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Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising strategy, harnessing the wealth of structure-activity data available for known targets to facilitate efficient few-shot model training for the specific target of interest. In this study, we assessed the effectiveness of two different meta-learning methods, namely model-agnostic meta-learning (MAML) and adaptive deep kernel fitting (ADKF), specifically in the regression setting. We investigated how factors such as dataset size and the similarity of training tasks impact predictability. The results indicate that ADKF significantly outperformed both MAML and a single-task baseline model on the inhibition data. However, the performance of ADKF varied across different test tasks. Our findings suggest that considerable enhancements in performance can be anticipated primarily when the task of interest is similar to the tasks incorporated in the meta-learning process., (© 2024 Wiley-VCH GmbH.)

Lead optimization supported by artificial intelligence (AI)-based generative models has become increasingly important in drug design. Success factors are reagent availability, novelty, and the optimization of multiple properties. Directed fragment-replacement is particularly attractive, as it mimics medicinal chemistry tactics. Here, we present variations of fragment-based reinforcement learning using an actor-critic model. Novel features include freezing fragments and using reagents as the fragment source. Splitting molecules according to reaction schemes improves synthesizability, while tuning network output probabilities allows us to balance novelty versus diversity. Combining fragment-based optimization with virtual library encodings allows the exploration of large chemical spaces with synthesizable ideas. Collectively, these enhancements influence design toward high-quality molecules with favorable profiles. A validation study using 15 pharmaceutically relevant targets reveals that novel structures are obtained for most cases, which are identical or related to independent validation sets for each target. Hence, these modifications significantly increase the value of fragment-based reinforcement learning for drug design. The code is available on GitHub: https://github.com/Sanofi-Public/IDD-papers-fragrl.

Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proof of concepts already published. Nevertheless, generative models are known for sometimes generating unrealistic, unstable, unsynthesizable, or uninteresting structures. This calls for methods to constrain those algorithms to generate structures in drug-like portions of the chemical space. While the concept of applicability domains for predictive models is well studied, its counterpart for generative models is not yet well-defined. In this work, we empirically examine various possibilities and propose applicability domains suited for generative models. Using both public and internal data sets, we use generative methods to generate novel structures that are predicted to be actives by a corresponding quantitative structure-activity relationships model while constraining the generative model to stay within a given applicability domain. Our work looks at several applicability domain definitions, combining various criteria, such as structural similarity to the training set, similarity of physicochemical properties, unwanted substructures, and quantitative estimate of drug-likeness. We assess the structures generated from both qualitative and quantitative points of view and find that the applicability domain definitions have a strong influence on the drug-likeness of generated molecules. An extensive analysis of our results allows us to identify applicability domain definitions that are best suited for generating drug-like molecules with generative models. We anticipate that this work will help foster the adoption of generative models in an industrial context., Competing Interests: The authors declare the following competing financial interest(s): All authors are or have been employed by Sanofi and may hold shares and/or stock options in the company., (© 2023 The Authors. Published by American Chemical Society.)

The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into "drug-like" chemical space, such as target-activity machine learning models, respectively., Competing Interests: Authors SS, HM, GH, and CG are employed by Sanofi., (Copyright © 2022 Sauer, Matter, Hessler and Grebner.)

The accurate prediction of protein-ligand binding affinity belongs to one of the central goals in computer-based drug design. Molecular dynamics (MD)-based free energy calculations have become increasingly popular in this respect due to their accuracy and solid theoretical basis. Here, we present a combined study which encompasses experimental and computational studies on two series of factor Xa ligands, which enclose a broad chemical space including large modifications of the central scaffold. Using this integrated approach, we identified several new ligands with different heterocyclic scaffolds different from the previously identified indole-2-carboxamides that show superior or similar affinity. Furthermore, the so far underexplored terminal alkyne moiety proved to be a suitable non-classical bioisosteric replacement for the higher halogen-π aryl interactions. With this challenging example, we demonstrated the ability of the MD-based non-equilibrium free energy calculation approach for guiding crucial modifications in the lead optimization process, such as scaffold replacement and single-site modifications at molecular interaction hot spots.

