RESUMO
In the pharmaceutical sector, evergreening is considered a range of practices applied to extend monopoly protection on existing products. Filing several patent applications related to the same active pharmaceutical ingredient (API) is one of the most common manifestations of evergreening. During the COVID-19 pandemic, several health technologies were developed. This study aimed to analyze the extension of evergreening for selected health technologies for SARS-CoV-2 through patent filing strategies. Starting with the selection of three antivirals, one biological and two vaccines, a patent landscape was built based on public and private databases. Regarding these selected technologies, we analyzed some of the evergreening strategies used by different applicants, academic institutions or pharmaceutical companies and found a total of 29 applications (10 after the pandemic) for antivirals, 3 applications for a biological drug (1 after the pandemic), and 41 applications for vaccines (23 after the pandemic). Despite differences among the technologies, a common aspect found in all analyzed cases is the intense patent filing after the pandemic, aligned to the fact that those technologies were moving through the R&D process up to regulatory approval. The evergreening approach pursued has already been found in other diseases, with the risk of monopoly extension and also bringing legal uncertainty due to the lack of transparency of newer patent applications covering specific medical indications. Therefore, efforts to address evergreening should be pursued by countries, including the adoption of a public health approach to the patent examination of those technologies to prevent the granting of undeserved patents.
RESUMO
Theoretical models of personality disorders can be complex and multifaceted, making it difficult to validate such models in a comprehensive, empirical fashion. One such model of borderline personality disorder (BPD) is the emotional cascade model (Selby & Joiner, 2009), which has garnered empirical support in piecemeal fashion but has not been examined in a gestalt fashion. One way to test comprehensive models of personality pathology is with Temporal Bayesian Network (TBN) modeling, in which the relations between multiple subcomponents of a model can be specified and examined over a dynamic time frame, allowing for the modeling of positive feedback processes in addition to comprehensive model utility. In this study, we applied TBN modeling to examine the emotional cascade model in a sample of adolescents and young adults who actively self-injure, including those with BPD. TBN modeling was applied to ecological momentary assessment data provided via participant smartphone assessments for a period of 2 weeks. TBN analysis suggested that the emotional cascade model has considerable predictive utility, demonstrating substantial accuracy in predicting BPD diagnosis (with accuracy estimates around 90%) and momentary prediction of rumination, negative emotion, and dysregulated behaviors (with accuracy estimates consistently above 70% and reaching up to 100%, depending on the level of momentary prediction specificity). These findings provide support and validity to the notion that BPD may emerge from a dynamic interplay between emotional cascades and dysregulated behaviors. Implications of TBN modeling of BPD and personality disorders, in general, are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
