Human challenge models for infection resistance as valuable tools to substantiate the beneficial health effects of food ingredients
Presenter: Alwine Kardinaal
Human clinical trials to support health benefits of food ingredients in the healthy population are generally large-sized studies, requiring substantial investments. There is a large gap between preclinical work, and the generation of evidence in humans. To bridge this gap, the use of human challenge models can reduce the risk of failure in field trials. In these smaller-sized trials, a controlled challenge is provided to healthy subjects, to measure the effect of food ingredients on resilience to the stressor. For infection resistance, we apply an E. coli challenge model (gut infection), a rhinovirus challenge model (upper respiratory tract infection), and a vaccination model (immune function related to infection resistance). The presentation will discuss the design and primary outcomes of these models, illustrated with results from interventions.
Challenge models and data driven experimental design…? How to improve the efficiency of your experimental design?
Presenter: Wynand Alkema
In order to capture the most value from your clinical trial, a large number of secondary or tertiary endpoints can be measured for analysis. With the technological advances in multiplexing and miniaturization techniques, it is now possible to simultaneously measure the levels of thousands of metabolites, proteins and transcripts in human blood as well as the occurrence of hundreds of microorganism at various body sites.
Harvesting, processing and understanding these big data sets, can shed light on the underlying mechanisms of (lack of) efficacy and possible side effects of the intervention. Similarly, such data can be obtained on predictive in-vitro or animal models, which may help to prospectively improve trial design with respect to e.g. subject selection, dose optimisation and biomarker identification.
In this talk we will outline and illustrate such data driven clinical trial approaches.
