aceBayesian Additive Regression Trees modeling of pharmacogenomic and clinical data predicts amlodipine response in hypertension
The adoption of precision medicine in antihypertensive treatment remains challenging, largely because of substantial interindividual variability in drug response. This study aimed to determine whether genotyping combined with Bayesian machine-learning methods could improve the prediction of response to amlodipine. We conducted a multicenter prospective cohort study of 292 Chinese patients with hypertension treated with amlodipine. Eight single-nucleotide polymorphisms (SNPs) in key pharmacokinetic and pharmacody…
A Bayesian machine-learning model combining six gene variants with clinical data predicted blood pressure control on amlodipine with an external validation AUC of 0.846, well above the comparison models.
The cohort was limited to 292 Chinese patients on amlodipine monotherapy, and two of the eight gene variants had to be excluded because of insufficient variation, narrowing the genetic information used.
Evidence
- Peer-reviewedPharmacogenetics and Genomics2026-10-30
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Truvace Impact Record TRV-2026-2325, v1: “Bayesian Additive Regression Trees modeling of pharmacogenomic and clinical data predicts amlodipine response in hypertension.” Truvace, 2026-10-31. /record/TRV-2026-2325 (accessed at citation time). sha256 43f5c27c613a55d8…
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