Ongoing projects
Antivirus Pandemic Preparedness EuropeAn pLatform (APPEAL)
Developing new antiviral treatment against viruses and particularly high-priority emerging and re-emerging viruses as listed by the WHO, remains crucial as, among other aspects, the viruses evolve under selection. Broad spectrum host-directed antiviral drugs (HDA) are promising therapeutic options, however the robust identification of relevant host factors by genome-wide knockout screens is challenging due to low consistency in the resulting hits. Our project aims to (1) establish a computational and experimental pipeline to identify and validate an antiviral for these viruses, and (2) identify at least one broad spectrum antiviral drug against potential emerging and re-emerging viruses as listed by the WHO. To address these, we will implement a drug selection pipeline following several strategies: We will employ (i) machine learning, based on data from knockout screens, proteomics, protein interaction, transcriptomics of infected cells with pandemic-related viruses, Genome-Wide Association Studies, and generic gene descriptors (initial work see e.g. [1,2]), (ii) High Density Cell Arrays, which provide much more detailed readouts compared to state of the art pooled knockout screens, (iii) primary cell cultures obtained from a diverse array of human tissues, as they are more appropriate to study the host cell physiology during infection, compared to cancer cell lines, and (iv) use the pipeline to identify host restriction factors, which, when activated, challenge the virus; followed by innovative drug development and delivery based on small activating RNA. The pipeline comprises an Expedited Arm for which broad spectrum antiviral drugs for repurposing will be selected followed by in vitro and in vivo efficacy testing, and a clinical trial as a proof of concept. The elaborated Arm includes all strategies (i)-(iv) and will provide a sustainable pandemic preparedness pipeline ready-to-operate to identify the appropriate treatment against the emerging virus at the early time of a new outbreak.
Participating institutions:
iRECORDS (International - Rapid rEcognition of CORticosteroiDs sensitivity or resistance in Sepsis)
Sepsis and COVID-19 are both placing a major burden on societies and populations worldwide. Deregulated host response to infection is the hallmark supporting the routine use of corticosteroids (CS), a low-cost and highly efficient class of immuno-modulators, in sepsis/COVID-19. Stratifying patients based on their individual immune response may improve the balance of benefit to risk of CS treatment. Only recently, we identified the ratio of IFNgamma/IL10 as a good biomarker for this [3]. This project aims to integrate data of DNA, RNA, proteins such as cytokines and hormones, or metabolite compounds to define the CS sensitivity/resistance of individual patients. Partners of all over Europe are involved. On our part we contribute to methods of artificial intelligence integrating the high dimensional multi-level data exploring gene networks.
Developing an individualised treatment approach for antibiotics therapy for patients hospitalised with moderate community-acquired pneumonia (CAP)
The role of macrolides, β-lactams and fluoroquinolones in CAP is controversially discussed. We aim to identify patient characteristics with which patients can be identified benefitting from a specific antibiotic therapy to implement this later into the clinical routine. We apply machine learning/artificial intelligence concepts to data available on admission of hospitalised patients from the observational, prospective, multinational CAPNETZ study [4] to investigate patient variables of all relevant aspects for CAP treatment and management based on appr. 12,000 patients. In a pilot study, we identified a treatment rule for macrolide/beta-lactam combination therapy in respect to a beta-lactam mono therapy [5].
Testing the microbiological effectiveness of UV-C radiation to break the infection chain in neonatal incubators
In Germany, each year 63,000 children are born too early. Worldwide, nearly one out of ten children are given prenatal birth (incidence: 9.7%). By this, premature infants are the largest group of patients in the peadiatrics. The improvements in cure and treatment options of prenatal infants have considerably contributed to a higher survival rate. Particularly very early born prenatal infants (Very Low Birth Weight, VLBW) are at high infection risk due to their immature organ system and immune response. Besides this, there is a high selection pressure to pathogens at nosokomial conditions. Prenatal infants are placed in incubators in humid and warm conditions (32-34 0C, 60-80% humidity). This environment unfortunately serves also germs ideal growing conditions. Initial microbiological investigations at a neonatal intensive care unit (NICU) showed that Gram positive and Gram negative bacteria were detected particularly at objects close to the patients including incubators.
The aim of our project is to microbiologically evaluate the performance of a novel device which disinfects the surface of the interior of incubators based on radiation by UVC-LEDs. A laboratory model developed by collaboration partners (SAVUNA GmBH Augsburg, Fraunhofer Institute IOSB Ilmenau, Micro-Hybrid Hermsdorf) will be tested within a clinical study. Our part in this project is the data analysis, and particulalrly the analysis of the sequenced microbiomes.
SARS-CoV-2-DX as part of the Leibniz Center for Photonics in Infection Research, which is currently being established.
The goal of the project is to establish new methods, concepts, and strategies to detect COVID-19 more quickly and reliably, to develop methods and technologies to identify outbreaks earlier and contain them more effectively, and to analyze the pathogenesis of SARS-CoV-2 in order to derive principles for therapy and prevention. The focus of our work is on gene expression-based analysis of tissue samples, and thus the identification of specific infection-related molecular changes and markers.
Collaborative Project on Core Technology 3 as part of the Leibniz Center for Photonics in Infection Research (LPI)
The LPI as a whole aims to bring promising diagnostic and therapeutic procedures with the corresponding technological maturity into translation for infection research through a modularly structured process along a diagnostic and therapeutic service pipeline. To utilize the generated photonic and molecular data for infection-relevant questions, the data must be translated into biomedical information. This translation is ensured by tools such as AI-based methods developed and applied in the core technology 3. Our project deals with the modeling and simulation of pathomechanisms investigating cellular pathways and gene regulatory networks.
