Dihydropyrimidine dehydrogenase (DPD)-deficient patients might only become aware of their genotype after exposure to dihydropyrimidines, if testing is performed. Case reports to pharmacovigilance databases might only contain phenotypical manifestations of DPD, without information on the genotype. This poses a difficulty in estimating the cases due to DPD. Auto machine learning models were developed to train patterns of phenotypical manifestations of toxicity, which were then used as a surrogate to estimate the number of cases of DPD-related toxicity. Results indicate that between 8,878 (7.0%) and 16,549 (13.1%) patients have a profile similar to DPD deficient status. Results of the analysis of variable importance match the known end-organ damage of DPD-related toxicity, however, accuracies in the range of 90% suggest presence of overfitting, thus, results need to be interpreted carefully. This study shows the potential for use of machine learning in the regulatory context but additional studies are required to better understand regulatory applicability.

中文翻译：

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机器学习在药物警戒中的应用：从氟嘧啶毒性的表型表现中估计患者的基因型。

如果进行了测试，缺乏二氢嘧啶脱氢酶（DPD）的患者可能仅在暴露于二氢嘧啶后才意识到其基因型。药物警戒数据库的病例报告可能仅包含DPD的表型表现，而没有有关基因型的信息。这给估计由DPD引起的案件带来了困难。开发了自动机器学习模型来训练毒性的表型表现形式，然后将其用作替代品来估计DPD相关毒性的病例数。结果表明，有8,878（7.0％）至16,549（13.1％）位患者的状况与DPD缺陷状态相似。重要性的可变性分析结果与DPD相关毒性的已知终末器官损害相匹配，但是，准确度在90％的范围内表明存在过度拟合，因此，结果需要仔细解释。这项研究显示了在监管环境中使用机器学习的潜力，但是还需要进行其他研究才能更好地理解监管的适用性。