Liver fibrosis affects up to 9 percent of the adult European population with no effective drug therapies currently available for patients. New drug development is time consuming and costly, but strategies in predictive toxicology utilizing adverse outcome pathways (AOP) have the potential to bridge this gap. AOPs are structured representations of biological events that lead to harmful effects and can be used for various risk assessments. Although detailed AOPs for liver fibrosis are available, quantitative data on the driving cellular events are lacking. The BRIDGE Discovery project “AOP Plug & Play” aims to offer a quantitative solution for in vitro AOP analysis, through an easy-to-use state-of-the-art microfluidic platform. Here, we present a characterization of enzymatic electrochemical sensor candidates that will be integrated into the multi-sensor microfluidic platform.
To mimic sequential events in an AOP, the microfluidic platform will use fluidic control systems to regulate fluid exchange between the cellular components and the sensor modules (Figure 1). Glucose, lactate, pH and reactive oxygen species sensors will all be integrated into the final platform. The sensor module will monitor cell health and metabolic status by measuring analytes in the cell culture medium, such as glucose and lactate. Initial characterization of a lactate sensor demonstrates a dynamic range from 0.1-10 mM while operating at 37°C in phosphate buffer for 2 hours (Figure 2). Characterization of electrochemical enzymatic glucose and lactate sensors will be presented with target ranges of 1-20 mM glucose and 0.5-10 mM lactate with a minimum 8 hour sensor lifetime.
Figure 1: Concept of the AOP "Plug & Play" fluid manipulation strategy leveraging pumps and valves to control exposure to different modules of the platform.
Data gathered by these sensors will be used to develop predictive in vitro models to assess cell health and response. Upon successful model development, in vitro-to-in vivo extrapolation (IVIVE) will be the next step to quantitatively support new therapies that may result in streamlining drug development based on AOPs.