Data-driven drug discovery: investigating the molecular mechanisms of safety and efficacy

项目摘要 我提出了一个精确的药理学和药物警戒研究计划， 通过前瞻性实验室实验进行数据分析，以提高药物的安全性和有效性。我们收集的能力 并存储大量的分子、临床和行为数据， 转变转化医学不难想象一个我们的设备和医生工作的世界 无缝地结合在一起，提供个性化的指导和治疗，以最大限度地提高我们的健康和长寿。 反过来，这些接触产生的数据将由生物医学专家收集、组织和分析， 研究人员发明下一代干预措施。然而，在禁止 朝着这一愿景取得了重大进展。我已经确定了我计划解决的四个问题： (1)许多亚人群，特别是少数民族，缺乏药理学知识 （非白人）、妇女和儿童; (2)观察数据，从设备捕获的数据到医疗记录中收集的数据， 可疑的有效性和价值; (3)对药物反应和药物-药物反应的分子机制的理解有限， 互动; (4)没有明确的方法在保护隐私的同时有意义地共享患者数据。 没有一个单一的解决方案可以解决所有这些挑战。每一个都需要独特的数据组合 科学、信息学和实验。在以前资助的项目中，我们在以下方面取得了重大进展： 药物不良反应和药物相互作用的表征， 药理学途径，以及统计数据挖掘在电子健康记录中的应用。我 通过利用彼此不同的数据源来实现这一点， 在各种条件下重复出现的假设。然后我验证了这些假设 用动物和细胞模型进行实验。挑战2和挑战3是上述挑战的自然延伸。 工作，在那里我将解决如何使用数据的目的以外，它是收集（二次使用） 并开发新的系统模型来解释药物-基因和药物-药物相互作用的生理效应。 挑战1和4代表了新的研究途径，我将在这里解决药理学的挑战。 在不同人群中进行的研究，以及平衡开放性和透明度这一日益重要的问题， 科学与病人隐私权的冲突上述挑战是重大的，而且可能不会是重大的。 在五年内解决。然而，追求这些挑战将产生新的知识， 具有显着改善药物设计、推进精准医学和指导药物安全治理的潜力。

Project Summary I am proposing a precision pharmacology and pharmacovigilance research program that couples observational data analysis with prospective laboratory experiments to advance drug safety and efficacy. Our ability to collect and store massive amounts of molecular, clinical, and behavioral data has the potential to fundamentally transform translational medicine. It is not difficult to imagine a world where our devices and doctors work together seamlessly to provide personalized guidance and treatment to maximize our health and longevity. And that, in turn, the data generated by these encounters be collected, organized, and analyzed by biomedical researchers to invent the next generation of interventions. However, there are significant challenges prohibiting meaningful progress toward this vision. I have identified four that I plan to address: (1) There is a dearth of pharmacological knowledge for many subpopulations, most notably minorities (non-Whites), women, and children; (2) Observational data, from what is captured by devices to what is collected in medical records, is of dubious validity and value; (3) There is a limited understanding of the molecular mechanisms of drug reactions and drug-drug interactions; (4) There is no clear method of meaningfully sharing patient data while preserving privacy. There is no single solution that will solve all of these challenges. Each will require a unique combination of data science, informatics, and experiments. In the previously funded project, we made significant advancements in the characterization of adverse drug reactions and drug-drug interactions, the molecular modeling of pharmacological pathways, and the application of statistical data mining to electronic health records. I accomplished this by leveraging distinct data sources against each other to focus attention on only those hypotheses that repeatedly replicate under a variety of conditions. I then validated those hypotheses experimentally using animal and cellular models. Challenges 2 and 3 are natural extensions of this previous work, where I will address how to use data for purposes other than what it was collected for (secondary use) and develop new systems models to explain the physiological effects of drug-gene and drug-drug interactions. Challenges 1 and 4 represent new avenues of research where I will address the challenges of pharmacological studies in diverse populations and the increasingly important issue of balancing openness and transparency in science with patients' rights to privacy. The challenges laid out above are significant and, likely, will not be solved in within five years. However, the pursuit of these challenges will generate new knowledge that has the potential to significantly improve drug design, advance precision medicine, and guide drug safety governance.