DOI: 10.25934/pr00007849
title: Determinants of study design for vaccines against opportunistic bacterial infections: a proof-of-concept for data-driven, individual patient data (IPD) analyses.

description: Background
In the era of antimicrobial resistance (AMR), there is the need to improve and speed up the drug development process for alternative therapies and preventive approaches to limit the spread of this ‘silent epidemic’. Despite the potential for vaccines to slow down AMR, the majority of the infections listed by World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) as AMR threats cannot be prevented by a licensed vaccine. Most of these multi-drug resistant pathogens cause opportunistic, hospital-acquired infections (HAI), which are known under the acronyms ESKAPE or ESCAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter). The lack of established correlates (immune markers statistically associated with vaccine-induced protection) and surrogates of protection (correlates on the direct causal pathway between vaccine and protection), which can be used as surrogate endpoints in clinical trials, and the low incidence of the disease, which makes it necessary to include large numbers of participants, represent major problems in the clinical development of vaccines against these pathogens. The COMBINE project, part of the Innovative Medicines Initiative (IMI) AMR Accelerator, aims to contribute to increasing the feasibility to conduct clinical trials for novel vaccines and antibiotics against AMR pathogens by critically assessing and improving the design and analysis of clinical trials.

Aim of the analysis
Individual patient data (IPD) (meta-)analyses have grown increasingly popular in various areas of evidence based medicine. However, they have never been used to investigate clinical trials of vaccines against opportunistic bacterial infections. 
In other branches of medicine and biology, data-driven investigations have informed basic research and helped discover previously unknown mechanisms, and identified features of patients likely to develop a disease or respond to treatment, thus driving research in personalized and precision medicine. 
We aim to provide a proof-of-concept that the data-driven merged analysis of individual patient data from multiple clinical trials can help understand past failures in the development of vaccines against opportunistic bacterial infection and inform future product development. For the success of this analysis, the availability of data from clinical trials by different sponsors investigating different products is key, as it is crucial to increase variability and to minimize systematic effects associated with a particular product or company operating procedures. 


Further clinical data are planned to be analysed as part of the same project. These are data originating from other clinical trials (Phase II/Phase III) for vaccine candidates against S. aureus, C. difficile, P. aeruginosa and extraintestinal pathogenic E. coli (ExPEC). Currently, we are not planning to submit further data requests on Vivli (data cannot be shared or companies do not have agreements with Vivli) but are submitting a request for data from one product outside of the Vivli platform.