Pharmacogenetic Analysis in Mice

描述(由申请人提供)： 这项研究项目的目标是证明遗传信息可以改善药物治疗方案。将在11个近交系小鼠身上测量至少4种不同治疗剂的药代动力学参数。菌株特定的药代动力学数据将使用基于单倍型的计算方法进行评估，以确定导致药代动力学反应变化的遗传位点。计算预测最初将通过近交系小鼠品系的等位基因和基因表达模式的相关性以及这些药物新陈代谢的已知信息进行评估。将使用体外和体内方法评估计算预测的基因内的遗传变化对这些药物新陈代谢的影响。这一实验系统将使影响药物代谢的因素能够被有效和快速地识别。将开发基于关键基因座基因分型信息的小鼠给药方案，并进行前瞻性测试，以评估遗传指导疗法的实用性。这个模型小鼠遗传系统将评估在治疗人群中遗传引导剂量调整的影响。如果这种方法成功应用于人类治疗，将对人类健康产生重大影响。 为了实现这一点，将完成以下工作：1)将鉴定影响药物代谢的150个小鼠基因的多态。2)测定香豆素、博莱霉素、异烟肼和利托那韦在11个近交系小鼠体内的药动学特征(母体和代谢物)。影响这种反应的遗传因素将通过计算确定，并将在体外和体内分析预测基因对这些药物新陈代谢的影响。3)将选择一种药物用于评估遗传引导剂量的效用，并将在实验小鼠模型中进行使用遗传引导剂量的临床试验。

DESCRIPTION (provided by applicant): The goal of this research project is to demonstrate that genetic information can improve drug treatment regimens. The pharmacokinetic parameters for at least 4 different therapeutic agents will be measured across 11 inbred mouse strains. The strain-specific pharmacokinetic data will be evaluated using a haplotype-based computational method to identify the genetic loci responsible for the variation in pharmacokinetic responses. The computational predictions will be initially evaluated by correlation of allelic and gene expression patterns across inbred mouse strains, and with known information on the metabolism of these drugs. The effect that genetic alterations within the computationally predicted genes have on the metabolism of these drugs will be evaluated using in vitro and in vivo methodology. This experimental system will enable the factors effecting the metabolism of a drug to be efficiently and rapidly identified. Dosing regimens in mice based on information obtained from genotyping key loci will be developed and prospectively tested to evaluate the utility of genetically guided therapeutics. This model murine genetic system will assess the impact of genetically guided dose adjustment within a treatment population. If this approach is successfully applied to human therapy, it would have a significant impact on human health. To enable this, the following will be completed: 1) Polymorphisms in 150 murine genes effecting drug metabolism will be identified. 2) The pharmacokinetic profile (parent and metabolites) of coumadin, bleomycin, isoniazid and ritonavir will be measured in 11 inbred mouse strains. The genetic factors effecting this response will be computationally identified, and the effect that the predicted genes have on the metabolism of these drugs will be analyzed in vitro and in vivo. 3) One drug will be selected for evaluation of the utility of genetically guided dosing, and a clinical trial using genetically guided dosing will be performed in an experimental murine model.