Comparison of Cell Lines and Culture Models to Evaluate Toxicity of Pesticides and Pesticide Mixtures
Bibliographic record
Abstract
There is a current shift in toxicology toward using in vitro cell-based assessments to screen environmental chemicals, such as pesticides, for their potential organ-specific toxicity as well as explore potential mechanisms of action. Many mammalian-derived cells lines are used to evaluate the organ-specific toxicity of chemicals; however, the ultimate use of the data obtained from these models in terms of translation to human toxicity remains challenging. Cell-based assays present distinct advantages and limitations, necessitating careful consideration by toxicologists when designing tests to address specific questions or interpret results. The primary goal of this thesis was to deepen our comprehension of cell-specific toxicity and in vitro assays. It is anticipated that understanding how individual factors impact variability in toxicity responses will help improve the accuracy and applicability of toxicity data derived from cell-based studies.\nFirst, we assessed the cytotoxic effects of pesticide chlorpyrifos (CPF), and its active metabolite (chlorpyrifos oxon, CPFO), across two human cell lines representative of liver (HepG2) and kidney (HK-2). The cytotoxicity to CPF and CPFO differed between cell lines, which was attributed to lower basal expression and inducibility of metabolizing enzymes, efflux transporters, and nuclear receptors in HK-2 cells. Co-exposure of CPF with specific inhibitors of efflux transporters enhanced CPF and CPFO cytotoxicity in HepG2 cells, indicating the role of these transporters in eliminating either CPF or CPFO. Co-incubation with transporter inhibitors also increased CPF accumulation in HepG2 cells, supporting the role of efflux transporters in elimination of CPF. These results underscore the crucial role of efflux transporter expression levels in selected cell lines for assessing the potential toxicity of environmental pollutants, such as pesticides.\nUnderstanding the role of efflux transporters in cell-specific toxicity responses to individual pesticides in cell lines prompted questions about their potential influence on mixture toxicity in vitro. Many pesticides act as substrates or inhibitors of efflux transporters; thus, their inhibitory effects could impede the elimination of other pesticides in a mixture, resulting in chemosensitization and increased cellular toxicity. We examined the combined toxicity of CPF with two other known P-glycoprotein (P-gp) inhibitor pesticides, endosulfan-α and heptachlor, in HepG2 cells. Binary mixtures of CPF with either endosulfan-α or heptachlor at concentrations causing less than 20% cytotoxicity (
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".