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.

项目摘要我提出了一项精确的药理学和药物宣传研究计划，该计划将观察性融合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功效。我们收集的能力并存储大量分子，临床和行为数据具有根本上的潜力转化翻译医学。不难想象我们的设备和医生工作的世界无缝共同提供个性化的指导和治疗，以最大程度地提高我们的健康和寿命。然后，通过生物医学来收集，组织和分析这些相遇产生的数据研究人员发明下一代干预措施。但是，禁止存在重大挑战有意义的进步。我已经确定了我打算解决的四个：（1）对于许多亚群，最著名的少数群体缺乏药理学知识（非白人），妇女和儿童；（2）观察数据，从设备捕获到医疗记录中收集的内容，可疑的有效性和价值；（3）对药物反应和药物的分子机制的理解有限互动；（4）没有明确的方法可以在保留隐私时有意义地共享患者数据。没有一个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。在先前资助的项目中，我们在不良药物反应和药物 - 药物相互作用的表征，药理途径以及统计数据挖掘在电子健康记录中的应用。我通过利用不同的数据源相互对立来实现这一目标，以将注意力集中在那些假设在各种条件下反复复制。然后我验证了这些假设实验使用动物和细胞模型。挑战2和3是以前的自然扩展工作，我将在其中讨论如何将数据用于目的以外的其他目的（二次使用）并开发新的系统模型来解释药物和药物相互作用的生理影响。挑战1和4代表研究的新途径，我将解决药理的挑战对不同人群的研究以及平衡开放性和透明度的越来越重要的问题具有患者隐私权的科学。上面提出的挑战是重大的，很可能不会在五年内解决。但是，对这些挑战的追求将产生具有的新知识有可能显着改善药物设计，提高精度医学和指导药物安全治理的潜力。