Hans Matter

Evidence for CCl/CBr⋅⋅⋅π Interactions as an Important Contribution to Protein–Ligand Binding Affinity†

Attractive chlorine: Noncovalent interactions between chlorine or bromine atoms and aromatic rings in proteins open up a new method for the manipulation of molecular recognition. Substitution at distinct positions of two factor Xa inhibitors improves the free energy of binding by interaction with a tyrosine unit. The generality of this motif was underscored by multiple crystal structures as well as high-level quantum chemical calculations (see picture).

Fragment Deconstruction of Small, Potent Factor Xa Inhibitors: Exploring the Superadditivity Energetics of Fragment Linking in Protein-Ligand Complexes.

Predictable thermodynamic additivity is one of the cornerstones of classical covalent chemistry, allowing accurate calculation of energy terms for complete processes by addition of terms for individual components. However this principle breaks down in complex noncovalent systems, such as biological systems, in which the energetics of individual components are not truly independent of each other. This complicates predicting protein structure and folding and, the focus of this work, the prediction of ligand binding to proteins. Molecular recognition in protein–ligand complexes predominantly occurs through multiple noncovalent interactions, whereas their contribution to the total free-energy of binding (DG) is often unevenly distributed over the contact interface. The identification of ligands as “molecular anchors” for high affinity regions in proteins (“hot spots”) is fundamental for fragment-based drug discovery, 3] indicating the similarity of ligandand protein-centric concepts. Often highaffinity ligands encompass more than one fragment in proximal protein sites; in a few cases, individual fragments in two neighboring sites could be linked to result in high binding affinity. Ideally, the DG of linked fragments should be significantly greater than the sum of DG increments from each fragment. This overproportional increase (“superadditivity”) is attributed to the fact that each fragment loses a significant part of its rigid body rotational and translational entropy upon complex formation. Thus, the sum of DG for two fragments includes two unfavorable rigid body entropy barrier terms, whereas the joined molecule is only affected by one of these terms. Any ligand has to overcome this barrier because of entropy loss upon association to its site. The nonadditivity for DG contributions is defined as linker coefficient E corresponding to the difference between the sums of fragment affinity and the final ligand [Eq. (1)]. DGfinal 1⁄4 DGfrag1 þ DGfrag2 þ DGlink with DGlink 1⁄4 R T ln E ð1Þ

Discovery, Structure Elucidation, and Biological Characterization of Nannocystin A, a Macrocyclic Myxobacterial Metabolite with Potent Antiproliferative Properties

Microbial natural products are a rich source of bioactive molecules to serve as drug leads and/or biological tools. We investigated a little-explored myxobacterial genus, Nannocystis sp., and discovered a novel 21-membered macrocyclic scaffold that is composed of a tripeptide and a polyketide part with an epoxyamide moiety. The relative and absolute configurations of the nine stereocenters was determined by NMR spectroscopy, molecular dynamics calculations, chemical degradation, and X-ray crystallography. The compound, named nannocystin A (1), was found to inhibit cell proliferation at low nanomolar concentrations through the early induction of apoptosis. The mode of action of 1 could not be matched to that of standard drugs by transcriptional profiling and biochemical experiments. An initial investigation of the structure-activity relationship based on seven analogues demonstrated the importance of the epoxide moiety for high activity.

Sulfonylthiadiazoles with an Unusual Binding Mode as Partial Dual Peroxisome Proliferator‐Activated Receptor (PPAR) γ/δ Agonists with High Potency and in vivo Efficacy

Compounds that simultaneously activate the peroxisome proliferator‐activated receptor (PPAR) subtypes PPARγ and PPARδ have the potential to effectively target dyslipidemia and type II diabetes in a single pharmaceutically active molecule. The frequently observed side effects of selective PPARγ agonists, such as edema and weight gain, are expected to be overcome by using partial instead of full agonists for this nuclear receptor family. Herein we report the discovery, synthesis, and optimization of a novel series of sulfonylthiadiazoles that are active as partial agonists. The initial compound 6 was discovered by high‐throughput screening as a moderate partial PPARδ agonist; its optimization was based on the X‐ray crystal structure in complex with PPARδ. In contrast to other PPARδ agonists, this ligand does not interact directly with residues from the activation helix AF‐2, which might be linked to its partial agonistic effect. Interestingly, the thiadiazole moiety fills a novel subpocket, which becomes accessible after moderate conformational rearrangement. The optimization was focused on introducing conformational constraints and replacing intramolecular hydrogen bonding interactions. Highly potent molecules with activity as dual partial PPARγ/δ agonists in the low nanomolar range were then identified. One of the most active members, compound 20 a, displayed EC50 values of 1.6 and 336 nM for PPARδ and γ, respectively. The X‐ray crystal structure of its complex with PPARδ confirms our design hypothesis. Compound 20 a clearly displayed in vivo activity in two chronic mice studies. Lipids were modified in a beneficial way in normolipidemic mice, and the development of overt diabetes could be prevented in pre‐diabetic db/db mice. However, body weight gain was similar to that observed with the PPARγ agonist rosiglitazone. Hence, active compounds from this series can be considered as valuable tools to elucidate the complex roles of dual PPARγ/δ agonists for potential treatment of metabolic syndrome.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules.

