PAASPort: A web-based tool for nonclinical research quality assessment and risk of bias management to advance development of innovative medications

PAASPort：一种基于网络的工具，用于非临床研究质量评估和偏倚风险管理，以促进创新药物的开发

• 批准号: 10217084
• 负责人: ANDRE DER-AVAKIAN
• 金额: $ 72.55万
• 依托单位: PAASP US, LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10217084
• 关键词: Academia; Address; Advanced Development; Area; Automatic Information Processing; Automation; Biomedical Research; Businesses; Capital; Categories; Certification; Clinical; Clinical Research; Clinical Treatment; Code; Computer Simulation; Costs and Benefits; Data; Data Analyses; Decision Making; Development; Disease; Early identification; Economics; Elements; Employee; Evaluation; Evaluation Research; FAIR principles; Feedback; Foundations; Funding; Generations; Goals; Guidelines; Human; Individual; Industry; Investments; Laboratories; Link; Manuals; Measures; Modeling; Neurons; Neurosciences; Online Systems; Outcome; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Privatization; Probability; Process; Quality Control; Questionnaires; Records; Reporting; Research; Research Design; Research Support; Resources; Risk; Risk Assessment; Risk Factors; Science; Scientist; Secure; Services; Substance Use Disorder; Targeted Research; Testing; Therapeutic; Time; Training; Translating; Translations; Validation; Visit; Weight; base; computerized data processing; cost; cost effective; data format; data integrity; design; digital; drug discovery; improved; innovation; interest; model design; novel; pressure; protective factors; prototype; research and development; research in practice; research to practice; response; substance use treatment; tool; translational approach; web app; web-based tool

PROJECT SUMMARY / ABSTRACT Robust nonclinical data is an absolute requirement for building an effective translational strategy and developing clinically safe and effective medications. Most current efforts to facilitate generation of robust nonclinical research data focus on producing guidelines and checklists pertaining to study design and data analysis, but: a) do so only from a reporting perspective; b) do not consider processes other than study design and data analysis, such as compliance of data records with FAIR principles; and c) are at risk of triggering normative responses, whereby scientists simply satisfy the guidelines at a time when it is too late to take corrective actions. In this regard, and in line with RFA-DA-19-031, the current application proposes to develop, validate and introduce into common practice a novel web-based tool called PAASPort for early identification of potential risks of bias and support of decision making related to nonclinical research practice. PAASPort consists of a web application with proprietary analytics supporting project-specific and research unit-specific certification. In Phase 1, we will (Aim 1) convert the existing PAASPort prototype, which is currently a questionnaire in Microsoft Word format where data are manually organized and analyzed by a PAASP employee, into a web- based, interactive digital tool that can be accessed online at the research unit’s convenience and data will be automatically processed and prepared to provide feedback to the PAASPort customer (e.g., foundations, private investors, VCs, pharma); (Aim 2) enhance the commercial value of PAASPort by asking different groups of private investors how they value the different aspects of bias in research practice related to 1) study design and conduct, 2) data integrity, 3) quality culture, and 4) organizational and individual factors. In Phase 2, we will: (Aim 1) implement analytical mechanisms to support semi-automated processing of information collected from the online assessment; (Aim 2) minimize normative responses to the online assessment by introducing onsite audit-based feedback control, and (Aim 3) demonstrate economic benefits of improved nonclinical research practice quality assessments for private investors. The tool will be developed and validated with neuroscience and, more specifically, substance use disorder drug discovery in focus, but it can be extrapolated to other disease indications and therapeutic areas. The most immediate impact of the proposed tool is to avoid normative responses to emerging standards in research quality. However, given that PAASPort-supported data are expected to be more robust, the ultimate goal is to facilitate decision making, industry-academia partnerships, and the advancement of fundamental discoveries into commercial development.

项目总结/摘要 稳健的非临床数据是构建有效转化策略的绝对要求， 开发临床安全有效的药物。目前为促进生成强有力的 非临床研究数据侧重于制定与研究设计相关的指南和检查表， 数据分析，但：a）仅从报告角度进行; B）不考虑除 研究设计和数据分析，例如数据记录是否符合FAIR原则;以及c）载于 触发规范性反应的风险，科学家只是在它 采取纠正措施为时已晚。在这方面，根据RFA-DA-19-031， 应用程序提出开发，验证和引入到共同的做法，一种新的基于网络的工具 称为PAASPort，用于早期识别潜在的偏倚风险，并支持与以下相关的决策 非临床研究实践。PAASPort由一个具有专有分析功能的Web应用程序组成 支持特定项目和特定研究单位的认证。 在第一阶段，我们将（目标1）转换现有的PAASPort原型，目前是一个调查问卷， Microsoft Word格式，其中数据由PAASP员工手动组织和分析到Web- 基于，交互式数字工具，可以在研究单位的方便和数据在线访问将 自动处理和准备以向PAASPort客户提供反馈（例如，地基， 私人投资者，风险投资，制药）;（目标2）通过要求不同的PAASPort 私人投资者群体如何评价与1）研究相关的研究实践中的不同方面的偏见 设计和执行，2）数据完整性，3）质量文化，以及4）组织和个人因素。同相 2、我们将：（目标1）实施分析机制，支持半自动化信息处理 从在线评估中收集;（目标2）尽量减少对在线评估的规范性反应， 引入基于现场反馈的反馈控制，以及（目标3）证明改进的经济效益 为私人投资者提供非临床研究实践质量评估。该工具将得到开发， 经过神经科学的验证，更具体地说，药物使用障碍药物发现的焦点，但它可以 可以外推到其他疾病适应症和治疗领域。最直接的影响是 拟议的工具是避免对研究质量方面的新标准作出规范性反应。但鉴于 PAASPort支持的数据预计将更加强大，最终目标是促进决策， 产业与学术界的伙伴关系，以及将基础发现转化为商业应用的进展 发展