Christoph W. Grathwol

Neuroinflammation is a key feature of neurological disorders such as Alzheimer’s or Parkinson’s disease, depression or amyotrophic lateral sclerosis. This study investigates the anti-neuroinflammatory effects of three cardenolide derivatives, digitoxigenin, digitoxigenone, and coroglaucigenin (all at concentrations of 1, 5, 10, and 25 µM), in IL-1β-stimulated human neuroblastoma SK-N-SH cells. All compounds significantly reduced PGE₂ production without affecting cell viability by downregulation of cyclooxygenase (COX)-2 and microsomal prostaglandin E synthases (mPGES)-1 expression and synthesis. Among them, digitoxigenin notably inhibited phosphorylation of p38 mitogen-activated protein kinase (MAPK) and nuclear factor-kappa B (NF-κB) and suppressed interleukin (IL)-6 and IL-8 expression and release. These findings demonstrate that cardenolide derivatives exert potent anti-neuroinflammatory effects by at least partially targeting key enzymes of the arachidonic acid pathway and modulating MAPK/NF-κB signaling, highlighting their potential as novel therapeutic candidates for neuroinflammatory diseases. Future research should focus on effects of the cardenolides in mixed-cell cultures, organoids and in vivo experiments, to evaluate possible side effects and the potential in disease models.

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype associated with poor clinical outcomes and limited treatment options. The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that regulates xenobiotic metabolism through the induction of many cytochrome P450 enzymes such as CYP1A1. In this study, we investigated three 2-phenyl-imidazo[1,2-a]pyridine derivates (X19724, X19728, and X15695), previously developed as selective estrogen receptor degraders, for their activity in TNBC cell lines. All three compounds activated AHR-dependent transcriptional activity and exhibited selective cytotoxicity toward MDA-MB-468 cells among a panel of TNBC and nontumorigenic mammary epithelial cell lines. Nonresponsive TNBC cell lines lacked either detectable AHR expression or inducible CYP1A1 expression. In sensitive MDA-MB-468 cells, cytotoxicity was accompanied by membrane permeabilization. Genetic ablation or pharmacological antagonism of AHR, as well as inhibition of CYP1A1 activity, markedly attenuated compound-induced cytotoxicity. Proteomic analysis further revealed enrichment of pathways related to phospholipid synthesis and base excision repair, along with increased abundance of proteins associated with DNA damage responses. Finally, among the three compounds, X15695 exhibited the greatest metabolic stability following incubation with human liver microsomes. Collectively, these findings support a model in which activation of AHR-CYP1A1 axis contributes to selective cytotoxicity in TNBC cells, highlighting this pathway as a potential mechanistic vulnerability that can be exploited by imidazopyridine derivatives.

We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling direct data exchange with it. A pivotal feature of OpenStats is its ability to record each analysis step in a structured history. This record allows users to retrace their work and enables automatic replay of the entire analysis, promoting reproducibility and long-term data integrity. In the modern research landscape, Research Data Management (RDM) tools like electronic lab notebooks (ELNs) are crucial for generating reproducible, repeatable, and transparently documented data. However, integrating statistical analysis tools into RDM systems remained a challenge. We demonstrated how OpenStats can be seamlessly linked to Research Data Management (RDM) systems, using Chemotion ELN as a reference implementation, by our application to a standard dose-response assay scenario. This linkage allows the integration of statistical data analysis in the form of a closed, traceable workflow into the RDM world. It therefore complements systems that are aiming for reproducible and standardized workflows in the scientific environment.

Aqueous solubility is one key property of a chemical compound that determines its possible use in different applications, from drug development to materials sciences. In this work, we present a model for the prediction of aqueous solubility that leverages a curated data set merged from four distinct sources. This data set encompasses a diverse range of organic compounds, providing a robust foundation for our investigation of solubility prediction. Our approach involves employing a variety of machine learning and deep learning models that combine an extensive array of chemical descriptors, fingerprints, and functional groups. This methodology is designed to address the complexities of solubility prediction and is tailored to achieve high accuracy and generalization. We tested the finalized model on a diverse data set of 1282 unique organic compounds from the Huuskonen data set. The results of our analysis demonstrate the success of our model, which, given an R2 value of 0.92 and an MAE value of 0.40, outperforms existing prediction methods for aqueous solubility on one of the most diverse data sets in the field.

Coumarins are known for their multiple biological effects and have been established as anti-coagulative drugs for years. Furthermore, some coumarins can promote anti-inflammatory effects via the GPR55 receptor, and dual target coumarins have been synthesized. Anti-inflammatory drugs might be beneficial in the treatment of neuropsychiatric disorders, as the inflammatory hypothesis suggests. For the current study, we compared isomeric 3-pyridinylmethylcoumarins with altered N-atom position regarding their effects on cytokine and chemokine synthesis and expression in LPS-stimulated BV2 microglial cells. The 3-pyridin-4-yl-methylcoumarin showed the most potent anti-inflammatory effects, followed by the 3-pyridin-2-ylmethylcoumarin analog. The observed effects might be mediated by an inhibition of ERK phosphorylation.