Human glucose transporters (GLUTs) are responsible for cellular uptake of hexoses. Elevated expression of GLUTs, particularly GLUT1 and GLUT3, is required to fuel the hyperproliferation of cancer cells, making GLUT inhibitors potential anticancer therapeutics. Meanwhile, GLUT inhibitor-conjugated insulin is being explored to mitigate the hypoglycemia side effect of insulin therapy in type 1 diabetes. Reasoning that exofacial inhibitors of GLUT1/3 may be favored for therapeutic applications, we report here the engineering of a GLUT3 variant, designated GLUT3exo, that can be probed for screening and validating exofacial inhibitors. We identify an exofacial GLUT3 inhibitor SA47 and elucidate its mode of action by a 2.3 Å resolution crystal structure of SA47-bound GLUT3. Our studies serve as a framework for the discovery of GLUTs exofacial inhibitors for therapeutic development., (© 2022. The Author(s).)

In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important for activity. However, this information is crucial for capturing the underlying structure-activity relationships (SARs) to guide further optimization. To address this interpretation gap, "Explainable Artificial Intelligence" (XAI) methods recently became popular. Herein, we apply and compare multiple XAI methods to projects of lead optimization data sets with well-established SARs and available X-ray crystal structures. As we can show, easily understandable and comprehensive interpretations are obtained by combining DNN models with some powerful interpretation methods. In particular, SHAP-based methods are promising for this task. A novel visualization scheme using atom-based heatmaps provides useful insights into the underlying SAR. It is important to note that all interpretations are only meaningful in the context of the underlying models and associated data.

Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a framework for leveraging predictive models. Nevertheless, various available model architectures differ in their global applicability and performance in lead optimization projects, such as stability over time and interpretability of the results. Here, we describe and compare the value of established DNN-based methods for the prediction of key ADME property trends and biological activity in an industrial drug discovery environment, represented by microsomal lability, CYP3A4 inhibition and factor Xa inhibition. Three architectures are exemplified, our earlier described multilayer perceptron approach (MLP), graph convolutional network-based models (GCN) and a vector representation approach, Mol2Vec. From a statistical perspective, MLP and GCN were found to perform superior over Mol2Vec, when applied to external validation sets. Interestingly, GCN-based predictions are most stable over a longer period in a time series validation study. Apart from those statistical observations, DNN prove of value to guide local SAR. To illustrate this important aspect in pharmaceutical research projects, we discuss challenging applications in medicinal chemistry towards a more realistic picture of artificial intelligence in drug discovery., (© 2021 Wiley-VCH GmbH.)

Mining the steadily increasing amount of chemical and biological data is a key challenge in drug discovery. Graph databases offer viable alternatives for capturing interrelationships between molecules and for generating novel insights for design. In a graph database, molecules and their properties are mapped to nodes, while relationships are described by edges. Here, we introduce a graph database for navigation in chemical space, analogue searching, and structure-activity relationship (SAR) analysis. We illustrate this concept using hERG channel inhibitors from ChEMBL to extract SAR knowledge. This graph database is built using different relationships, namely 2D-fingerprint similarity, matched molecular pairs, topomer distances, and structure-activity landscape indices (SALI). Typical applications include retrieving analogues linked by single or multiple edge paths to the query compound as well as detection of nonadditive SAR features. Finally, we identify triplets of linked molecules for clustering. The speed of searching and analysis allows the user to interactively navigate the database and to address complex questions in real-time.

Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects of artificial intelligence based de novo design pertaining to its integration into real-life workflows. First, different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions. With the trained neuronal networks different biologically active molecules could be regenerated. Excluding molecules with substructures such as five-membered rings from training spaces nevertheless produced results containing these moieties. Furthermore, different scoring functions in RL were investigated and produced different design ensembles. In summary, some of these design proposals are close in chemical space to the query, thus supporting lead optimization, while 3D-shape or QSAR (quantitative structure-activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation. Therefore, RL provides a good framework to tailored design approaches for different discovery phases.

