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.

项目总结/摘要 与任何药物一样，大分子药物的仿制药的批准需要严格的评估， 与参比药物的治疗等效性，以确保相似的疗效和安全性。较 药物是有机小分子，大分子的化学组成和特性 药物--通常是蛋白质或多糖--本质上更易变，因为这些药物 分子被产生和分离。有一种方法来确定分子量将是有利的 相似性和隐含的等效性，而无需在动物和人类中进行昂贵的体内试验。 该提案的具体目的是制定和测试一个强有力的数据驱动统计方法， 评估治疗性大分子的不同样品之间的相似性，无论是来自不同批次还是 改变的过程，或者甚至完全由不同的实体生产。该方法是基于一个遗传 一种从大型复杂数据集中提取相关特征的算法。 指导这项工作的假设是，通过适当的数据解释和建模， 可以从使用光谱和色谱测量确定的分子相似性推断 揭示了关键的分子属性，最终负责的重要性质的通用和 参考大分子：功效，副作用，稳定性等。即使不知道这些 属性指定了这些属性，它们确实指定了这些属性，这意味着这些数据是等价的 可以最好地在不进行体内测试的情况下确定。 将收集多个批次和多个批次的蛋白质和多糖引入模型的数据 原料药和产品。数据将来自各种高分辨率质谱仪 方法，高场核磁共振光谱分析，和其他几个光谱和 用于表征溶液中大分子的色谱方法。生物活性数据将 从已建立适当测定的外包测试实验室获得。 拟议研究的结果将是一个工作方法，用于评估 任何大分子药物的通用和参考版本。这将导致更快地批准仿制药 大分子药物的版本，从而更广泛地获得这些药物， 对治疗许多严重疾病至关重要。