Psych-STRATA - A Stratified Treatment Algorithm in Psychiatry: A program on stratified pharmacogenomics in severe mental illness

精神卫生的一个关键问题是，多达三分之一的严重精神障碍患者对药物治疗产生了抗药性。然而，显示出治疗抵抗（TR）的早期迹象的患者没有接受足够的早期强化药物治疗，而是接受逐步试错治疗方法。这种情况源于三个主要的知识和翻译差距：我们缺乏有效的方法来识别在疾病过程早期处于TR风险中的个体，ii.）我们缺乏基于对TR生物学基础的见解的有效的个性化治疗策略，以及iii.）我们缺乏有效的流程来将有关TR的科学见解转化为临床实践、初级保健和治疗指南。PSYCH-STRATA的中心目标是弥合这些差距，并为转向为TR风险个体量身定制的治疗决策过程铺平道路。为此，我们的目标是建立循证标准，以决定在精神分裂症、双相情感障碍和重度抑郁症等主要精神疾病中，对有TR风险的个体进行早期强化治疗。PSYCH-STRATA will i.）剖析TR的生物学基础，并建立标准，以便根据对前所未有的遗传、生物、数字心理健康和临床数据的综合分析，早期发现有TR风险的个体。（二）此外，我们将根据SCZ、BD和MDD的泛欧临床试验，在治疗过程的早期确定TR风险个体的有效治疗策略。这些努力将使新型多模态机器学习模型的建立能够预测TR风险和治疗反应。最后，三）。我们将使这些发现转化为临床实践的原型整合个性化治疗决策支持和面向患者的决策心理健康委员会。

A key problem in Mental Health is that up to one third of patients suffering from major mental disorders develop resistance against drug therapy. However, patients showing early signs of treatment resistance (TR) do not receive adequate early intensive pharmacological treatment but instead they undergo a stepwise trial-and-error treatment approach. This situation originates from three major knowledge and translation gaps: i.) we lack effective methods to identify individuals at risk for TR early in the disease process, ii.) we lack effective, personalized treatment strategies grounded in insights into the biological basis of TR, and iii.) we lack efficient processes to translate scientific insights about TR into clinical practice, primary care and treatment guidelines. It is the central goal of PSYCH-STRATA to bridge these gaps and pave the way for a shift towards a treatment decision-making process tailored for the individual at risk for TR. Tothat end, we aim to establish evidence-based criteria to make decisions of early intense treatment in individuals at risk for TR across the major psychiatric disorders ofschizophrenia, bipolar disorder and major depression. PSYCH-STRATA will i.) dissect the biological basis of TR and establish criteria to enable early detection of individuals at risk for TR based on the integrated analysis of an unprecedented collection of genetic, biological, digital mental health, and clinical data. ii.) Moreover, we will determine effective treatment strategies of individuals at risk for TR early in the treatment process, based on pan-European clinical trials in SCZ, BD and MDD. These efforts will enable the establishment of novel multimodal machine learning models to predict TR risk and treatment response. Lastly, iii.) we will enable the translation of these findings into clinical practice by prototyping the integration of personalized treatment decision supportand patient-oriented decision-making mental health boards.