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.

项目总结/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%的新疗法进入临床，这表明我们需要更好的模式。 来预测药效有趣的是，细胞对刺激的反应具有多重和级联效应， 例如药物治疗。例如，药物刺激可以诱导多种结果， 症状或引起不期望的副作用，并且此外，药物可以影响其下游的蛋白质。 预定目标。然而，药物治疗并不是常规设计的，其多重，级联效应或多- 蛋白质结合特性，因为我们缺乏定量的机制模型来理解这些 有趣的细胞效应蛋白质-蛋白质相互作用（PPI）网络模型已经确定了下游蛋白质 与药物治疗相关的疾病和副作用相关，证明了将多路复用 一个刺激的结果。这些关联模型不足以优先考虑药物靶蛋白， PPI网络缺乏机制细节来描述细胞响应的大小或相对贡献， 多蛋白质刺激。本研究的总体目标是得出多个变量之间的定量关系， 蛋白刺激和下游反应蛋白。这项建议是创新的，因为 情境特定交互（CSI）方法：与其他PPI网络方法相比，CSI分析 在预测准确度和精确度方面分别提高了50%和76-95%， 使用药物靶点下游的PPI识别严重不良事件，并强调临床数据 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， 具体的数据，以更好地了解所有网络相关的表型，并证明了衍生的可行性 PPI网络模型中的机械细节。这一计划意义重大，因为它将减少整体侧- 通过促进对细胞多重反应的更好理解， 多蛋白质刺激。此外，我还展示了稳定的生产力以及灵活性和适应性， 追求研究项目，以建立一个杰出的独立的职业生涯。