Helland Thomas, Alsomairy Sarah, Lin Chenchia, Søiland Håvard, Mellgren Gunnar, Hertz Daniel Louis
Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA.
Hormone Laboratory, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, 5021 Bergen, Norway.
J Pers Med. 2021 Mar 13;11(3):201. doi: 10.3390/jpm11030201.
Tamoxifen is an endocrine treatment for hormone receptor positive breast cancer. The effectiveness of tamoxifen may be compromised in patients with metabolic resistance, who have insufficient metabolic generation of the active metabolites endoxifen and 4-hydroxy-tamoxifen. This has been challenging to validate due to the lack of measured metabolite concentrations in tamoxifen clinical trials. activity is the primary determinant of endoxifen concentration. Inconclusive results from studies investigating whether genotype is associated with tamoxifen efficacy may be due to the imprecision in using genotype as a surrogate of endoxifen concentration without incorporating the influence of other genetic and clinical variables. This review summarizes the evidence that active metabolite concentrations determine tamoxifen efficacy. We then introduce a novel approach to validate this relationship by generating a precision endoxifen prediction algorithm and comprehensively review the factors that must be incorporated into the algorithm, including genetics of and other pharmacogenes. A precision endoxifen algorithm could be used to validate metabolic resistance in existing tamoxifen clinical trial cohorts and could then be used to select personalized tamoxifen doses to ensure all patients achieve adequate endoxifen concentrations and maximum benefit from tamoxifen treatment.
他莫昔芬是一种用于激素受体阳性乳腺癌的内分泌治疗药物。对于存在代谢抵抗的患者,他莫昔芬的疗效可能会受到影响,这些患者体内活性代谢物内昔芬和4-羟基他莫昔芬的代谢生成不足。由于他莫昔芬临床试验中缺乏代谢物浓度的测量数据,这一点难以得到验证。活性是内昔芬浓度的主要决定因素。研究基因是否与他莫昔芬疗效相关的研究结果尚无定论,可能是因为在未纳入其他基因和临床变量影响的情况下,将基因作为内昔芬浓度的替代指标不够精确。本综述总结了活性代谢物浓度决定他莫昔芬疗效的证据。然后,我们介绍一种新方法,通过生成精确的内昔芬预测算法来验证这种关系,并全面回顾必须纳入该算法的因素,包括基因和其他药物基因的遗传学。精确的内昔芬算法可用于验证现有他莫昔芬临床试验队列中的代谢抵抗情况,然后可用于选择个性化的他莫昔芬剂量,以确保所有患者达到足够的内昔芬浓度,并从他莫昔芬治疗中获得最大益处。