Limaye Nimita
Biometrics and Medical Writing, Tata Consultancy Services, India.
Appl Transl Genom. 2013 May 14;2:17-21. doi: 10.1016/j.atg.2013.05.002. eCollection 2013 Dec 1.
While the potential for the application of pharmacogenomics and theranostics to develop personalized healthcare solutions is enormous, multiple challenges will need to be addressed to get there. Understanding the complex interactions and detailed characterization of the functional variants of individual ADME (Absorption Distribution Metabolism Excretion) genes and drug target genes is needed to demonstrate clinical utility, using both a bottoms-up as well as a top-down approach. Clinical trials need to be designed appropriately so as to identify not only individual but also population variations. The impact of non-genetic and environmental factors, epigenetic variations and circadian rhythms on an individual's response need to be assessed to make pharmacogenomics clinically indicated. More advanced algorithms and appropriate study designs need to be developed to allow this pipeline to grow and to be used effectively in the clinical setting. Another challenge lies in the value proposition to the pharmaceutical industry. Fearing the impact of the slice and dice approach on revenues, companies are going slow on developing pharmacogenomic solutions; yet many are hedging their bets, amassing huge amounts of single nucleotide polymorphisms (SNP) data. They are being used as predictors of drug efficacy and safety to zero in on subpopulations that are at risk for either a bad response or no response in clinical trials, supporting the , approach. In addition, the growth of theranostics is impeded by the fear that the approval of both the diagnostic and the drug would get delayed. Education of the health care provider, payor, regulator and the patient is also required and an exercise of change management needs to occur. Countries such as India should exploit the joint benefit of the reduced cost of tests today, complemented by a large and a highly genetically diverse population.
虽然药物基因组学和治疗诊断学在开发个性化医疗解决方案方面的应用潜力巨大,但要实现这一目标还需要应对多重挑战。要证明临床效用,需要采用自下而上和自上而下的方法,了解个体药物代谢动力学(吸收、分布、代谢、排泄)基因和药物靶基因功能变体的复杂相互作用并进行详细表征。临床试验需要进行适当设计,以便不仅识别个体差异,还能识别群体差异。需要评估非遗传和环境因素、表观遗传变异以及昼夜节律对个体反应的影响,以使药物基因组学具有临床指征。需要开发更先进的算法和适当的研究设计,以使这一流程得以发展并在临床环境中有效应用。另一个挑战在于对制药行业的价值主张。由于担心“细分”方法对收入的影响,各公司在开发药物基因组学解决方案方面进展缓慢;然而,许多公司仍在观望,积累了大量单核苷酸多态性(SNP)数据。这些数据被用作药物疗效和安全性的预测指标,以锁定在临床试验中可能出现不良反应或无反应风险的亚群体,支持“细分”方法。此外,治疗诊断学的发展受到担心诊断试剂和药物的批准都会延迟的阻碍。还需要对医疗服务提供者、付款方、监管机构和患者进行教育,并开展变革管理工作。像印度这样的国家应该利用如今检测成本降低的优势,再加上庞大且基因高度多样化的人口这一有利条件。