51. IMPLEMENTATION OF PHARMACOGENETIC TESTING AMONG PSYCHIATRIC INPATIENTS WITH SEVERE MENTAL ILLNESS IN ONTARIO, CANADA

Background Pharmacogenetic research has identified several genes associated with responses to mental health medications and established several evidence-based guidelines. However, implementation is lagging. As first of its kind, the Ontario Shores Centre has recently begun implementation for in- and outpatients as part of routine clinical care. Here, we report PGx testing results for CYP2D6 and CYP2C19 from our clinical cohort. Methods This implementation study was conducted beginning in September 2024. Psychiatric inpatients with severe mental illness were invited to undergo PGx testing to optimize medication treatment. Metabolism status identification followed Clinical Pharmacogenetics Implementation Consortium (CPIC) nomenclature. Medication order data were also included in the analysis to track each individual’s prescription timeline before PGx testing. Genotype-sensitive medications were identified according to CPIC guidelines. Descriptive analyses were used to summarize phenotype distributions and medication exposure patterns. Results Between September 2024 and March 2026, 546 inpatients underwent PGx testing. For CYP2C19, 38.5% exhibited normal metabolizer status, while 31.5% and 21.2% were intermediate and rapid metabolizers, respectively. Poor metabolizers accounted for 4.8%, and ultrarapid metabolizers for 4.0%. For CYP2D6, 61.2% were normal metabolizers and 32.2% were intermediate metabolizers, while 3.3% and 1.1% were classified as poor and ultrarapid metabolizers, respectively. Overall, only 23.4% (n = 123) of inpatients exhibited normal metabolism for both genes, and 0.5% (n = 3) exhibited extreme metabolism for both. Among patients with non-normal metabolizer phenotypes, 18.1% (n = 57) for CYP2C19 and 41.2% (n = 75) for CYP2D6 had been prescribed at least one CPIC-designated sensitive antipsychotic or antidepressant before PGx testing. Discussion PGx testing revealed a high prevalence of non-normal metabolizer phenotypes among inpatients with severe mental illness, which may impact medication efficacy. The observed distribution is consistent with previous reports in populations of predominantly European ancestry. Notably, exposure to CPIC-designated sensitive medications was relatively high among patients with CYP2D6 non-normal metabolizer phenotypes. These findings support the potential utility of PGx guided prescribing in psychiatric settings and highlight the need for further studies to evaluate its impact on treatment outcomes and medication safety.

Authors

Institutions

Publication Details

Journal
European Neuropsychopharmacology
Published
2026-09-21
DOI
https://doi.org/10.1016/j.euroneuro.2026.113078
Primary Topic
Pharmacogenetics and Drug Metabolism
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

51. IMPLEMENTATION OF PHARMACOGENETIC TESTING AMONG PSYCHIATRIC INPATIENTS WITH SEVERE MENTAL ILLNESS IN ONTARIO, CANADA

J V Forrester, Gokulan Kandasamy, Carman Brown, Daniel Mueller et al.
European Neuropsychopharmacology
Pharmacogenetics and Drug Metabolism
article

51. IMPLEMENTATION OF PHARMACOGENETIC TESTING AMONG PSYCHIATRIC INPATIENTS WITH SEVERE MENTAL ILLNESS IN ONTARIO, CANADA

J V Forrester, Gokulan Kandasamy, Carman Brown, Daniel Mueller, Phil Klassen, Ben Rogers, Susan Wei, Mugeng Liu
article en

Abstract

Background Pharmacogenetic research has identified several genes associated with responses to mental health medications and established several evidence-based guidelines. However, implementation is lagging. As first of its kind, the Ontario Shores Centre has recently begun implementation for in- and outpatients as part of routine clinical care. Here, we report PGx testing results for CYP2D6 and CYP2C19 from our clinical cohort. Methods This implementation study was conducted beginning in September 2024. Psychiatric inpatients with severe mental illness were invited to undergo PGx testing to optimize medication treatment. Metabolism status identification followed Clinical Pharmacogenetics Implementation Consortium (CPIC) nomenclature. Medication order data were also included in the analysis to track each individual’s prescription timeline before PGx testing. Genotype-sensitive medications were identified according to CPIC guidelines. Descriptive analyses were used to summarize phenotype distributions and medication exposure patterns. Results Between September 2024 and March 2026, 546 inpatients underwent PGx testing. For CYP2C19, 38.5% exhibited normal metabolizer status, while 31.5% and 21.2% were intermediate and rapid metabolizers, respectively. Poor metabolizers accounted for 4.8%, and ultrarapid metabolizers for 4.0%. For CYP2D6, 61.2% were normal metabolizers and 32.2% were intermediate metabolizers, while 3.3% and 1.1% were classified as poor and ultrarapid metabolizers, respectively. Overall, only 23.4% (n = 123) of inpatients exhibited normal metabolism for both genes, and 0.5% (n = 3) exhibited extreme metabolism for both. Among patients with non-normal metabolizer phenotypes, 18.1% (n = 57) for CYP2C19 and 41.2% (n = 75) for CYP2D6 had been prescribed at least one CPIC-designated sensitive antipsychotic or antidepressant before PGx testing. Discussion PGx testing revealed a high prevalence of non-normal metabolizer phenotypes among inpatients with severe mental illness, which may impact medication efficacy. The observed distribution is consistent with previous reports in populations of predominantly European ancestry. Notably, exposure to CPIC-designated sensitive medications was relatively high among patients with CYP2D6 non-normal metabolizer phenotypes. These findings support the potential utility of PGx guided prescribing in psychiatric settings and highlight the need for further studies to evaluate its impact on treatment outcomes and medication safety.

European NeuropsychopharmacologyVol. 111
Ontario Shores Centre for Mental Health Sciences (CA)
No poverty
Openalex Percentile: Top 9%
Pharmacogenetics and Drug Metabolism
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.