Detection of drug effect in small groups using PET

描述（由申请人提供）： 该提案的目的是开发一种基于正电子发射断层扫描（PET）成像和先进图像分析方法的工具，以满足制药行业面临的关键需求。 针对大脑的药物的评估可能是困难和主观的;因此，制药公司经常花费数百万美元用于临床开发一种化合物，但由于在人体测试中遇到的问题，这种化合物最终被放弃。因此，需要可以帮助对候选药物做出简单的去/不去决定的方法，以便在其评估过程的早期消除无效药物。 FDG PET成像有望成为解决这一问题的有效方法。基于PET，我们建议开发一种新产品，直接回答药物是否值得进一步研究的基本问题。具体来说，我们将在第一阶段研究是否有可能仅使用大约5到8名受试者来确定候选药物是否对大脑有任何可测量的代谢影响。如果发现效应，则可以扫描额外的受试者以获得更详细的药物表征所需的全部补充。如果没有发现效果，则可以从进一步考虑中排除该药物，并将资源应用于下一个候选药物。 技术挑战将是确定优化的机器学习技术是否可以允许使用少量PET扫描做出这种初始的去/不去决定。我们将使用预测准确度作为药物作用强度的度量，并确定该度量是否可以在小受试者组中可靠地计算。在最近的学术工作中，我们已经证明，通过使用多变量机器学习方法可以大大提高预测精度。我们的目标将是采用这些新方法，以便以最低的成本从数据中获得尽可能多的信息。 具体目标是：1）实现一个按模型复杂度从低到高排序的学习算法体系：白色噪声假设下的广义似然比检验（GLRT）、典型变量分析（CVA）、二次判别分析（QDA）和相关向量机（RVM）; 2）使用分析统计模型和NPAIRS响应框架来估计每种方法的预测准确度，其将用作药物作用强度的量度;以及3）计算性能与受试者数量N、空间平滑和所使用的主成分数量的关系。优化算法配置的性能将揭示所提出的概念的可行性。

DESCRIPTION (provided by applicant): The purpose of this proposal is to develop a tool, based on positron emission tomography (PET) imaging and advanced image-analysis methods, to address a critical need faced by the pharmaceutical industry. Evaluation of Pharmaceuticals that target the brain can be difficult and subjective; therefore, pharmaceutical companies often spend millions of dollars in clinical development of a compound that is ultimately dropped due to problems encountered in human testing. Thus, methods are needed which can help make a simple go/no-go decision about a candidate drug, so as to eliminate ineffective drugs early in their evaluation process. PET imaging with FDG promises to be an effective solution to this problem. Based on PET, we propose to develop a new product that will directly answer the basic go/no-go question of whether a drug merits further study. Specifically, we will investigate in Phase I whether it is possible to determine, using only around five to eight subjects, whether a candidate drug has any measurable metabolic effect on the brain. If an effect is found, then additional subjects can be scanned to obtain the full complement needed for more detailed characterization of the drug. If no effect is found, then the drug can be eliminated from further consideration, and resources can be applied to the next candidate. The technical challenge will be to determine whether optimized machine-learning techniques can permit this initial go/no-go decision to be made with a small number of PET scans. We will use prediction accuracy as a measure of the strength of drug effect, and determine whether this metric can be computed reliably in small groups of subjects. In recent academic work, we have demonstrated that prediction accuracy can be tremendously enhanced by using multivariate machine-learning methods. Our goal will be to bring these new approaches to bear, so as to obtain the greatest amount of information possible from the data at the least cost. The specific aims will be to: 1) Implement a hierarchy of learning algorithms ranked in terms of model complexity, from low to high, as follows: generalized likelihood ratio test (GLRT) with white noise assumption, canonical variates analysis (CVA), quadratic discriminate analysis (QDA), and relevance vector machines (RVM); 2) Use analytical statistical models, and the NPAIRS resampling framework, to estimate prediction accuracy for each method, which will be used as a measure of the strength of drug effect; and 3) Compute performance versus number of subjects N, spatial smoothing, and number of principal components used. The performance of optimized algorithmic configurations will reveal the feasibility of the proposed concept.