525. Precision psychiatry in practice: mechanism, algorithms, and clinical choice

Vollständiger Abstract

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Abstract Background Despite the availability of more than 25 licensed antipsychotics worldwide, prescribing decisions remain constrained by oversimplified classifications and fragmented guideline recommendations. The conventional dichotomy between “typical” and “atypical” antipsychotics fails to capture meaningful differences in receptor pharmacology, clinical efficacy, and side-effect burden. At the same time, side-effects substantially impair quality of life, physical health, and treatment adherence, yet clinicians lack concise tools that synthesise these multidimensional risks. There is a growing need for approaches that integrate mechanistic pharmacology, clinical outcomes, and pragmatic decision support to enable personalised, evidence-based prescribing in routine care. Aims & Objectives This presentation aims to demonstrate how pharmacodynamically informed classification systems, international algorithmic guidelines, and digital decision-support tools can be combined to improve antipsychotic selection. Specifically, it will: (i) describe a receptor affinity–based classification of antipsychotics and its ability to predict clinical effects; (ii) outline how these insights informed the development of the INTEGRATE international algorithmic guidelines for schizophrenia; and (iii) show how digital tools can operationalise complex efficacy and side-effect data to support shared, patient-centred decision-making. Method First, receptor binding affinities of 27 antipsychotics across 42 receptors, derived from over 3,000 in vitro studies, were analysed using clustering algorithms to generate pharmacodynamically coherent drug groups. Machine learning models tested whether these groupings predicted clinical efficacy and side-effect profiles derived from umbrella reviews of clinical trials and treatment guidelines. Second, these mechanistic and clinical insights were incorporated into the INTEGRATE guideline development process, which combined umbrella reviews, expert consensus, international collaboration, and lived-experience focus groups to produce a concise, algorithmic framework for schizophrenia pharmacotherapy. Third, comprehensive databases of antipsychotic side-effects were created from meta-analyses and guideline data, and integrated into a digital decision-support tool (Psymatik Treatment Optimizer) using multicriteria decision analysis. Results Receptor affinity–based clustering identified four distinct groups of antipsychotics with characteristic pharmacological and clinical “fingerprints,” including differences in efficacy and specific side-effect risks such as extrapyramidal symptoms, metabolic disturbance, and anticholinergic effects. These groupings predicted out-of-sample clinical effects of individual drugs. The INTEGRATE guidelines translated this evidence into a pragmatic algorithm emphasising early metabolic monitoring, timely identification of non-response, symptom-domain–specific interventions, and prompt use of clozapine in treatment resistance. Digital tools further enabled clinicians to navigate trade-offs between competing side-effects by incorporating patient-specific priorities into treatment rankings. Discussion & Conclusions Mechanism-anchored antipsychotic classification provides a clinically meaningful alternative to traditional drug categories and offers a foundation for more rational prescribing. When embedded within international algorithmic guidelines and supported by digital decision tools, this approach facilitates personalised, transparent, and implementable treatment decisions. Together, these advances demonstrate how precision pharmacology can be translated into pragmatic, patient-centred care that addresses both psychiatric symptoms and long-term physical health outcomes in schizophrenia.