# Pharmacogenomics of risperidone in autism spectrum disorder: a minireview

## TL;DR

This paper reviews how genetic factors influence how people with autism respond to risperidone, a drug used to manage irritability.

## Contribution

The paper synthesizes current pharmacogenomic findings and discusses emerging approaches like polygenic models and machine learning.

## Key findings

- CYP2D6 is a strong predictor of risperidone's pharmacokinetics and toxicity.
- Genes like ABCB1, DRD3, and HTR2A show inconsistent associations with treatment outcomes.
- Emerging methods like polygenic models and pharmacoepigenomics are being explored for better predictions.

## Abstract

Risperidone is one of the most widely prescribed antipsychotics for the management of irritability and associated behavioral symptoms in autism spectrum disorder (ASD), yet clinical response and adverse-effect risk vary widely among individuals. Pharmacogenomic (PGx) research has sought to explain this variability, with accumulating evidence pointing to contributions from metabolic, transporter, and neurotransmitter pathways. In this narrative minireview, we synthesize current findings on PGx factors influencing risperidone outcomes in children and adolescents with ASD. CYP2D6 emerges as the most robust predictor of pharmacokinetics and toxicity, while pharmacodynamic associations involving dopaminergic, serotonergic, and metabolic pathways in genes such as ABCB1, DRD3, HTR2A, HTR2C, and LEP remain inconsistent and largely derived from small cohorts. We also discuss methodological challenges in assessing treatment response, current clinical guidelines, barriers to implementation, and emerging approaches including polygenic models, pharmacoepigenomics, and machine learning. Together, the available evidence points to both the promise and the limitations of PGx in guiding safer and more individualized risperidone therapy in ASD.