Physiological processes rely on initial recognition events between cellular components and other molecules or modalities. Biomolecules can have multiple sites or mode of interaction with other molecular entities, so that a resolution of the individual binding events in terms of spatial localization as well as association and dissociation kinetics is required for a meaningful description. Here we describe a trichromatic fluorescent binding- and displacement assay for simultaneous monitoring of three individual binding sites in the important transporter and binding protein human serum albumin. Independent investigations of binding events by X-ray crystallography and time-resolved dynamics measurements (switchSENSE technology) confirm the validity of the assay, the localization of binding sites and furthermore reveal conformational changes associated with ligand binding. The described assay system allows for the detailed characterization of albumin-binding drugs and is therefore well-suited for prediction of drug-drug and drug-food interactions. Moreover, conformational changes, usually associated with binding events, can also be analyzed., (© 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

Relative binding free energy (RBFE) prediction methods such as free energy perturbation (FEP) are important today for estimating protein-ligand binding affinities. Significant hardware and algorithmic improvements now allow for simulating congeneric series within days. Therefore, RBFE calculations have an enormous potential for structure-based drug discovery. As typically only a few representative crystal structures for a series are available, other ligands and design proposals must be reliably superimposed for meaningful results. An observed significant effect of the alignment on FEP led us to develop an alignment approach combining docking with maximum common substructure (MCS) derived core constraints from the most similar reference pose, named MCS-docking workflow. We then studied the effect of binding pose generation on the accuracy of RBFE predictions using six ligand series from five pharmaceutically relevant protein targets. Overall, the MCS-docking workflow generated consistent poses for most of the ligands in the investigated series. While multiple alignment methods often resulted in comparable FEP predictions, for most of the cases herein, the MCS-docking workflow produced the best accuracy in predictions. Furthermore, the FEP analysis data strongly support the hypothesis that the accuracy of RBFE predictions depends on input poses to construct the perturbation map. Therefore, an automated workflow without manual intervention minimizes potential errors and obtains the most useful predictions with significant impact for structure-based design.

The therapeutic success of peptidic GLP-1 receptor agonists for treatment of type 2 diabetes mellitus (T2DM) motivated our search for orally bioavailable small molecules that can activate the GLP-1 receptor (GLP-1R) as a well-validated target for T2DM. Here, the discovery and characterization of a potent and selective positive allosteric modulator (PAM) for GLP-1R based on a 3,4,5,6-tetrahydro-1 H -1,5-epiminoazocino[4,5- b ]indole scaffold is reported. Optimization of this series from HTS was supported by a GLP-1R ligand binding model. Biological in vitro testing revealed favorable ADME and pharmacological profiles for the best compound 19 . Characterization by in vivo pharmacokinetic and pharmacological studies demonstrated that 19 activates GLP-1R as positive allosteric modulator (PAM) in the presence of the much less active endogenous degradation product GLP1(9-36)NH 2 of the potent endogenous ligand GLP-1(7-36)NH 2 . While these data suggest the potential of small molecule GLP-1R PAMs for T2DM treatment, further optimization is still required towards a clinical candidate.

Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excretion, metabolism, and toxicity) databases are constantly growing, deep neural nets (DNN) emerged as transformative artificial intelligence technology to analyze those challenging data. Relevant features are automatically identified, while appropriate data can also be combined to multitask networks to evaluate hidden trends among multiple ADME-Tox parameters for implicitly correlated data sets. Here we describe a novel, fully industrialized approach to parametrize and optimize the setup, training, application, and visual interpretation of DNNs to model ADME-Tox data. Investigated properties include microsomal lability in different species, passive permeability in Caco-2/TC7 cells, and logD. Statistical models are developed using up to 50 000 compounds from public or corporate databases. Both the choice of DNN hyperparameters and the type and quantity of molecular descriptors were found to be important for successful DNN modeling. Alternate learning of multiple ADME-Tox properties, resulting in a multitask approach, performs statistically superior on most studied data sets in comparison to DNN single-task models and also provides a scalable method to predict ADME-Tox properties from heterogeneous data. For example, predictive quality using external validation sets was improved from R 2 of 0.6 to 0.7 comparing single-task and multitask DNN networks from human metabolic lability data. Besides statistical evaluation, a new visualization approach is introduced to interpret DNN models termed "response map", which is useful to detect local property gradients based on structure fragmentation and derivatization. This method is successfully applied to visualize fragmental contributions to guide further design in drug discovery programs, as illustrated by CRCX3 antagonists and renin inhibitors, respectively.

