Pharmacological modelling and data analysis in decision support of antimalarial drug dosing regimens

抗疟药物给药方案决策支持中的药理学建模和数据分析

• 批准号: G1100522/1
• 负责人: Ian Hastings
• 金额: $ 49.38万
• 依托单位: Liverpool School of Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G1100522%2F1
• 关键词: Pharmacological; modelling; data; analysis; decision

Successful treatment of an individual infected with malaria means that the drug resolves their symptoms and clears all parasites from their blood without causing side effects that harm the patient or make them hesitant to use the drug in the future. Treatment success at population level means that the drug is effective and safe, develops a good reputation that optimises patient compliance, reduces malaria transmission from treated patients, reduces the likelihood of new drug resistant mutations arising, and slows the rate of spread of any existing resistant parasites. A clear, explicit methodology is needed to design and optimise dosing regimens to achieve these aims.Treatment outcome rests on a complex variety of factors, including natural variability in how patients processes the drug (PK), variation in parasite sensitivity to the drug (PD) and variation in patient drug intake depending on their weight, age or height, and patient compliance. Recent developments have resulted in increasingly powerful and accurate simulation models for malaria which has allowed us to simulate drug treatment more accurately. As these models mature it is increasingly recognised that an integrated modelling method that combines different sources of data will help support the development of optimal drug combinations and dosing regimens for new and currently implemented antimalarial drugs.We have been developing and refining simulation models of antimalarial drug treatment as one of the partners of a large malaria modelling research consortium, OpenMalaria, to the state that they can now successfully capture the broad patterns of variation in treatment outcome noted in real-life. We now propose to further refine and calibrate this methodology, and specifically develop them to combine data from several field and clinical sources to address operational questions surrounding deployment of the latest generation of antimalarial drugs, the artemisinin-combination therapies (ACTs). We do this with a team of experts that range in expertise from modelling, pharmacology, clinical and policy knowledge on antimalarials to ensure that the models are developed according to the needs of drug developers and policy makers.

成功治疗疟疾感染者意味着该药物可以解决他们的症状并清除血液中的所有寄生虫，而不会引起伤害患者或使他们在未来犹豫使用该药物的副作用。在人群水平上的治疗成功意味着该药物是有效和安全的，建立了良好的声誉，优化了患者的依从性，减少了疟疾从治疗患者的传播，降低了新的耐药突变的可能性，并减缓了任何现有的耐药寄生虫的传播速度。治疗结果取决于多种复杂的因素，包括患者处理药物的自然变异性（PK）、寄生虫对药物的敏感性（PD）、患者药物摄入量的变化（取决于体重、年龄或身高）以及患者依从性。最近的发展导致疟疾的模拟模型越来越强大和准确，这使我们能够更准确地模拟药物治疗。随着这些模型的成熟，越来越多的人认识到，结合不同数据来源的综合建模方法将有助于支持开发最佳药物组合和新的和目前实施的抗疟药物的给药方案。我们一直在开发和完善抗疟药物治疗的模拟模型，作为大型疟疾建模研究联盟OpenMalaria的合作伙伴之一，他们现在可以成功地捕捉到现实生活中治疗结果的广泛变化模式。我们现在建议进一步完善和校准这一方法，并专门制定这一方法，以便将来自若干实地和临床来源的数据联合收割机结合起来，解决围绕部署最新一代抗疟药物-青蒿素综合疗法-的业务问题。我们与一个专家团队一起开展这项工作，该团队具有抗疟药建模、药理学、临床和政策知识等方面的专业知识，以确保根据药物开发者和政策制定者的需求开发模型。