Bibliographic record
Abstract
The genetic basis for drug dependence has focused on genes that encode receptors involved in the reinforcing properties of drugs of abuse or that determine drug-taking behavior (e.g. impulsivity, etc.). Pharmacogenetic variations in the patterns of metabolism among individuals can also importantly modulate the risk of drug dependence. Cytochrome P450 drug metabolizing enzymes (CYPs), can activate (e.g. codeine to morphine) or deactivate (e.g. nicotine to cotinine) drugs of abuse. Some CYPs are polymorphic, that is, there are gene mutations which result in individuals with no (null mutations) or decreased enzyme activity (e.g. CYP2D6*10). Individuals with two null mutations appear in the population as phenotypic poor metabolizers. Using in vitro studies, we have identified drugs of abuse that are substrates of the polymorphic enzymes CYP2D6 (codeine, amphetamines, dextromethorphan), CYP2A6 (nicotine) and CYP2C19 (flunitrazepam). In human experimental studies, we have shown that CYP phenotype and genotype affect abuse liability of CYP2D6 metabolized drugs of abuse. In addition, we inhibited CYP2D6 and decreased individuals' risk of dependence experimentally (codeine, dextromethorphan) and treated codeine dependence. In epidemiologic studies CYP2D6 and CYP2A6 null mutations protect individuals from becoming codeine and tobacco dependent, respectively. With respect to CYP2A6, individuals with mutations, smoke fewer cigarettes and can quit more easily. Inhibiting CYP2A6 (e.g. tranylcypromine, methoxsalen) decreases smoking and the activation of procarcinogens. By mimicking these gene defects the risk of dependence can be decreased in individuals and new treatments developed.
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.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".