Using genetic variants as a treatment decision aid for the optimization of antipsychotic treatments: a critical appraisal of the literature.

抗精神病药物被广泛用于治疗精神分裂症。然而，它们只对大约60%的患者有效，而且许多服用这些药物的人都有副作用。这些症状从轻微的头痛和头晕，到严重的危及生命。例如，抗精神病药物氯氮平有患心肌炎的风险，这是一种可能致命的心脏病。目前，很难判断哪种药物会给哪些病人带来哪些副作用。最近的研究集中在试图找到我们可以测量个体内部的东西，以预测他们对药物的反应。这些东西被称为“生物标志物”，可以是基因突变、血液中的蛋白质或其他测量结果。为了证明生物标记物、药物及其副作用之间存在关联，需要大量高质量的证据。问题是有很多不同的抗精神病药物，有数百种已知的副作用。其中一些关联在小型研究中得到了证实，但要真正理解这些发现并确保它们是可靠的，有必要将这些数据结合起来进行更大规模的分析。这些分析涉及到称为“系统评价”和“元分析”的方法，我们利物浦大学的团队在其他医学领域对这些方法非常有经验。与此同时，加拿大的研究小组在与抗精神病药物反应相关的特定基因突变方面做了大量工作。虽然需要这些更大的分析，但它们既耗时又费力。它们需要多名研究人员来定位、评估和分析数百篇（有时是数千篇）科学论文。美国的一个研究小组开创了一种新方法，利用机器学习将这一过程自动化。这是非常新的方法，虽然它可能有用，但我们需要检查并确保它与传统方法一样有效。这将是这次实习的主要目标。我们将比较机器学习方法与传统文献检索方法的结果，以识别抗精神病药物反应的生物标志物。这将有助于我们评估机器学习方法，并提供该领域的全面概述。然后，我们将检查所产生的证据，并使用为此目的制定的正式标准评估其可靠性。通过这样做，我们希望我们能够识别出能够预测药物反应的生物标志物，并有强有力和可靠的证据支持它们。然后，我们将能够推荐这些生物标志物用于临床实践。该项目利用了利物浦大学和加拿大集团的优势。它将促进各国之间的知识转移，并创造当前和未来的合作机会。

Antipsychotic drugs are widely used to treat schizophrenia. However, they only work in about 60% of patients, and many people who take these drugs suffer from side-effects. These range from mild ones, like headaches and dizziness, to serious and life-threatening. For example, the antipsychotic clozapine carries a risk of myocarditis, a heart condition that can be fatal. Presently, it is very hard to tell which patients will suffer from which side-effects for a particular drug. Recent research has focussed on trying to find things we can measure within individuals to predict how they will react to a drug. These things are known as 'biomarkers' which can be genetic mutations, proteins in the blood, or other measurements. In order to prove there is an association between a biomarker, a drug, and its side-effects, there needs to be a lot of high-quality evidence. The problem is that there are many different antipsychotic drugs, with hundreds of known side-effects. Some of these associations have been shown in small studies, but to really understand these findings and ensure they are reliable, it is necessary to combine these data in larger analyses. These analyses involve methods called 'systematic reviews' and 'meta-analyses' and our group at the University of Liverpool are very experienced with these methods, in other areas of medicine. Meanwhile, the group in Canada has done lots of work on specific genetic mutations linked to antipsychotic drug responses.While these larger analyses are needed, they are time-consuming and labour-intensive. They need multiple researchers to locate, assess, and analyse hundreds (sometimes thousands) of scientific papers. A new approach has been pioneered by a group in the USA using machine learning to automate this process. This is very new, and while it is potentially useful, we need to check and make sure it works as well as the traditional approaches. That will be the main goal of this proposed internship. We will compare the results of the machine learning approach to the traditional literature searching approach for identifying biomarkers of antipsychotic drug response. This will help us evaluate the machine learning method, as well as provide a comprehensive overview of the field. We will then examine the evidence produced and evaluate how reliable it is by using formal criteria drawn up for this purpose. By doing this, we hope that we can identify biomarkers capable of predicting drug response, with strong and reliable evidence behind them. We will then be able to make recommendations for these biomarkers to be used in clinical practice. This project utilises the strengths of both the University of Liverpool and the Canadian group. It will facilitate knowledge transfer between the countries and create ongoing and future collaboration opportunities.