Aqueous solubility is one key property of a chemical compound that determines its possible use in different applications from drug development to materials sciences. In this work, we present an aqueous solubility prediction study that leverages a curated dataset merged from four distinct sources. This unified dataset encompasses a diverse range of organic compounds, providing a robust foundation for our investigation of solubility prediction. Our approach involves employing a variety of machine learning and deep learning models that combine an extensive array of chemical descriptors, fingerprints, and functional groups. This methodology is designed to address the complexities of solubility prediction, and it is tailored to achieve high accuracy and generalization. We tested the finalized model on a diverse dataset of 1282 unique organic compounds from the Husskonnen dataset. The results of our analysis demonstrate the success of our model, which, given an R2 value of 0.92 and an MAE value of 0.40, outperforms existing prediction methods for aqueous solubility on one of the most diverse datasets in the field.

The DNA repair protein PARP-1 emerged as a valuable target in the treatment of tumor entities with deficiencies of BRCA1/2, such as breast cancer. More recently, the application of PARP inhibitors (PARPi) such as olaparib has been expanded to other cancer entities including colorectal cancer (CRC). We previously demonstrated that PARP-1 is overexpressed in human CRC and promotes CRC progression in a mouse model. However, acquired resistance to PARPi and cytotoxicity-mediated adverse effects limit their clinical applicability. Here, we detailed the role of PARP-1 as a therapeutic target in CRC and studied the efficacy of novel PARPi compounds in wildtype (WT) and DNA repair-deficient CRC cell lines together with the chemotherapeutics irinotecan (IT), 5-fluorouracil (5-FU), and oxaliplatin (OXA). Based on the ComPlat molecule archive, we identified novel PARPi candidates by molecular docking experiments in silico, which were then confirmed by in vitro PARP activity measurements. Two promising candidates (X17613 and X17618) also showed potent PARP-1 inhibition in a CRC cell-based assay. In contrast to olaparib, the PARPi candidates caused no PARP-1 trapping and, consistently, were not or only weakly cytotoxic in WT CRC cells and their BRCA2- or ATR-deficient counterparts. Importantly, both PARPi candidates did not affect the viability of nonmalignant human colonic epithelial cells. While both olaparib and veliparib increased the sensitivity of WT CRC cells towards IT, no synergism was observed for X17613 and X17618. Finally, we provided evidence that all PARPi (olaparib > veliparib > X17613 > X17618) synergize with chemotherapeutic drugs (IT > OXA) in a BRCA2-dependent manner in CRC cells, whereas ATR deficiency had only a minor impact. Collectively, our study identified novel lead structures with potent PARP-1 inhibitory activity in CRC cells but low cytotoxicity due to the lack of PARP-1 trapping, which synergized with IT in homologous recombination deficiency.

Chronic inflammation is driven by proinflammatory cytokines such as interleukin 6 (IL-6), tumor necrosis factor-α (TNF-α), and chemokines, such as c-c motif chemokine ligand 2 (CCL2), CCL3, C-X-C motif chemokine ligand 2 (CXCL2), and CXCL10. Inflammatory processes of the central nervous system (CNS) play an important role in the pathogenesis of various neurological and psychiatric disorders like Alzheimer’s disease, Parkinson’s disease, and depression. Therefore, identifying novel anti-inflammatory drugs may be beneficial for treating disorders with a neuroinflammatory background. The G-protein-coupled receptor 55 (GPR55) gained interest due to its role in inflammatory processes and possible involvement in different disorders. This study aims to identify the anti-inflammatory effects of the coumarin-based compound KIT C, acting as an antagonist with inverse agonistic activity at GPR55, in lipopolysaccharide (LPS)-stimulated BV2 microglial cells in comparison to the commercial GPR55 agonist O-1602 and antagonist ML-193. All compounds significantly suppressed IL-6, TNF-α, CCL2, CCL3, CXCL2, and CXCL10 expression and release in LPS-treated BV2 microglial cells. The anti-inflammatory effects of the compounds are partially explained by modulation of the phosphorylation of p38 mitogen-activated protein kinase (MAPK), p42/44 MAPK (ERK 1/2), protein kinase C (PKC) pathways, and the transcription factor nuclear factor (NF)-κB, respectively. Due to its potent anti-inflammatory properties, KIT C is a promising compound for further research and potential use in inflammatory-related disorders.

Inflammation processes of the central nervous system (CNS) play a vital role in the pathogenesis of several neurological and psychiatric disorders like depression. These processes are characterized by the activation of glia cells, such as microglia. Clinical studies showed a decrease in symptoms associated with the mentioned diseases after the treatment with anti-inflammatory drugs. Therefore, the investigation of novel anti-inflammatory drugs could hold substantial potential in the treatment of disorders with a neuroinflammatory background. In this in vitro study, we report the anti-inflammatory effects of a novel hexacyclic peptide-peptoid hybrid in lipopolysaccharide (LPS)-stimulated BV2 microglial cells. The macrocyclic compound X15856 significantly suppressed Interleukin 6 (IL-6), tumor necrosis factor-α (TNF-α), c-c motif chemokine ligand 2 (CCL2), CCL3, C-X-C motif chemokine ligand 2 (CXCL2), and CXCL10 expression and release in LPS-treated BV2 microglial cells. The anti-inflammatory effects of the compound are partially explained by the modulation of the phosphorylation of p38 mitogen-activated protein kinases (MAPK), p42/44 MAPK (ERK 1/2), protein kinase C (PKC), and the nuclear factor (NF)-κB, respectively. Due to its remarkable anti-inflammatory properties, this compound emerges as an encouraging option for additional research and potential utilization in disorders influenced by inflammation, such as depression.