Aimo Kannt

Acetaminophen‐induced acute liver failure is characterized by reactive metabolite formation, resulting in profound oxidative stress and mitochondrial dysfunction, leading to oxidative DNA damage and loss of hepatocyte viability. While oxidative stress is well recognized as a central pathogenic driver, the contribution of DNA damage resolution pathways to hepatocyte survival remains poorly defined. Here we report the development of a small molecule, CMM‐98, that installs a robust AP‐site processing function in the DNA glycosylase OGG1 by binding the active site. Acting on the Schiff base intermediate, the molecule effectively rewires the enzyme's catalytic outcome under oxidative stress, boosting AP site turnover by 57‐fold. Using cellular and in vivo models of acetaminophen‐induced liver failure, we demonstrate that OGG1 catalysis manipulated by CMM‐98 reduces oxidative DNA damage burden, promotes the resolution of oxidative DNA lesions, and ameliorates hepatocyte injury. These findings identify DNA repair capacity as a modifiable determinant of acute liver injury outcome and establish chemical switching of OGG1 as a strategy to support hepatocyte resilience under extreme oxidative stress.

Targeted degradation of extracellular proteins via endo-lysosomal pathways constitutes a promising therapeutic strategy by enabling irreversible target removal and potentially reducing systemic effects of antigen accumulation. In this study, we present a systematic approach for the design and evaluation of Biological Degraders (BioDegs) that leverage receptor-mediated uptake through triantennary N-acetylgalactosamine (TGN) induced activation of the asialoglycoprotein receptor (ASGPR). While this principle has been established, there is still a lack about molecular determinants that influence uptake efficiency. Using interleukin-6 (IL-6) and its soluble receptor (sIL-6R) as therapeutically relevant model antigens, we systematically compared a range of BioDeg formats, including full-length antibodies (siltuximab, tocilizumab), a VHH against IL-6, and an IL-6 binding decoy-receptor design, with respect to binding affinity, thermal stability, cellular uptake, lysosomal trafficking, and degradation efficiency in HepG2 cells. Our results demonstrate that combined properties of scaffold architecture and receptor engagement critically influence degradation outcomes. This work provides a comparative framework for BioDeg design and highlights key parameters for developing lysosome-targeting degraders for extracellular proteins.

The direct-to-biology (D2B) approach enables biological screening of crude reaction mixtures, eliminating the need for purification steps and thereby accelerating hit identification and PROTAC development.

Targeted degradation of extracellular proteins via lysosomal pathways offers a promising therapeutic strategy by enabling irreversible target removal and potentially reducing systemic effects of antigen accumulation. In this study, we present a modular platform for the design and evaluation of Biological Degraders (BioDegs) that leverage receptor-mediated uptake through triantennary N-acetylgalactosamine (TGN) induced activation of the asialoglycoprotein receptor (ASGPR). Using interleukin-6 (IL-6) and its soluble receptor (sIL-6R) as therapeutically relevant model antigens, we systematically compared a range of BioDeg formats, including full-length antibodies (siltuximab, tocilizumab), a VHH against IL-6, and an IL-6 binding decoy-receptor design, with respect to binding affinity, thermal stability, cellular uptake, lysosomal trafficking, and degradation efficiency in HepG2 cells. Our results demonstrate that both scaffold architecture and receptor engagement critically influence degradation outcomes. This work provides a comparative framework for BioDeg design and highlights key parameters for developing lysosome-targeting degraders for extracellular proteins.

