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The ligand-sensing transcription factor Nurr1 emerges as a promising therapeutic target for neurodegenerative pathologies but Nurr1 ligands for functional studies and therapeutic validation are lacking. Here pronounced Nurr1 modulation by statins for which clinically relevant neuroprotective effects are demonstrated, is reported. Several statins directly affect Nurr1 activity in cellular and cell-free settings with low micromolar to sub-micromolar potencies. Simvastatin as example exhibits anti-inflammatory effects in astrocytes, which are abrogated by Nurr1 knockdown. Differential gene expression analysis in native and Nurr1-silenced cells reveals strong proinflammatory effects of Nurr1 knockdown while simvastatin treatment induces several neuroprotective mechanisms via Nurr1 involving changes in inflammatory, metabolic and cell cycle gene expression. Further in vitro evaluation confirms reduced inflammatory response, improved glucose metabolism, and cell cycle inhibition of simvastatin-treated neuronal cells. These findings suggest Nurr1 involvement in the well-documented but mechanistically elusive neuroprotection by statins.
Publicly available compound and bioactivity databases provide an essential basis for data-driven applications in life-science research and drug design. By analyzing several bioactivity repositories, we discovered differences in compound and target coverage advocating the combined use of data from multiple sources. Using data from ChEMBL, PubChem, IUPHAR/BPS, BindingDB, and Probes & Drugs, we assembled a consensus dataset focusing on small molecules with bioactivity on human macromolecular targets. This allowed an improved coverage of compound space and targets, and an automated comparison and curation of structural and bioactivity data to reveal potentially erroneous entries and increase confidence. The consensus dataset comprised of more than 1.1 million compounds with over 10.9 million bioactivity data points with annotations on assay type and bioactivity confidence, providing a useful ensemble for computational applications in drug design and chemogenomics.
Non-alcoholic steatohepatitis (NASH) - a hepatic manifestation of the metabolic syndrome - is a multifactorial disease with alarming global prevalence. It involves steatosis, inflammation and fibrosis in the liver, thus demanding multiple modes of action for robust therapeutic efficacy. Aiming to fuse complementary validated anti-NASH strategies in a single molecule, we have designed and systematically optimized a scaffold for triple activation of farnesoid X receptor (FXR), peroxisome proliferator-activated receptor (PPAR) α and PPARδ. Pilot profiling of the resulting triple modulator demonstrated target engagement in native cellular settings and in mice, rendering it a suitable tool to probe the triple modulator concept in vivo. In DIO NASH in mice, the triple agonist counteracted hepatic inflammation and reversed hepatic fibrosis highlighting the potential of designed polypharmacology in NASH.
Several lines of evidence suggest the ligand-sensing transcription factor Nurr1 as a promising target to treat neurodegenerative diseases. Nurr1 modulators to validate and exploit this therapeutic potential are rare, however. To identify novel Nurr1 agonist chemotypes, we have employed the Nurr1 activator amodiaquine as template for microscale analogue library synthesis. The first set of analogues was based on the 7-chloroquiolin-4-amine core fragment of amodiaquine and revealed superior N-substituents compared to diethylaminomethylphenol contained in the template. A second library of analogues was subsequently prepared to replace the chloroquinolineamine scaffold. The two sets of analogues enabled a full scaffold hop from amodiaquine to a novel Nurr1 agonist sharing no structural features with the lead but comprising superior potency on Nurr1. Additionally, pharmacophore modeling based on the entire set of active and inactive analogues suggested key features for Nurr1 agonists.
The nsP3 macrodomain is a conserved protein interaction module that plays essential regulatory roles in host immune response by recognizing and removing posttranslational ADP-ribosylation sites during SARS-CoV-2 infection. Thus, targeting this protein domain may offer a therapeutic strategy to combat the current and future virus pandemics. To assist inhibitor development efforts, we report here a comprehensive set of macrodomain crystal structures complexed with diverse naturally-occurring nucleotides, small molecules as well as nucleotide analogues including GS-441524 and its phosphorylated analogue, active metabolites of remdesivir. The presented data strengthen our understanding of the SARS-CoV-2 macrodomain structural plasticity and it provides chemical starting points for future inhibitor development.
