Validation and early development of a blood-based rapid diagnostic test for sepsis endotypes

摘要 脓毒症是一种全身性炎症性疾病，作为感染的并发症发生，在严重的情况下， 与急性和危及生命的器官功能障碍有关。仅在美国，败血症就夺去了约27万人的生命， 在每年诊断的约170万患者中，全球范围内，脓毒症相关死亡率估计 占死亡总数的20%。虽然脓毒症治疗方案的改进和及时 抗生素的使用降低了死亡率，但没有一种治疗败血症的药物能够成功地 尽管进行了100多项干预性试验，但它仍然被推向市场，今天被视为60年前的抗生素， 支持性护理。脓毒症是不寻常的，因为大量的免疫反应实际上是适应性的（有益的） vs.致病（适应不良），因此逆转炎症反应可导致感染 未经检查。我们认为，治疗应该扭转适应不良（器官损伤）的组成部分， 免疫反应，同时保持适应性（病原体战斗）组件完好无损。我们认为，要实现 为了取得有影响力的进展，需要利用免疫亚组的新方法。在这个项目中，我们建议 将脓毒症患者细分为可治疗亚类或“内型”的精确医学方法 使用配套诊断测试HostDx Endotypes。我们预计，将脓毒症患者分类为 内型可能使我们能够确定改进的治疗方案，从而发现新的靶点， 内型特异性治疗和/或现有药物的再利用途径。 在1期工作中，我们已经通过鉴定3种脓毒症内在型（炎症性， 自适应和凝血病），并在多个外部数据集上进行验证。我们已经证明，我们的HostDx Endotypes可以使用几种方法将患者分为3篇出版物中的新型、潜在可治疗亚组 回顾性队列和两个前瞻性队列。我们还证明了这种方法的临床实用性。 InSepTM是我们基于HostDx床旁检测的强大的急性传染病分类器， 系统平台 为了将我们的概念验证工作与HostDx-Endotypes的产品开发联系起来，我们将（目标1）采用 严格的机器学习算法和过程，以最终确定和锁定最佳分类器;（目标2）应用 药物再利用创新方法，以识别和排序用于临床的内型特异性候选药物 脓毒症治疗的研究，以及（目的3）将最终的mRNA组翻译到Inflammatix的qLAMP盒中， 平台在第二阶段完成时，我们将生产出HostDx Endotypes研究用检测盒， 与预期临床试验的再利用药物清单，并将准备进入正式的产品 开发阶段;我们将启动FDA预提交。最终，该产品的目标将是 明确将患者败血症内型与治疗干预联系起来，以实现更好的治疗选择 和资源分配。

ABSTRACT Sepsis, a systemic inflammatory condition, occurs as a complication of infection and in severe cases may be associated with acute and life-threatening organ dysfunction. In the US alone, sepsis claims ~270,000 lives each year among ~1.7 million patients diagnosed annually, and globally, sepsis-related mortality is estimated to account for 20% of total deaths. Although improvements in the treatment protocol for sepsis and timely administration of antibiotics have reduced mortality rates, not a single drug for sepsis has ever been successfully brought to market, despite over 100 interventional trials, and it is treated today as 60 years ago: antibiotics and supportive care. Sepsis is unusual in that a great deal of the immune response is actually adaptive (beneficial) vs. disease-causing (maladaptive), so reversing the inflammatory response can cause the infection to go unchecked. We believe that a treatment should reverse the maladaptive (organ-damaging) components of the immune response, while keeping the adaptive (pathogen-fighting) components intact. We believe that to achieve impactful progress, new approaches are required that harness immune sub-groups. In this project, we propose a precision medicine approach to subdivide sepsis patients into treatable subclasses or “endotypes” using a companion diagnostic test, HostDx-Endotypes. We anticipate that classifying sepsis patients into such endotypes may allow us to identify improved treatment regimens, leading to the discovery of new targets or pathways for endotype-specific therapies and/or repurposing of available drugs. In Phase 1 work we already demonstrated proof-of-concept by identifying 3 sepsis endotypes (Inflammopathic, Adaptive, and Coagulopathic) and validated across multiple external data sets. We have shown that our HostDx Endotypes can stratify patients into novel, potentially treatable subgroups across 3 publications using several retrospective cohorts, and two prospective cohorts. We have also demonstrated the clinical utility of this paradigm with InSepTM, our robust classifier for acute infectious disease, based on our HostDx point of care test system platform. To bridge our proof-of-concept work to product development for HostDx-Endotypes, we will (Aim 1) employ rigorous machine learning algorithms and processes to finalize and lock an optimal classifier; (Aim 2) apply an innovative approach of drug repurposing to identify and rank-order endotype-specific drug candidates for clinical studies on sepsis treatment, and (Aim 3) translate the final mRNA panel to Inflammatix’s qLAMP cartridge and platform. At Phase II completion we will have produced the HostDx Endotypes research-ready cartridge, together with a list of repurposed drugs for intended clinical trials, and will be ready to enter the formal product development phase; we will have initiated the FDA pre-submission. Ultimately, the goal of this product will be to definitively link a patient sepsis endotype with a therapeutic intervention, to enable better therapy selection and resource allocation.