Shah Rashmi R, Gaedigk Andrea, LLerena Adrián, Eichelbaum Michel, Stingl Julia, Smith Robert L
8 Birchdale, Gerrards Cross, Buckinghamshire, UK.
Clinical Pharmacology, Toxicology &, Therapeutic Innovation, Children's Mercy-Kansas City, 2401 Gillham Rd, Kansas City, MO 64108, USA.
Pharmacogenomics. 2016 Feb;17(3):259-75. doi: 10.2217/pgs.15.172. Epub 2016 Jan 18.
Despite strong pharmacological support, association studies using genotype-predicted phenotype as a variable have yielded conflicting or inconclusive evidence to promote personalized pharmacotherapy. Unless the patient is a genotypic poor metabolizer, imputation of patient's metabolic capacity (or metabolic phenotype), a major factor in drug exposure-related clinical response, is a complex and highly challenging task because of limited number of alleles interrogated, population-specific differences in allele frequencies, allele-specific substrate-selectivity and importantly, phenoconversion mediated by co-medications and inflammatory co-morbidities that modulate the functional activity of drug metabolizing enzymes. Furthermore, metabolic phenotype and clinical outcomes are not binary functions; there is large intragenotypic and intraindividual variability. Therefore, the ability of association studies to identify relationships between genotype and clinical outcomes can be greatly enhanced by determining phenotype measures of study participants and/or by therapeutic drug monitoring to correlate drug concentrations with genotype and actual metabolic phenotype. To facilitate improved analysis and reporting of association studies, we propose acronyms with the prefixes 'g' (genotype-predicted phenotype) and 'm' (measured metabolic phenotype) to better describe this important variable of the study subjects. Inclusion of actually measured metabolic phenotype, and when appropriate therapeutic drug monitoring, promises to reveal relationships that may not be detected by using genotype alone as the variable.
尽管有强大的药理学支持，但使用基因型预测表型作为变量的关联研究，在促进个性化药物治疗方面得出了相互矛盾或不确定的证据。除非患者是基因型代谢不良者，否则对患者代谢能力（或代谢表型）进行推断是一项复杂且极具挑战性的任务，因为所检测的等位基因数量有限、等位基因频率存在人群特异性差异、等位基因特异性底物选择性，更重要的是，由联合用药和炎症合并症介导的表型转换会调节药物代谢酶的功能活性，而患者的代谢能力（或代谢表型）是药物暴露相关临床反应的一个主要因素。此外，代谢表型和临床结果并非二元函数；存在较大的基因型内和个体内变异性。因此，通过确定研究参与者的表型测量值和/或通过治疗药物监测将药物浓度与基因型和实际代谢表型相关联，可以大大提高关联研究识别基因型与临床结果之间关系的能力。为便于改进关联研究的分析和报告，我们提出使用前缀“g”（基因型预测表型）和“m”（测量的代谢表型）的首字母缩写，以更好地描述研究对象的这一重要变量。纳入实际测量的代谢表型，并在适当情况下进行治疗药物监测，有望揭示仅使用基因型作为变量可能无法检测到的关系。
