From the Origins of Pharmacogenetics to First Applications in Psychiatry
Bibliographic record
Abstract
Pharmacogenetics is the division of science addressing how genetic factors contribute to the metabolism, response, and side effects of a given medication. What was once regarded as a subdivision of genetics and pharmacology is now recognized as its own field and has its own unique story of origin. While the term "pharmacogenetics" was coined by Friedrich Vogel in 1959, the relevance of inherited genetic traits in affecting the clinical outcome to xenobiotics has been observed long before. In fact, there is much hope that pharmacogenetics can help unravel the "mysteries" as to why different people may display variable responses to the same medication as well as identify new drug targets. This article will highlight the conceptual framework for pharmacogenetics advanced by pioneer scientists Arno Motulsky and Friedrich Vogel (both human geneticists), as well as Werner Kalow (clinical pharmacologist), leading up to the creation of modern pharmacogenetics. Finally, the practical implications and first steps toward implementation for current psychiatric treatment are reviewed followed by an outlook on future studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".