Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.
Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Clin Pharmacol Ther. 2018 Oct;104(4):709-718. doi: 10.1002/cpt.1020. Epub 2018 Feb 2.
Deleterious variants in dihydropyrimidine dehydrogenase (DPD, DPYD gene) can be highly predictive of clinical toxicity to the widely prescribed chemotherapeutic 5-fluorouracil (5-FU). However, there are very limited data pertaining to the functional consequences of the >450 reported no-synonymous DPYD variants. We developed a DPYD-specific variant classifier (DPYD-Varifier) using machine learning and in vitro functional data for 156 missense DPYD variants. The developed model showed 85% accuracy and outperformed other in silico prediction tools. An examination of feature importance within the model provided additional insight into functional aspects of the DPD protein relevant to 5-FU toxicity. In the absence of clinical data for unstudied variants, prediction tools like DPYD-Varifier have great potential to individualize medicine and improve the clinical decision-making process.
二氢嘧啶脱氢酶（DPD，DPYD 基因）中的有害变异可高度预测广泛应用的化疗药物 5-氟尿嘧啶（5-FU）的临床毒性。然而，关于 >450 种报道的无义 DPYD 变异的功能后果的信息非常有限。我们使用机器学习和 156 种错义 DPYD 变异的体外功能数据开发了一种 DPYD 特异性变异分类器（DPYD-Varifier）。该模型的准确率为 85%，优于其他计算预测工具。对模型中的特征重要性的检查提供了对与 5-FU 毒性相关的 DPD 蛋白的功能方面的更多见解。在缺乏对未研究变异的临床数据的情况下，像 DPYD-Varifier 这样的预测工具具有很大的潜力来实现个体化医疗并改善临床决策过程。
