[Show abstract][Hide abstract] ABSTRACT: 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.
No preview · Article · Jun 2015 · Angewandte Chemie
[Show abstract][Hide abstract] ABSTRACT: Most drug candidates are active against more than a single target. Some interactions
can lead to drug toxicity, so the hERG potassium channel and the serotonin
receptor 5HT2B have been linked to severe cardiovascular side effects of
drugs. Combined in silico/in vitro off-target profiling strategies are most effective
to identify critical liabilities in drug discovery. A stepwise approach is required
to optimally support drug safety profiling. Computational models for numerous
off-targets allow for systematic prediction of drug-target interactions. Target engagement
in adverse pathways can be analyzed with pathway databases, supporting
the toxicity hazard prediction and mode-of-toxicity evaluation of drugs. - The
CTLink method for off-target prediction offers several algorithms to link new
chemical samples to a curated reference set of several million biologically active
samples. Prospective validation for in vitro assay data revealed a predictive accuracy
of up to 81%. More than 4000 different protein targets can be predicted this way,
each limited by an individual model applicability domain. - Quantitative QSAR
models were built for ~ 400 important off-targets (200 kinases, >200 receptors&
channels) in alignment to corporate in vitro panels. These models were extensively
validated and the best have good r2/r2(cv)/q2 >= 0.6. - A biological network analysis
approach, based on pathway databases (IPA®, MetaCore®, WikiPathways
and Reactome), provides signals for potential drug toxicity. This approach links
validated predictions to pathologies, assuming that drug tissue concentrations exceed
a given threshold. In silico predictions help to design experimental follow-up
studies and to enhance compound selection. Relevant application examples will
be presented, focusing on organ toxicities (e.g. cardiotoxicity, hepatotoxicity and
nephrotoxicity) and reproductive toxicity.
[Show abstract][Hide abstract] ABSTRACT: 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.
No preview · Article · Mar 2014 · Future medicinal chemistry
[Show abstract][Hide abstract] ABSTRACT: Absorption, distribution, metabolism, excretion, and toxicology (ADMET) strongly influence the pharmacokinetic and safety behavior of drugs. Therefore, ADMET parameters determined by in vitro assays are used in drug discovery to prioritize hit and lead structures as well as to support compound optimization. The large datasets obtained from these efforts provide a good opportunity to develop global in silico models, which can successfully be used in the selection of hit or lead structure with as few liabilities as possible. Such models, if carefully validated, offer additional opportunities in lead optimization. Here, we describe the development of a global model for metabolic lability from a large in-house human liver microsome dataset. The model building strategy comprised the evaluation of different molecular descriptors and decision tree enhancements provided by the software Cubist to identify the machine learning algorithm and the descriptors performing best for this dataset. To ensure proper application of the model, a similarity-based applicability domain estimation was implemented, together with a continuous model update procedure. This combination ensures reliable prediction of metabolic lability, which can be used along the early drug discovery process. A case study is given, illustrating the application of the model in a multidimensional compound optimization.
[Show abstract][Hide abstract] ABSTRACT: Abstract The electron transport chain (ETC) couples electron transfer between donors and acceptors with proton transport across the inner mitochondrial membrane. The resulting electrochemical proton gradient is used to generate chemical energy in the form of adenosine triphosphate (ATP). Proton transfer is based on the activity of complex I-V proteins in the ETC. The overall electrical activity of these proteins can be measured by proton transfer using Solid Supported Membrane technology. We tested the activity of complexes I, III, and V in a combined assay, called oxidative phosphorylation assay (oxphos assay), by activating each complex with the corresponding substrate. The oxphos assay was used to test in-house substances from different projects and several drugs currently available on the market that have reported effects on mitochondrial functions. The resulting data were compared to the influence of the respective compounds on mitochondria as determined by oxygen consumption and to data generated with an ATP depletion assay. The comparison shows that the oxidative phosphorylation assay provides both a rapid approach for detecting interaction of compounds with respiratory chain proteins and information on their mode of interaction. Therefore, the oxphos assay is a useful tool to support structure activity relationship studies by allowing early identification of mitotoxicity and for analyzing the outcome of phenotypic screens that are susceptible to the generation of mitotoxicity-related artifacts.
No preview · Article · Aug 2013 · Assay and Drug Development Technologies
[Show abstract][Hide abstract] ABSTRACT: We have used a set of four local properties based on semiempirical molecular orbital calculations (the electron density (ρ), hydrogen bond donor field (HDF), hydrogen bond acceptor field (HAF) and molecular lipophilicity potential (MLP)) for 3D-QSAR studies to overcome the limitations of the current force-field based molecular interaction fields (MIFs). These properties can be calculated rapidly and are thus amenable to high-throughput industrial applications. Their statistical performance was compared with that of conventional 3D-QSAR approaches using nine datasets (angiotensin converting enzyme inhibitors (ACE), acetylcholinesterase inhibitors (AchE), benzodiazepine receptor ligands (BZR), cyclooxygenase-2 inhibitors (COX2), dihydrofolate reductase inhibitors (DHFR), glycogen phosphorylase b inhibitors (GPB), thermolysin inhibitors (THER), thrombin inhibitors (THR) and serine protease factor Xa inhibitors (fXa)). The 3D-QSAR models generated were tested thoroughly for robustness and predictive ability. The average performance of the quantum mechanical molecular interaction field (QM-MIF) models for the nine datasets is better than that of the conventional force-field-based MIFs. In the individual datasets, the QM-MIF models always perform better than, or as well as, the conventional approaches. It is particularly encouraging that the relative performance of the QM-MIF models improves in the external validation. In addition, the models generated showed statistical stability with respect to model building procedure variations such as grid spacing size and grid orientation. QM-MIF contour maps reproduce the features important for ligand binding for the example dataset (factor Xa inhibitors), demonstrating the intuitive chemical interpretability of QM-MIFs.
