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. 2017 Jul;43(7):1002-1012.
doi: 10.1007/s00134-017-4802-4. Epub 2017 May 2.

Appropriate endpoints for evaluation of new antibiotic therapies for severe infections: a perspective from COMBACTE's STAT-Net

Jean-François Timsit  1   2 , Marlieke E A de Kraker  3 , Harriet Sommer  4 , Emmanuel Weiss  5   6 , Esther Bettiol  7 , Martin Wolkewitz  4 , Stavros Nikolakopoulos  8 , David Wilson  9 , Stephan Harbarth  7 ; COMBACTE-NET consortium
Affiliations

Appropriate endpoints for evaluation of new antibiotic therapies for severe infections: a perspective from COMBACTE's STAT-Net

Jean-François Timsit et al. Intensive Care Med. 2017 Jul.

Abstract

Purpose: In this era of rising antimicrobial resistance, slowly refilling antibiotic development pipelines, and an aging population, we need to ensure that randomized clinical trials (RCTs) determine the added benefit of new antibiotic agents effectively and in a valid way, especially for severely ill patients. Unfortunately, universally accepted endpoints for the evaluation of new drugs in severe infections are lacking.

Methods: We review and discuss the current practices and challenges regarding endpoints in RCTs in this field and propose novel approaches.

Results: Usual endpoints actually recommended for drug development suffer from important flaws. Mortality requires large sample size and only partly related to the infectious process. Clinical cure rate is highly subjective in critically ill patients where symptoms may be related to other intercurrent events. Currently, composite endpoints, hierarchical nested designs, and competing risks analysis seem to be the most promising new tools for designing and analyzing clinical trials in this area, although they require further validation.

Conclusion: Regulatory authorities, pharmaceutical companies, and clinicians need to agree on the most appropriate clinical endpoints for severe infections to ensure efficient approval of new, effective antibiotic agents.

Keywords: Antibiotic therapy; Consensus; Endpoints; Randomized clinical trials; Severe infections.

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Conflict of interest statement

Conflicts of interests

JFT has received speaker fees from Astellas, Gilead, Novartis, Merck, and Pfizer, and is Advisory Board member for 3M, Bayer, Abbott, and Merck. SH has received consulting fees from GSK, Janssen, Abbott, and Novartis, as well as research support from Pfizer.

Funding

For all authors, the research leading to this manuscript has received support from the Innovative Medicines Initiative Joint Undertaking under Grant agreement No. 115523 [Combatting Bacterial Resistance in Europe projects (COMBACTE)], resources of which are composed of financial contribution from the European Union’s 7th Framework Programme (FP7/2007 ± 2013) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) companies’ in-kind contribution. MW has also received funding from the German Research Foundation (Deutsche Forschungsgemeinschaft) under Grant No. WO 1746/1-2. DW belongs to an EFPIA member company in the IMI JU and costs related to his part in the research was carried by the respective company as an in-kind contribution under the IMI JU scheme. The funders had no role in data collection and analysis, decision to publish, or preparation of the manuscript.

Figures

Fig. 1
Fig. 1
These graphs show the impact of the chosen endpoint, effect size, and non-inferiority margins on the required sample size. Scenario 1: required sample size for a non-inferiority trial with a 28-day mortality endpoint with an estimated mortality difference of 0% (green circle) or 2% (blue square) and a non-inferiority margin of 10% (dashed line) or 5% (solid line) (a). Scenario 2: required sample size for a superiority trial for a difference in antibiotic-free days of 7 days (green circle) (b). Scenario 3: required sample size for a superiority trial for a probability of 66% (green circle) to have a better outcome in the intervention arm (DOOR/RADAR composite endpoint) (green circle) (c). All simulations are based on a power of 80%
Fig. 2
Fig. 2
An illustration of the follow-up time over 30 days for ten patients with cure as the primary endpoint. On the x-axis, time from infection is displayed in days. Death can happen early in time (e.g., patients 2 and 9) preventing a patient from being cured, but death can also be observed after cure (patients 6 and 10)
See this image and copyright information in PMC

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