Statistical Research in Drug Discovery and Development

药物发现和开发的统计研究

• 批准号: 0305996
• 负责人: C. F. Jeff Wu
• 金额: $ 39.88万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-15 至 2008-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0305996&HistoricalAwards=false
• 关键词: Statistical; Research; Drug; Discovery; Development

Three broad issues in drug discovery and development are studied. For target identification, use of microarrays for gene expression has become a standard practice. To test the significance of gene expression for thousands of genes, the issue of multiplicity in testing is central. A new graphical procedure is proposed. For drug discovery, the research focuses on high throughput screening (HTS). A central issue in HTS is to be able to identify as many potent drugs as possible and to achieve this within an economic timeframe. To increase the "hits", both false positive and false negative errors need to be accurately estimated and the cutoff point be chosen optimally. The major research effort focuses on the accurate estimation of false positive and false negative errors, determination of optimal cutoff points, and estimation strategy. The results will be pivotal to the planning of validation studies that are used before the screen is put into production. For drug development, it is proposed to apply modern techniques in design and analysis of experiments to pharmaceutical sciences (formulations and stability) and process R&D (chemical and biological scale-up). Two new techniques are considered: estimation of interactions in experiments with complex aliasing, and robust parameter design for process improvement. Since these techniques were developed in the context of manufacturing and hi-tech industries, new features in the pharmaceutical applications should lead to the development of new methods. The research is a jointly effort by the research group of Jeff Wu (PI) at Georgia Institute of Technology and the nonclinical biostatistics and genetics groups headed by David Stock and Kim Zerba at Bristol-Myers Squibb. Statistical tools have been widely used in drug discovery and development in the pharmaceutical industry. With the increasing competition in the industry and the explosion of disease targets, it is becoming increasingly important to have an efficient system for discovering new compounds and developing them into drugs for clinical trials and scale-up production. There has been a lot of collaborative research between academia and industries on clinical trials. Much less collaboration has been done on preclinical research like drug discovery and development. Successful implementation of this project can serve as a role model for this collaboration. It can have a major societal impact in accelerating the identification of disease target and discovery of compounds for blockbuster drugs, resulting in savings of lives and health care costs. The work should lead to new advances in theory and methodology and the research findings will be presented in professional meetings and published in trade journals. Because of the novelty of applications and the scientific relevance, the methodology and theory developed in this project will be of a broad and generic nature. They will benefit the industrial partner as well as the industry in general. Several Ph.D. students will participate in the project, splitting their time between university and industrial labs. The project will provide a new opportunity for graduate students to be exposed to cutting-edge research in drug discovery and development. It can enrich their educational experience and broaden the prospects for their careers.

研究了药物发现和开发中的三个广泛问题。对于靶标识别，使用微阵列进行基因表达已成为一种标准做法。为了测试数千个基因的基因表达的重要性，测试中的多样性问题是核心。提出了一种新的图解方法。对于药物发现，研究的重点是高通量筛选(HTS)。HTS的一个核心问题是能够识别出尽可能多的强效药物，并在经济的时间框架内实现这一点。为了增加命中率，需要准确地估计误报和漏报错误，并优化选择截止点。主要的研究工作集中在对误判和漏判误差的准确估计、最佳截止点的确定和估计策略上。这些结果将对在屏幕投入生产之前使用的验证研究的规划至关重要。对于药物开发，建议将现代实验设计和分析技术应用于制药科学(配方和稳定性)和过程研发(化学和生物放大)。考虑了两种新技术：复杂混叠实验中交互作用的估计和用于过程改进的稳健参数设计。由于这些技术是在制造业和高科技行业的背景下发展起来的，制药应用的新特点应该导致新方法的开发。这项研究是由佐治亚理工学院的Jeff Wu(Pi)研究小组与百时美施贵宝的David Stock和Kim Zerba领导的非临床生物统计学和遗传学小组共同努力的。统计工具在制药行业的药物发现和开发中得到了广泛的应用。随着行业竞争的加剧和疾病靶点的爆炸性增长，拥有一个有效的系统来发现新的化合物并将其开发成用于临床试验和规模化生产的药物变得越来越重要。学术界和产业界在临床试验方面进行了大量的合作研究。在药物发现和开发等临床前研究方面的合作要少得多。该项目的成功实施可以作为这种协作的榜样。它可以在加速确定疾病目标和发现重磅炸弹药物的化合物方面产生重大的社会影响，从而节省生命和医疗费用。这项工作应该在理论和方法上带来新的进步，研究结果将在专业会议上提出，并在行业期刊上发表。由于应用的新颖性和科学相关性，本项目开发的方法和理论将具有广泛和一般性。它们将使行业合作伙伴和整个行业受益。几名博士生将参与这个项目，他们的时间被分配在大学和工业实验室之间。该项目将为研究生提供一个接触药物发现和开发方面的前沿研究的新机会。它可以丰富他们的教育经验，拓宽他们的职业前景。