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

数据驱动的药物发现：研究安全性和有效性的分子机制

基本信息

批准号：
9920189
负责人：
Nicholas P Tatonetti
金额：
$ 48.11万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9920189
关键词：
Address Affect Age Animal Experimentation Animal Model Attention Behavioral Biological Models Cell model Child Clinical Clinical Trials Couples Dangerousness Data Data Analyses Data Science Data Sources Devices Drug Design Drug Interactions Electronic Health Record Equilibrium Funding Gender Genes Health Informatics Intervention Knowledge Longevity Medical Records Medicine Methods Minority Modern Medicine Molecular Pathway interactions Patient Rights Patients Pharmaceutical Preparations Pharmacology Pharmacology Study Physiological Effects of Drugs Population Heterogeneity Privacy Reaction Research Research Personnel Safety Science Vision Woman Work adverse drug reaction data mining drug discovery drug efficacy experimental study improved laboratory experiment medication safety molecular modeling next generation patient population pharmacovigilance precision medicine privacy preservation programs prospective sex side effect translational medicine

项目摘要

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.

项目概要我正在提议一项精准药理学和药物警戒研究计划，该计划将观察性结合起来通过前瞻性实验室实验进行数据分析，以提高药物安全性和有效性。我们的收集能力并存储大量的分子、临床和行为数据有可能从根本上转变转化医学。不难想象我们的设备和医生工作的世界无缝地共同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寿命。反过来，生物医学部门也会收集、组织和分析这些遭遇产生的数据。研究人员发明下一代干预措施。然而，存在重大挑战，禁止朝着这一愿景取得有意义的进展。我已经确定了我计划解决的四个问题： (1) 许多亚人群缺乏药理学知识，尤其是少数群体（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，都是有效性和价值可疑； (3)对药物反应和药物间相互作用的分子机制了解有限。互动； (4) 没有明确的方法可以在保护隐私的同时有意义地共享患者数据。没有单一的解决方案可以解决所有这些挑战。每个都需要独特的数据组合科学、信息学和实验。在之前资助的项目中，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、药物的分子模型药理学途径，以及统计数据挖掘在电子健康记录中的应用。我通过利用相互不同的数据源来仅关注那些数据源来实现这一点在各种条件下重复复制的假设。然后我验证了这些假设使用动物和细胞模型进行实验。挑战 2 和 3 是前一个挑战的自然延伸工作中，我将讨论如何将数据用于收集目的以外的目的（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，我将在其中解决药理学的挑战对不同人群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患者的隐私权。上述挑战是重大的，而且很可能不会是五年内解决。然而，对这些挑战的追求将产生新的知识，这些知识具有具有显着改善药物设计、推进精准医疗、指导药物安全治理的潜力。