Amare Azmeraw T, Schubert Klaus Oliver, Baune Bernhard T
Discipline of Psychiatry, School of Medicine, University of Adelaide, North Terrace, Adelaide, SA 5005 Australia.
Northern Adelaide Local Health Network, Mental Health Services, Adelaide, SA Australia.
EPMA J. 2017 Sep 5;8(3):211-227. doi: 10.1007/s13167-017-0112-8. eCollection 2017 Sep.
Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressants , , , , , , , , , , , , , and and (b) mood stabilizers (lithium) -, , , , , , , , , , , , , , and . We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual's genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.
个性化医疗（特定环境下的个性化精神病学）是一种迈向个性化护理的新模式，在这种模式中，来自基因组学和其他组学领域（微生物组、表观基因组、蛋白质组和代谢组）的知识将与临床数据相结合，以指导新药研发以及现有治疗方案的靶向处方。在本综述中，我们总结了可能为个性化精神病学奠定基础的情绪障碍药物基因组学研究。此外，我们还讨论了整合来自组学领域数据的可能策略，作为迈向个性化精神病学的未来路径。到目前为止，在揭示情绪障碍治疗疗效背后的单核苷酸多态性（SNP）方面所取得的进展（例如，与双相情感障碍和重度抑郁症患者中选择性5-羟色胺再摄取抑制剂或锂治疗反应相关的SNP）令人鼓舞，但还不够充分。基因研究已指出一些位于候选基因上的SNP，这些SNP可能影响对以下药物的反应：（a）抗抑郁药 、 、 、 、 、 、 、 、 、 、 、 以及 ，和（b）情绪稳定剂（锂） 、 、 、 、 、 、 、 、 、 、 、 、 以及 。我们提出三种替代且互补的策略来应用从药物基因组学研究中获得的知识。第一种策略可以是基于候选基因或基因产物进行诊断、治疗或预后基因检测。第二种选择是进行整合分析（系统基因组学方法），将从组学研究的不同领域获得的组学数据相结合，包括基因组学、表观基因组学、蛋白质组学、代谢组学和微生物组学。系统基因组学的主要目标是识别和理解药物反应背后的生物途径、网络和模块。第三种策略旨在开发多变量诊断或预后算法（工具），将个体的基因组信息（多基因评分）与其他预测因素（例如，组学领域、神经影像学和临床特征）相结合，以最终预测治疗结果。将分子科学与传统临床实践相结合是药物发现和新型治疗方法的前进方向，也是对精神疾病进行特征描述以在精神病学中实现更好的预测、预防和个性化医疗（PPPM）的前进方向。随着组学技术的未来进步以及数据整合方法的发展，精神病学中PPPM的目标前景广阔。