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

CROSS: An Efficient Workflow for Reaction-Driven Rescaffolding and Side-Chain Optimization Using Robust Chemical Reactions and Available Reagents

A novel procedure (CROSS: Computational Rescaffolding and Optimization using Synthetic Schemes) for in silico rescaffolding and side-chain optimization is reported with explicit consideration of the route of synthesis and availability of compatible chemical reagents. We have defined a set of retrosynthetic disconnections representing robust reactions, amenable for combinatorial chemistry. This rule set is used to generate virtual fragment databases from available reagents. Each reactive center is annotated with its compatibility with regard to the chemical reactions. The rule set is then applied to a new molecule to obtain separate query subunits for rescaffolding by 3D similarity searching in specific reagent-derived fragment databases. Thus, only fragments compatible with the chemistry and shape of the corresponding query moiety are investigated further. The identified fragment hits directly indicate (1) available chemical reagents that can replace the query moiety in the starting molecule and (2) the route for the synthesis of the proposed molecules.

Predictive in silico off-target profiling in drug discovery

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein-ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

Discovery and pharmacological characterization of SAR707 as novel and selective small molecule inhibitor of stearoyl-CoA desaturase (SCD1)

Stearoyl-CoA desaturase (SCD1) is linked to the pathogenesis of obesity, dyslipidemia and type 2 diabetes. It is the rate-limiting enzyme in the synthesis of monounsaturated 16:1 n-7 and 18:1 n-9 fatty acyl-CoAs and catalyzes an essential part of lipogenesis. Here, we describe the identification, in vitro properties and in vivo efficacy of a novel class of heterocyclic small molecule hexahydro-pyrrolopyrrole SCD1 inhibitors. SAR707, a compound representative for the series, was optimized to high in vitro potency, selectivity and favorable overall properties in enzymatic and cellular assays. In vivo, this compound reduced serum desaturation index, decreased body weight gain and improved lipid parameters and blood glucose levels of obese Zucker diabetic fatty rats treated for 4 weeks in a chronic study. In parallel, fissures of the eye lid, alopecia and inflammation of the skin were observed from day 11 on in all animals treated with the same metabolically active dose. In summary, we described in vitro and in vivo properties of a novel, potent and selective SCD1 inhibitor that improved body weight, blood glucose and triglycerides in an animal model of obesity, type 2 diabetes and dyslipidemia. However, the favorable in vivo properties of systemic SCD1 inhibition shown in our study were accompanied by dose-dependently occurring adverse target-related effects observed in skin. Thus, systemic SCD1 inhibition by small molecules might therefore not represent a feasible approach for the treatment of chronic metabolic diseases.

Identification and Application of Antitarget Activity Hotspots to Guide Compound Optimization

The optimization of a lead structure to a development candidate often requires removal of undesirable antitarget activities. To this end, we have developed an approach to extract antitarget activity hotspots from larger databases and to transfer this knowledge onto novel chemical series. These antitarget activity hotspots will be captured as pairs of informative molecules, which are chemically closely related, but differ significantly in biological activity. We illustrate the application of antitarget activity hotspots as informative compound pairs for the optimization of side effects in lead structures for relevant antitargets in pharmaceutical research. The use for prospective design requires establishing a structural link between known antitarget hotspot pairs and a new lead structure: we employ 3D‐based similarity comparison for this task. The entire workflow serves as idea generator in early optimization. The feasibility of this approach is demonstrated in several optimization problems related to hERG inhibition, and CYP3A4 inhibition. Several structural examples demonstrate the ability of the 3D‐shape searching to identify related scaffolds and the usefulness of the antitarget hotspot information to guide optimization by modulating the undesirable antitarget activity. Such a concept based on the analysis of local similarities and the transfer to 3D‐related series is especially promising in those cases, where the construction of antitarget QSAR models fails to detect local SAR trends for guiding the next optimization cycle.

Identification and Characterization of a Single High‐Affinity Fatty Acid Binding Site in Human Serum Albumin

A single high-affinity fatty acid binding site in the important human transport protein serum albumin (HSA) is identified and characterized using an NBD (7-nitrobenz-2-oxa-1,3-diazol-4-yl)-C12 fatty acid. This ligand exhibits a 1:1 binding stoichiometry in its HSA complex with high site-specificity. The complex dissociation constant is determined by titration experiments as well as radioactive equilibrium dialysis. Competition experiments with the known HSA-binding drugs warfarin and ibuprofen confirm the new binding site to be different from Sudlow-sites I and II. These binding studies are extended to other albumin binders and fatty acid derivatives. Furthermore an X-ray crystal structure allows locating the binding site in HSA subdomain IIA. The knowledge about this novel HSA site will be important for drug depot development and for understanding drug-protein interaction, which are important prerequisites for modulation of drug pharmacokinetics.