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已批准仿制药的上市后风险投诉 药品尽管仿制药被认为在安全性、强度、 质量、性能特征，处方者仍然担心安全性和 从品牌转向仿制产品后的疗效。为了清楚地界定通用化合物是否真的是 无效或不良事件的原因，数据挖掘需要大量的时间和资源。 因此，迫切需要从这些不良事件或无效信号中滤除噪声， 上市后报告。先进的定量方法，如药物计量分析， 以简单的方式分析复杂和大量数据以做出明智决策的能力。 药物计量学在仿制药评价中的作用具有很大的潜力，通过这一应用， 旨在证明如何在仿制药上市后风险评估中利用这种潜力。 将进行基于模拟的分析，以确定响应率（或信噪比）的重要性 比率），在批准该品牌产品时使用低信噪比和高信噪比 场景模拟演习将包括确定响应率后，虚拟患者将 从品牌转向普通产品。药代动力学变异性以及内源性和外源性 (such如身体大小、药物-药物相互作用、器官功能障碍等）因素将被考虑-其 使这项研究具有创新性和更丰富的信息。目标是对所有治疗领域进行分类， 遗传药物办公室优先考虑进一步调查报告。 此外，我们打算采用定量贝叶斯框架，以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 还将用来说明如何在员额增加时更新潜在信号 品牌和非专利产品的市场报告。最后，我们将应用上述发现 从不良事件报告系统（AERS）数据库访问真实的数据。的 可交付成果将是使用贝叶斯框架的器械治疗区域特定潜在信号。

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.