Personalization of Therapeutic Efficacy and Risk

描述：非甾体抗炎药（NSAIDs）是由世界上数以千万计的消费。虽然它们可以缓解疼痛和炎症，但我们对其作用机制知之甚少。它们还造成严重的胃肠道和心血管不良影响，据信已造成数千人死亡。尽管在随机试验中招募了超过100，000名患者，但我们仍然不知道关节炎和心脏病患者的首选NSAID，或者NSAID的临床疗效是否不同。在这里，我们提出了一个范式转变，战略方法，收获利益和管理风险的个性化治疗非甾体抗炎药。 来自酵母、哺乳动物细胞、斑马鱼、小鼠和人类的研究数据将被整合，以开发反映模型NSAID干扰的信号网络，并产生最终由前瞻性随机人体试验解决的假设。我们的希望是，这些迭代改进的模型，逐步了解人类数据，将导致算法的增量价值，临床医生在预测疗效和不良反应。这一跨学科战略将提供创新的工具和技术，药物反应的定量模型和生物标志物，如果成功，将允许更合理的NSAID处方，以最大限度地降低风险并最大限度地提高个体获益，为药物的开发和批准，随机试验的设计和慢性疾病的治疗创造一个新的范例。

DESCRIPTION: Nonsteroidal anti-inflammatory drugs (NSAIDs) are consumed by tens of millions worldwide. Although they relieve pain and inflammation, we understand poorly their mechanism of action. They also cause serious gastrointestinal and cardiovascular adverse effects and are thought to have caused thousands of deaths. Despite enrolling more than 100,000 patients in randomized trials, we still do not know the NSAID of choice for patients with arthritis and heart disease or if NSAIDs differ in clinical efficacy. Here we propose a paradigm shifting, strategic approach to harvest benefit and manage risk by personalizing therapy with NSAIDs. Data from studies from yeast, mammalian cells, zebrafish, mice and humans will be integrated to develop signaling networks that reflect perturbation by model NSAIDs and that generate hypotheses ultimately addressed by prospective, randomized trials in humans. Our hope is that these iteratively refined models, progressively informed by human data, will lead to algorithms of incremental value to clinicians in the prediction of efficacy and adverse effects. This interdisciplinary strategy will deliver innovative tools and technologies, quantitative models and biomarkers of drug response and if successful will allow more rational prescription of NSAIDs to minimize risk and maximize benefit to individuals, creating a novel paradigm for the development and approval of drugs, the design of randomized trials and the treatment of chronic disease.