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

脓毒症内型基于血液的快速诊断测试的验证和早期开发

• 批准号: 10462722
• 负责人: Timothy E Sweeney
• 金额: $ 101.8万
• 依托单位: INFLAMMATIX, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10462722
• 关键词: Acute; Affect; Antibiotics; Biological; Biological Assay; Blood; Blood Tests; Cell Line; Cessation of life; Characteristics; Classification; Clinic; Clinical; Clinical Research; Clinical Trials; Cohort Analysis; Communicable Diseases; Complication; Custom; Data; Data Set; Development; Devices; Diagnosis; Diagnostic tests; Disease; Drug Targeting; Face; Failure; Functional disorder; Future; Gene Expression Profile; Genes; Goals; Heterogeneity; Immune; Immune response; Infection; Inflammatory; Inflammatory Response; Intervention; Intervention Trial; Libraries; Life; Link; Machine Learning; Measures; Messenger RNA; Methods; Modeling; Molecular; Network-based; Oncology; Organ; Outcome; Paper; Pathway interactions; Patients; Pharmaceutical Preparations; Phase; Phase I/II Clinical Trial; Phenotype; Process; Prognostic Marker; Prospective Studies; Prospective cohort; Public Health; Publications; Rapid diagnostics; Reproducibility; Research; Resource Allocation; Retrospective cohort; Safety; Sampling; Selection for Treatments; Sepsis; Series; Severities; Subgroup; Supportive care; System; Technology; Testing; Therapeutic; Therapeutic Intervention; Time; Training; Translating; Translations; Treatment Protocols; United States National Institutes of Health; Update; Validation; Venipunctures; Work; base; biomarker signature; clinically actionable; cohort; companion diagnostics; design; diagnostic biomarker; diagnostic tool; drug action; drug candidate; drug repurposing; fighting; genetic signature; immunoregulation; improved; innovation; machine learning algorithm; machine learning classifier; machine learning pipeline; mortality; novel; novel diagnostics; novel strategies; pathogen; patient stratification; point of care; point of care testing; precision medicine; product development; rapid test; response; septic patients; success; transcriptomics

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.

摘要