Potential Complementary Therapy for Adverse Drug Reactions to Sulfonamides: Chemoprotection Against Oxidative and Nitrosative Stress by TCM Constituents and Defined Mixtures
Bibliographic record
Abstract
PURPOSE: Our working hypothesis is that bioactive phytochemicals that are important constituents of Traditional Chinese Medicine and their defined mixtures have potential as complementary therapy for chemoprotection against adverse drug reactions whose toxicity is not related to the pharmacological action of the drug but where oxidative and nitrosative stress are causative factors. METHODS: In this investigation we measured cytotoxicity, lipid peroxidation, protein carbonylation and ROS/NOS-mediated changes in the disulfide proteome of Jurkat E6.1 cells resulting from exposure to sulfamethoxazole N-hydroxylamine with or without pre-treatment with low µM concentrations of baicalein, crocetin, resveratrol and schisanhenol alone and in defined mixtures to compare the ability of these treatment regimens to protect against ROS/RNS toxicity to Jurkat E6.1 cells in culture. RESULTS: Each of the Traditional Chinese Medicine constituents and defined mixtures tested had significant chemoprotective effects against the toxicity of ROS/RNS formed by exposure of Jurkat E6.1 cells to reactive metabolites of sulfamethoxazole implicated as the causative factors in adverse drug reactions to sulfa drugs used for therapy. At equimolar concentrations, the defined mixtures tended to be more effective chemoprotectants overall than any of the single constituents against ROS/RNs toxicity in this context. CONCLUSIONS: At low µM concentrations, defined mixtures of TCM constituents that contain ingredients with varied structures and multiple mechanisms for chemoprotection have excellent potential for complementary therapy with sulfa drugs to attenuate adverse effects caused by oxidative/nitrosative stress. Typically, such mixtures will have a combination of immediate activity due to short in vivo half-lives of some ingredients cleared rapidly following metabolism by phase 2 conjugation enzymes; and some ingredients with more prolonged half-lives and activity reliant on phase 1 oxidation enzymes for their metabolic clearance. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".