Understanding the effect of anti-psychotic medication on cellular metabolism.

了解抗精神病药物对细胞代谢的影响。

• 批准号: 2746429
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2746429
• 关键词: Understanding; effect; anti; psychotic; medication

Antipsychotic medications are the mainstay of treatment of psychosis. One of the major side-effects of these medications is metabolic impairment, which increases the risk of cardiometabolic diseases. Patients with psychosis are twice as likely, compared to general populations, to die of cardiometabolic diseases. Antipsychotics differ in their propensity to cause metabolic derangements: clozapine and olanzapine (high risk), risperidone and quetiapine (moderate risk), and haloperidol and lurasidone (low risk). Patients also differ in their susceptibility to develop metabolic side-effects with different antipsychotics. It is only after patients are on antipsychotic treatment, often for weeks and months, before these side-effects become problematic and strategies, like lifestyle changes and medications like metformin, to mitigate these are instituted. Besides increasing the risk of cardiometabolic diseases, these side-effects also affect the therapeutic relationship adversely, contribute to poor treatment concordance and increase the risk of relapse. There is a critical need for a a better understanding of the cellular response to these medications which will lead to strategies to inform treatment options, which minimise cardiometabolic risks and reduce associated morbidity and mortality.Mitochondrial dysfunction is a crucial process that can explain antipsychotic induced metabolic derangements. Mitochondrial metabolism can be accurately profiled using high-resolution respirometry (HRR) techniques. The student will use HRR to accurately define the metabolic signatures that different antipsychotics elicit in cell lines, primary cells and human treated and untreated (control) blood cells, to stratify the risk of developing metabolic syndrome and identify which among the treatment options, like metformin, mitigate or reverse those changes. Using detailed metabolic profiling, they will be able to specify precisely which molecules and pathways are indicative of onset and amelioration of metabolic syndromes. There is potential for the student to work towards development of a set of biomarkers that characterise the effects of each of the medications currently used in the clinic. Respirometry information will be enriched with proteomics analysis of treated and untreated cells to define the molecular pathways associated with metabolic perturbations. Epigenetic analysis will define regulatory mechanisms that may be important in controlling the side effects.

抗精神病药物是精神病治疗的主要手段。这些药物的主要副作用之一是代谢障碍，这会增加心脏代谢疾病的风险。与普通人群相比，精神病患者死于心脏代谢疾病的可能性是普通人群的两倍。抗精神病药物引起代谢紊乱的倾向不同：氯氮平和奥氮平（高风险），利培酮和奎替利（中等风险），氟哌啶醇和鲁拉西酮（低风险）。患者对不同抗精神病药物产生代谢副作用的易感性也不同。只有在患者接受抗精神病药物治疗（通常持续数周或数月）后，这些副作用才会成为问题，并制定了减轻这些副作用的策略，如生活方式的改变和二甲双胍等药物。除了增加心脏代谢疾病的风险外，这些副作用还对治疗关系产生不利影响，导致治疗一致性差并增加复发风险。我们迫切需要更好地了解细胞对这些药物的反应，这将导致策略，以告知治疗方案，最大限度地减少心脏代谢风险，降低相关的发病率和死亡率。线粒体功能障碍是一个关键的过程，可以解释抗精神病药诱导的代谢紊乱。线粒体代谢可以使用高分辨率呼吸测定（HRR）技术准确地分析。学生将使用HRR来准确定义不同抗精神病药物在细胞系，原代细胞和人类治疗和未治疗（对照）血细胞中引起的代谢特征，对发展代谢综合征的风险进行分层，并确定哪些治疗方案，如二甲双胍，减轻或逆转这些变化。使用详细的代谢分析，他们将能够精确地指定哪些分子和途径指示代谢综合征的发作和改善。学生有可能致力于开发一套生物标志物，以证明目前临床上使用的每种药物的效果。呼吸测定信息将通过处理和未处理细胞的蛋白质组学分析来丰富，以确定与代谢紊乱相关的分子途径。表观遗传学分析将定义可能在控制副作用方面很重要的调节机制。