An Automated Software Tool for Efficient Processing and Analysis of Ligand-Observed 1H and 19F NMR Binding Data

Due to their exceptional sensitivity and robustness, NMR spectroscopy-based binding assays are routinely applied in hit finding and validation during early stages of drug discovery, in particular in the field of fragment-based lead generation. To this end, compound libraries comprised of 500-3000 molecules are screened in mixtures by ligand-observed NMR binding experiments, and in addition, focused sets comprised of individual compounds are routinely assessed in follow-up activities. Most commonly, proton-detected experiments such as STD, T1ρ and WaterLOGSY are utilized and more recently fluorine-detected relaxation-edited experiments. While some level of automation is generally implemented for sample preparation and data acquisition, the subsequent data analysis of a high number of spectra remains largely a manual and slow process, which presents a critical bottleneck in NMR-based binding studies. Here, we report a novel software tool that processes, analyzes, and qualifies ligand-observed proton and fluorine NMR binding data in a fully automated fashion. The challenges associated with such complex data are addressed by carefully designed processes and filters built into the analysis. The output consists of a hit list and an interactive graphical presentation of the spectra and analysis results for easy user inspection and validation. The scope, performance and limitations of the tool are demonstrated on three datasets comprised of 19F-detected mixture screening binding experiments, as well as 1H-detected mixture and single compound binding experiments. From the comparison of automated and manual analysis results, we conclude that the program delivers robust, high-confidence hit lists in a fraction of the time needed for manual analysis and greatly facilitates the visual inspection of the associated NMR spectra. These features enable considerably shorter turn-around times, higher throughput and thereby greater impact of NMR-based binding experiments

Study of the selectivity of insulin-like growth factor-1 receptor (IGF1R) inhibitors

The insulin-like growth factor-1 receptor (IGF1R) is a drug target for oncology, and many studies are ongoing to identify compounds that inhibit its tyrosine kinase activity. IGF1R is highly homologous to the insulin receptor (IR) and IGF1R inhibition might be beneficial for patients, while IR inhibition may lead to limiting toxicity. Therefore selectivity for IGF1R over IR is the aim for drug design in this context. A few compounds that selectively inhibit IGF1R over IR in cells have been identified, but none of them show the same levels of selectivity in enzymatic assays. To determine whether this discrepancy is linked to the conditions used in the enzymatic assays, we have studied the interaction between known IGF1R inhibitors (NVP-AEW541, OSI906, AG538, NVP-TAE226) and phosphorylated/unphosphorylated IGF1R/IR proteins with both biophysical (isothermal calorimetry and surface plasmon resonance) and enzymatic methods. In this report, we describe the results of this study and comment on the different degrees of selectivity IGF1R versus IR measured in biochemical and cellular assays. Finally, our study provides new information on the biochemical and mechanism of action of these small molecular weight IGF1R inhibitor

Fast and Efficient Fragment-Based Lead Generation by Fully Automated Processing and Analysis of Ligand-Observed NMR Binding Data

NMR binding assays are routinely applied in hit finding and validation during early stages of drug discovery, particularly for fragment-based lead generation. To this end, compound libraries are screened by ligand-observed NMR experiments such as STD, T1ρ, and CPMG to identify molecules interacting with a target. The analysis of a high number of complex spectra is performed largely manually and therefore represents a limiting step in hit generation campaigns. Here we report a novel integrated computational procedure that processes and analyzes ligand-observed proton and fluorine NMR binding data in a fully automated fashion. A performance evaluation comparing automated and manual analysis results on ¹⁹F- and ¹H-detected data sets shows that the program delivers robust, high-confidence hit lists in a fraction of the time needed for manual analysis and greatly facilitates visual inspection of the associated NMR spectra. These features enable considerably higher throughput, the assessment of larger libraries, and shorter turn-around times

Discovery of a selective and biologically active low-molecular weight antagonist of human interleukin-1β

Abstract Human interleukin-1β (hIL-1β) is a pro-inflammatory cytokine involved in many diseases. While hIL-1β directed antibodies have shown clinical benefit, an orally available low-molecular weight antagonist is still elusive, limiting the applications of hIL-1β-directed therapies. Here we describe the discovery of a low-molecular weight hIL-1β antagonist that blocks the interaction with the IL-1R1 receptor. Starting from a low affinity fragment-based screening hit 1, structure-based optimization resulted in a compound (S)-2 that binds and antagonizes hIL-1β with single-digit micromolar activity in biophysical, biochemical, and cellular assays. X-ray analysis reveals an allosteric mode of action that involves a hitherto unknown binding site in hIL-1β encompassing two loops involved in hIL-1R1/hIL-1β interactions. We show that residues of this binding site are part of a conformationally excited state of the mature cytokine. The compound antagonizes hIL-1β function in cells, including primary human fibroblasts, demonstrating the relevance of this discovery for future development of hIL-1β directed therapeutics