The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.
Druggability Evaluation of the Neuron Derived Orphan Receptor (NOR-1) Reveals Inverse NOR-1 Agonists
(2022)
The neuron derived orphan receptor (NOR-1, NR4A3) is among the least studied nuclear receptors. Its physiological role and therapeutic potential remain widely elusive which is in part due to the lack of chemical tools that can directly modulate NOR-1 activity. To probe the possibility of pharmacological NOR-1 modulation, we have tested a drug fragment library for NOR-1 activation and repression. Despite low hit-rate (<1 %), we have obtained three NOR-1 ligand chemotypes one of which could be rapidly expanded to an analogue comprising low micromolar inverse NOR-1 agonist potency and altering NOR-1 regulated gene expression in a cellular setting. It confirms druggability of the transcription factor and may serve as an early tool to assess the role and potential of NOR-1.
Nuclear receptors (NRs) activate transcription of target genes in response to binding of ligands to their ligand-binding domains (LBDs). Typically, in vitro assays use either gene expression or the recruitment of coactivators to the isolated LBD of the NR of interest to measure NR activation. However, this approach ignores that NRs function as homo- as well as heterodimers and that the LBD harbors the main dimerization interface. Cofactor recruitment is thereby interconnected with oligomerization status as well as ligand occupation of the partnering LBD through allosteric cross talk. Here we present a modular set of homogeneous time-resolved FRET–based assays through which we investigated the activation of PPARγ in response to ligands and the formation of heterodimers with its obligatory partner RXRα. We introduced mutations into the RXRα LBD that prevent coactivator binding but do not interfere with LBD dimerization or ligand binding. This enabled us to specifically detect PPARγ coactivator recruitment to PPARγ:RXRα heterodimers. We found that the RXRα agonist SR11237 destabilized the RXRα homodimer but promoted formation of the PPARγ:RXRα heterodimer, while being inactive on PPARγ itself. Of interest, incorporation of PPARγ into the heterodimer resulted in a substantial gain in affinity for coactivator CBP-1, even in the absence of ligands. Consequently, SR11237 indirectly promoted coactivator binding to PPARγ by shifting the oligomerization preference of RXRα toward PPARγ:RXRα heterodimer formation. These results emphasize that investigation of ligand-dependent NR activation should take NR dimerization into account. We envision these assays as the necessary assay tool kit for investigating NRs that partner with RXRα.
Designed polypharmacology presents as an attractive strategy to increase therapeutic efficacy in multi-factorial diseases by a directed modulation of multiple involved targets with a single molecule. Such an approach appears particularly suitable in non-alcoholic steatohepatitis (NASH) which involves hepatic steatosis, inflammation and fibrosis as pathological hallmarks. Among various potential pharmacodynamic mechanisms, activation of the farnesoid X receptor (FXRa) and inhibition of leukotriene A4 hydrolase (LTA4Hi) hold promise to counteract NASH according to preclinical and clinical observations. We have developed dual FXR/LTA4H modulators as pharmacological tools, enabling evaluation of this polypharmacology concept to treat NASH and related pathologies. The optimized FXRa/LTA4Hi exhibits well-balanced dual activity on the intended targets with sub-micromolar potency and is highly selective over related nuclear receptors and enzymes rendering it suitable as tool to probe synergies of dual FXR/LTA4H targeting.
Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of the most promising computational designs remains challenging. Herein, we leveraged the probabilities learnt by chemical language models with the beam search algorithm as a model-intrinsic technique for automated molecule design and scoring. Prospective application of this method yielded novel inverse agonists of retinoic acid receptor-related orphan receptors (RORs). Each design was synthesizable in three reaction steps and presented low-micromolar to nanomolar potency towards RORγ. This model-intrinsic sampling technique eliminates the strict need for external compound scoring functions, thereby further extending the applicability of generative artificial intelligence to data-driven drug discovery.