Penetration rate of anti-TNF biosimilars and savings at 5 years after their introduction in French hospitals.

OBJECTIVE: Since the expiry of the patents on originator anti-TNF agents in Europe, France has authorized the sale of biosimilars. The penetration rate of anti-TNF agents and their biosimilars, and the cost savings driven by the introduction of biosimilars appears to vary widely. This study aimed to describe the market share of anti-TNFs and their biosimilars, and the cost savings generated by the introduction of biosimilars in French hospitals 5 years ago. METHODS: The pharmaceutical component of the French national uniform hospital discharge data set database (PMSI) was used to study sales of infliximab, etanercept and adalimumab originators and biosimilars, and to estimate cost savings generated by the introduction of biosimilars onto the market, using the historical tariffs of the originators. RESULTS: The penetration rate of anti-TNF biosimilars in France in 76% for infliximab, 74% for etanercept and 77% for adalimumab. In 2020, Inflectra® (41%) was the Remicade® biosimilar with the highest sales volume, while Erelzi® (57%) and Amgevita® (64%) were the most widely sold biosimilars of Enbrel® and Humira® respectively. In terms of cost savings since the launch of biosimilars, overall, for all biosimilars taken together, over the 5-year period, a total of 824 million Euro was saved, in relation to the historical tariffs of the originators. CONCLUSIONS: This study shows firstly that the penetration rate of anti-TNF biosimilars in France 5 years after their launch is close to 80%. Secondly, we show that the cost savings generated by the use of biosimilars to anti-TNF agents exceed 820 million Euro over 5 years.

Competition From Biosimilars Drives Price Reductions For Biologics In The French Single-Payer Health System.

France has a single-payer health insurance system that has the authority to impose pharmaceutical price reductions but relies on decentralized market negotiations between hospitals and manufacturers to establish prices for injected and infused biologics. Hospitals rely on biosimilars-less expensive but therapeutically equivalent variants of biologic medications-to stimulate competition. Price reductions negotiated by hospitals subsequently are adopted by the health insurance system, driving hospitals to negotiate a new round of discounts. This article measures 2004-20 trends in prices, price reductions, utilization, and market shares for three prominent biologics-Remicade, Enbrel, and Humira-and their eleven competing biosimilars. Biosimilar launches are associated with a sequence of price reductions for the reference biologic, for other biologics that treat similar conditions, and for all related biosimilars. The French experience provides lessons for the US in its efforts to use competition from biosimilars to drive price reductions and savings from biologics.

An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study.

INTRODUCTION: Signal detection from healthcare databases is possible, but is not yet used for routine surveillance of drug safety. One challenge is to develop methods for selecting signals that should be assessed with priority. AIM: The aim of this study was to develop an automated system combining safety signal detection and prioritization from healthcare databases and applicable to drugs used in chronic diseases. METHODS: Patients present in the French EGB healthcare database for at least 1 year between 2005 and 2015 were considered. Noninsulin glucose-lowering drugs (NIGLDs) were selected as a case study, and hospitalization data were used to select important medical events (IME). Signal detection was performed quarterly from 2008 to 2015 using sequence symmetry analysis. NIGLD/IME associations were screened if one or more exposed case was identified in the quarter, and three or more exposed cases were identified in the population at the date of screening. Detected signals were prioritized using the Longitudinal-SNIP (L-SNIP) algorithm based on strength (S), novelty (N), and potential impact of signal (I), and pattern of drug use (P). Signals scored in the top 10% were identified as of high priority. A reference set was built based on NIGLD summaries of product characteristics (SPCs) to compute the performance of the developed system. RESULTS: A total of 815 associations were screened and 241 (29.6%) were detected as signals; among these, 58 (24.1%) were prioritized. The performance for signal detection was sensitivity = 47%; specificity = 80%; positive predictive value (PPV) 33%; negative predictive value = 82%. The use of the L-SNIP algorithm increased the early identification of positive controls, restricted to those mentioned in the SPCs after 2008: PPV = 100% versus PPV = 14% with its non-use. The system revealed a strong new signal with dipeptidylpeptidase-4 inhibitors and venous thromboembolism. CONCLUSION: The developed system seems promising for the routine use of healthcare data for safety surveillance of drugs used in chronic diseases.