Longitudinal Plasma Multiomics Characterization of Nephrotoxicity in Beagles Following Repeated Dosing of Enteric-Coated Propyl Gallate Tablets
Bibliographic record
Abstract
Permeation enhancers (PEs) are excipients used in oral biotherapeutic formulations to facilitate the transport of bioactive compounds across the intestinal barrier and prevent their degradation. Concerns associated with the chronic use of PEs demand comprehensive approaches to elucidate their potential toxicity mechanisms. A recent publication from our group reported nephrotoxicity in beagles after daily administration of enteric-coated (EC) tablets containing propyl gallate (PG) as a PE. To further characterize EC-PG-mediated nephrotoxicity mechanisms, we conducted a longitudinal mass spectrometry (MS)-based multiomics analysis of the dog plasma lipidome and proteome. Time-course analyses revealed elevation across multiple lipid classes and, in particular, species containing arachidonic acid, which may reflect EC-PG treatment-induced inflammation. At the protein level, alterations in biological processes associated with coagulation, complement activation, protein degradation and metabolism, and lipid transport and metabolism were observed. Integrative multiomics analyses provided additional insights into toxicity mechanisms at the interface between lipids and proteins. This holistic approach highlighted lipid transport and metabolism, oxidative stress, and inflammation as altered biological processes by EC-PG administration. Altogether, longitudinal multiomics profiling and integrative analysis provided additional mechanistic hypotheses for EC-PG induced renal toxicity, demonstrating the value of such an approach to investigate mechanisms relevant to drug safety.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".