the stable expression of human (Hs Pgp), cotton bollworm (Ha Pgp), and malaria mosquito (Ag Pgp) homologs, enabling a direct comparative analysis of efflux kinetics across different pesticide target and non-target biological systems. Functional validation using the substrate Digoxin confirmed high P-gp dependency within the platform. Methyl-parathion, an organophospate insecticide, was recognised and transported by both insect and human Pgps. This is consistent with the low mammalian selectivity of the compound. In contrast, Triflumezopyrim exhibited a high efflux ratio that remained remarkably stable even in the absence of MDR1, indicating that the uptake and pharmacokinetics of this compound are not Pgp-dependent. By successfully differentiating between transporter-specific and independent pathways these cell lines can serve as a high-fidelity screening tool for predicting novel pesticide selectivity and possibly validating the potential role of transporters in resistance. The system can be expanded and exploit also other transporters.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Skouloudaki, K., Denecke, S., Vogelsang, K., Pergantis, S., Vontas, J.. 2026-07-24. An Insectified Caco-2 Cell-Based assay to monitor pesticide Transport Processes and Pharmacokinetics. https://doi.org/10.64898/2026.07.21.739763

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Translational Pharmacokinetics and Pharmacodynamics of a Cationic mRNA-Lipid Nanoparticle from Mice to Non-Human Primates

Cationic lipid nanoparticles have demonstrated unique potential for extrahepatic mRNA delivery, particularly enabling selective targeting of the pulmonary endothelium. However, their translational development has been hampered by reports of infusion-related immune reactions and innate immune system activation, most notably transient complement activation. Here, we present a case study illustrating the discovery and translational advancement of a selected cationic LNP into non-human primates (NHPs) for initial pharmacokinetic assessment and evaluation of potential immunostimulatory side effects. We show surface charge dependent organ-selective expression of reporter mRNAs from different LNPs in vivo. An mRNA encoding the Tie2 agonist COMP-Angl, was formulated with LNP002, and respective pharmacokinetic and pharmacodynamic readouts were analyzed in two independent non-human primate studies. Notably, dose-dependent transient complement activation could be abrogated by extending the infusion time. Finally, we identified the blood-borne pharmacodynamic biomarker PDGFB for LNP002/mRNA-76 treatment reflecting activated Tie2-signalling in healthy pulmonary endothelium in vivo supported by single cell sequencing and cluster-alignment of downstream effector genes with the same spatial profile as the delivered mRNA.

pharmacology and toxicology↗

Cytotoxic Effects of Multiple Pesticides and their Mixtures on Caco-2 Cells Evaluated by Using MTT and Trypan Blue Assays

BACKGROUND: Pesticides are extensively used in agriculture, raising concerns about their potential impact on human health through dietary and environmental exposure. OBJECTIVES: This study evaluated the in vitro cytotoxicity of ten commonly used pesticides and their mixtures (lambda-cyhalothrin, cypermethrin, deltamethrin, tebuconazole, glyphosate, acetamiprid, cyprodinil, piperonyl butoxide, fluopyram, and imazalil) on human intestinal Caco-2 cells. METHODS: Cytotoxicity was assessed using the MTT assay, as a measure of metabolic activity, and the trypan blue exclusion test, as an indicator of cell membrane integrity. FINDINGS: Results showed that high concentrations (100 mg/L) of all pesticides significantly reduced cell viability and vitality. Notably, glyphosate and tebuconazole exhibited significant toxicity even at lower concentrations, respectively 0.1 mg/L and 10 mg/L. Combination treatments (Top 3 and Top 8 pesticide mixtures) retained the cytotoxic effects observed for individual compounds, showing additive (non-synergistic) effects. CONCLUSIONS: Overall, these findings indicate that certain pesticides-based herbicides can exert cytotoxic effects on intestinal cells even at relatively low concentrations and highlight the importance of using the component-based approach in mixture risk assessment for humans. This study was performed as part of the EU SPRINT (Sustainable Plant Protection Transition: A Global Health Approach) project.

pharmacology and toxicology↗

Assessing chemical toxicity across Eukaryota using multimodal transformers

Biodiversity is globally threatened by chemical pollution, yet toxicity data remain unavailable for millions of species and tens of thousands of chemicals, severely limiting our ability to assess ecological impacts. Here we present TRIDENT-2, a multimodal artificial intelligence model for predicting chemical toxicity across evolutionarily diverse eukaryotic species. Trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios, TRIDENT-2 accurately predicts toxicity across Eukaryota with an average median absolute error ranging from 1.76 to 3.80. By jointly learning from chemical, biological, and experimental information, it remains accurate across broad chemical and taxonomic distances, allowing for toxicity assessment for species and chemicals beyond the current experimental evidence. Our findings demonstrate that artificial intelligence can help overcome longstanding data limitations in ecotoxicology, paving the way for improved decision-making and reducing chemical impacts on biodiversity and ecosystems.

pharmacology and toxicology↗