Statistical Methodology for Characterization of Macromolecular Similarity

大分子相似性表征的统计方法

• 批准号: 8883055
• 负责人: JOHN R CORT
• 金额: $ 59.99万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-10 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8883055
• 关键词: Statistical; Methodology; Characterization; Macromolecular; Similarity

PROJECT SUMMARY/ABSTRACT As with any drug, approval of generic versions of macromolecular drugs requires rigorous evaluation of therapeutic equivalence to the reference drug in order to assure similar efficacy and safety. Compared with drugs that are small organic molecules, the chemical composition and characteristics of macromolecular drugs—typically proteins or polysaccharides—are inherently more variable because of the way these molecules are produced and isolated. It would be advantageous to have a way to determine molecular similarity and, by implication, equivalence without using costly in vivo testing in animals and humans. The specific aim of the proposal is to develop and test a robust data-driven statistical methodology for assessing similarity among distinct samples of therapeutic macromolecules, whether from different batches or altered processes, or even if produced by different entities entirely. The methodology is based on a genetic algorithm designed to extract relevant features from large, complex datasets. The hypothesis guiding the work is that with proper data interpretation and modeling, therapeutic equivalence can be inferred from molecular similarity determined using spectroscopic and chromatographic measurements that reveal the critical molecular attributes ultimately responsible for important properties of the generic and reference macromolecules: efficacy, side-effects, stability, and so on. Even without knowing how these attributes specify these properties, the fact that they do specify them means that this data is where equivalence can best be determined without going to in vivo testing. Data introduced into the model will be collected for multiple lots and batches of protein and polysaccharide drug substances and products. The data will come from an assortment of high-resolution mass-spectrometry methods, high-field nuclear magnetic resonance spectroscopy analyses, and several other spectroscopic and chromatographic methods used to characterize macromolecules in solution. Biological activity data will be obtained from outsource testing labs that have established assays in place. The outcome of the proposed research will be a working methodology for evaluation of similarity between generic and reference versions of any macromolecular drug. This will lead towards faster approval of generic versions of macromolecular drugs, and thereby to broader access and lower cost for these drugs which are critical to treating many serious diseases.

项目摘要/摘要 与任何药物一样，批准大分子药物的通用版本需要严格评估 与参考药物的治疗等效性，以确保相似的效率和安全性。与 小有机分子的药物，大分子的化学成分和特征 药物（通常是蛋白质或多糖）的固有变化，因为这些 产生和分离分子。有一种确定分子的方法是有利的 相似性，并暗示相似性，而无需在动物和人类中使用昂贵的体内测试。 该提案的具体目的是开发和测试针对数据驱动的强大统计方法 评估不同批次或来自​​不同批次的热大分子样本之间的相似性 更改过程，甚至完全由不同实体产生。该方法基于遗传 算法旨在从大型复杂数据集中提取相关功能。 指导工作的假设是，通过适当的数据解释和建模，治疗等价 可以通过使用光谱和色谱测量确定的分子相似性来推断 这揭示了关键分子属性最终负责通用和 参考大分子：有效性，副作用，稳定性等。即使不知道这些 属性指定这些属性，它们确实指定它们的事实意味着此数据是等价的地方 可以最好地确定而无需进行体内测试。 引入模型中的数据将用于多个批次和蛋白质和多糖的批次 药物和产品。数据将来自各种高分辨率质谱法 方法，高场核磁共振光谱分析以及其他几种光谱和 色谱方法用于表征溶液中的​​大分子。生物活动数据将是 从已建立测定的外包测试实验室获得。 拟议研究的结果将是评估相似性的工作方法 任何大分子药物的通用和参考版本。这将导致更快地批准通用 大分子药物的版本，从而使这些药物更广泛获取和较低的成本 对于治疗许多严重疾病至关重要。