Visualizing classification of drugs used in psychotic disorders: A ‘subway map’ representing mechanisms, established classes and informal categories
Bibliographic record
Abstract
Drugs used to treat psychotic disorders ('antipsychotics') have been widely used in psychiatry since the introduction of chlorpromazine in the mid-1950s. The categorization of these drugs evolved in a piecemeal way, relying initially on grouping by chemical structure (e.g. phenothiazines, butyrophenones), then by epoch of introduction (e.g. first generation ('conventional') vs second generation ('atypical')). As psychopharmacological expertise has advanced, it has become possible to quantify affinities for each drug in this class for relevant receptors including dopamine D2, 5HT2A, 5HT2C, histamine H1 and others. However, until the recent emergence of a new generation of agents known collectively as dopamine D2 receptor partial agonists (e.g. aripiprazole, brexpiprazole and cariprazine), there had been little reference in drug classification to specific pharmacological properties. An overview of data on receptor affinities across multiple drugs and receptor types would permit categorization according to binding affinities and putative pharmacological mechanisms. In this paper, we have attempted to construct a 'subway map' of 32 drugs used for treatment of psychotic disorders. This design allows a visualization of both the historical classifications by structure and epoch of introduction, and of the binding affinities for key receptors based on appraisal of scientific literature. The map represents a step towards categorization by mechanism, allowing prescribers and patients to understand which drugs share common biological features and the extent to which drugs may have similarities and differences in their mechanisms. In addition, this approach may encourage more logical groupings of drugs to be used in systematic reviews and meta-analyses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".