Mechanisms of protein-carbohydrate recognition attract a lot of interest due to their roles in various cellular processes and metabolism disorders. We have performed a large-scale analysis of protein structures solved in complex with glucose, galactose and their substituted analogues. We found that, on average, sugar molecules establish five hydrogen bonds (HBs) in the binding site, including one to three HBs with bridging water molecules. The free energy contribution of bridging and direct HBs was estimated using the free energy perturbation (FEP+) methodology for mono- and disaccharides that bind to l-ABP, ttGBP, TrmB, hGalectin-1 and hGalectin-3. We show that removing hydroxy groups that are engaged in direct HBs with the charged groups of Asp, Arg and Glu residues, protein backbone amide or buried water dramatically decreases binding affinity. In contrast, all solvent-exposed hydroxy groups and hydroxy groups engaged in HBs with the solvent-exposed bridging water molecules contribute weakly to binding affinity and so can be replaced to optimize ligand potency. Finally, we rationalize an effect of binding site water replacement on the binding affinity to l-ABP., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Water is an essential part of protein binding sites and mediates interactions to ligands. Its displacement by ligand parts affects the free binding energy of resulting protein-ligand complexes. Therefore the characterization of solvation properties is important for design. Of particular interest is the propensity of localized water to be favorably displaced by a ligand. This review discusses two popular computational approaches addressing these questions, namely WaterMap based on statistical mechanics analysis of MD simulations and 3D RISM based on integral equation theory of liquids. The theoretical background and recent applications in structure-based design will be presented., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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)-C 12 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., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Water molecules play an essential role for mediating interactions between ligands and protein binding sites. Displacement of specific water molecules can favorably modulate the free energy of binding of protein-ligand complexes. Here, the nature of water interactions in protein binding sites is investigated by 3D RISM (three-dimensional reference interaction site model) integral equation theory to understand and exploit local thermodynamic features of water molecules by ranking their possible displacement in structure-based design. Unlike molecular dynamics-based approaches, 3D RISM theory allows for fast and noise-free calculations using the same detailed level of solute-solvent interaction description. Here we correlate molecular water entities instead of mere site density maxima with local contributions to the solvation free energy using novel algorithms. Distinct water molecules and hydration sites are investigated in multiple protein-ligand X-ray structures, namely streptavidin, factor Xa, and factor VIIa, based on 3D RISM-derived free energy density fields. Our approach allows the semiquantitative assessment of whether a given structural water molecule can potentially be targeted for replacement in structure-based design. Finally, PLS-based regression models from free energy density fields used within a 3D-QSAR approach (CARMa - comparative analysis of 3D RISM Maps) are shown to be able to extract relevant information for the interpretation of structure-activity relationship (SAR) trends, as demonstrated for a series of serine protease inhibitors.

The design, synthesis, and structure-activity relationship of 1-phenoxy-2-aminoindanes as inhibitors of the Na + /H + exchanger type 3 (NHE3) are described based on a hit from high-throughput screening (HTS). The chemical optimization resulted in the discovery of potent, selective, and orally bioavailable NHE3 inhibitors with 13d as best compound, showing high in vitro permeability and lacking CYP2D6 inhibition as main optimization parameters. Aligning 1-phenoxy-2-aminoindanes onto the X-ray structure of 13d then provided 3D-QSAR models for NHE3 inhibition capturing guidelines for optimization. These models showed good correlation coefficients and allowed for activity estimation. In silico ADMET models for Caco-2 permeability and CYP2D6 inhibition were also successfully applied for this series. Moreover, docking into the CYP2D6 X-ray structure provided a reliable alignment for 3D-QSAR models. Finally 13d, renamed as SAR197, was characterized in vitro and by in vivo pharmacokinetic (PK) and pharmacological studies to unveil its potential for reduction of obstructive sleep apneas.

