Integrating, Validating & Applying Pharmacogenetic Data

描述（由申请人提供）：基因安全网络（GSN）的使命是创建一个系统，使临床医生能够使用来自临床试验和治疗记录的汇总遗传和表型数据，为每位患者做出最安全、最有效的治疗决策。患者对临床治疗的独特反应取决于他或她的遗传组成以及疾病过程的生物分子性质。学术机构正在迅速积累临床数据，代表着个性化医疗趋势的先锋，但缺乏用于数据整合和验证的技术系统和格式标准，使得难以成功解释和预测个体患者的反应。对于第一阶段，我们专注于GSN使命的三个关键组成部分：i）创建标准化本体和翻译引擎，以有效整合和验证药代动力学数据，ii）使用翻译引擎将多组药代动力学数据整合到标准化本体中，以及iii）开发统计方法，利用整合的遗传和表型数据进行数据验证和结果预测。为了证明我们的方法的实用性，我们正在与斯坦福大学的PharmGKB项目合作。PharmGKB管理着一个开放共享的临床试验数据互联网存储库，旨在揭示个体遗传变异如何导致对药物的独特反应。作为NIH药物遗传学研究网络（PGRN）的成员，PharmGKB的数据库包括来自心血管、肺和癌症研究的广泛的药代动力学和基因组记录。在这里，我们专注于乳腺癌和结肠癌的治疗，这两者都可以通过将不同的遗传和表型数据整合到一个标准化的本体中，验证数据，并对数据进行统计分析来预测药物疗效和副作用来大大增强。欠定和病态的数据集是常见的这些疾病，因为许多基因型和表型建模问题，在可能的预测因子的数量？基因、蛋白质或突变位点？ 相对于测量结果的数量来说是很大的。对于特定的目标我，我们专注于创建一个标准化的本体和PharmGKB数据的翻译引擎。对于目标2，我们专注于与PharmGKB的乳腺癌和结肠癌数据相关的药代动力学数据的整合和分析。对于目标3，我们在综合数据上训练统计模型，以展示如何使用这些数据来提高某些药物的疗效和安全性。在随后的阶段，原型系统将被扩展以适应其他形式的数据和疾病类型，并将为临床医生提供功能，以选择试验，提交新患者的相关数据，并使用在综合试验数据上训练的模型查看针对该患者的不同干预措施的关键结果的预测和置信界限。详情将在第一阶段完成后的第二阶段申请中提供。临床医生必须汇编和消化的数据量，以提供最佳的护理他们的病人正在迅速扩大，越来越令人生畏。基因安全网络将显著减轻这一负担，并大大提高临床决策的速度和准确性。

DESCRIPTION (provided by applicant): The mission of the Gene Security Network (GSN) is to create a system that enables clinicians to use aggregated genetic and phenotypic data from clinical trials and treatment records to make the safest, most effective treatment decisions for each patient. A patient's unique response to clinical therapy is dependent on his or her genetic composition, as well as the biomolecular nature of the disease process. Academic institutions are rapidly accumulating clinical data, representing a vanguard in the trend towards personalized medicine, but a lack of technology systems and format standards for the integration and validation of data makes it difficult to successfully interpret and predict individual patient responses. For Phase I, we focus on three key components of the GSN mission: i) to create a standardized ontology and translation engine for efficient integration and validation of pharmacokinetic data, ii) to use the translation engine to integrate multiple sets of pharamacokinetic data into the standardized ontology, and iii) to develop statistical methods to perform data validation and outcome prediction with the integrated genetic and phenotypic data. To demonstrate the utility of our approach, we are collaborating with the PharmGKB Project at Stanford University. PharmGKB manages an openly-shared Internet repository for clinical trial data with the intent to uncover how individual genetic variation contributes to distinctive reactions to Pharmaceuticals. As a member of the NIH Pharmacogenetics Research Network (PGRN), PharmGKB's database includes extensive pharmacokinetic and genomic records from cardiovascular, pulmonary, and cancer research. Here we focus on breast and colon cancer treatment, both of which could be considerably enhanced by the integration of diverse genetic and phenotypic data into a standardized ontology, validation of the data, and statistical analysis of data to predict drug efficacy and side-effect profiles. Underdetermined and ill-conditioned data sets are common for these diseases, as for many genotypic and phenotypic modeling problems, where the number of possible predictors? genes, proteins, or mutation sites? Is large relative to the number of measured outcomes. For specific Aim I, we focus on creating a standardized ontology and translation engine for PharmGKB data. For Aim 2, we concentrate on the integration and analysis of pharmacokinetic data associated with PharmGKB's breast cancer and colon cancer data. For Aim 3, we train statistical models on the integrated data to show how the data can be used to enhance the efficacy and safety of certain drugs. In subsequent phases the prototype system will be extended to accommodate other forms of data and types of diseases, and functionality will be provided for a clinician to select a trial, submit relevant data for a new patient, and view predictions and confidence bounds for key outcomes given different interventions for that patient using models trained on the integrated trial data. Details are to be provided in a phase II application subsequent to completion of Phase I. The amount of data that clinicians must compile and digest to provide their patients with optimal care is rapidly expanding and is increasingly daunting. The Gene Security Network stands to significantly reduce this burden and greatly improve the speed and accuracy of clinical decision-making.