Subtopic 2: Pharmacometric medeling and simulation for a generic drug substitutability evaluation and post marketing risk assessment

项目摘要 仿制药办公室收到大量批准的仿制药上市后风险投诉 药品。尽管仿制药被认为在安全性、强度、 质量，性能特征，仍然是处方者对安全性和 从品牌转向仿制药后的功效。为了清楚地描述仿制化合物是否真的是 对于无效或不良事件的原因，数据挖掘需要相当多的时间和资源。 因此，迫切需要从信号中滤除这些不良事件或无效的噪声 上市后的报告。先进的定量方法，如药物计量分析，具有 能够以简单化的方式分析复杂和海量的数据，做出明智的决策。 药物计量学在仿制药评价中的作用有很大的潜力，通过这一应用，我们 旨在展示如何在仿制药上市后风险评估中利用这一潜力。 将进行基于模拟的分析，以确定响应率(或信噪比)的重要性 在批准品牌产品时使用低信噪比和高信噪比) 场景。模拟练习将包括在虚拟患者将 从品牌转向仿制产品。药代动力学变异性及其内源性和外源性影响 (例如身体大小、药物与药物的相互作用、器官功能障碍等)因素会被考虑在内- 使这项研究更具创新性和信息量。目标是对所有治疗领域进行分类以帮助 基因药物办公室在进一步调查报告方面的优先次序。 此外，我们打算将定量贝叶斯框架应用于常用的信号检测方法(用于 不良事件)，如比例报告比(PRR)和信息标准(IC)。贝叶斯框架 还将用来演示如何在应计帖子后更新潜在信号 品牌和非专利产品的营销报告。最后，我们将应用上面提到的发现 将从不良事件报告系统(AERS)数据库访问的真实数据的步骤。这个 交付将是使用贝叶斯框架的设备治疗区域特定的潜在信号。

Project Summary Office of Generic Drugs receives a huge number of post marketing risk complaints for the approved generic drug products. Even though generics are believed to be similar to the brand name drugs in safety, strength, quality, performance characteristics, still there is a concern amongst prescribers regarding the safety and efficacy after switching from brand to generic products. To clearly delineate if the generic compound is truly the cause of inefficacy or adverse events, considerable time and resources are required in data mining. Hence, there is a grave need to filter out the noise from the signal for these adverse events or inefficacy from the post-marketing reports. Advanced quantitative approaches such as pharmacometric analysis has the capability of analyzing complex and huge amount of data in a simplistic manner to make informed-decisions. The role of pharmacometrics in generic drug evaluation has a lot of potential and through this application we intend to demonstrate how this potential can be utilized in generic drugs' post-marketing risk assessment. Simulations based analysis will be carried out to determine the importance of response rate (or signal to noise ratio) at the time of approval for the brand product using a low signal to noise and a high signal to noise ratio scenarios. The simulation exercise will comprise of determining the response rate after the virtual patients will switch from brand to the generic product. Pharmacokinetic variability and the effect of intrinsic and extrinsic (such as body size, drug-drug interaction, organ dysfunction etc) factors will be taken into account – which renders this research innovative and more informative. The objective is to classify all therapeutic areas to aid office of genetic drugs in prioritizing the further probing of reports. Further we intend to employ quantitative Bayesian framework to commonly used signal detection methods (for adverse events) such as proportional reporting ratio (PRR) and information criterion (IC). Bayesian framework will also be employed to demonstrate how the potential signal could be updated upon accrual of post marketing reports for both brand and generic products. Finally we will apply the finding of above mentioned steps to the real data that will be accessed from the adverse events reporting system (AERS) database. The deliverable will be to device therapeutic area specific potential signals using a Bayesian framework.