No preview · Article · May 2013 · Journal of Chemical Information and Modeling
[Show abstract][Hide abstract] ABSTRACT: A novel procedure for in-silico rescaffolding and side chain optimization is reported with explicit consideration of the route of chemical synthesis and availability of compatible chemical reagents. We have defined a set of retrosynthetic disconnections representing robust chemical reactions, amenable for combinatorial chemistry. This rule set is used to generate virtual fragment databases from available chemical 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 shape matching in specific reagent-derived fragment databases. Thus, only fragment hits 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.
No preview · Article · Apr 2013 · Journal of Medicinal Chemistry
[Show abstract][Hide abstract] ABSTRACT: 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 catalyses 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 optimised to high in vitro potency, selectivity and favourable 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 favourable 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.
Preview · Article · Mar 2013 · European journal of pharmacology
[Show abstract][Hide abstract] ABSTRACT: The discovery of potent benzimidazole stearoyl-CoA desaturase (SCD1) inhibitors by ligand-based virtual screening is described. ROCS 3D-searching gave a favorable chemical motif that was subsequently optimized to arrive at a chemical series of potent and promising SCD1 inhibitors. In particular, compound SAR224 was selected for further pharmacological profiling based on favorable in vitro data. After oral administration to male ZDF rats, this compound significantly decreased the serum fatty acid desaturation index, thus providing conclusive evidence for SCD1 inhibition in vivo by SAR224.
No preview · Article · Jan 2013 · Bioorganic & medicinal chemistry letters
[Show abstract][Hide abstract] ABSTRACT: The pressure on research efficiency and cost in the pharmaceutical industry has resulted in a paradigm shift to bring active molecules earlier to the market (Wess 2002; Lawrence 2002). Increasing expenses by attrition in late stage development are partially attributed to an inadequate understanding of pharmacokinetic and toxicological behavior of drugs (Prentis et al. 1988; Kennedy 1997; Drews 2000). The conversion of biologically active molecules into effective and safe pharmaceuticals adds substantial value to the drug discovery process. Consequently, the improvement of a compound profile toward a clinical candidate is one of the essential skills in integrated drug discovery teams. Those candidate requirements include multiple parameters including potency and efficacy, selectivity against related proteins or “antitargets,” favorable physicochemical and pharmacokinetic properties leading to the required bioavailability after oral administration, and an acceptable half-life of elimination of the final candidate. A simultaneous optimization of multiple parameters in carefully planned iterations is therefore required to arrive at molecules with suitable properties and profiles.
[Show abstract][Hide abstract] ABSTRACT: Current 3D-QSAR methods such as CoMFA or CoMSIA make use of classical force-field approaches for calculating molecular fields. Thus, they can not adequately account for noncovalent interactions involving halogen atoms like halogen bonds or halogen-π interactions. These deficiencies in the underlying force fields result from the lack of treatment of the anisotropy of the electron density distribution of those atoms, known as the "σ-hole", although recent developments have begun to take specific interactions such as halogen bonding into account. We have now replaced classical force field derived molecular fields by local properties such as the local ionization energy, local electron affinity, or local polarizability, calculated using quantum-mechanical (QM) techniques that do not suffer from the above limitation for 3D-QSAR. We first investigate the characteristics of QM-based local property fields to show that they are suitable for statistical analyses after suitable pretreatment. We then analyze these property fields with partial least-squares (PLS) regression to predict biological affinities of two data sets comprising factor Xa and GABA-A/benzodiazepine receptor ligands. While the resulting models perform equally well or even slightly better in terms of consistency and predictivity than the classical CoMFA fields, the most important aspect of these augmented field-types is that the chemical interpretation of resulting QM-based property field models reveals unique SAR trends driven by electrostatic and polarizability effects, which cannot be extracted directly from CoMFA electrostatic maps. Within the factor Xa set, the interaction of chlorine and bromine atoms with a tyrosine side chain in the protease S1 pocket are correctly predicted. Within the GABA-A/benzodiazepine ligand data set, PLS models of high predictivity resulted for our QM-based property fields, providing novel insights into key features of the SAR for two receptor subtypes and cross-receptor selectivity of the ligands. The detailed interpretation of regression models derived using improved QM-derived property fields thus provides a significant advantage by revealing chemically meaningful correlations with biological activity and helps in understanding novel structure-activity relationship features. This will allow such knowledge to be used to design novel molecules on the basis of interactions additional to steric and hydrogen-bonding features.
No preview · Article · Aug 2012 · Journal of Chemical Information and Modeling
[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: This chapter contains sections titled: IntroductionGeneral Aspects of Halogen Atoms in Medicinal ChemistryFluorine: A Unique Halogen AtomInteractions of Higher Halogen AtomsInteractions of Higher Halogen Atoms to Aromatic RingsConclusions
[Show abstract][Hide abstract] ABSTRACT: The superadditivity of fragment linking on affinities was quantified by systematic deconstruction of a fXa inhibitor. In their Communication (DOI: 10.1002/anie.201107091), M. Nazaré, H. Matter, and co-workers show that by connecting two fragments with a single bond, a linker contribution of -14.0 kJ mol(-1) results, which corresponds to an affinity improvement of about 2.5 orders of magnitude relative to the sum of fragment affinities.
No preview · Article · Jan 2012 · Angewandte Chemie International Edition