Understanding cascading cellular protein responses following multi-protein stimuli using network modeling and real-world evidence

使用网络模型和现实世界证据了解多蛋白刺激后的级联细胞蛋白反应

• 批准号: 10685328
• 负责人: Jennifer Lynn Wilson
• 金额: $ 31.24万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685328
• 关键词: Adverse drug event; Binding; Binding Proteins; Cells; Clinic; Clinical Data; Complex; Data; Disease; Drug Targeting; Healthcare; Learning; Link; Medicine; Mind; Modeling; Outcome; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Productivity; Property; Proteins; Research; Research Project Grants; Serious Adverse Event; Stimulus; Symptoms; Treatment Efficacy; career; data integration; design; flexibility; improved; innovation; network models; programs; protein protein interaction; response; side effect

Project Summary/ Abstract: Drug adverse events have been estimated to contribute to 16% of healthcare spending in the US, and less than 10% of new treatments reach the clinic, suggesting we need better models for anticipating drug effects. Intriguingly, cells have multiplexed, and cascading effects in response to stimuli, such as drug therapies. For instance, a drug stimulus can induce multiple outcomes including modifying disease symptoms or causing undesirable side-effects, and further, drugs can influence proteins downstream of their intended targets. Yet, drug therapies are not routinely designed with their multiplexed, cascading effects or multi- protein binding properties in mind because we lack quantitative mechanistic models for understanding these interesting cellular effects. Protein-protein interaction (PPI) network models have identified downstream proteins associated with diseases and side-effects relevant to drug therapies, demonstrating the ability to link multiplexed outcomes with a stimulus. These associative models are insufficient for prioritizing drug target proteins because PPI networks lack mechanistic detail to describe the magnitude or relative contribution of cellular responses to multi-protein stimuli. The overall objective of this research is to derive quantitative relationships between multi- protein stimuli and downstream response proteins using clinical data. This proposal is innovative because of the context-specific interaction (CSI) approach: compared to other PPI network approaches, CSI analysis demonstrated a 50% and 76-95% improvement in prediction accuracy and precision, respectively, when identifying severe adverse events using PPIs downstream of drug targets and the emphasis on clinical data integration. This approach emphasized the importance of learning PPI network parameters using phenotype- specific data to better understand all network-associated phenotypes and demonstrated the feasibility of deriving mechanistic details in PPI network models. This program is significant because it stands to reduce overall side- effects and increase therapeutic efficacy by advancing a better understanding of cellular multiplexed responses to multi-protein stimuli. Further, I have demonstrated consistent productivity and the flexibility and adaptability in pursuing research projects to establish a distinguished independent career.

项目摘要/摘要：据估计，药物不良事件占医疗保健的 16% 在美国，只有不到 10% 的新疗法到达诊所，这表明我们需要更好的模型 用于预测药物作用。有趣的是，细胞对刺激的反应具有多重和级联效应， 比如药物治疗。例如，药物刺激可以引起多种结果，包括改变疾病 症状或引起不良副作用，此外，药物可以影响其下游的蛋白质 预期目标。然而，药物疗法的常规设计并未考虑其多重、级联效应或多重效应。 考虑到蛋白质结合特性，因为我们缺乏理解这些特性的定量机制模型 有趣的细胞效应。蛋白质-蛋白质相互作用 (PPI) 网络模型已识别出下游蛋白质 与药物治疗相关的疾病和副作用相关，证明了连接多重的能力 刺激的结果。这些关联模型不足以优先考虑药物靶蛋白，因为 PPI 网络缺乏机制细节来描述细胞反应的幅度或相对贡献 多种蛋白质刺激。本研究的总体目标是得出多因素之间的定量关系 使用临床数据的蛋白质刺激和下游反应蛋白质。这个提议之所以具有创新性是因为 上下文特定交互（CSI）方法：与其他 PPI 网络方法相比，CSI 分析 表现出预测准确度和精确度分别提高了 50% 和 76-95% 使用药物靶标下游的 PPI 识别严重不良事件并重视临床数据 一体化。这种方法强调了使用表型学习 PPI 网络参数的重要性 具体数据可以更好地理解所有与网络相关的表型，并证明了推导的可行性 PPI 网络模型中的机制细节。该计划意义重大，因为它可以减少总体副作用 通过促进对细胞多重反应的更好理解来提高治疗效果 多蛋白质刺激。此外，我还表现出了一贯的生产力以及灵活性和适应性。 追求研究项目以建立杰出的独立职业生涯。