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.
Generic drugs (copies of brand-name drugs) are important options that allow greater access to health care for all Americans. This project will aim at utilizing advanced pharmacometric analysis to device a novel signal to noise ratio classification system for rank-ordering the therapeutic areas for further probing in post marketing risk assessment of generic products.