Data Driven Taxonomy for Antipsychotic Medication: A New Classification System

Abstract

Background: There are over 25 licensed antipsychotic medications with diverse pharmacological and clinical profiles. Antipsychotics are commonly described as either 'typical' or 'atypical', but this does not accurately reflect pharmacological profiles. There is thus a need for a data driven antipsychotic classification scheme suitable for clinicians and researchers which maps onto both pharmacological and clinical effects. Method: We analysed affinities of 27 antipsychotics for 42 receptors from 3,325 receptor binding studies. We used a clustering algorithm to group antipsychotics based on their pattern of receptor affinity. Using a machine learning model, we examined the ability of this grouping to predict antipsychotic-induced side effects quantified according to an umbrella review of clinical trial and treatment guideline data. Results: Clustering resulted in four groups of antipsychotics. The predominant receptor affinity and effect/side effect 'fingerprints' of these four groups were defined, as follows: Group 1 - Muscarinic (M3-M5) receptor antagonism; Cholinergic and metabolic side effects. Group 2 - Dopamine (D2) partial agonism and adrenergic antagonism; Globally low side effect burden. Group 3 - Serotonergic and dopaminergic antagonism; Globally moderate side effect burden. Group 4 - Dopaminergic antagonism; Extrapyramidal and motor side effects. Groups 1 and 4 were more efficacious than clusters 2 and 3. The novel classification was superior to existing approaches when predicting side effects. Conclusions: A receptor affinity-based grouping not only reflects compound pharmacology but also detects meaningful clinical differences to a greater extent than existing approaches. The approach has the potential to benefit both patients and researchers by guiding treatment and informing drug development.

Ayahuasca in the treatment of bipolar disorder with psychotic features–A retrospective case study

“…Later, Mudge adopted a practice of maintenance dosing with ayahuasca. The described patient appeared to have adopted a similar maintenance practice, but with antipsychotics (with regard to antipsychotics, McCutcheon et al presented a new, receptor affinity-based classification system for antipsychotic medication (McCutcheon et al, 2023); it would be interesting to see where ayahuasca and its MAOI components would be located in this taxonomy). The maintenance practice likely allowed for a gradual processing of the underlying trauma.…”

“…Healy reviewed the history of bipolar disorder (Healy, 2008). A preprint by McCutcheon et al presented a new, receptor affinity-based classification system for antipsychotic medication (McCutcheon et al, 2023).…”

How to classify antipsychotics: time to ditch dichotomies?

“…Using this method it is apparent that the typical/typical divide captures only a minimum of pharmacological differences at best. 5…”

“…An alternative to an expert consensus approach is to use a data-driven approach. This was recently applied to classify antipsychotics on the basis of their receptor affinity profile, 5 using a multivariate approach to identify clusters of drugs with similar receptor profiles. This identified four clusters, one with high affinity for muscarinic receptors (e.g.…”

“…The next three columns show the relative affinity for the dopamine D 2 receptor, the serotonin (5-HT) 2A receptor and the ratio between the two. Affinities obtained from McCutcheon et al 5 …”