Background: The COVID-19 pandemic has profoundly affected daily life and posed significant challenges for politics, the economy, and the education system. To better prepare for such situations and implement effective measures, it is crucial to accurately assess, monitor, and forecast the progression of a pandemic. This study examines the potential of integrating wastewater surveillance data to enhance an autoregressive COVID-19 forecasting model for Germany and its federal states. Methods: First, we explore the cross-correlations between SARS-CoV-2 viral RNA load measured in wastewater and COVID-19 hospitalization considering different time-lags. Further, the study compares the performance of different models, including Random Forest regressors, XGBoost regressors, ARIMA models, linear regression, and ridge regression models, both with and without the use of wastewater data as predictors. For decision tree-based models, we also analyze the performance of fully cross-modal models that rely solely on viral load measurements to predict COVID-19 hospitalization rates. Results: Our retrospective analysis suggest that wastewater data can potentially serve as an early warning indicator of impending trends in hospitalization at a national level, as it shows a strong correlation with hospitalization figures of up to 86% and tends to lead them by up to 8 days. Despite this, including wastewater data in the prediction models did not statistical significantly enhance the accuracy of COVID-19 hospitalization forecasts. The ARIMA model without the inclusion of wastewater viral load data emerged as the best-performing model, achieving a Mean Absolute Percentage Error of 4.76% forecasting hospitalization 7 days ahead. However, wastewater viral load proved to be a valuable standalone predictor, offering an objective alternative to classical surveillance methods for monitoring pandemic trends. Conclusion: This study reinforces the potential of wastewater surveillance as an early warning tool for COVID-19 hospitalizations in Germany. While strong correlations were observed, the integration of wastewater data into predictive models did not improve their performance. Nevertheless, wastewater viral load serves as a valuable indicator for monitoring pandemic trends, suggesting its utility in public health surveillance and resource allocation. Further research may help to clarify the real-time applicability of wastewater data and expand its use to other pathogens and data sources.

Targeted protein degradation (TPD) mediated by molecular glues is an innovative pharmaceutical paradigm. By binding to and modulating the surface of an E3-ligase component, molecular glue degraders can facilitate the recruitment of a specific target protein (or vice versa) and ultimately invoke target degradation. This mode of action results in specific challenges for the development of rational discovery strategies, and complex hit validation workflows may be required to reliably eliminate compounds that elicit nonspecific effects. With the aim to guide screening efforts, we optimized two orthogonal cell-based, target-centric assays for degrader discovery: (1) a time-resolved FRET assay directly quantifying the levels of a target protein and its degradation (signal inhibition) and (2) an assay coupling TPD to cell growth (signal rescue). To enable a deeper understanding of the individual assays' strengths and limitations, we compared their statistical performance as well as respective hit populations by screening a specifically designed collection of about 1000 compounds containing well-annotated reference compounds and known frequent hitters (FHs). We found that the signal rescue format reliably and specifically captured active target degraders while efficiently filtering out interfering or FH compounds. Importantly, this format achieved to retrieve lower potency hits, which might be desirable in order to confidently include as many diverse chemical starting points as possible at the start of a drug discovery project.

The bifunctional soluble epoxide hydrolase (sEH) represents a promising target for inflammation-related diseases. Although potent inhibitors targeting each domain are available, sEH-PROTACs offer the unique ability to simultaneously block both enzymatic functions, mimicking the sEH knockout phenotype, which has been associated with reducing inflammation, including neuroinflammation, and delaying the progression of Alzheimer's disease. Herein, we report the structure-based development of a potent sEH-PROTAC as a useful pharmacological tool. In order to facilitate a rapid testing of the PROTACs, a cell-based sEH degradation assay was developed utilizing HiBiT technology. We designed and synthesized 24 PROTACs. Furthermore, cocrystallization of sEH with two selected PROTACs allowed us to explore the binding mode and rationalize the most optimal linker length. After comprehensive biological and physicochemical characterization of this series, the most optimal PROTAC 23 was identified in primary human and murine cells, highlighting the potential of using 23 in disease-relevant cell and tissue models.

Background/Objectives: Preclinical studies suggest that the deleterious effect of a high serum carnosinase 1 (CN1) concentration is attributed to its adverse effects on insulin sensitivity and glucose metabolism. However, there is little evidence for a modulating role of CN1 in glucose metabolism in humans. Methods: We measured serum CN1 concentration in an observational type 1 diabetes cohort of 172 patients in whom glucose variability (MAGE, MODD, SD of individual blood glucose, mean, and CV) was recorded by blinded continuous glucose monitoring for 5–7 days. Furthermore, insulin dose per kg body weight was compared. Results: Insulin sensitivity (insulin dosage) and glucose variability parameters did not differ between different CN1 tertiles (p > 0.05). Conclusions: There was no association of serum CN1 with indices of glucose variability in this type 1 diabetes cohort.