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., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The discovery of Streptomyces-produced streptomycin founded the age of tuberculosis therapy. Despite the subsequent development of a curative regimen for this disease, tuberculosis remains a worldwide problem, and the emergence of multidrug-resistant Mycobacterium tuberculosis has prioritized the need for new drugs. Here we show that new optimized derivatives from Streptomyces-derived griselimycin are highly active against M. tuberculosis, both in vitro and in vivo, by inhibiting the DNA polymerase sliding clamp DnaN. We discovered that resistance to griselimycins, occurring at very low frequency, is associated with amplification of a chromosomal segment containing dnaN, as well as the ori site. Our results demonstrate that griselimycins have high translational potential for tuberculosis treatment, validate DnaN as an antimicrobial target, and capture the process of antibiotic pressure-induced gene amplification., (Copyright © 2015, American Association for the Advancement of Science.)

Thermodisinfection of human femoral heads from living donors harvested during hip joint replacement is an established processing procedure. This study was designed to examine the influence of heat sterilization on pull out strength of cancellous bone and storage at different temperatures up to 2 years since we had previously studied the storage of unprocessed cancellous bone. Porcine cancellous bone resembling human bone structure was obtained from 140 proximal humerus of 6-8 months old piglets. Pull out strength of screws after thermodisinfection was compared with unprocessed cancellous bone and tested immediately and after 6, 12 and 24 months of storage at -20 and -80 °C. A three-way ANOVA was performed and significance level was 5 %. The thermodisinfected bone showed a pull out force of 2729 N (1657-3568 N). The reduction of pull out strength compared with unprocessed bone over all periods of storage was 276 N on average with 95 % confidence interval ranging from 166 N to 389 N (p < 0.0001). Different freezing temperatures did not influence this mechanic property within 24 months storage and showed no difference compared with fresh frozen bone. Thermodisinfection of cancellous bone preserves tensile strength necessary for clinical purposes. The storage at -20 °C for at least 2 years did not show relevant decrease of pull out strength compared with -80 °C without difference between thermodisinfected and fresh frozen bone. The increase of the storage temperature to -20 °C for at least 2 years should be considered.

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.

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.

Background: Emergency departments (EDs) are an essential component of any developed health care system. There is, however, no national description of EDs in Switzerland. Our objective was to establish the number and location of EDs, patient visits and flow, medical staff and organization, and capabilities in 2006, as a benchmark before emergency medicine became a subspecialty in Switzerland., Methods: In 2007, we started to create an inventory of all hospital-based EDs with a preliminary list from the Swiss Society of Emergency and Rescue Medicine that was improved with input from ED physicians nationwide. EDs were eligible if they offered acute care 24 h per day, 7 days per week. Our goal was to have 2006 data from at least 80% of all EDs. The survey was initiated in 2007 and the 80% threshold reached in 2012., Results: In 2006, Switzerland had a total of 138 hospital-based EDs. The number of ED visits was 1.475 million visits or 20 visits per 100 inhabitants. The median number of visits was 8,806 per year; 25% of EDs admitted 5,000 patients or less, 31% 5,001-10,000 patients, 26% 10,001-20,000 patients, and 17% >20,000 patients per year. Crowding was reported by 84% of EDs with >20,000 visits/year. Residents with limited experience provided care for 77% of visits. Imaging was not immediately available for all patients: standard X-ray within 15 min (70%), non-contrast head CT scan within 15 min (38%), and focused sonography for trauma (70%); 67% of EDs had an intensive care unit within the hospital, and 87% had an operating room always available., Conclusions: Swiss EDs were significant providers of health care in 2006. Crowding, physicians with limited experience, and the heterogeneity of emergency care capabilities were likely threats to the ubiquitous and consistent delivery of quality emergency care, particularly for time-sensitive conditions. Our survey establishes a benchmark to better understand future improvements in Swiss emergency care.

