Near, far, wherever you are: phenotype-related variation in pharmacogenomic effect sizes across the psychiatric drug literature

TL;DR

This paper finds that genetic effects on drug response are stronger for drug absorption than for treatment outcomes in psychiatry.

Contribution

The study introduces a dashboard and shows pharmacogenomic effects are larger for proximal than distal outcomes in psychiatric drugs.

Findings

Pharmacogenomic effect sizes for proximal outcomes are significantly larger than for distal outcomes.

Future studies need larger samples to detect distal outcome effects compared to proximal ones.

The trend holds for common gene-drug pairings in the dataset.

Abstract

Pharmacogenomics is viewed as one route to understanding inter-individual variability in drug response. However, clinical uptake in psychiatry is slower than in other medical fields such as oncology, so assessing evidence for psychiatric genotype-drug pairs and understanding what influences the magnitude of these effects is essential.

We performed a systematic search for studies investigating pharmacogenomic variation in the context of antipsychotic and antidepressant use. Outcomes varied, including those related to drug bioavailability (“proximal”) or side effects, symptom severity, and other treatment outcomes (“distal”). We performed a meta-analysis, moderated by outcome type, to quantify the average pharmacogenomic effect size across proximal and distal outcomes and assess whether they differ significantly from one another. We developed a Pharmacogenomic (PGx) Effect Size Explorer for Psychiatric Drugs dashboard that allows users to explore the dataset and perform simplified meta-analyses, power calculations, and Bayesian shrinkage analyses based on drugs, enzymes, and outcomes of interest (see: https://locksk.shinyapps.io/pgx-effect-sizes/).

We analysed 2,102 standardised mean differences (SMDs) from 184 studies, finding evidence that pharmacogenomic effect sizes for proximal outcomes were significantly larger than distal (Δβ = −0.203 [95% CI −0.288 to −0.118], p = 6 × 10−6). This trend was consistent across sub-groups restricted to the most common gene-drug pairings in the dataset. Power calculations for hypothetical future studies using two-sample t-tests showed that, to attain at least 80% statistical power, analyses of distal outcomes require a larger sample size than proximal outcomes.

We demonstrate that pharmacogenomic effect sizes are significantly larger for proximal outcomes related to pharmacokinetics than for distal outcomes related to efficacy and toxicity. Understanding how the biological mechanisms underlying different outcomes might impact pharmacogenomic effect sizes could help to inform participant recruitment for future psychiatric pharmacogenomic studies, alongside the development of pharmacogenomic guidelines for psychiatric medications.

Figures