ArticleBiological psychiatry2023
Data-Driven Taxonomy for Antipsychotic Medication: A New Classification System.
Article in Biological psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Dopaminergic modulation of pancreatic beta-cell insulin secretion and implications for antipsychotic-induced glucose dysregulation: a systematic review and meta-analysis.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Pooled it
- Guideline
- Non-Clozapine interventions in treatment-resistant schizophrenia: a systematic review and meta-analysis.Molecular psychiatry · 2026Pooled it
- Switching antipsychotics versus continued current treatment in people with non-responsive schizophrenia.The Cochrane database of systematic reviews · 2025Pooled it
- Effects of antipsychotics on human cognitive function: causal evidence from healthy volunteers following sustained D2/D3 antagonism, D2/D3 partial agonism and placebo.Molecular psychiatry · 2025Trial
- Refining Drug Classes: A Multidimensional, Data-Driven Map of Antidepressant Pharmacology.Biological psychiatry global open science · 2026Article
- Reproducible human reward imaging phenotypes exhibit differential sensitivity to dopamine D2 receptor antagonism.Nature communications · 2026Article
- Functional coupling between MC4R and Kir7.1 contributes to clozapine-induced weight gain in female mice.Nature communications · 2026Article
- Sexual Dysfunction with Antipsychotics: Emerging Clues from a Disproportionality Analysis of the World Health Organization VigiBase.Drug safety · 2026Article
- The influence of sex on cognition in psychosis.Schizophrenia research. Cognition · 2026Article
- LiquidGAN for Handwriting-Based Detection and Severity Classification of Extrapyramidal Symptoms.Sensors (Basel, Switzerland) · 2026Article
- Real-world comprehensive care of people living with schizophrenia: recommendations across different settings and clinical stages.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Dissociation between the dynamics of symptom scores and network metrics in psychosis treatment: a proof-of-concept study using network intervention analysis.Psychopharmacology · 2026Article
- Whole brain grey matter synaptic terminal density, age and intellectual functioning in schizophrenia: an in vivo [Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Absolute Receptor Occupancy, Not Rate of Decline, Predicts Relapse.Schizophrenia bulletin · 2026Review
- Asenapine for delirium in patients with cancer: A scoping review.PCN reports : psychiatry and clinical neurosciences · 2026Review
- The relevance of phenotypic definition in treatment resistant forms of major depressive disorder: a narrative review.Frontiers in pharmacology · 2026Review
- Applying advanced psychometrics to antipsychotic treatment trials: A protocol comparing effect estimates.Open research Europe · 2026Article
- Schizophrenia.Nature reviews. Disease primers · 2025Review
- Antipsychotic drugs at 75: the past, present, and future of psychosis management.British medical bulletin · 2025Review
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6 authors.
Funding
Abstract
backgroundGlobally, there are more than 25 licensed antipsychotic medications. Antipsychotics are commonly described as either typical or atypical, but this dichotomous classification does not reflect the diversity of their pharmacological and clinical profiles. There is a need for a data-driven antipsychotic classification scheme suitable for clinicians and researchers that maps onto both pharmacological and clinical effects. Receptor affinity provides one starting point for such a scheme.
methodsWe analyzed affinities of 27 antipsychotics for 42 receptors from 3325 in vitro receptor binding studies. We used a clustering algorithm to group antipsychotics based on receptor affinity. Using a machine learning model, we examined the ability of this grouping to predict antipsychotic-induced clinical effects quantified according to an umbrella review of clinical trial and treatment guideline data.
resultsClustering resulted in 4 groups of antipsychotics. The predominant receptor affinity and clinical effect "fingerprints" of these 4 groups were defined as follows: group 1, muscarinic (M
conclusionsA receptor affinity-based grouping not only reflects compound pharmacology but also detects meaningful clinical differences. This approach has the potential to benefit both patients and researchers by guiding treatment and informing drug development.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.