We have used a set of four local properties based on semiempirical molecular orbital calculations (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 data sets (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 data sets is better than that of the conventional force field-based MIFs. In the individual data sets, 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 data set (factor Xa inhibitors), demonstrating the intuitive chemical interpretability of QM-MIFs.

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.

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., (Copyright © 2013 Elsevier B.V. All rights reserved.)

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

BACKGROUND: The recommendations for storage temperature of allogeneic bone are varying between -20 °C and -70 °C and down to -80 °C. The necessary temperature of storage is not exactly defined by scientific data, and the effect of different storage temperatures onto the biomechanical and the biological behavior is discussed controversially. METHODS: The historical development of storage temperature of bone banks is described. A survey on literature concerning the biomechanical and biological properties of allograft bone depending on the procurement and storage temperature is given as well as on national and international regulations on storage conditions of bone banks (European Council, American Association of Tissue Banks (AATB), European Association of Tissue Banks (EATB)). RESULTS: Short-term storage up to 6 months is recommended with -20 °C and -40 °C for a longer period (AATB), and EATB recommends storage at -40 °C and even -80 °C while the regulations of the German German Medical Association (Bundesärztekammer) from 2001 recommend storage at -70 °C. Duration of storage at -20 °C can be maintained at least for 2 years. The potential risk of proteolysis with higher storage temperatures remains, but a definite impairment of bone ingrowth due to a storage at -20 °C was not shown in clinical use, and no adverse biomechanical effects of storage at -20 °C could be proven. CONCLUSION: Biomechanical studies showed no clinically relevant impairment of biomechanical properties of cancellous bone due to different storage temperatures. Sterilization procedures bear the advantage of inactivating enzymatic activity though reducing the risk of proteolysis. In those cases a storage temperature of -20 °C can be recommended for at least a period of 2 years, and the risk of undesired effects seems to be low for native unprocessed bone.

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., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The selective inhibition of the aspartyl protease renin is of high interest to control hypertension and associated cardiovascular risk factors. Following on preceding contributions, we report herein on the optimization of two series of azaindoles to arrive at potent and non-chiral renin inhibitors. The previously discovered azaindole scaffold was further explored by structure-based drug design in combination with parallel synthesis. This results in the identification of novel 5- or 7-azaindole derivatives with remarkable potency for renin inhibition. The best compounds on both series show IC(50) values between 3 and 8nM., (Copyright © 2011. Published by Elsevier Ltd.)

The control of hypertension and associated cardiovascular risk factors is possible by selective inhibition of the aspartyl protease renin due to its unique position in the renin-angiotensin system. Starting from a previously disclosed series of potent and nonchiral indole-3-carboxamides, we further explored this motif by structure-based drug design guided by X-ray crystallography in combination with efficient parallel synthesis. This resulted in the discovery of 4- or 6-azaindole derivatives with remarkable potency for renin inhibition. The best compound from these series showed an IC(50) value of 1.3 nM., (Copyright © 2011. Published by Elsevier Ltd.)

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., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Selective inhibition of the aspartyl protease renin has gained attraction as an interesting approach to control hypertension and associated cardiovascular risk factors given its unique position in the renin-angiotensin system. Using a combination of high-throughput screening, parallel synthesis, X-ray crystallography and structure-based design, we identified and optimized a novel series of potent and non-chiral indole-3-carboxamides with remarkable potency for renin. The most potent compound 5k displays an IC(50) value of 2nM., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

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).

