540 Chemie und zugeordnete Wissenschaften
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- Biochemie, Chemie und Pharmazie (8) (remove)
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.
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 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.
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.
Designed multitarget ligands are a popular approach to generating efficient and safe drugs, and fragment-based strategies have been postulated as a versatile avenue to discover multitarget ligand leads. To systematically probe the potential of fragment-based multiple ligand discovery, we have employed a large fragment library for comprehensive screening on five targets chosen from proteins for which multitarget ligands have been successfully developed previously (soluble epoxide hydrolase, leukotriene A4 hydrolase, 5-lipoxygenase, retinoid X receptor, farnesoid X receptor). Differential scanning fluorimetry served as primary screening method before fragments hitting at least two targets were validated in orthogonal assays. Thereby, we obtained valuable fragment leads with dual-target engagement for six out of ten target combinations. Our results demonstrate the applicability of fragment-based approaches to identify starting points for polypharmacological compound development with certain limitations.
Hepatocyte nuclear factor 4α (HNF4α) is a ligand-sensing transcription factor and presents as a potential drug target in metabolic diseases and cancer. In humans, mutations in the HNF4α gene cause maturity-onset diabetes of the young (MODY), and the elevated activity of this protein has been associated with gastrointestinal cancers. Despite the high therapeutic potential, available ligands and structure–activity relationship knowledge for this nuclear receptor are scarce. Here, we disclose a chemically diverse collection of orthogonally validated fragment-like activators as well as inverse agonists, which modulate HNF4α activity in a low micromolar range. These compounds demonstrate the druggability of HNF4α and thus provide a starting point for medicinal chemistry as well as an early tool for chemogenomics.