Epigenetics in Traditional Chinese Pharmacy: A Bioinformatic Study at Pharmacopoeia Scale
Bibliographic record
Abstract
Epigenetics is a phenomenon of heritable changes in the chromatin structure of a genomic region, resulting in a transcriptional silent or active state of the region over cell mitosis. Mounting evidence has demonstrated phenotypic consequence of alternations in the patterns of DNA methylation and histone modifications, two of the well-studied epigenetic mechanisms. The epigenome thus represents an interesting therapeutic target. Traditional Chinese medicine (TCM) is a system of therapies that has developed through empiricism for over 2100 years and has remained a popular alternative medicine in some Far East Asian populations. We searched 3294 TCM medicinals (TCMMs) containing 48 491 chemicals for chemicals that interact with the epigenetics-related proteins and found that 29.8% of the TCMMs are epigenome- and miRNA-modulating via, mainly, interactions with Polycomb group and methyl CpG-binding proteins. We analyzed 200 government-approved TCM formulas (TCMFs) and found that a statistically significant proportion (99%) of them are epigenome- and miRNA-interacting. The epigenome and miRNA interactivity of the Monarch medicinals is found to be most prominent. Histone modifications are heavily exploited by the TCMFs, many of which are tonic. Furthermore, epigenetically, the Assistant medicinals least resemble the Monarch. We quantified the role of epigenetics in TCM prescription and found that epigenome- and miRNA-interaction information alone determined, to an extent of 20%, the clinical application areas of the TCMFs. Our results provide (i) a further support for the notion of the epigenomes as a drug target and (ii) a new set of tools for the design of TCM prescriptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".