Empirical scoring functions to calculate binding affinities of protein-ligand complexes have been calibrated based on experimental structure and affinity data collected from public and industrial sources. Public data were taken from the AffinDB database, whereas access to industrial data was gained through the Scoring Function Consortium (SFC), a collaborative effort with various pharmaceutical companies and the Cambridge Crystallographic Data Center. More than 850 complexes were obtained by the data collection procedure and subsequently used to setup different training sets for the parameterization of new scoring functions. Over 60 different descriptors were evaluated for all complexes, including terms accounting for interactions with and among aromatic ring systems as well as many surface-dependent terms. After exploratory correlation and regression analyses, stepwise variable selection procedures and systematic searches, the most suitable descriptors were chosen as variables to calibrate regression functions by means of multiple linear regression or partial least squares analysis. Eight different functions are presented herein. Cross-validated r(2) (Q(2)) values of up to 0.72 and standard errors (s(PRESS)) generally below 1.15 pK(i) units suggest highly predictive functions. Extensive unbiased validation was carried out by testing the functions on large data sets from the PDBbind database as used by Wang et al. (J Chem Inf Comput Sci 2004;44:2114-2125) in a comparative analysis of other scoring functions. Superior performance of the SFCscore functions is observed in many cases, but the results also illustrate the need for further improvements., ((c) 2008 Wiley-Liss, Inc.)

Background and Objectives: In 2002 the first endemic hantavirus infection in Switzerland was detected only by chance following a broad spectrum of diagnostics. This raised the question, whether Hantavirus infection should be included in the differential diagnosis of febrile illness of patients in Switzerland. In order to estimate the frequency of hantavirus infections in Switzerland, this survey on hantaviral seroprevalence was conducted in the Canton St. Gallen., Methods: A total of 1693 sera from farmers, forestry workers, and young soldiers as well as blood donors, as a cross-section of the average adult population of the Canton St. Gallen, were screened for hantavirus-specific antibodies by a microsphere-based assay. All volunteers with positive screening results obtained a questionnaire for assessment of details of previous rodent encounter and illnesses compatible with hantavirus infection., Results: This first survey on hantavirus-specific IgG in populations of eastern Switzerland revealed low seroprevalence-rates not significantly different among populations with higher risk for hantavirus infection (0.0%-1.9%) and the average adult population (0.5%)., Conclusions: As hantavirus infections among different populations are rare, and no evidence for hantaviral nephropathy could be found, serological investigation of suspected endemic hantavirus infection in eastern Switzerland should be confined to patients with acute nephropathy and/or a history of recent rodent encounter.

To gain insight into the structural determinants for the matrix metalloproteinase (MMP) family, we characterized the binding sites of 56 MMP structures and one TACE (tumor necrosis factor alpha converting enzyme) structure using molecular interaction fields (MIFs). These MIFs were produced by two approaches: the GRID force field and the knowledge-based potential DrugScore. The subsequent statistical analysis using consensus principal component analysis (CPCA) for the entire binding site and each subpockets revealed both approaches to encode similar information about discriminating regions. However, the relative importance of the probes varied between both approaches. The CPCA models provided the following ranking of the six subpockets based on the opportunity for selective interactions with different MMPs: S1' > S2, S3, S3' > S1, S2'. The interpretation of these models agreed with experimental binding modes inferred from crystal structures or docking.

We present a novel approach for ligand-based virtual screening by combining query molecules into a multiple feature tree model called MTree. All molecules are described by the established feature tree descriptor, which is derived from a topological molecular graph. A new pairwise alignment algorithm leads to a consistent topological molecular alignment based on chemically reasonable matching of corresponding functional groups. These multiple feature tree models find application in ligand-based virtual screening to identify new lead structures for chemical optimization. Retrospective virtual screening with MTree models generated for angiotensin-converting enzyme and the alpha1a receptor on a large candidate database yielded enrichment factors up to 71 for the first 1% of the screened database. MTree models outperformed database searches using single feature trees in terms of hit rates and quality and additionally identified alternative molecular scaffolds not included in any of the query molecules. Furthermore, relevant molecular features, which are known to be important for affinity to the target, are identified by this new methodology.

