mRNA expression changes following drug treatment and pharmacogenomic responses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inter-individual variability in drug response represents a major challenge in clinical therapeutics, with genetic polymorphisms in drug-metabolizing enzymes contributing substantially to observed differences in efficacy and toxicity. This research examined mRNA expression changes in pharmacogenes following drug treatment and characterized the influence of genetic variants on transcriptional responses. A prospective pharmacogenomic investigation was conducted at the University of Toronto from October 2022 to June 2024, enrolling 156 healthy volunteers with documented genotypes for major cytochrome P450 and transporter polymorphisms. Participants received standardized doses of five probe drugs metabolized by CYP2D6, CYP2C19, CYP3A4, and transport proteins. Peripheral blood mononuclear cells were collected at multiple timepoints for RNA extraction and quantitative gene expression analysis. RNA sequencing identified 847 differentially expressed genes following drug exposure, with enrichment in xenobiotic metabolism and cellular stress response pathways. Genotype-stratified analysis revealed significant differences in mRNA expression patterns according to metabolizer phenotype: poor metabolizers exhibited 2.4-fold higher baseline CYP2D6 expression compared to normal metabolizers (p
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it