ABSTRACT
Objectives: Starting from April 2017, the Italian Medicine Agency (AIFA) has approved new criteria for defining any new medicinal product with an innovative indication. The purpose of the study is to analyze the activity of innovativeness evaluation according to the new approach, to estimate the weight of each criterion considered for innovativeness definition, and to evaluate how the new approach works in terms of consistency and reproducibility. Methods: A retrospective analysis was performed on the final reports evaluating the drug innovativeness assessment published on the AIFA's website between April 2017 and January 2021. Descriptive statistics, chi-square test, whether the conditions were respected, or Fisher's exact test was used to explore the association between characteristics of drugs and the innovativeness status and the association between the three criteria. Profiles of the decision process and their relationship with innovativeness response were described. In order to evaluate the weight of each criterion in predicting the innovativeness status, a Classification Tree (CT) algorithm was applied. Results: Overall, of the 109 published drugs reports, 37 (33.9%) were recognized as fully innovative, 29 (26.6%) were considered conditionally innovative, while for 43 (39.4%) reports innovativeness was not recognized. Considering the three criteria of the decision process, the added therapeutic value was the only criterion statistically associated with a drug's degree of innovation (p < 0.001). The therapeutic need and the quality of clinical evidence were statistically associated (p = 0.008) even if only a mild association was observed. The added therapeutic value was the most important variable in predicting the innovativeness status according to the classification tree (CT) model applied, achieving an accuracy of 89.4%. No difference was found between orphans and non-orphan drugs or oncological and non-oncological drugs. Discussion: The added therapeutic value is the most important criterion of the multidimensional approach for the innovativeness status definition of a new medical product. A mild association was found between the therapeutic need and the quality of evidence. Overall, similar decision profiles bring the same evaluation of innovativeness status, indicating a good consistency and reproducibility between decisions.
ABSTRACT
The Italian Medicines Agency has started, since the first months of 2020, a monitoring of drug use during the covid-19 pandemic. This made it possible to identify specific trends in hospital and local purchases, such as the extensive use of off-label drugs with little evidence of efficacy during the first weeks of the epidemic, and to progressively assess the degree of implementation of regulatory and ministerial recommendations.Fin dalle prime fasi dell'emergenza covid-19 è emersa per l'Agenzia Italiana del Farmaco (AIFA) la necessità di monitorare in modo specifico l'uso dei farmaci utilizzati nel corso dell'epidemia. È infatti fondamentale, in un contesto caratterizzato da grande incertezza e da continui aggiornamenti delle linee guida, disporre di informazioni utili a una corretta lettura e interpretazione dei dati. È stato quindi realizzato un primo rapporto dell'Osservatorio Nazionale sull'Impiego dei Medicinali (OsMed) sull'uso dei farmaci utilizzati, a livello ospedaliero e territoriale, nella fase iniziale dell'epidemia1. Questo metteva a confronto i consumi relativi al periodo compreso tra marzo a maggio del 2020 con quelli del trimestre immediatamente precedente, da dicembre 2019 a febbraio 2020. In seguito, il 4 marzo 2021, è stato pubblicato un aggiornamento dei dati in riferimento alle fasi successive dell'epidemia2. Il monitoraggio ha preso in considerazione diverse categorie di farmaci, andando a valutare le oscillazioni negli acquisti in riferimento al progressivo aggiornamento degli indirizzi regolatori. I volumi osservati per ciascun farmaco sono stati standardizzati per 10.000 abitanti/die, andando a valutare le differenze pre- e post-covid-19 in termini di differenza assoluta, differenza percentuale e di p-value (p).