In this paper, we compare protein- and ligand-based virtual screening techniques for identifying the ligands of four biogenic amine-binding G-protein coupled receptors (GPCRs). For the screening of the virtual compound libraries, we used (1) molecular docking into GPCR homology models, (2) ligand-based pharmacophore and Feature Tree models, (3) three-dimensional (3D)-similarity searches, and (4) statistical methods [partial least squares (PLS) and partial least squares discriminant analysis (PLS-DA) models] based on two-dimensional (2D) molecular descriptors. The comparison of the different methods in retrieving known antagonists from virtual libraries shows that in our study the ligand-based pharmacophore-, Feature Tree-, and 2D quantitative structure-activity relationship (QSAR)-based screening techniques provide enrichment factors that are higher than those provided by molecular docking into the GPCR homology models. Nevertheless, the hit rates achieved when docking with GOLD and ranking the ligands with GoldScore (up to 60% among the top-ranked 1% of the screened databases) are still satisfying. These results suggest that docking into GPCR homology models can be a useful approach for lead finding by virtual screening when either little or no information about the active ligands is available.

Nitric oxide synthesized from l-arginine by nitric oxide synthase isoforms (NOS-I-III) is physiologically important but also can be deleterious when overproduced. Selective NOS inhibitors are of clinical interest, given their differing pathophysiological roles. Here we describe our approach to target the unique NOS (6R,1'R,2'S)-5,6,7,8-tetrahydrobiopterin (H(4)Bip) binding site. By a combination of ligand- and structure-based design, the structure-activity relationship (SAR) for a focused set of 41 pteridine analogues on four scaffolds was developed, revealing selective NOS-I inhibitors. The X-ray crystal structure of rat NOS-I dimeric-oxygenase domain with H(4)Bip and l-arginine was determined and used for human isoform homology modeling. All available NOS structural information was subjected to comparative analysis of favorable protein-ligand interactions using the GRID/concensus principal component analysis (CPCA) approach to identify the isoform-specific interaction site. Our interpretation, based on protein structures, is in good agreement with the ligand SAR and thus permits the rational design of next-generation inhibitors targeting the H(4)Bip binding site with enhanced isoform selectivity for therapeutics in pathology with NO overproduction.

Structure-activity relationships within a series of highly potent 2-carboxyindole-based factor Xa inhibitors incorporating a neutral P1 ligand are described with particular emphasis on the structural requirements for addressing subpockets of the factor Xa enzyme. Interactions with the subpockets were probed by systematic substitution of the 2-carboxyindole scaffold, in combination with privileged P1 and P4 substituents. Combining the most favorable substituents at the indole nucleus led to the discovery of a remarkably potent factor Xa inhibitor displaying a K(i) value of 0.07 nM. X-ray crystallography of inhibitors bound to factor Xa revealed substituent-dependent switching of the inhibitor binding mode and provided a rationale for the SAR obtained. These results underscore the key role played by the P1 ligand not only in determining the binding affinity of the inhibitor by direct interaction but also in modifying the binding mode of the whole scaffold, resulting in a nonlinear SAR.

The design, synthesis, and structure-activity relationship of 3-oxybenzamides as potent inhibitors of the coagulation protease factor Xa are described on the basis of X-ray structures, privileged structure motifs, and SAR information. A total of six X-ray structures of fXa/inhibitor complexes led us to identify the major protein-ligand interactions. The binding mode is characterized by a lipophilic dichlorophenyl substituent interacting with Tyr228 in the protease S1 pocket, while polar parts are accommodated in S4. This alignment in combination with docking allowed derivation of 3D-QSAR models and tailored scoring functions to rationalize biological affinity and provide guidelines for optimization. The resulting models showed good correlation coefficients and predictions of external test sets. Furthermore, they correspond to binding site topologies in terms of steric, electrostatic, and hydrophobic complementarity. Two approaches to derive tailored scoring functions combining binding site and ligand information led to predictive models with acceptable predictions of the external set. Good correlations to experimental affinities were obtained for both AFMoC (adaptation of fields for molecular comparison) and the novel TScore function. The SAR information from 3D-QSAR and tailored scoring functions agrees with all experimental data and provides guidelines and reasonable activity estimations for novel fXa inhibitors.