Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
Open Biol. 2018 May;8(5). doi: 10.1098/rsob.180031.
Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.
个体化医学在许多医学领域变得越来越重要，有望实现更有效的药物治疗和更早的干预。个体化医学的发展伴随着生物标志物和分类算法的识别，这些生物标志物和分类算法有助于预测不同患者对不同药物的反应。在过去的 10 年中，美国食品和药物管理局（FDA）已经批准了几种在肿瘤学、肺病学、胃肠病学、血液学、神经病学、风湿病学，甚至精神病学等领域以药物基因组学预先筛选的基因药物。临床医生早就警告说，看似相似的患者报告的症状实际上可能来自不同的生物学原因。随着越来越多的人被诊断出患有不同的精神疾病，科学家和临床医生开发针对个体情况的精准药物至关重要。全基因组关联研究突出了精神疾病（如精神分裂症、双相情感障碍、重度抑郁症和自闭症谱系障碍）的复杂性质。在这些研究之后，需要进行关联研究，以寻找针对特定疾病人群中个体患者可用药物反应的基因组标记物。除了 GWAS 之外，脑成像、细胞重编程、测序和基因编辑等新技术的出现，为我们提供了寻找更多药物反应生物标志物的机会，这些生物标志物可用于药物治疗，并可用于确定治疗方案。在这篇综述中，我们讨论了为寻找针对四种脑部疾病（双相情感障碍、精神分裂症、重度抑郁症和自闭症谱系障碍）的不同可用药物的反应生物标志物而进行的研究。我们为使用集成方法提供了建议，该方法将使用现有技术来更好地预测最合适的药物。
