Identifying Greener and Safer Plasticizers: A 4-Step Approach
Bibliographic record
Abstract
The health and economic burden of endocrine disrupting chemicals, such as the plasticizer di(2-ethylhexyl) phthalate (DEHP), is prompting industry to develop alternatives. However, the absence of requirements for manufacturers to ensure the safety of these alternatives has led to the generation of replacements that may have similar or worse effects than the original chemicals. Consequently, there is increasing recognition by scientists, regulators and industry that proactive approaches are needed to develop safe chemical substitutes. We propose a 4-step approach for the design, characterization and toxicological testing of responsible alternative chemicals that we illustrate with our ongoing studies on DEHP replacements. Our approach is comprised of: (1) the design and characterization of alternative chemicals based on innovative chemical structures and environmental considerations; (2) large-scale in vitro cell-based high throughput and selective ex vivo studies to preselect the most innocuous alternatives; (3) an acute toxicity in vivo study to rule out overt toxicity of the selected candidates; and (4) an in utero and lactational exposure study comparing the effects of selected candidates to those currently in use, emphasizing commonly described phenotypes after exposure to the latter. Using this 4-step approach, we have identified 2 alternative chemicals displaying good plasticizing properties, better biodegradability, and less leaching than DEHP without any apparent toxicity in vivo. This process has thus far proven useful in the proactive identification of responsible chemical replacements for DEHP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".