Guo Yingying, Shafer Steven, Weller Paul, Usuka Jonathan, Peltz Gary
Roche Palo Alto, Department of Genetics and Genomics, S3-1, 3431 Hillview Ave, Palo Alto, CA 94304, USA.
Pharmacogenomics. 2005 Dec;6(8):857-64. doi: 10.2217/14622416.6.8.857.
It is generally anticipated that pharmacogenomic information will have a large impact on drug development and will facilitate individualized drug treatment. However, there has been relatively little quantitative modeling to assess how pharmacogenomic information could be best utilized in clinical practice. Using a quantitative model, this review demonstrates that efficacy is increased and toxicity is reduced when a genetically-guided dose adjustment strategy is utilized in a clinical trial. However, there is limited information available regarding the genetic variables affecting the disposition or mechanism of action of most commonly used medications. These genetic factors must be identified to enable pharmacogenomic testing to be routinely used in the clinic. A recently described murine haplotype-based computational genetic analysis method provides one strategy for identifying genetic factors regulating the pharmacokinetics and pharmacodynamics of commonly used medications.
一般预计，药物基因组学信息将对药物开发产生重大影响，并有助于实现个体化药物治疗。然而，相对较少有定量模型来评估如何在临床实践中最佳利用药物基因组学信息。本综述使用一个定量模型证明，在临床试验中采用基因指导的剂量调整策略时，疗效会提高，毒性会降低。然而，关于影响最常用药物处置或作用机制的基因变量的可用信息有限。必须识别这些遗传因素，以便在临床中常规使用药物基因组学检测。最近描述的一种基于小鼠单倍型的计算遗传分析方法提供了一种识别调节常用药物药代动力学和药效学的遗传因素的策略。
