Statistical Research in Drug Discovery and Development

研究了药物发现和开发中的三个广泛问题。对于靶标鉴定，使用微阵列进行基因表达已成为一种标准做法。为了测试基因表达对数千个基因的意义，测试中的多样性问题是中心问题。提出了一种新的图解方法。对于药物发现，研究重点是高通量筛选（HTS）。HTS的一个中心问题是能够识别尽可能多的强效药物，并在经济时限内实现这一目标。为了增加“命中”，需要准确估计假阳性和假阴性误差，并选择最优的截止点。主要研究工作集中在假阳性和假阴性误差的准确估计、最佳截止点的确定和估计策略上。结果将是关键的验证研究的计划，在屏幕投入生产之前使用。对于药物开发，建议将现代技术应用于制药科学（配方和稳定性）和工艺研发（化学和生物放大）的实验设计和分析。考虑了两种新技术：复杂混叠实验中相互作用的估计和过程改进的鲁棒参数设计。由于这些技术是在制造业和高科技产业的背景下发展起来的，因此在制药应用中的新特点应该导致新方法的发展。这项研究是由乔治亚理工学院Jeff Wu （PI）的研究小组和百时美施惠宝公司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结果。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激增，拥有一个有效的系统来发现新化合物并将其开发成用于临床试验和大规模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。这个项目的成功实施可以作为这种合作的榜样。它可以在加速疾病目标的识别和重磅药物化合物的发现方面产生重大的社会影响，从而节省生命和医疗保健费用。这项工作应导致理论和方法的新进展，研究结果将在专业会议上提出并在贸易期刊上发表。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通用的性质。它们将使工业合作伙伴以及整个行业受益。几名博士生将参与该项目，他们将在大学和工业实验室之间分配时间。该项目将为研究生提供接触药物发现和开发前沿研究的新机会。它可以丰富他们的教育经验，拓宽他们的职业前景。

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.