MICA: Towards using historical data for research prioritisation in children

研究背景：必须严格评估儿童新药，以确保它们既安全又有效。为了避免在儿童身上进行不必要的临床试验，对一种新药的风险和益处的评估应该纳入现有的相关数据。例如，如果成人数据被认为是相关的，我们可以从证据中推断一种药物对成人有效，从而得出结论，如果处方剂量达到血液中对成人有治疗作用的浓度，它对儿童也会有益。然而，为了使这一结论有效，成人和儿童的疾病进展以及药物浓度与临床反应之间的关系必须相似。错误地假设类似的浓度-反应关系将导致儿童接受过度毒性或亚治疗剂量。监管机构已经提出了算法方法，以确定需要在儿童中进行哪些研究以支持药物开发，但这些方法无法适应外推假设的不确定性。此外，假设可能只适用于某些儿童亚群。目的和目标：拟议的项目旨在发展统计方法，以量化成人和儿童之间假定的相似性的不确定性。具体来说，该项目的第一个工作包将开发一种方法，使用历史临床试验的数据来衡量支持一种新药在成人和儿童中具有相似浓度-反应关系的主张的证据的强度。历史数据将被降低权重，以考虑历史和未来患者之间的差异。将制定决策规则，利用这些信息来决定是否需要额外的儿童临床数据来验证类似浓度-反应关系的假设。工作还将为临床试验制定设计，目的是验证外推假设；设计将是高效的，因为决策将基于所有可用的数据。该项目的第二个工作包将探索基于模型的方法，将儿童按不同的集中-反应关系分类。潜在应用和益处：研究将在三个条件下进行：虽然我们的主要重点是癫痫研究，但出于比较目的，也将考虑将其应用于艾滋病毒和哮喘药物。拟议研究的预期受益者包括监管机构、公共部门临床试验单位、制药业和学术界。这项研究有可能有益于儿童的健康，因为研究方法将帮助调查人员在以下方面做出明智的决定：a)成人和儿童之间相似之处的合理性；b)评估一种药物的风险和益处需要哪些儿童数据。该项目将与葛兰素史克和诺华公司合作。将向公众提供实施已开发方法的软件，以鼓励从业者采用这些方法。

Context of the research: New medicines for children must be rigorously assessed to ensure that they are both safe and effective. So that unnecessary clinical trials in children may be avoided, assessments of the risks and benefits of a new medicine should incorporate existing relevant data. For example, if adult data are considered relevant, we may extrapolate from evidence a medicine is effective in adults to conclude that it will be beneficial in children also if prescribed at doses yielding concentrations in the blood that are therapeutic in adults. However, for this conclusion to be valid, disease progression and the relationship between drug concentration and clinical response must be similar in adults and children. Erroneously assuming similar concentration-response relationships will lead to children receiving excessively toxic or sub-therapeutic doses. Regulators have proposed algorithmic approaches for determining which studies are needed in children to support medicine development but these do not accommodate uncertainty about extrapolation assumptions. Furthermore, assumptions may only hold in certain subgroups of children. Aims and objectives: The proposed project aims to develop statistical methods for quantifying uncertainty about assumed similarities between adults and children. Specifically, the first workpackage of this project will develop an approach using data from historical clinical trials to measure the strength of evidence supporting a claim of similar concentration-response relationships in adults and children for a new medicine. Historical data will be down-weighted to account for differences between historical and future patients. Decision rules will be formulated which use this information to make decisions about whether additional clinical data in children are needed to verify an assumption of similar concentration-response relationships. Work will also formulate designs for clinical trials conducted with the aim of verifying an extrapolation assumption; designs will be efficient because decisions will be based on all available data. The second workpackage of the project will explore model-based approaches for classifying children into groups with different concentration-response relationships. Potential applications and benefits: Research will be embedded in three conditions: while our primary focus will be on epilepsy research, for comparative purposes, applications to HIV and asthma medicine will also be considered. Anticipated beneficiaries of the proposed research span regulatory agencies, public sector clinical trials units, the pharmaceutical industry and academia. The research has the potential to benefit the health of children because methods will help investigators to make informed decisions about: a) the plausibility of similarities between adults and children; and b) which data are needed in children to evaluate the risks and benefits of a medicine. The project will work with GlaxoSmithKline and Novartis. Software implementing developed methodologies will be made publicly available to encourage their uptake by practitioners.