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Precision pharmacotherapy: psychiatry’s future direction in preventing diagnosing and treating mental disorders

机译:精准药物疗法:精神病学在预防诊断和治疗精神疾病方面的未来方向

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摘要

Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems. However, in the last decades, the understanding of biological mechanisms underlying mental disorders has grown and can be used for the development of precision medicine, that is, to deliver a patient-tailored individual treatment. Precision medicine may incorporate genetic variants contributing to the mental disorder and the response to pharmacotherapies, but also consider gene >¥ environment interactions, blood-based markers, neuropsychological tests, data from electronic health records, early life adversity, stressful life events, and very proximal factors such as lifestyle, nutrition, and sport. Methods such as artificial intelligence and the underlying machine learning and deep learning approaches provide the framework to stratify patients, initiate specific tailored treatments and thus increase response rates, reduce adverse effects and medical errors. In conclusion, precision medicine uses measurable health parameters to identify individuals at risk of a mental disorder, to improve the diagnostic process and to deliver a patient-tailored treatment.
机译:精神障碍占全世界残疾的三分之一,并造成巨大的个人和社会负担。当前的药物治疗和非药物治疗确实对许多患者有帮助,但是仍然有很高的部分或无反应率,延迟的作用和不利的不良反应。 《精神疾病诊断和统计手册》和《国际疾病分类》目前对精神疾病的诊断分类法依靠呈现体征和症状,但没有反映出神经生物学和行为系统的证据。然而,在过去的几十年中,对精神障碍的生物学机制的了解不断增长,可用于开发精密医学,即提供针对患者的个性化治疗。精密医学可能会整合导致精神障碍和对药物治疗反应的遗传变异,但也要考虑基因>¥环境相互作用,血液标记,神经心理学测试,电子健康记录数据,早期生活逆境,压力大的生活事件,以及非常接近的因素,例如生活方式,营养和运动。诸如人工智能以及潜在的机器学习和深度学习方法之类的方法提供了对患者进行分层,启动特定的定制治疗方法的框架,从而提高了响应率,减少了不良反应和医疗错误。总之,精密医学使用可测量的健康参数来识别有精神障碍风险的个体,改善诊断过程并提供针对患者的治疗方案。

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