Crystal Structures of Human MdmX (HdmX) in Complex with p53 Peptide Analogues Reveal Surprising Conformational Changes*

p53 tumor suppressor activity is negatively regulated through binding to the oncogenic proteins Hdm2 and HdmX. The p53 residues Leu26, Trp23, and Phe19 are crucial to mediate these interactions. Inhibiting p53 binding to both Hdm2 and HdmX should be a promising clinical approach to reactivate p53 in the cancer setting, but previous studies have suggested that the discovery of dual Hdm2/HdmX inhibitors will be difficult. We have determined the crystal structures at 1.3 Å of the N-terminal domain of HdmX bound to two p53 peptidomimetics without and with a 6-chlorine substituent on the indole (which binds in the same subpocket as Trp23 of p53). The latter compound is the most potent peptide-based antagonist of the p53-Hdm2 interaction yet to be described. The x-ray structures revealed surprising conformational changes of the binding cleft of HdmX, including an “open conformation” of Tyr99 and unexpected “cross-talk” between the Trp and Leu pockets. Notably, the 6-chloro p53 peptidomimetic bound with high affinity to both HdmX and Hdm2 (Kd values of 36 and 7 nm, respectively). Our results suggest that the development of potent dual inhibitors for HdmX and Hdm2 should be feasible. They also reveal possible conformational states of HdmX, which should lead to a better prediction of its interactions with potential biological partners

New insights from old data - Hunting for compounds with novel mechanisms using cellular high-throughput screening profiles with Grey Chemical Matter

Identifying high quality chemical starting points is a critical and challenging step in drug discovery, which typically involves screening large compound libraries or repurposing of compounds with known mechanisms of actions (MoAs). Here we introduce a novel cheminformatics approach that mines existing large-scale, phenotypic high throughput screening (HTS) data. Our method aims to identify bioactive compounds with distinct and specific MoAs, serving as a valuable complement to existing focused library collections. This approach identifies chemotypes with selectivity across multiple cell-based assays and characterized by persistent and broad structure activity relationships (SAR). We prospectively demonstrate the validity of the approach in broad cellular profiling assays (cell painting, DRUG-seq, Promotor Signature Profiling) and chemical proteomics experiments where the compounds behave similarly to known chemogenetic libraries, but with a bias towards novel protein targets and required no synthetic effort to improve compound properties. A public set of such compounds is provided based on the PubChem BioAssay dataset for use by the scientific community

Fragment-Based Drug Discovery of Inhibitors of Phosphopantetheine Adenylyltransferase from Gram-Negative Bacteria

The discovery and development of new antibiotics capable of curing infections due to multidrug-resistant and pandrug-resistant Gram-negative bacteria are a major challenge with fundamental importance to our global healthcare system. Part of our broad program at Novartis to address this urgent, unmet need includes the search for new agents that inhibit novel bacterial targets. Here we report the discovery and hit-to-lead optimization of new inhibitors of phosphopantetheine adenylyltransferase (PPAT) from Gram-negative bacteria. Utilizing a fragment-based screening approach, we discovered a number of unique scaffolds capable of interacting with the pantetheine site of <i>E. coli</i> PPAT and inhibiting enzymatic activity, including triazolopyrimidinone <b>6</b>. Structure-based optimization resulted in the identification of two lead compounds as selective, small molecule inhibitors of bacterial PPAT: triazolopyrimidinone <b>53</b> and azabenzimidazole <b>54</b> efficiently inhibited <i>E. coli</i> and <i>P. aeruginosa</i> PPAT and displayed modest cellular potency against the efflux-deficient <i>E. coli</i> Δ<i>tolC</i> mutant strain

Fragment-Based Drug Discovery of Inhibitors of Phosphopantetheine Adenylyltransferase from Gram-Negative Bacteria

The discovery and development of new antibiotics capable of curing infections due to multidrug-resistant and pandrug-resistant Gram-negative bacteria are a major challenge with fundamental importance to our global healthcare system. Part of our broad program at Novartis to address this urgent, unmet need includes the search for new agents that inhibit novel bacterial targets. Here we report the discovery and hit-to-lead optimization of new inhibitors of phosphopantetheine adenylyltransferase (PPAT) from Gram-negative bacteria. Utilizing a fragment-based screening approach, we discovered a number of unique scaffolds capable of interacting with the pantetheine site of <i>E. coli</i> PPAT and inhibiting enzymatic activity, including triazolopyrimidinone <b>6</b>. Structure-based optimization resulted in the identification of two lead compounds as selective, small molecule inhibitors of bacterial PPAT: triazolopyrimidinone <b>53</b> and azabenzimidazole <b>54</b> efficiently inhibited <i>E. coli</i> and <i>P. aeruginosa</i> PPAT and displayed modest cellular potency against the efflux-deficient <i>E. coli</i> Δ<i>tolC</i> mutant strain