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Objectives: The patent linkage system upgraded patent challenges to an important factor in granting timely market approval for generic drugs. We aim to understand patent challenges and identify the factors that are associated with successful patent challengers under the patent linkage system in South Korea.Methods: We constructed a novel dataset that combined information on manufacturers with detailed data about their patent challenges after introduction of the patent linkage system. Based on the number of successful patent challenges, manufacturers were categorized into non-challengers, passive challengers, and aggressive challengers. Then, two types of logistic models were applied to identify the factors associated with successful and aggressive challengers.Findings: Only 39 active ingredients were challenged by 77 manufacturers from March 2015 to December 2019. Of 171 manufacturers, 94 (55 %) were non-challengers, 58 (34 %) were passive challengers who had succeeded in fewer than 4 patent challenges, and 19 (11 %) were aggressive challengers who had succeeded in 4 or more patent challenges. Higher sales, more employees, and a greater number of reimbursed drugs were associated with being a patent challenger, while a greater number of reimbursed drugs was associated with being an aggressive challenger.Conclusion: Some manufacturers utilize patent challenges to strengthen their product portfolios in the market. However, under the patent linkage system, the frequency of patent challenges is limited in South Korea compared to the United States. In particular, patent challenges against drugs in injection form and biologics are very rare. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data.Methods: To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability.Results: CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table.Conclusions: CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects. [ABSTRACT FROM AUTHOR]