Lukas Königer,  Universitätsklinikum Würzburg, Lehrstuhl Tissue Engineering und Regenerative Medizin

The discovery and design of specific drugs and therapeutic agents are major drivers for increasing investment in healthcare worldwide. The costs for their development are mainly determined by the expenditures of the clinical phase. On average, only one in ten drug candidates passes clinical trials despite having shown good results in the preclinical phase. One strategy to reduce this discrepancy in the drug evaluation might be to use in vitro testing systems with improved predictive power in research before a drug candidate is used in clinical trials. Already two-dimensional (2D) cell culture enables the evaluation of drugs and their impact on human cells of the tissue of interest. However, a monolayer of human cells does not resemble the complexity and functionality of a whole organ or tissue. Thus, essential tissue functions are not represented in a 2D cell culture.

Kjell Kochale, IUTA Duisburg 

Automation is increasingly finding its way into the biochemical laboratory. While many individual workstations are already automated, the question of feasibility often arises for entire processes. In the case of high sample throughput and fixed work steps, full automation is the obvious choice. However, these usually have to be implemented by external companies at high cost and can only be adapted by them if necessary. Frequently, however, the process is subject to constant change and must therefore be adapted on an ongoing basis. Flexible laboratory automation can then be used to exploit the advantages of automation, i.e. increased sample throughput with better reproducibility. In this presentation, the automation of two different effect-related assays, the acetylcholinesterase assay and the A-YES assay, will be explained. Here, the focus will be on the abstraction of the individual steps, the implementation in automation stations and the subsequent coupling to a process. The entire implementation takes place without IT or automation domain experts. After the implementation, an improved reproducibility with increased sample throughput could be achieved.


Arne Kusserow Merck BSSN, Darmstadt 

Laboratories produce scientific data. This is their basic purpose. Today, only a small fraction of the produced data is used. The vast majority of data, including most metadata are not used and are not even accessible for the lab user. Therefore, many experiments are done again and again. Ideally producing identical results. Wouldn’t it be nice to have all data in one place, no matter by which instrument they are produced? All data, easy to access and securely stored according to FAIR and ALCOA+. AI and ML ready? But it isn’t just nice, it reduces costs, makes labs more efficient and reduces time to market and time to publish.