Discovery of Novel UDP-N-Acetylglucosamine Acyltransferase (LpxA) Inhibitors with Activity against Pseudomonas aeruginosa.

This study describes a novel series of UDP-N-acetylglucosamine acyltransferase (LpxA) inhibitors that was identified through affinity-mediated selection from a DNA-encoded compound library. The original hit was a selective inhibitor of Pseudomonas aeruginosa LpxA with no activity against Escherichia coli LpxA. The biochemical potency of the series was optimized through an X-ray crystallography-supported medicinal chemistry program, resulting in compounds with nanomolar activity against P. aeruginosa LpxA (best half-maximal inhibitory concentration (IC50) <5 nM) and cellular activity against P. aeruginosa (best minimal inhibitory concentration (MIC) of 4 µg/mL). Lack of activity against E. coli was maintained (IC50 > 20 µM and MIC > 128 µg/mL). The mode of action of analogues was confirmed through genetic analyses. As expected, compounds were active against multidrug-resistant isolates. Further optimization of pharmacokinetics is needed before efficacy studies in mouse infection models can be attempted. To our knowledge, this is the first reported LpxA inhibitor series with selective activity against P. aeruginosa.

In-silico guided discovery of novel CCR9 antagonists.

Antagonism of CCR9 is a promising mechanism for treatment of inflammatory bowel disease, including ulcerative colitis and Crohn's disease. There is limited experimental data on CCR9 and its ligands, complicating efforts to identify new small molecule antagonists. We present here results of a successful virtual screening and rational hit-to-lead campaign that led to the discovery and initial optimization of novel CCR9 antagonists. This work uses a novel data fusion strategy to integrate the output of multiple computational tools, such as 2D similarity search, shape similarity, pharmacophore searching, and molecular docking, as well as the identification and incorporation of privileged chemokine fragments. The application of various ranking strategies, which combined consensus and parallel selection methods to achieve a balance of enrichment and novelty, resulted in 198 virtual screening hits in total, with an overall hit rate of 18%. Several hits were developed into early leads through targeted synthesis and purchase of analogs.

Structure-based discovery of LpxC inhibitors.

The emergence and spread of multidrug-resistant (MDR) Gram negative bacteria presents a serious threat for public health. Novel antimicrobials that could overcome the resistance problems are urgently needed. UDP-3-O-(R-3-hydroxymyristol)-N-acetylglucosamine deacetylase (LpxC) is a cytosolic zinc-based deacetylase that catalyzes the first committed step in the biosynthesis of lipid A, which is essential for the survival of Gram-negative bacteria. Our efforts toward the discovery of novel LpxC inhibitors are presented herein.

Discovery of Pyrazolopyridones as a Novel Class of Gyrase B Inhibitors Using Structure Guided Design.

The ATPase subunit of DNA gyrase B is an attractive antibacterial target due to high conservation across bacteria and the essential role it plays in DNA replication. A novel class of pyrazolopyridone inhibitors was discovered by optimizing a fragment screening hit scaffold using structure guided design. These inhibitors show potent Gram-positive antibacterial activity and low resistance incidence against clinically important pathogens.

Fragment-based discovery of DNA gyrase inhibitors targeting the ATPase subunit of GyrB.

Inhibitors of the ATPase function of bacterial DNA gyrase, located in the GyrB subunit and its related ParE subunit in topoisomerase IV, have demonstrated antibacterial activity. In this study we describe an NMR fragment-based screening effort targeting Staphylococcus aureus GyrB that identified several attractive and novel starting points with good ligand efficiency. Fragment hits were further characterized using NMR binding studies against full-length S. aureus GyrB and Escherichia coli ParE. X-ray co-crystal structures of select fragment hits confirmed binding and suggested a path for medicinal chemistry optimization. The identification, characterization, and elaboration of one of these fragment series to a 0.265 µM inhibitor is described herein.

Rank order entropy: why one metric is not enough.

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the misapplication of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r(2), PRESS r(2), F-tests, etc.) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted end point values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a "rank order entropy" evaluation or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes and provides confidence in the use of a particular model within a specific domain of applicability.

Highly potent inhibitors of methionine aminopeptidase-2 based on a 1,2,4-triazole pharmacophore.

High-throughput screening for inhibitors of the human metalloprotease, methionine aminopeptidase-2 (MetAP2), identified a potent class of 3-anilino-5-benzylthio-1,2,4-triazole compounds. Efficient array and interative synthesis of triazoles led to rapid SAR development around the aniline, benzylthio, and triazole moeities. Evaluation of these analogs in a human MetAP2 enzyme assay led to the identification of several inhibitors with potencies in the 50-100 picomolar range. The deleterious effects on inhibitor potency by methylation of the anilino-triazole nitrogens, as well as the X-ray crystal structure of triazole 102 bound in the active site of MetAP2, confirm the key interactions between the triazole nitrogens, the active site cobalt atoms, and the His-231 side-chain. The structure has also provided a rationale for interpreting SAR within the triazole series. Key aniline (2-isopropylphenyl) and sulfur substituents (furanylmethyl) identified in the SAR studies led to the identification of potent inhibitors (103 and 104) of endothelial cell proliferation. Triazoles 103 and 104 also exhibited dose-dependent activity in an aortic ring tissue model of angiogenesis highlighting the potential utility of MetAP2 inhibitors as anticancer agents.

4-Aryl-1,2,3-triazole: a novel template for a reversible methionine aminopeptidase 2 inhibitor, optimized to inhibit angiogenesis in vivo.

Inhibitors of human methionine aminopeptidase type 2 (hMetAP2) are of interest as potential treatments for cancer. A new class of small molecule reversible inhibitors of hMetAP2 was discovered and optimized, the 4-aryl-1,2,3-triazoles. Compound 24, a potent inhibitor of cobalt-activated hMetAP2, also inhibits human and mouse endothelial cell growth. Using a mouse matrigel model, this reversible hMetAP2 inhibitor was also shown to inhibit angiogenesis in vivo.

Six-coordinate Co(2+) with H(2)O and NH(3) ligands: which spin state is more stable?

Octahedral, six-coordinate Co(2+) can exist in two spin states. For biological ligands, H(2)O and NH(3), the most stable spin state is high spin (S = (3)/(2)). The difference in energy between high and low spin is dependent upon the ligand mix and coordination stereochemistry. High spin optimized geometries for these model compounds give structures close to octahedral symmetry. Low spin permits significant Jahn-Teller distortion. H(2)O ligands preferentially assume axial positions. Continuum solvent has a greater effect on low spin Co(2+), and it reduces the energy difference between the two spin states. For some ligand combinations optimized in the presence of solvent, there is no significant difference in energy between spin states.