Effect of Therapeutic Class on Generic Drug Substitutions

The use of generic drugs in place of branded drug products results in lower prices to insurers and patients. The U.S. Food and Drug Administration (FDA) assures that an approved generic product provides the same quality, safety, and efficacy as the corresponding brand. Despite this, some clinicians and patients are reluctant to use generic medications. Sometimes, this is based on a concern that even a small difference in absorption or metabolism impacts outcomes [such as drugs with very narrow therapeutic indices (NTI)]. In other cases, however, the reasons for “generic reluctance” are ambiguous. If the societal goal is to allow patients to access worthy medications while containing costs within the health care system, we need to understand why there is differential acceptance of generic drugs. As requested by the FDA, we propose to enter into a cooperative agreement to advance knowledge about usage of generic drugs, substitution of generic drugs for brand-name drugs, and the modifiers of generic drug use at the patient, prescriber, and health system level. We hypothesize that the factors which influence generic drug use vary by therapeutic class. We propose to: 1) quantify generic drug usage in the U.S. by therapeutic classes and assess predictors of generic substitution; 2) quantify patient-reported concerns about generic drugs, by therapeutic class, and to identify risk factors for patient-reported concerns in response to generic substitution; and 3) develop an evaluation scheme to prioritize drugs requiring generic product development and investment and to apply the evaluation scheme to identify priority drugs. To address the aims of this project, we propose three activities using datasets with highly complementary information. The first is data from an integrated health system (Sutter Health) that, importantly, will allow us to look at written prescriptions, in addition to pharmacist-filled prescriptions. These predictors of usage will then be confirmed in a separate, very large dataset from Truven Health Analytics(MarketScan Commercial Claims). These data include pharmacy fill data from patients who are representative of commercially-insured patients across the country. Between these two sources, we will be able enumerate which therapeutic classes have much and little generic usage, and using generalized hierarchical models, look at predictors of generic usage. We will also analyze the FDA Adverse Event Reporting System data (FAERS). We will isolate patient-generated reports and, specifically, those with mention difficulties with switching to a generic product. We will primarily analyze the publically accessible coded data but will use a subset of the submitted case reports to validate our methodology. Finally, with the knowledge gained, we will develop a scheme to prioritize drugs requiring generic product development and investment and apply the evaluation scheme to identify priority drugs. We will use a structured process with experts and key stakeholders to inform the development of a taxonomy that can be applied to drugs currently with market exclusivity.