Authors' Affiliations: Department of Clinical, Social, and Administrative Sciences, University of Michigan College of Pharmacy, Ann Arbor, Michigan; Moffitt Cancer Center; and DeBartolo Family Personalized Medicine Institute, Tampa, Florida.
Authors' Affiliations: Department of Clinical, Social, and Administrative Sciences, University of Michigan College of Pharmacy, Ann Arbor, Michigan; Moffitt Cancer Center; and DeBartolo Family Personalized Medicine Institute, Tampa, FloridaAuthors' Affiliations: Department of Clinical, Social, and Administrative Sciences, University of Michigan College of Pharmacy, Ann Arbor, Michigan; Moffitt Cancer Center; and DeBartolo Family Personalized Medicine Institute, Tampa, Florida
Clin Cancer Res. 2014 May 15;20(10):2530-40. doi: 10.1158/1078-0432.CCR-13-2780.
The patient (germline) genome can influence the pharmacokinetics and pharmacodynamics of cancer therapy. The field of pharmacogenetics (PGx) has primarily focused on genetic predictors of pharmacokinetics, largely ignoring pharmacodynamics, using a candidate approach to assess single-nucleotide polymorphisms (SNP) with known relevance to drug pharmacokinetics such as enzymes and transporters. A more comprehensive approach, the genome-wide association study, circumvents candidate selection but suffers because of the necessity for substantial statistical correction. Pharmacogene panels, which interrogate hundreds to thousands of SNPs in genes with known relevance to drug pharmacokinetics or pharmacodynamics, represent an attractive compromise between these approaches. Panels with defined or customizable SNP lists have been used to discover SNPs that predict pharmacokinetics or pharmacodynamics of cancer drugs, most of which await successful replication. PGx discovery, particularly for SNPs that influence drug pharmacodynamics, is limited by weaknesses in both genetic and phenotypic data. Selection of candidate SNPs for inclusion on pharmacogene panels is difficult because of limited understanding of biology and pharmacology. Phenotypes used in analyses have primarily been complex toxicities that are known to be multifactorial. A more measured approach, in which sensitive phenotypes are used in place of complex clinical outcomes, will improve the success rate of pharmacodynamics SNP discovery and ultimately enable identification of pharmacodynamics SNPs with meaningful effects on treatment outcomes. See all articles in this CCR focus section, "Progress in pharmacodynamic endpoints."
患者（种系）基因组会影响癌症治疗的药代动力学和药效动力学。药物遗传学（PGx）领域主要侧重于药物代谢动力学的遗传预测因子，在很大程度上忽略了药效动力学，采用候选方法来评估与药物代谢动力学相关的单核苷酸多态性（SNP），例如酶和转运蛋白。更全面的方法，即全基因组关联研究，避免了候选物的选择，但由于需要进行大量的统计校正而受到限制。药物基因检测面板可检测与药物代谢动力学或药效动力学相关的基因中的数百至数千个 SNP，这是这些方法之间的一种有吸引力的折衷方案。具有定义或可定制 SNP 列表的面板已被用于发现可预测癌症药物药代动力学或药效动力学的 SNP，其中大多数仍在等待成功复制。PGx 的发现，特别是对影响药物药效动力学的 SNP 的发现，受到遗传和表型数据的局限性的限制。由于对生物学和药理学的了解有限，候选 SNP 被纳入药物基因检测面板的选择具有一定难度。用于分析的表型主要是已知具有多因素的复杂毒性。更具针对性的方法是使用敏感表型代替复杂的临床结果，这将提高药效动力学 SNP 发现的成功率，并最终能够识别对治疗结果有意义影响的药效动力学 SNP。查看 CCR 重点部分的所有文章，“药效终点的进展。”
