利用基因谱预测癌症疼痛患者的阿片类药物剂量：支持向量机学习目前还无法实现。
Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning.
作者信息
Olesen Anne Estrup, Grønlund Debbie, Gram Mikkel, Skorpen Frank, Drewes Asbjørn Mohr, Klepstad Pål
机构信息
Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark.
Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
出版信息
BMC Res Notes. 2018 Jan 27;11(1):78. doi: 10.1186/s13104-018-3194-z.
OBJECTIVE
Use of opioids for pain management has increased over the past decade; however, inadequate analgesic response is common. Genetic variability may be related to opioid efficacy, but due to the many possible combinations and variables, statistical computations may be difficult. This study investigated whether data processing with support vector machine learning could predict required opioid dose in cancer pain patients, using genetic profiling. Eighteen single nucleotide polymorphisms (SNPs) within the µ and δ opioid receptor genes and the catechol-O-methyltransferase gene were selected for analysis.
RESULTS
Data from 1237 cancer pain patients were included in the analysis. Support vector machine learning did not find any associations between the assessed SNPs and opioid dose in cancer pain patients, and hence, did not provide additional information regarding prediction of required opioid dose using genetic profiling.
目的
在过去十年中，阿片类药物用于疼痛管理的情况有所增加；然而，镇痛反应不足很常见。基因变异性可能与阿片类药物疗效有关，但由于存在许多可能的组合和变量，统计计算可能很困难。本研究调查了使用支持向量机学习进行数据处理是否能够通过基因谱分析预测癌症疼痛患者所需的阿片类药物剂量。选择了μ和δ阿片受体基因以及儿茶酚-O-甲基转移酶基因中的18个单核苷酸多态性（SNP）进行分析。
结果
1237例癌症疼痛患者的数据纳入分析。支持向量机学习未发现评估的SNP与癌症疼痛患者的阿片类药物剂量之间存在任何关联，因此，未提供有关使用基因谱分析预测所需阿片类药物剂量的额外信息。
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